The 100 Top AI Engineers In America

“Scientists study the world as it is; engineers create the world that never has been,” Nobel laureate Theodore von Kármán once said. That spirit of engineering, of building what does not yet exist has defined the story of artificial intelligence from its earliest experiments to today’s breakthroughs.

When AIM Media House first began spotlighting AI talent, the focus was on researchers and engineers laying the technical foundations of data science. Over time, as AI moved from papers and prototypes into startups, enterprises, and public policy, the scope of our recognition evolved as well. Today, the Top 100 AI Engineers in the USA reflects that breadth: academics advancing fundamental research, startup founders pushing new frontiers, big tech leaders scaling systems globally, and practitioners applying AI responsibly across industries.

(In alphabetic order within the category)

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Big Tech Leaders & Distinguished Scientists

Alborz Geramifard is a Distinguished Scientist at LinkedIn. Prior to this, he was Senior Research Director at Meta AI supporting Conversational AI, and before that led the Conversational AI team at Amazon Alexa, where he created and shipped more than a dozen NLU models into production. He began his career as a postdoctoral fellow at MIT’s Laboratory for Information & Decision Systems after earning his PhD in 2011 from MIT and his MSc in 2008 from the University of Alberta, both focused on reinforcement learning. He was a recipient of the NSERC postgraduate scholarship (2010–2012) and has contributed to the AI community as guest editor for Machine Learning Journal and AI Magazine, as well as Area Chair for EMNLP and ACL.

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Alex Acero is an AI Venture Partner at Cadenza Capital and a consultant to AI startups. Until 2024, he served as Senior Distinguished Engineer and Siri Chief Scientist at Apple, leading teams in speech recognition, synthesis, language understanding, and dialog for Siri, and contributing to accessibility features such as Voice Control, screen readers, and spoken navigation in CarPlay. Prior to Apple, he spent 20 years at Microsoft Research, managing teams across speech, audio, multimedia, computer vision, NLP, machine translation, machine learning, and information retrieval, with contributions including Bing Translator, Xbox Kinect, and advances in large-vocabulary speech recognition using deep neural networks. He also managed the speech team at Spain’s Telefonica (1991–1993) and had an earlier stint at Apple in 1990. Alex is Affiliate Faculty at the University of Washington.

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Amar Subramanya is Corporate Vice President of AI at Microsoft, bringing more than two decades of experience in engineering, research, and leadership. An alumnus of Bangalore University with a Bachelor of Engineering in Electrical, Electronics, and Communications, he later earned his PhD from the University of Washington. Subramanya spent 16 years at Google in pivotal roles, including Vice President of Engineering, Distinguished Engineer for Google Assistant, Principal Engineer, and Staff Research Scientist, where he contributed significantly to large-scale AI and product innovation. Earlier in his career, he worked as a Software Engineer at IBM and as a Visiting Researcher at Microsoft. Now leading AI initiatives at Microsoft, Subramanya draws on his deep technical expertise and leadership experience to advance cutting-edge AI capabilities and applications globally.

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Bill Dally is Chief Scientist at NVIDIA and a pioneer in computer systems, parallel computing, and network architecture. He previously served as a professor and chair of the Computer Science Department at Stanford University, and earlier held research positions at MIT and Caltech, where he developed experimental parallel systems like the J-Machine and M-Machine and pioneered wormhole routing and virtual-channel flow control. He co-founded Stream Processors, Inc. and Velio Communications, and collaborated with Intel, Cray, and Avici Systems to commercialize advanced technologies. Bill is a member of the National Academy of Engineering, a fellow of AAAS, IEEE, and ACM, and has received the ACM Eckert-Mauchly Award, IEEE Seymour Cray Award, and ACM Maurice Wilkes Award.

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Bhuvana Ramabhadran leads a research team at Google focused on semi-supervised and multilingual speech recognition. Previously, she was a Distinguished Research Staff Member and Manager at IBM Research AI, where she directed global efforts in speech technologies. She has held leadership roles across IEEE and ISCA, including Chair of the IEEE Speech and Language Technical Committee, Area Chair for ICASSP and Interspeech, and Member-at-Large of the IEEE SPS Board of Governors. With over 150 publications and 40 U.S. patents, she has also served as an adjunct professor at Columbia University and as PI on projects funded by NSF, EU, and iARPA. Her research spans speech recognition, synthesis, statistical modeling, and self-supervised learning.

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Blaise Agüera y Arcas is an author, AI researcher, and Vice President / Fellow at Google, where he serves as CTO of Technology & Society and founded Paradigms of Intelligence (Pi), an organization focused on fundamental research in AI, neural computing, active inference, sociality, evolution, and Artificial Life. At Google, he has driven innovations in on-device machine learning for Android and Pixel, invented Federated Learning for decentralized model training, and launched the Artists + Machine Intelligence program. Blaise has authored numerous papers, essays, and books, including Who Are We Now? and Ubi Sunt, with his forthcoming book What Is Intelligence? set to release via Antikythera and MIT Press in September 2025.

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Bryan Catanzaro is Vice President of Applied Deep Learning Research at NVIDIA, where he leads a team exploring novel applications of AI across language understanding, computer graphics, and chip design. His research at NVIDIA contributed to the creation of cuDNN and, more recently, he helped lead the team that developed DLSS 2.0. Prior to NVIDIA, Bryan worked at Baidu, developing next-generation systems for end-to-end deep learning-based speech recognition. He holds a Ph.D. in Electrical Engineering and Computer Sciences from the University of California, Berkeley and applies deep learning to diverse challenges ranging from video games to hardware design.

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Ece Kamar is the Managing Director of Microsoft’s AI Frontiers Lab, a research group advancing foundation models and platform capabilities to make AI more efficient, reliable, and controllable. Formerly Deputy Director of Microsoft Research Redmond, she has spent over a decade building AI systems that collaborate with people and operate reliably in the real world, with a focus on trustworthiness, fairness, and responsible deployment.

She played a key role in shaping Microsoft’s Responsible AI efforts, serves as Technical Advisor to the company’s Internal Committee on AI, Engineering and Ethics, and helped integrate models like GPT-4 into Microsoft products across E+D, Azure Machine Learning, and Cognitive Services. Her work spans AI, HCI, responsible AI, and AI safety, earning multiple best paper awards. Beyond Microsoft, she is Affiliate Faculty in the Department of Computer Science and Engineering at the University of Washington, has served on Stanford’s AI100 study panel, and is a member of the National Academies’ Computer Science and Telecommunications Board (CSTB).

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François Chollet is a French software engineer, AI researcher, and Co-founder of Ndea, also associated with the ARC Prize. Best known as the creator of the widely adopted Keras deep learning library, released in 2015, he has significantly influenced the accessibility and democratization of AI. His work spans computer vision, formal reasoning, and the study of abstraction, with a focus on algorithms capable of autonomous abstraction, considered a key step toward general intelligence.

Beyond technical contributions, Chollet is deeply interested in leveraging AI to enhance human potential through applications in education, steerable recommendation systems, and personal productivity technologies. He also explores the early stages of human cognitive development, drawing insights from developmental psychology and robotics to inform his approach to building more generalizable AI systems.

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Greg Corrado is a Distinguished Scientist at Google Research and co-founder of the Google Brain Team, which helped drive the widespread adoption of deep neural networks across the tech industry. At Google, he has led teams applying deep learning to areas including image recognition, machine translation, search ranking, text synthesis, advertising, and recommendation systems. He currently heads Google Health Research & Innovations and the Imaging & Diagnostics product pillar, reflecting his passion for applying AI for social good. An accomplished academic, Greg has published across fields such as behavioral economics, neuromorphic device physics, systems neuroscience, artificial intelligence, and scalable machine learning.

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Jaime Teevan is Chief Scientist and Technical Fellow at Microsoft, where she sets the research agenda for the company’s Experiences + Devices division including Office, Windows, Surface, and Teams and founded the Office of Applied Research. A leader in developing AI for productivity, she drove the creation of M365 Copilot by initiating the use of large language models in Microsoft’s core products, and earlier invented the first personalized search algorithm used by Bing.

She also led company-wide research into hybrid work during the pandemic, influencing Microsoft’s product strategy, workplace policy, and academic research, and sponsored Responsible AI efforts across E+D. Previously Technical Advisor to CEO Satya Nadella, Jaime is an ACM Fellow, a member of the SIGIR and CHI Academies, and serves on the Yale Corporation and the board of Shutterstock. She holds a Ph.D. in AI from MIT, a B.S. from Yale, and is an Affiliate Professor at the University of Washington.

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Jakub Pachocki is the Chief Scientist at OpenAI, where he has led transformative research initiatives since 2017. As former Director of Research, he spearheaded the development of GPT-4, OpenAI Five, and foundational advances in large-scale reinforcement learning and deep learning optimization, helping shape the company’s focus on scaling deep learning systems. Pachocki earned his PhD in Theoretical Computer Science from Carnegie Mellon University and his undergraduate degree in Computer Science from the University of Warsaw, where his team won a gold medal and placed second overall in the 2012 International Collegiate Programming Contest. That same year, he was also the champion of Google Code Jam.

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Dean now serves as Google’s Chief Scientist, co-leading strategy for the company’s frontier models (Gemini) and long-horizon AI research. Earlier he co-created the infrastructure and abstractions that made web-scale AI possible, MapReduce, Bigtable, Spanner, DistBelief/TensorFlow and co-founded the Google Brain effort that normalized deep nets across Google’s products. That continuum, from systems to learning to foundation models explains his present mandate: set technical direction for the combined Google DeepMind + Research organization and shepherd the most consequential AI programs.

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John Giannandrea is Senior Vice President of Machine Learning and AI Strategy at Apple, reporting to CEO Tim Cook. Since joining Apple in 2018, he oversees AI and machine learning strategy across the company, including the development of Core ML and Siri technologies. Previously, he spent eight years at Google, leading the Machine Intelligence, Research, and Search teams, and earlier co-founded Tellme Networks and Metaweb Technologies. John began his career as a senior engineer at General Magic. He earned a B.Sc. with Honors in Computer Science from the University of Strathclyde in Glasgow, where he was also awarded a Doctorate Honoris Causa, and serves on the board of trustees at the SETI Institute.

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Li Deng has been the Chief Artificial Intelligence Officer at Citadel since May 2017. Prior to Citadel, he was Chief Scientist of AI, founder of the Deep Learning Technology Center, and Partner Research Manager at Microsoft and Microsoft Research. Before joining Microsoft, he held academic positions at the University of Waterloo (Assistant Professor to Full Professor, 1989–1999), with additional research appointments at MIT, ATR (Kyoto, Japan), and HK University of Science and Technology. He is an IEEE Fellow, Acoustical Society of America Fellow, ISCA Fellow, and an Affiliate Professor at the University of Washington. He was elected a Fellow of the Academy of Engineering of Canada in 2019 and received the IEEE SPS Industrial Leader Award for his leadership in large-scale deep learning, natural language processing, and financial engineering.

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Mark Chen is the Chief Research Officer at OpenAI, where he leads work on multimodal modeling and reasoning. He directed the teams behind DALL·E, the integration of visual perception into GPT-4, and the development of Codex, while also contributing to advances in GPT models such as Image GPT. Before OpenAI, he worked as a quantitative trader at firms including Jane Street Capital, building machine learning algorithms for equities and futures trading. Chen holds a bachelor’s degree in Mathematics with Computer Science from MIT and also serves as a coach for the USA Computing Olympiad team.

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Norman P. Jouppi is a Google Fellow and the technical lead for Google Tensor Processing Units (TPUs). He is renowned for his innovations in computer memory systems, microprocessor architecture, and graphics accelerators, with many of his designs adopted in high-performance processors. Norm was one of the principal architects of the MIPS microprocessor and developed techniques for MOS VLSI timing verification. He has held faculty positions at Stanford University, taught courses in computer architecture, VLSI, and circuit design, and served as a Staff Fellow and Senior Fellow at HP/Compaq. He earned his Ph.D. in Electrical Engineering from Stanford University and an M.S. from Northwestern University.

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Sumit Gulwani is a computer scientist recognized for connecting ideas, people, and research with practice. He is the inventor of the Flash Fill feature in Excel and the founder of Microsoft’s PROSE research and engineering team, which has shipped cutting-edge AI research into products such as Excel (Formula-by-Example), PowerApps, PowerAutomate, Visual Studio’s IntelliCode Suggestions, PowerQuery’s Data Connectors, and various Copilots.

He also started the PROSE Research Fellowship Program in India to nurture research talent while amplifying the impact of team projects. An expert in neuro-symbolic AI, program synthesis, programming-by-examples, and LLM-based compound AI systems, he has authored more than 165 publications (h-index 71) across top-tier conferences, earning 13 research paper awards, including three test-of-time most influential paper honors.

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Shivakumar Venkataraman is the Vice President and General Manager of Google Cloud – Applied AI, leading global strategy for enterprise AI solutions. He spent over two decades at Google in senior roles including VP/GM of Google Labs, VP/GM of Search Ads, and VP of Engineering for Ads Infrastructure and Payments, driving major advances in advertising platforms, distributed data systems, and generative AI. In 2024, he briefly served as VP at OpenAI before returning to Google. A seasoned leader with deep expertise in AI, machine learning, and scalable infrastructure, Shivakumar continues to shape the future of applied AI in the enterprise.

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Tara Sainath is a Distinguished Research Scientist at Google DeepMind, with a focus on advancing deep neural networks for automatic speech recognition. She received her S.B., M.Eng., and PhD in Electrical Engineering and Computer Science from MIT, and spent five years at IBM’s T.J. Watson Research Center before joining Google. An IEEE and ISCA Fellow, she has served as Program Chair for ICLR, Associate Editor for IEEE/ACM Transactions on Audio, Speech, and Language Processing, and organized numerous leading workshops and conferences. Her contributions have been recognized with the 2021 IEEE SPS Industrial Innovation Award and the 2022 IEEE SPS Signal Processing Magazine Best Paper Award. At DeepMind, she co-leads the Gemini Audio Pillar and is the Modeling Lead for Project Astra.

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Vincent Vanhoucke is a Distinguished Engineer at Waymo whose work spans artificial intelligence, machine learning, robotics, and audio-visual perception. He previously founded and led Google’s Robotics research team, was an early member of Google Brain, and co-created the Conference on Robot Learning, where he now serves as President of the Robot Learning Foundation. A Fellow of the IEEE, he has contributed to fields ranging from speech recognition to computer vision and robotics, and his Udacity deep learning course has introduced over 100,000 students to the field. Vincent holds a doctorate from Stanford University and a diplôme d’ingénieur from the École Centrale Paris.

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Xuedong David Huang is the Chief Technology Officer at Zoom, where he leads the company’s AI transformation to enhance communication and productivity for hundreds of millions of users. A pioneer in spoken language processing and artificial intelligence, he previously spent 30 years at Microsoft as Technical Fellow and Azure AI CTO, leading large-scale AI services and achieving several industry-first human parity milestones across speech, language, and multimodal AI. Dr. Huang holds over 170 U.S. patents, is a member of the National Academy of Engineering and the American Academy of Arts and Sciences, and a fellow of both IEEE and ACM. He earned his Ph.D. from the University of Edinburgh, co-authored the widely used textbook Spoken Language Processing, and is a passionate advocate for AI for Accessibility and AI for Cultural Heritage.

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Yangqing Jia is the VP of AI System Software at NVIDIA and Co-Founder of Lepton AI. He is best known as the creator of Caffe and has led influential open-source projects including Caffe2, ONNX, and PyTorch 1.0. Earlier in his career, he worked at Google Brain, contributing to TensorFlow and co-creating GoogLeNet, and at Facebook, where he helped build the company’s large-scale AI platform. He later served as VP at Alibaba and President of Alibaba Cloud’s Computing Platform business unit, driving cloud-based AI and big data initiatives. Jia holds a Ph.D. in Computer Science from UC Berkeley, where he studied under Prof. Trevor Darrell, following his bachelor’s and master’s degrees from Tsinghua University.

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Jiang Bian is Partner Research Manager at Microsoft Research, where he leads the MSR Asia Industry Innovation Center (MIIC) in advancing disruptive AI technologies and frameworks for real-world applications across finance, supply chain, healthcare, and sustainability. Previously, he was a Scientist at Yahoo! Labs, driving personalization and web search for Yahoo! Homepage, and later a core member of Yidian Inc., where he focused on recommendation model development. He has authored numerous research papers, filed multiple US patents, and contributed as a Program Committee Member and peer reviewer for top international conferences and journals. Dr. Bian earned his bachelor’s degree from Peking University and a Ph.D. in computer science from the Georgia Institute of Technology.

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Kaisheng Yao is a Tech Lead at Google working on foundation models, with extensive experience across research and development in multimodal machine learning, conversational systems, natural language understanding, speech recognition, and speech signal processing. He previously held roles at AWS AI, Ant Group, Microsoft Research, and Texas Instruments . His work has contributed algorithms and systems to products serving tens of millions of users, and his research has earned honors including the IEEE Signal Processing Society Best Paper Award and NLPCC Best Paper Award. He is a Senior Member of IEEE.

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Marc’Aurelio Ranzato is a Research Scientist Director at Google DeepMind, where he leads the continual learning team, with broad interests spanning machine learning, computer vision, natural language processing, and artificial intelligence. Originally from Padova, Italy, he earned his Ph.D. in Computer Science from New York University in 2009 under Prof. Yann LeCun and later worked as a postdoctoral fellow with Prof. Geoffrey Hinton at the University of Toronto.

He was an early member of the Google Brain team in 2011, a founding member of Facebook AI Research in 2013, and has been at DeepMind since 2021. Ranzato has served as Program Chair for ICLR (2017–2018), Program Chair for NeurIPS (2020), and General Chair for NeurIPS (2021). His long-term research goal is to enable machines to learn more efficiently with less supervision by transferring and accumulating knowledge over time.

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Ryen W. White is Partner Research Director at Microsoft Research in Redmond, where he leads the LEAP (Language, Learning, Audio, Privacy) research area, overseeing five research groups as well as Central Engineering and Research Technology Engineering. His work spans multi-agent systems (AutoGen), world and human action models for gameplay ideation (WHAM, featured in Nature), audio innovations for Teams, and differential privacy for synthetic data (Private Evolution).

With research interests in search and AI assistance, he previously led applied science for Cortana and served as Chief Scientist at Microsoft Health, contributing to AI breakthroughs across Bing, Xbox, Skype, Dynamics, Office, Windows, Copilot, and Azure. An ACM and BCS Fellow, and member of the ACM SIGIR and SIGCHI Academies, he has received more than 20 research awards, including multiple best paper and test-of-time honors, and also serves as an Affiliate Full Professor at the University of Washington.

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Saravan Rajmohan is Partner Director and General Manager of the M365 Research group within M365 Core at Microsoft, where he leads a global team of over 45 researchers advancing AI systems, generative AI, and large language model infrastructure. An industry expert with numerous top-tier publications and patents, he focuses on building the next generation of generative AI that is efficient, trustworthy, private, and adaptable across domains.

His team drives innovation through three research pillars: advancing cloud-scale AI operations for reliability and efficiency, rethinking the AI stack for cross-platform resource optimization, and enabling contextual, customizable, and domain-adaptable generative AI solutions. By bridging foundational research with real-world product impact, Saravan champions responsible, scalable, and personalized AI for enterprises and individuals alike.

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Susan Dumais is a Technical Fellow and Managing Director at Microsoft Research, overseeing labs in New England, New York City, and Montreal. A leading researcher at the intersection of information retrieval and human-computer interaction, she is widely recognized for co-developing Latent Semantic Indexing, a pioneering method for concept-based retrieval.

Since joining Microsoft Research in 1997, her work has advanced areas including gaze-enhanced interaction, user modeling and personalization, search evaluation, and novel retrieval interfaces, while earlier contributions spanned personal information management, desktop search, question answering, and collaborative filtering. She has collaborated extensively with product teams across Microsoft such as Bing, Windows Desktop Search, SharePoint, and Office—bringing search innovations into products used worldwide.

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Yann LeCun is Chief AI Scientist at Meta (Facebook) AI Research – FAIR and Silver Professor at New York University, where he is affiliated with the Courant Institute of Mathematical Sciences and the Center for Data Science, which he helped found. A pioneer of modern artificial intelligence, LeCun is best known for inventing convolutional neural networks (ConvNets), the foundation of today’s breakthroughs in computer vision, speech recognition, and natural language processing.

He received his Ph.D. in Computer Science from Université Pierre et Marie Curie in 1987 and has held influential research positions at AT&T Bell Labs, NEC Research Institute, and NYU. In recognition of his transformative contributions, LeCun was awarded the 2018 ACM A.M. Turing Award, alongside Geoffrey Hinton and Yoshua Bengio, for advances that made deep neural networks a cornerstone of modern computing. He is a member of the U.S. National Academy of Engineering, a Fellow of AAAI, and a Chevalier de la Légion d’Honneur, with honors including the IEEE Neural Network Pioneer Award and the IEEE PAMI Distinguished Researcher Award.

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Emerging Researchers & Technical Staff

Angela Fan is a Member of Technical Staff at OpenAI, with nearly a decade of experience as a research scientist at Meta’s Facebook AI Research. At Meta, she contributed to major initiatives such as No Language Left Behind, which built translation systems for hundreds of languages, and Universal Speech Translation for Unwritten Languages, which enabled direct speech-to-speech translation for languages without writing systems. Earlier in her career, she focused on on-device models for NLP and computer vision as well as text generation. Angela began her career as a data science intern at Riot Games and holds a bachelor’s degree in statistics from Harvard University.

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Addy Osmani is an Irish software engineer and technology leader at Google, where he works on the Chrome web browser and contributes to AI initiatives with Google DeepMind’s Gemini. With over 25 years of experience, including 13+ years at Google, he leads global engineering teams focused on reducing friction for developers and enabling high-quality web applications, serving 40M developers and 3B+ Chrome users worldwide. His AI-focused projects include improving Gemini models for coding, integrating Gemini assistance into Chrome DevTools, and driving rollouts across multiple developer platforms. A passionate advocate for AI-assisted engineering and developer tools, Addy is also the author of several books including Learning JavaScript Design Patterns and Leading Effective Engineering Teams and a prolific speaker who has delivered over 175 talks globally.

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Abhimanyu is a Senior Staff Research Scientist at Meta, where he began as a Research Scientist at Facebook AI Research, working on core machine learning challenges. He earned his Ph.D. in Computer Science from MIT in 2021 under Professor Alex Pentland, focusing on differential privacy, heavy-tailed robustness, and out-of-distribution generalization in multi-agent online learning. Earlier, he completed his master’s in Computer Science and bachelor’s in Electrical Engineering at IIT Delhi under Professor Sumeet Agarwal, and was a post-baccalaureate fellow in economics at Harvard University, advised by Professor Ed Glaeser.

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Adina Williams is a Research Scientist at FAIR Labs NYC, Meta, where she leads the Science of Robust Assessment and Interpretability team, focusing on evaluating generative model performance and safety as well as advancing mechanistic interpretability of large language models. She earned her Ph.D. in Linguistics from New York University, where she studied the brain basis of syntactic and semantic processing, building on earlier training in cognitive neuroscience and Mandarin Chinese language and culture. Her research bridges linguistics, cognitive science, and AI: applying linguistic and cognitive insights to improve NLP systems while also leveraging statistical and corpus-based methods from NLP to uncover new cross-linguistic facts about human language.

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Aleksander Madry is the Cadence Design Systems Professor of Computing in the MIT EECS Department, a member of CSAIL, and a Member of Technical Staff at OpenAI. He directs the MIT Center for Deployable Machine Learning and co-leads the MIT AI Policy Forum, focusing on making machine learning systems safe, reliable, and responsibly deployable in real-world settings. His research spans optimization, algorithmic graph theory, and machine learning, with particular emphasis on robustness and trustworthy AI. Madry received his Ph.D. in computer science from MIT in 2011, was a postdoctoral researcher at Microsoft Research New England, and served as a faculty member at EPFL before joining MIT in 2015.

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Christina Kim is a Member of Technical Staff at OpenAI, currently on the mid-training team, where she works on advancing large-scale language model development. Since joining OpenAI in 2020 as a Research Scholar and later as a Member of Technical Staff, she has contributed to projects including WebGPT, ChatGPT, ChatGPT with browsing, and GPT-4, as well as research on reinforcement learning and scaling laws for language transfer learning. Previously, she was a founding engineer at Sourceress and a Fellow at the Recurse Center.

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Greta Tuckute is a neuroscientist studying how language is implemented in both biological and artificial systems, working at the intersection of neuroscience, AI, and cognitive science. Starting September 2025, she will join the Kempner Institute at Harvard University as a Research Fellow after completing her PhD in Brain and Cognitive Sciences at MIT under Dr. Ev Fedorenko. Originally from Lithuania, she earned her BSc and MSc at KU/DTU with additional research experience at MIT, Caltech, and Hokkaido University. Her research focuses on using neural networks to build more precise models of brain language processing, with applications in developing better brain–machine interfaces and improving technologies such as cochlear implants. Recognized for her innovative work, Tuckute has explored how GPT-based models can predict neural responses to language, offering non-invasive ways to understand and even modulate brain activity.

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Haşim Sak is a Research Scientist at Google whose work focuses on speech recognition, speech synthesis, and statistical language modeling. He received his B.S. in Computer Engineering from Bilkent University in 2000, and both his M.S. and Ph.D. in Computer Engineering from Boğaziçi University, where his dissertation addressed language modeling and speech decoding challenges in agglutinative languages with rich morphology. From 2000 to 2005, he worked in industry developing speech technologies for Turkish. His research interests span morphological parsing, spelling correction, morphological disambiguation, and core challenges in speech and language processing.

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John Langford is a computer scientist specializing in machine learning and learning theory, known for foundational contributions to algorithms such as Isomap, CAPTCHA, Cover Trees, Learning Reductions, and Contextual Bandits, a term he coined. He is the principal developer of the open-source machine learning system Vowpal Wabbit and the author of the influential weblog hunch.net.

Currently at Microsoft Research New York, where he was a founding member, Langford has also held research positions at Yahoo!, the Toyota Technological Institute at Chicago, and IBM’s Watson Research Center. He earned dual bachelor’s degrees in Physics and Computer Science from Caltech in 1997 and a Ph.D. in Computer Science from Carnegie Mellon University in 2002. A leader in the research community, he has served as Program Co-Chair (2012) and General Chair (2016) of ICML, and was President of ICML from 2019 to 2021.

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Isa Fulford is a Member of Technical Staff at OpenAI, where she leads deep research and works on ChatGPT agent research as part of the post-training team. She previously gained experience as a Scout at Sequoia Capital and a Mayfield Fellow with Stanford Technology Ventures Program (STVP), as well as through engineering roles at Mem Labs and Amazon Web Services, where she contributed to automated reasoning and verification for critical systems. A Phi Beta Kappa graduate of Stanford University, she holds both a Master’s in Computer Science and a Bachelor’s in Mathematical and Computational Science, with academic experiences at Oxford and Florence.

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Jennifer Wortman Vaughan is a Senior Principal Research Manager at Microsoft Research in New York City, where she works at the intersection of responsible AI and human-AI interaction. As part of Microsoft’s FATE group and co-chair of the Aether working group on transparency, her research focuses on AI transparency, interpretability, evaluation practices, and fairness, with a strong emphasis on developing AI that augments rather than replaces human abilities.

Trained in machine learning and algorithmic economics, she began her career in theoretical research but has since expanded her methods to include human-subject experiments and qualitative studies to better understand sociotechnical systems. Deeply engaged in the research community, she is Program Co-Chair for FAccT 2025 and previously served as Program Co-Chair for NeurIPS 2021, in addition to leading roles at HCOMP, ICML, EC, and NeurIPS. She is also a co-founder of Women in Machine Learning (WiML), where she continues to serve as a long-term Senior Advisor, and has held positions such as Steering Committee Member of ACM FAccT and Secretary-Treasurer of SigEcom.

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Michel Galley is a Sr. Principal Researcher in the Deep Learning group at Microsoft Research, specializing in Artificial Intelligence, Natural Language Processing, and Machine Learning. His work spans text generation, dialog systems, interactive systems, and machine translation, with recent research focused on advancing large language models (LLMs) through augmentation with external memory and tools, as well as developing methods for LLM verification.

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Noam Brown is a Research Scientist at OpenAI, where he focuses on multi-step reasoning, self-play, and multi-agent AI. Previously, he worked at FAIR (Meta), contributing to CICERO, the first AI to achieve human-level performance in the strategy game Diplomacy. Earlier in his career, he co-created Libratus and Pluribus at Carnegie Mellon University, AI systems that defeated top human professionals in no-limit poker; Libratus received the Marvin Minsky Medal, and Pluribus was featured on the cover of Science and named runner-up for Science’s 2019 Breakthrough of the Year. Noam earned his Ph.D. in Computer Science from Carnegie Mellon and previously worked at the Federal Reserve Board and in algorithmic trading. He was also recognized as one of MIT Technology Review’s 35 Innovators Under 35.

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Ruoming Pang is a renowned engineer who played a key role in shaping Apple’s AI efforts, where he reportedly led a team of over 100 engineers focused on developing foundational models for Siri and other on-device features. Before joining Apple in 2021 from Google’s parent Alphabet, Pang spent 15 years at Google working on large-scale AI and infrastructure systems.

He co-led the development of the Babelfish/Lingvo deep learning framework within Google Brain’s speech recognition team, contributed to Tacotron 2, co-founded and served as sole tech lead for Zanzibar, Google’s global authorization system, and co-developed a search system on Bigtable that was later adopted by over 1,000 projects. With a master’s in computer science from the University of Southern California and a Ph.D. from Princeton University, Pang’s career has been marked by his leadership in foundational AI research and engineering that underpins core technology at both Google and Apple.

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Sonal Gupta, now a Member of Technical Staff on Mustafa Suleyman’s Microsoft AI team, is a seasoned AI leader with a track record of driving research-to-product innovation across some of the industry’s most influential organizations. At Google DeepMind, she served as Area Tech Lead for agentive feature modeling and engineering lead for Gemini’s Canvas product, while previously at Meta she was a founding member of the GenAI org, co-led media generation at FAIR including the release of Make-A-Video, one of the first strong text-to-video models and led teams behind AI Stickers, Meta’s first GenAI media product.

Earlier, she shaped strategy and product development in the Facebook Assistant org, shipping features for Portal and Ray-Ban smart glasses. Holding a PhD in NLP from Stanford under Chris Manning and a Master’s from UT Austin, Gupta’s work has consistently bridged cutting-edge research in NLP and multimodal AI with impactful user-facing applications.

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Soumith Chintala is an AI researcher, engineer, and community builder, currently leading PyTorch and AI infrastructure initiatives at Meta while conducting research at NYU, including work on robotics. Known as an “AI Fixer,” he specializes in machine learning platforms, generative models, and advanced AI algorithms, with highly-cited research in GANs (Wasserstein GAN, DCGAN, LAPGAN) and contributions spanning SysML, NLP, computer vision, and game AI.

Soumith co-created and led PyTorch from its inception to industry-wide adoption, previously maintaining the Torch-7 framework used by Facebook, DeepMind, and Twitter. He has also developed benchmarking tools like convnet-benchmarks, guided FAIR’s hardware and software infrastructure, and consulted on diverse AI projects, combining research, engineering, and leadership to impact millions of users globally.

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Sebastien Bubeck is a Member Of Technical Staff at OpenAI, where he leads work on the “Physics of AGI,” studying how intelligence emerges in large language models by analyzing interactions across parameters, neurons, layers, and data curricula to improve their capabilities toward AGI. Previously, he was Vice President of AI and a Distinguished Scientist at Microsoft, spending a decade at Microsoft Research after serving as an assistant professor at Princeton. Earlier in his career, he focused on convex optimization, online algorithms, and adversarial robustness, earning multiple best paper awards, including STOC 2023, NeurIPS 2018 and 2021, COLT 2016, and several student paper awards in collaboration with Microsoft Research interns.

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Uday Ruddarraju is the Head of Compute Infrastructure at OpenAI, where he oversees the systems powering the training and deployment of advanced AI models. He previously served as Head of Infrastructure Engineering at xAI, where he played a pivotal role in building Colossus, a supercomputer of more than 200,000 GPUs and in training Grok 3, one of the company’s most advanced models. Recognized as one of four high-profile hires at OpenAI, Ruddarraju brings deep expertise in large-scale compute systems and infrastructure engineering to support the future of frontier AI development.

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Ethics, Safety & Policy Leaders

Margaret Mitchell is Chief Ethics Scientist at Hugging Face, where she leads research on machine learning, responsible AI development, and ethics-informed approaches to technology. A computer scientist specializing in natural language processing, natural language generation, assistive technology, and AI ethics, she co-founded and co-led Google’s Ethical AI team and previously worked at Microsoft Research on computer vision-to-language systems. She also co-founded WiNLP and has championed initiatives advancing diversity and inclusion in computer science.

Widely recognized for pioneering “Model Cards” for AI transparency, developing “Seeing AI” for blind and low-vision individuals, and co-authoring “Stochastic Parrots,” her work has shaped both academic research and industry practices. Named one of TIME’s Most Influential People in 2023, Mitchell’s contributions have been honored by awards from U.S. Secretary of Defense Ash Carter and the American Foundation for the Blind.

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Paul Christiano is the Head of AI Safety at the U.S. Artificial Intelligence Safety Institute, where he leads efforts to design and conduct evaluations of frontier AI models, particularly for capabilities with national security implications. He previously founded the Alignment Research Center, a nonprofit focused on aligning machine learning systems with human interests, and launched the initiative now known as Model Evaluation and Threat Research (METR), which conducts third-party evaluations of advanced AI models. Before that, Christiano led the language model alignment team at OpenAI, where he pioneered reinforcement learning from human feedback (RLHF), a technique now foundational to AI safety. He earned his Ph.D. in computer science from the University of California, Berkeley, and holds a B.S. in mathematics from MIT.

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Dr. Sarah Bird is the Chief Product Officer of Responsible AI at Microsoft, where she drives the adoption of responsible AI principles, tools, and practices across the company. She has led responsible AI development for products such as GitHub Copilot and the new Bing, and helped build open-source tools including Fairlearn, SmartNoise, and InterpretML. A founding researcher in Microsoft’s FATE group, she also contributed to the Microsoft Responsible AI Standard and serves on the AETHER committee. Beyond Microsoft, she co-founded ONNX, Fairlearn, and OpenDP’s SmartNoise, and played a leadership role in PyTorch 1.0 and MLSys, as well as in shaping the machine learning systems community. She holds a Ph.D. in computer science from UC Berkeley, where she was advised by Dave Patterson, Krste Asanovic, and Burton Smith.

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Timnit Gebru is the founder and executive director of The Distributed Artificial Intelligence Research (DAIR) Institute, where she leads independent research on AI’s societal impacts. Previously, she co-led Google’s Ethical AI team until her dismissal in 2020 after co-authoring a paper highlighting the risks of large language models and raising concerns about workplace discrimination.

She is also the co-founder of Black in AI, a nonprofit dedicated to increasing the inclusion and visibility of Black professionals in artificial intelligence, and serves on the board of AddisCoder, which teaches programming to Ethiopian and Jamaican high school students. Recognized as one of Nature’s Ten for shaping science and named to the TIME 100 list of the most influential people in the world, Gebru continues to challenge power structures in tech. She is currently working on her forthcoming book, The View from Somewhere, a memoir and manifesto envisioning a technological future that serves communities rather than surveillance, warfare, or corporate control.

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Industry Practitioners & Applied AI Executives

Alessya Visnjic is an engineering leader at Apple and the former CEO and Co-Founder of WhyLabs, an AI observability company focused on building the interface between AI systems and human operators. Before founding WhyLabs, she served as CTO-in-residence at the Allen Institute for AI (AI2), assessing the commercial potential of cutting-edge AI research. She previously spent nine years at Amazon, where she played a key role in driving machine learning adoption and was a founding member of Amazon’s first ML research center in Berlin. Alessya also founded Rsqrd AI, a global community of 1,000+ practitioners dedicated to advancing robust and responsible AI.

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Barak Turovsky is the Chief AI Officer at General Motors and a seasoned product and business leader with over 25 years of experience spanning Generative AI, computer vision, enterprise software, cloud applications, ads, and commerce. Prior to GM, he served as Vice President of AI at Cisco, leading research, engineering, and product teams, and spent a decade at Google as Director and Head of Product for the Languages AI group, where he helped shape products such as Search, Translate, Ads, and Cloud. His leadership background also includes roles as Chief Product and Technology Officer at Trax and Executive in Residence at Scale Venture Partners.

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David Lau is a software engineering executive best known for his tenure at Tesla, where he served as Vice President of Software. Joining the company in 2012 as a Senior Firmware Engineering Manager, he rose to lead the development of vehicle firmware, user interfaces, and Tesla’s signature over-the-air update systems. His scope also included mobile apps, server-side infrastructure for telemetry and diagnostics, data analytics, automation controls, and product security. Recognized as a central figure in shaping Tesla’s software-driven approach to vehicles and operations, Lau played a pivotal role in advancing features that blurred the line between cars and consumer technology.

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David Heckerman spent 25 years at Microsoft Research (1992–2017), where he founded the first AI, machine-learning, bioinformatics, and genomics groups. He invented the world’s first machine-learning spam filter, the Answer Wizard (later powering Clippy), and Windows Troubleshooters, and led the team that built Microsoft’s first ML platform in SQL Server. He also recruited leading researchers including Eric Horvitz and Chris Bishop. His research contributions include probabilistic expert systems (ACM best dissertation, 1990), Bayesian network learning for causal discovery, HIV vaccine design using ML, and genome association studies. He earned his Ph.D. (1990) and M.D. (1992) from Stanford University and is a Fellow of ACM, AAAI, and ACMI.

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David Petrou is the founder and CEO of Continua AI, Inc., a stealth startup building AI-powered tools that transform group collaboration by structuring conversations and turning ideas into actionable outcomes. Before launching Continua, he spent over a decade at Google, where he rose to the position of Distinguished Software Engineer and played a key role in pioneering projects such as Google Goggles, one of the company’s earliest ventures into computer vision. With a PhD in Computer Engineering from Carnegie Mellon University and a BS in EECS from the University of California, Berkeley, David brings a unique blend of research depth, technical expertise, and product-building experience. 

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Deepak Agrawal is Chief AI Officer at LinkedIn, where he oversees AI strategy, ethics, and innovation to enhance user experiences, growth, and trust engineering. He previously served as Vice President of AI at LinkedIn for eight years, leading initiatives that shaped the platform’s data-driven systems and personalized member experiences. Before LinkedIn, he was Chief AI Officer and VP of Consumer Trust Engineering at Pinterest, and has held roles at Yahoo! and AT&T. Deepak brings expertise in statistical modeling, predictive analytics, and generative AI, and is committed to fostering ethical AI practices while building scalable, impactful solutions that empower teams globally.

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Emre Kıcıman is the Head of Research for Copilot Tuning and a Senior Principal Research Manager at Microsoft, previously a Senior Principal Researcher at Microsoft Research. His work focuses on causal machine learning and its applications for robust, scalable decision-making, with impacts spanning Bing Ads, Azure for Industries, and the open-source DoWhy library, widely adopted in retail, e-commerce, and energy sectors. Beyond machine learning, his research explores AI’s societal implications, data biases, and computational social science, particularly in health and mental health domains. He also played a key role in advancing AI security as the founding co-chair of Microsoft’s Aether working group on AI Security, where he helped shape company-wide processes and tools. Earlier in his career, he contributed to the reliability and operations of distributed systems, pioneering the use of machine learning for fault detection in Internet services, now a standard industry practice.

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Rahul Dodhia is the Deputy Director of Microsoft’s AI for Good Lab, where he leads a team of AI research scientists focused on creating solutions to address some of the world’s most pressing challenges, particularly in the Global South. With a PhD in mathematical psychology from Columbia University, he began his career at NASA Ames Research Center, studying human memory and decision-making, before moving into applied machine learning roles at Amazon and Microsoft.

Over two decades, Dodhia has built and led high-performing teams of scientists and engineers, translating advanced AI research into practical applications with meaningful social and business impact. His expertise lies in blending innovation with execution, fostering collaboration across diverse stakeholders, and driving AI solutions that deliver real-world outcomes.

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Dr. Rebecca (Sorla) Portnoff is Vice President of Data Science at Thorn, where she leads the development of ML/AI and algorithmic solutions used by hundreds of law enforcement agencies, hotlines, and technology companies worldwide to combat child sexual abuse. With over a decade of experience in machine learning, child safety, and trauma-informed leadership, she serves as an ecosystem leader, driving novel research, setting standards, and fostering cross-sector collaborations between technologists and child protection experts.

A graduate of Princeton University and UC Berkeley, where she earned her PhD in Computer Science, Portnoff has been recognized as an MIT Tech Review 35 Under 35 innovator and serves on the AI for Safer Children Advisory Board. Her work has been featured in The New York Times, The Wall Street Journal, AP, and Forbes, reflecting her commitment to building technology that defends children from sexual abuse.

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Ruhi Sarikaya is Vice President of Alexa AI at Amazon, leading engineering, science, and product management for AI capabilities across Alexa and Alexa+. He joined Amazon in 2016 and has overseen the development of natural language understanding, dialog systems, ranking and relevance, recommendation systems, search, personalization, and analytics using large language models.

Prior to Amazon, Ruhi founded and led the Language Understanding and Dialog Systems Group at Microsoft, building the core AI for Cortana, Xbox One, and bots. He also spent ten years at IBM T.J. Watson Research Center and two years at the Center for Spoken Language Research at the University of Colorado. Ruhi holds a Ph.D. in Electrical and Computer Engineering from Duke University, has published over 130 technical papers, holds 90+ patents, and serves on multiple university advisory boards.

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Yaron Singer is VP of AI and Security at Cisco, following the acquisition of Robust Intelligence, where he was CEO and co-founder. A leading expert in AI, machine learning, optimization, algorithms, and deep learning, he is on a mission to secure AI and harness it to transform cybersecurity. Before entering industry, Yaron was a tenured Professor of Computer Science and Applied Mathematics at Harvard, a researcher at Google AI, and a consulting researcher at Microsoft.

His work has earned him numerous honors, including the NSF CAREER Award, DARPA Award, Sloan Fellowship, Facebook Faculty Award, Google Faculty Award, and both the Facebook Graduate and Microsoft Research Ph.D. Fellowships, along with a best student paper award at the ACM Web Search and Data Mining conference. In addition to his academic and industry achievements, he actively invests in and advises startups at the intersection of AI and security.

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Startup Founders & Entrepreneurs

Adam Cheyer is an inventor, entrepreneur, and AI pioneer, known for his work in human-computer interfaces and intelligent systems. He has co-founded or been a founding member of five startups, including Siri (acquired by Apple), Change.org, Viv Labs (acquired by Samsung), Sentient, and GamePlanner.AI (acquired by Airbnb). At Apple and Samsung, he led AI and product engineering for voice assistants, and at Airbnb, he served as VP of AI Experience.

Previously, Adam was VP of Engineering at Verticalnet and Dejima, and spent over a decade at SRI International as Chief Architect of CALO/PAL, the U.S. government’s largest AI project. He holds over 50 patents, has authored 60+ publications, graduated with highest honors from Brandeis University, and was named “Outstanding Masters Student” at UCLA’s School of Engineering.

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Alexandr Wang is the Chief AI Officer at Meta, where he leads Meta Superintelligence Labs, a newly formed organization bringing together the company’s AI research and product teams under one roof. Recognized as one of the most influential young entrepreneurs in technology, Wang is best known as the founder of Scale AI, a data infrastructure company he launched in 2016 that became essential to frontier labs and enterprises for human-in-the-loop labeling, safety red-teaming, and evaluation tooling.

Under his leadership, Scale transformed data quality and testing into a discipline as rigorous as model training, enabling breakthroughs in autonomous driving, defense, and large language model deployments. At 28, Wang became the world’s youngest self-made billionaire and recently made headlines after Meta invested $14.3 billion in Scale for a 49% stake, doubling its valuation to $29 billion. A former MIT student with early roles at Addepar and Quora, Wang is now charting Meta’s next era of AI with a focus on superintelligence.

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Andrej Karpathy is a computer scientist known for his contributions to deep learning, computer vision, and AI education. A founding member of OpenAI, where he was a research scientist from 2015 to 2017, he later became Tesla’s Director of Artificial Intelligence and Autopilot Vision until his departure in 2022. Named one of MIT Technology Review’s Innovators Under 35 in 2020, Karpathy went on to focus on AI education, launching Eureka Labs in 2024 with its first course, LLM101n, and the “Zero to Hero” series on large language models. He has also promoted the idea of AI teaching assistants, sparking debate over privacy and the role of human educators, and in 2025 coined the term vibe coding to describe building apps and websites through natural language prompts.

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Ng co-founded Google Brain, showing that distributed compute plus deep nets unlock breakthrough representation learning across speech and vision. At Baidu he translated research into large product surfaces (speech/search/recs). His second act scaled AI education globally via Coursera and DeepLearning.AI, and he now focuses on bringing computer vision to manufacturing through Landing AI. His lasting contribution is movement-building: creating the pipelines, courses, and playbooks that made applied ML a mainstream industrial capability. 

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Anthony Goldbloom is the founder and former CEO of Kaggle, the global data science competition platform that enabled organizations such as NASA, Wikipedia, Ford, and Deloitte to crowdsource solutions to complex problems from mapping dark matter to advancing HIV/AIDS research. Under his leadership, Kaggle raised $11.25 million in Series A funding led by Khosla Ventures and Index Ventures and became a central hub for the world’s data science community. Recognized among Forbes’ “30 Under 30 in Technology,” Goldbloom has been profiled by Fast Company and featured in leading outlets including The New York Times, The Wall Street Journal, and The Independent. Since 2021, he has served as an Investment Partner at AIX Ventures, a fund focused on backing the next generation of artificial intelligence startups.

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Cassie Kozyrkov is the CEO of Kozyr and the founder of the field of Decision Intelligence, best known as Google’s first Chief Decision Scientist, where she helped lead the company’s transformation into an AI-first organization. Over nearly a decade at Google, she personally trained more than 20,000 employees and influenced over 500 initiatives, leaving a lasting cultural impact. Today, Cassie is a globally sought-after advisor and keynote speaker who has guided AI strategy for organizations including NASA, Gucci, Meta, Salesforce, and GSK. Recognized for making complex ideas engaging and actionable, she has spoken on major stages such as the United Nations, World Economic Forum, Web Summit, and SXSW, and is followed by over half a million professionals in the AI and data community.

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Daniel M. Bikel is the Head of AI at Writer, with over two decades of experience advancing natural language processing and machine learning. A Harvard graduate in Classics, he earned his M.S. and Ph.D. in computer science from the University of Pennsylvania, where he made foundational contributions to statistical parsing algorithms. He has held research and leadership roles at BBN Technologies, IBM Research, Google, LinkedIn, and Meta, working on problems spanning parsing, machine translation, information extraction, speech recognition, semantic parsing, and conversational AI.

His work includes contributions to YouTube captioning, Google Now (which became Google Assistant), LinkedIn’s NLP infrastructure, specialized domain understanding at Google Research, and safety and memory features for Meta’s Llama2 and chatbots. Widely published in leading NLP and AI venues, Dr. Bikel also co-edited the book Multilingual Natural Language Processing Applications: From Theory to Practice (IBM Press/Pearson, 2012). At Writer, he leads research and development of enterprise-ready large language models.

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Ilya Sutskever is an Israeli-Canadian computer scientist and one of the most influential figures in deep learning. He co-invented AlexNet with Alex Krizhevsky and Geoffrey Hinton, a breakthrough convolutional neural network that won the 2012 ImageNet competition and sparked the modern deep learning revolution. In 2015, he co-founded OpenAI with Sam Altman, Elon Musk, and others, serving as Chief Scientist and leading the development of the GPT series of language models that transformed natural language processing. A former OpenAI board member, he played a central role in its high-profile leadership changes in 2023 before stepping down. In June 2024, he co-founded Safe Superintelligence with Daniel Gross and Daniel Levy, continuing his mission to advance AI while addressing its safety challenges.

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Ion Stoica is a Romanian-American computer scientist, entrepreneur, and Professor of Electrical Engineering and Computer Sciences at UC Berkeley, where he holds the Xu Bao Chancellor’s Chair and directs SkyLab. He is best known as a co-founder and Executive Chairman of Databricks, a $62 billion cloud-based data and AI company built on Apache Spark, which he also helped develop.

Stoica co-founded Conviva, a video streaming quality startup, and Anyscale, focusing on cloud computing and AI systems. His research spans cloud computing, AI, and distributed systems, with past contributions including Ray, Apache Mesos, Tachyon, Chord DHT, and Dynamic Packet State (DPS). He is an ACM Fellow, an Honorary Member of the Romanian Academy, and recipient of numerous awards including the Mark Weiser Award (2019) and SIGOPS Hall of Fame Award (2015).

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After six years helping turn OpenAI’s research into blockbusters (ChatGPT, DALL·E, Sora) and a brief stint as interim CEO during OpenAI’s 2023 leadership crisis, Murati left in Sept 2024 to start Thinking Machines Lab. In 2025 she unveiled the company publicly, positioning it to build safer, collaborative multimodal AI and rapidly hiring senior researchers from OpenAI, Meta, and Mistral. Her early career blended hands-on product engineering (Tesla Model X) and AR at Leap Motion before moving into applied AI leadership at OpenAI experience she now channels into a product-and-safety-first research org backed by a massive seed round.

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Rodney Brooks is a pioneering robotics entrepreneur, researcher, and technologist, best known as the co-founder and CTO of Robust AI and the founder of Rethink Robotics and iRobot, where he helped bring intelligent robots into both industry and homes. A former director of MIT’s Artificial Intelligence Laboratory and the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL), Brooks has shaped the fields of robotics, AI, and artificial life through decades of influential research and leadership.

He earned his Ph.D. in Computer Science from Stanford after studying mathematics at Flinders University, and has held faculty and research roles at Stanford, Carnegie Mellon, and MIT. A member of the National Academy of Sciences, National Academy of Engineering, and multiple international academies, Brooks has received numerous honors including the IEEE Robotics and Automation Award, the Engelberger Robotics Award for Leadership, the IEEE Founders Medal, and the Computer History Museum Fellow Award.

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Sebastian Thrun is a research professor at Stanford, Google Fellow, and co-founder of Udacity, widely recognized for his groundbreaking work in artificial intelligence, robotics, and education. At Google, he founded Google X, the company’s innovation lab that launched projects such as the self-driving car and Google Glass. Earlier, he led the Stanford team that built Stanley, the autonomous vehicle that won the 2005 DARPA Grand Challenge and ignited today’s self-driving revolution. Thrun went on to co-found Udacity, pioneering accessible, industry-aligned online education through its nanodegree programs, with a mission to democratize higher learning. His career is defined by translating cutting-edge AI research into real-world applications while reimagining how technology can improve daily life and expand access to knowledge.

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Zachary Lipton is the Chief Technology Officer and Chief Scientist at Abridge, where he leads the builder organization overseeing AI research, applied science, product management, design, and engineering, responsible for delivering Abridge’s AI scribing products to over 60 major healthcare enterprises. He is also the Raj Reddy Associate Professor of Machine Learning at Carnegie Mellon University, directing the Approximately Correct Machine Intelligence (ACMI) Lab, which focuses on robust and adaptive machine learning, clinical prediction and decision-making, natural language processing, and societal impacts of AI systems. Zachary is the founder of the Approximately Correct blog and co-author of Dive Into Deep Learning, an interactive open-source book that has reached millions of readers.

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Academic & Research Pioneers

Alfred Aho, the Lawrence Gussman Professor of Computer Science at Columbia University, is a pioneering computer scientist whose contributions have shaped algorithms, programming languages, compilers, and theoretical computer science. Best known as the “A” in AWK and coauthor of The AWK Programming Language as well as the iconic “dragon book” on compilers, he also co-created the Aho-Corasick string-matching algorithm and authored foundational UNIX tools like fgrep and egrep.

Aho has coauthored numerous influential textbooks that have educated generations of computer scientists, including collaborations with John Hopcroft, Jeffrey Ullman, Brian Kernighan, and others. After earning his B.A.Sc. from the University of Toronto and a Ph.D. from Princeton, he built his career at Bell Labs during the formative years of UNIX, later serving as Columbia’s Computer Science Chair. A member of the National Academy of Sciences, National Academy of Engineering, American Academy of Arts and Sciences, and the Royal Society of Canada, he has been honored with the ACM A.M. Turing Award (2020), the NEC C&C Prize (2017), and the IEEE John von Neumann Medal (2003).

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Aaron Defazio is a leading optimization researcher whose work has advanced the stability, efficiency, and theoretical foundations of modern deep learning. Known for contributions such as SAGA, variance-reduced SGD methods, and adaptive algorithms, he has published extensively on stochastic optimization and convex analysis.

At Meta’s Fundamental AI Research (FAIR), he developed algorithms that improve training dynamics and scaling for large models in both vision and language, while also pushing frontiers in applied domains like MRI image reconstruction and automated theorem proving. His research spans from core methodological advances, performance optimization, initialization, and normalization to practical breakthroughs that shape the way machine learning is practiced at scale.

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Anima Anandkumar is a Bren Professor at Caltech and a pioneer in AI for scientific modeling and discovery, with applications ranging from extreme weather forecasting and drug discovery to scientific simulations and engineering design. She invented Neural Operators, a deep learning framework for multiscale physical phenomena, which enabled the first AI-based high-resolution weather model, now deployed at weather agencies, and helped create the field of AI-driven weather and climate modeling.

Her algorithms have also advanced medical device design, anti-cancer drug discovery, and safer autonomous drone operations. Anima is a fellow of IEEE, ACM, and AAAI, and has received numerous honors, including the Time 100 Impact Award, IEEE Kiyo Tomiyasu Award, Schmidt AI2050 Senior Fellowship, and awards from the Guggenheim, Sloan, and Blavatnik Foundations. She earned her B.Tech from IIT Madras, a Ph.D. from Cornell University, and conducted postdoctoral research at MIT, and has held leadership roles at Amazon Web Services and NVIDIA.

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Chien-Ming Huang is the John C. Malone Assistant Professor of Computer Science at Johns Hopkins University, where he directs the Intuitive Computing Laboratory. His research focuses on human-machine teaming, with an emphasis on creating intuitive, personalized technologies that provide social, physical, and behavioral support for diverse populations, including children with autism. An expert in human-robot and human-computer interaction, Huang draws on AI, robotics, and HCI to develop interactive systems that decipher human behavioral cues, enable intuitive robot collaboration, and design methods for re-skilling robots to perform custom tasks.

His work has broad applications in healthcare, education, and manufacturing. Huang is affiliated with the Malone Center for Engineering in Healthcare, the Laboratory for Computational Sensing and Robotics, the Institute for Assured Autonomy, and the Data Science and AI Institute. His honors include a 2022 NSF CAREER Award, the John C. Malone endowed chair, recognition as a 2012 Human-Robot Interaction Pioneer, and early career distinctions at ACM’s CHI and SIGCSE conferences.

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Cynthia Breazeal is a professor of media arts and sciences at MIT, where she founded and directs the Personal Robots Group at the Media Lab, and also serves as MIT’s dean for digital learning, leading Open Learning’s business, research, and engagement efforts. A pioneer in social robotics, human-robot interaction, and AI literacy, she is the founding director of MIT RAISE (Responsible AI for Social Empowerment and Education), which advances inclusive AI education for K12 students and the workforce, including the global Day of AI initiative that has reached over a million students in 170 countries.

She co-founded the consumer social robotics company Jibo, Inc., and has shaped the field through her landmark book Designing Sociable Robots. Her research explores how personified AI and social robots can foster human flourishing across education, healthcare, wellness, and aging, while also developing design justice frameworks for inclusive AI. Breazeal’s contributions have earned her numerous honors, including fellowships with AAAI and AAAS, recognition from TIME, Fortune, and Fast Company, as well as prestigious awards such as the TR35 and the National Academy of Engineering’s Gilbreth Lecture Award.

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Christopher Manning, the inaugural Thomas M. Siebel Professor in Machine Learning at Stanford University, is a leading figure in natural language processing (NLP) and deep learning whose work has transformed how machines understand human language. A Founder and Associate Director of Stanford’s Human-Centered AI Institute and former Director of the Stanford AI Lab, he pioneered neural approaches to natural language understanding, including contributions to sentiment analysis, paraphrase detection, the GloVe word vector model, attention mechanisms, neural machine translation, and self-supervised pretraining, earning honors such as two ACL Test of Time Awards and the IEEE John von Neumann Medal (2024).

Earlier, he helped establish probabilistic approaches to NLP and computational linguistics, advancing syntactic parsing, multilingual language processing, and natural language inference. Manning is also renowned for education and open-source contributions, having coauthored seminal textbooks on statistical NLP and information retrieval, created widely used tools like Stanford CoreNLP and Stanza, and reached global audiences with his popular CS224N course. An ACM, AAAI, and ACL Fellow and former ACL President.

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Raj Reddy, a Turing Award laureate and one of the foremost pioneers of artificial intelligence, is best known for creating the first systems capable of continuous speech recognition, including Hearsay I, Hearsay II, Harpy, and Dragon, which laid the foundations for modern speech technology and the widely adopted “blackboard architecture.” Born in 1937 in Andhra Pradesh, India, Reddy studied engineering in Chennai, earned a master’s degree in Sydney, and later became a professor at Stanford before joining Carnegie Mellon University, where he spent decades shaping AI, robotics, and human-computer interaction.

As founding director of CMU’s Robotics Institute and later Dean of the School of Computer Science, he spearheaded advances in robotics, machine learning, and language technologies while also championing education and digital inclusion initiatives in India, including the International Institute of Information Technology, Hyderabad, and the Rajiv Gandhi University of Knowledge Technologies. Honored with the Padma Bhushan, the Legion d’Honneur, the Honda Prize, the Okawa Prize, and numerous other distinctions, Reddy’s influence spans groundbreaking research, institution building, and global efforts to bridge the digital divide.

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Dan Jurafsky is Professor of Linguistics, Professor of Computer Science, and Reynolds Professor in Humanities at Stanford University, widely recognized as a pioneer in natural language processing (NLP) and its intersections with linguistics, cognitive science, and society. He is co-author of the standard textbook Speech and Language Processing, one of the most cited works in computational linguistics, and co-created one of the first massively open online courses on NLP.

A 2002 MacArthur Fellow, Jurafsky is also a Fellow of the ACL, LSA, and AAAS, and a member of the American Academy of Arts and Sciences. His trade book, The Language of Food: A Linguist Reads the Menu, was an international bestseller and a finalist for the 2015 James Beard Award, exemplifying his ability to bridge rigorous scholarship with public engagement.

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Edward Albert Feigenbaum, often called the “father of expert systems,” is a pioneering American computer scientist whose work laid the foundation for practical applications of artificial intelligence. Born in 1936 in Weehawken, New Jersey, he developed an early fascination with machines and cognition, which led him to study at the Carnegie Institute of Technology under Herbert Simon, one of AI’s founding figures.

After earning his Ph.D. in industrial administration in 1960, Feigenbaum taught at the University of California, Berkeley, before joining Stanford University in 1965, where he advanced the development of expert systems which are AI programs designed to emulate human expertise in specialized domains. His contributions helped shift AI from theoretical research to real-world problem solving, establishing him as one of the most influential figures in the field.

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Dr. Fei-Fei Li is the Sequoia Professor of Computer Science at Stanford University and the Founding Co-Director of Stanford’s Human-Centered AI Institute, widely recognized as one of the most influential figures in modern artificial intelligence. She is the inventor of ImageNet, a groundbreaking dataset that helped spark the deep learning revolution, and has published over 400 scientific papers spanning computer vision, machine learning, robotics, and healthcare AI.

Beyond academia, she has held leadership roles including Vice President at Google and Chief Scientist of AI/ML at Google Cloud, and is currently Co-founder and CEO of World Labs, an AI company specializing in spatial intelligence and generative AI. Her impact extends to policy and global discourse, having advised the U.S. government, the United Nations, and the State of California on AI and workforce issues. A member of the National Academy of Engineering, the National Academy of Medicine, and the American Academy of Arts and Sciences, she has received numerous accolades including the VinFuture Prize, Intel Lifetime Achievements Award and recognition in Time Magazine’s AI100. 

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Geoffrey G. Parker is a Professor of Engineering at Dartmouth’s Thayer School of Engineering and Director of the Irving Institute for Energy and Society, as well as a Visiting Scholar and Research Fellow at MIT Sloan’s Initiative for the Digital Economy, where he co-chairs the annual MIT Platform Strategy Summit. His research focuses on the economics and strategy of network platforms, including applications in energy, manufacturing, and healthcare, and he is a co-developer of the theory of two-sided networks, which explains pricing in networked markets. He works with organizations worldwide on platform strategy and is co-author of Platform Revolution (WW Norton, 2016), now published in 10 languages.

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Ian Goodfellow is a pioneering artificial intelligence researcher, best known as the inventor of Generative Adversarial Networks (GANs), a breakthrough that reshaped machine learning by introducing an adversarial training framework capable of generating highly realistic synthetic data. He is also the lead author of the widely acclaimed textbook “Deep Learning”, co-written with Yoshua Bengio and Aaron Courville, which has become a cornerstone reference in the field.

With academic roots at Stanford under Andrew Ng and a Ph.D. from Université de Montréal under Yoshua Bengio, Goodfellow has held influential roles at Google Brain, OpenAI, Apple, and DeepMind, consistently working at the forefront of AI research. Beyond technical innovation, his work on adversarial examples helped expose critical vulnerabilities in machine learning, shaping the global discourse on AI security, ethics, and responsible development, solidifying his reputation as one of the most influential minds in modern AI.

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Jeffrey Ullman, the Stanford W. Ascherman Professor of Engineering (Emeritus) in Computer Science at Stanford University and CEO of Gradiance Corp., is a leading figure in database theory, programming languages, and compiler design. After earning his B.S. from Columbia and Ph.D. from Princeton, he worked at Bell Labs and Princeton before joining Stanford in 1979, where he later chaired the Computer Science Department. Ullman has authored 16 influential books on databases, algorithms, automata theory, and compilers, many co-written with Alfred Aho, shaping how generations of computer scientists learn and practice the field.

His foundational contributions to database theory including query optimization and Datalog as well as compiler pedagogy, underpin key technologies from query planners in modern data systems to the code generation used in machine learning pipelines. A member of the National Academy of Engineering and the American Academy of Arts and Sciences, Ullman has received honors such as the Knuth Prize, the IEEE John von Neumann Medal, and the NEC C&C Prize, cementing his role as one of the most influential educators and theorists in computer science.

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Larry Heck is a Professor with a joint appointment in Electrical and Computer Engineering and Interactive Computing at the Georgia Institute of Technology, holding the Rhesa S. Farmer Advanced Computing Concepts Chair and serving as a Georgia Research Alliance Eminent Scholar. He is a pioneer in speech and dialogue technologies, co-founding Microsoft’s Cortana personal assistant and leading AI initiatives at Microsoft, Google, and Samsung Viv Labs.

Larry began his career at SRI International, where his team was the first to deploy large-scale deep neural networks for speech recognition in industrial applications. He holds a Ph.D. in Electrical Engineering from Georgia Tech, is a Fellow of the IEEE, has authored numerous scientific papers, and holds over 50 U.S. patents.

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Michael I. Jordan is the Pehong Chen Distinguished Professor at the University of California, Berkeley, with joint appointments in Electrical Engineering and Computer Science and Statistics, and is regarded as one of the most influential figures in artificial intelligence and machine learning. His work spans computation, statistics, cognition, biology, and the social sciences, making him a leading voice in shaping the theoretical foundations of modern AI. A member of the National Academy of Sciences, National Academy of Engineering, American Academy of Arts and Sciences, and a Foreign Member of the Royal Society, he has received some of the highest honors in science and engineering, including the WLA Prize (2022), IEEE John von Neumann Medal (2020), and the David E. Rumelhart Prize (2015).

His career has included faculty positions at MIT and UC Berkeley, and he has delivered landmark lectures such as the International Congress of Mathematicians Plenary in 2018 and the IMS Neyman Lecture in 2011. Named the “most influential computer scientist” in a 2016 Science article based on Semantic Scholar rankings, Prof. Jordan continues to shape the direction of AI research with his cross-disciplinary insights and lasting contributions.

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Peter Norvig is an American computer scientist and Distinguished Education Fellow at the Stanford Institute for Human-Centered AI (HAI). He previously served as Director of Research and Search Quality at Google, where he led teams focused on advancing artificial intelligence and improving search technologies. Norvig is the co-author, with Stuart J. Russell, of the influential textbook Artificial Intelligence: A Modern Approach, used in over 1,500 universities across 135 countries. Renowned for his contributions to AI research and education, he has helped shape the field through both groundbreaking work in machine learning and efforts to make AI knowledge widely accessible.

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Dr. Pranam Chatterjee is an Assistant Professor of Bioengineering and Computer and Information Science at the University of Pennsylvania, where he leads the Programmable Biology Group. His lab develops novel methods for de novo protein and peptide design by integrating generative sequence modeling with experimental platforms in vitro and in vivo, with applications spanning protein modulation, genome editing, and cell engineering.

Much of his work focuses on creating new therapeutics for rare pediatric neurodegenerative diseases and cancers, as well as molecules for bioremediation. A graduate of MIT with SB, SM, and PhD degrees, Dr. Chatterjee has received multiple NIH and foundation grants and is deeply committed to translating academic research into clinical practice. He is the co-founder of three biotech companies like Gameto Inc., UbiquiTx Inc., and AtomBioworks Inc. which build on his research to develop innovative protein-based cancer therapies and fertility solutions.

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Regina Barzilay is the School of Engineering Distinguished Professor for AI and Health at MIT, where she is a faculty member in Electrical Engineering and Computer Science and a researcher at the Computer Science and Artificial Intelligence Laboratory (CSAIL). Her work spans natural language processing, machine learning applications in chemistry, and AI-driven oncology, with groundbreaking contributions to early cancer detection and drug discovery.

A MacArthur Fellow (2017) and recipient of the AAAI Squirrel AI Award for Artificial Intelligence for the Benefit of Humanity (2020), she has been recognized for developing AI models that aid in the creation of antibiotics and improve breast cancer diagnosis. Barzilay is also an AAAI and ACL Fellow, and her honors include the NSF CAREER Award, MIT Technology Review’s TR-35 Award, and multiple best paper prizes at ACL and NAACL. She earned her Ph.D. in Computer Science from Columbia University and completed a postdoctoral fellowship at Cornell, establishing herself as one of the leading voices in applying AI for human health and societal benefit.

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Sanjeev Khudanpur is an Associate Professor of Electrical and Computer Engineering at Johns Hopkins University, with a secondary appointment in Computer Science. He applies information theory, statistics, and deep learning to human language technologies, including automatic speech recognition, machine translation, and natural language processing. His research contributed to the development of the Amazon Echo/Alexa and advanced neural language models, with alumni from his group now leading research globally.

He serves as Director of the Center for Language and Speech Processing and the JHU+Amazon Initiative for Interactive AI, and is a founding member of the Human Language Technology Center of Excellence. Khudanpur earned a BTech in Electrical Engineering from the Indian Institute of Technology and a Ph.D. in Electrical Engineering from the University of Maryland, College Park.

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Song leads a lab focused on advancing robot learning for contact-rich manipulation and rapid generalization in real-world environments. Her work blends computer vision rigor with systems-level robotics, combining 3D perception and manipulation to scale data-driven visuomotor skills, develop generalizable affordances, and enable category-level manipulation.

After earning her BEng in Computer Science from HKUST in 2013 and a Ph.D. in Computer Science from Princeton in 2018, she held research roles at Microsoft Research, Google, Princeton, Columbia, and Stanford, producing influential benchmarks, datasets, and algorithms that push robots from clean lab demos into messy kitchens and warehouses. Her accolades include the Facebook Fellowship (2014), Siebel Scholarship (2016), Wallace Fellowship (2017), and Princeton SEAS Award for Excellence (2017), and she was part of the MIT-Princeton team that placed 3rd in the 2016 Amazon Robotics Challenge and 1st (stow task) in 2017.

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Stuart Russell is a Professor of Electrical Engineering and Computer Sciences at UC Berkeley, where he holds the Smith-Zadeh Chair in Engineering and directs the Center for Human-Compatible AI. A leading voice on the future of artificial intelligence, his research spans machine learning, probabilistic reasoning, and the long-term societal impact of AI, with recent work focusing on developing systems that are provably beneficial to humans. He co-authored Artificial Intelligence: A Modern Approach with Peter Norvig, the world’s most widely used AI textbook, adopted in over 1,500 universities across 135 countries.

Beyond academia, Russell has contributed to global security through his work on seismic monitoring for nuclear test-ban verification and has been a prominent advocate for banning lethal autonomous weapons, including co-creating the widely discussed short film Slaughterbots. His recent book, Human Compatible, has been hailed as a pivotal work on aligning AI with human values. Widely recognized for his contributions, he is a Fellow of AAAI, ACM, and AAAS, an Andrew Carnegie Fellow, an Honorary Fellow of Wadham College, Oxford, and a recipient of the IJCAI Computers and Thought Award.

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Terry Winograd is a Professor of Computer Science at Stanford University and co-director of the Human–Computer Interaction Group. He is best known for his pioneering work in natural language understanding with the SHRDLU program and for co-authoring Understanding Computers and Cognition with Fernando Flores, which challenged traditional AI and introduced phenomenological approaches to design and communication.

At Stanford, he founded the Project on People, Computers, and Design and helped establish the d.school, advancing human-centered approaches to software design. A founding member and national president of Computer Professionals for Social Responsibility, Winograd also advised Larry Page during his PhD, contributing to the early development of Google. 

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Weiyan Shi is an Assistant Professor at Northeastern University, jointly appointed in Electrical and Computer Engineering and Khoury College of Computer Sciences. Her research spans natural language processing, human-AI interaction, and AI safety, with a particular focus on AI-driven persuasion exploring how to leverage its societal benefits while addressing potential harms. Recognized on MIT Technology Review’s 35 Under 35 list, she is advancing the understanding of how humans and AI systems communicate, collaborate, and influence one another.

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Yuke Zhu is an Assistant Professor of Computer Science at UT Austin and Director of the Robot Perception and Learning (RPL) Lab, as well as a Director and Distinguished Research Scientist at NVIDIA Research, where he co-leads the Generalist Embodied Agent Research (GEAR) group. His work bridges perception, language, and control to advance embodied intelligence and interactive learning, developing large-scale simulation and multi-task training regimes that enable robots to acquire reusable navigation and manipulation skills and transfer them to the real world. A consistent focus throughout his career has been creating open resources and competitions that have shaped the embodied AI community, with GEAR’s mission centered on building foundation models for embodied agents in both virtual and physical environments.

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