Claire Bowern is Professor of Linguistics at Yale University specializing in historical linguistics, language documentation, and Australian Indigenous languages. Her research employs computational phylogenetics to study language evolution and supports language revitalization through digital archives and fieldwork methodologies. Recent publications address: Phylogenetic signal in lexical evolution across language families Digital infrastructure for endangered language documentation (FLEx software analysis) Decolonizing linguistics pedagogy and research practices Her work consistently integrates linguistic, anthropological, and computational approaches to analyze language diversity and change. She contributes to global databases including Grambank and D-PLACE, examining links between linguistic, cultural, and environmental patterns.
Joshua D. Angrist is the Ford Professor of Economics at the Massachusetts Institute of Technology, where he has been a faculty member since 1996. He is also a co-founder and director of MIT's Blueprint Labs and a Research Associate at the National Bureau of Economic Research. Angrist shares the 2021 Nobel Prize in Economic Sciences with David Card and Guido Imbens for their methodological contributions to the analysis of causal relationships. Angrist received his B.A. from Oberlin College in 1982 and completed his Ph.D. in Economics at Princeton University in 1989. Prior to joining MIT, he taught at Harvard University and the Hebrew University of Jerusalem. His academic journey began somewhat unconventionally, as he left high school early after 11th grade, worked for over a year, and only later discovered his passion for economics through an inspiring teacher at Oberlin. Angrist's research focuses on developing and applying innovative econometric methods to answer important economic questions using natural experiments. His work spans labor economics, education economics, and causal inference methodology. He is particularly known for his contributions to instrumental variables methods and the Local Average Treatment Effect (LATE) framework developed with Guido Imbens. His research explores the economics of education and school reform, the impact of social programs on labor markets, and the effects of immigration and regulation. His recent publications reveal a continued focus on causal inference methods applied to education policy questions, labor market issues, and health economics. The trend shows increasing sophistication in research design, with particular attention to addressing selection bias and developing methods for external validity. His work spans theoretical econometric contributions alongside empirical applications in education, labor markets, and health. Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel (2021) Fama Prize for Graduate Education (2018) Fellow of the American Academy of Arts and Sciences Fellow of the Econometric Society Angrist is deeply committed to teaching and mentoring. He has developed influential econometrics textbooks including 'Mostly Harmless Econometrics' and 'Mastering Metrics' with Jörn-Steffen Pischke. At MIT, he teaches courses including Labor Economics I (14.661), Econometric Data Science (14.32), and Labor Economics and Public Policy (14.64). He emphasizes selecting UROP students who have mastered foundational economics through courses like 14.64 and 14.32. Beyond MIT, Angrist co-founded Avela, a software startup using cutting-edge research to help schools improve enrollment and operations. Angrist co-founded and directs MIT's Blueprint Labs, which brings together researchers from economics, computer science, and education to develop innovative solutions for educational challenges. Through Blueprint Labs and Avela, his work bridges academic research with practical applications in education policy and technology.
Adrian Weller is a prominent researcher and academic at the University of Cambridge, serving as a Director of Research in Machine Learning within the Department of Engineering. He holds multiple significant leadership roles including Programme Director for Trust and Society at the Leverhulme Centre for the Future of Intelligence (CFI), and previously served as Programme Director for AI at The Alan Turing Institute, the UK national institute for data science and AI. His work bridges theoretical machine learning research with practical applications and societal implications of artificial intelligence. Weller's research interests span a broad spectrum of AI and machine learning topics with a particular focus on ensuring beneficial societal outcomes. His work encompasses explainability, fairness, robustness, scalability, privacy, safety, and ethics in AI systems. He has made significant contributions to trustworthy machine learning, including developing frameworks for AI governance, certification, and human-AI collaboration. His research group actively investigates neuro-symbolic approaches, privacy-preserving techniques, and methods for improving the reliability and interpretability of AI systems. His recent publications demonstrate a strong trend toward addressing the practical challenges of deploying AI systems in real-world contexts, particularly focusing on certification frameworks, governance mechanisms, and human-centered approaches. His work spans theoretical advances in machine learning architectures while maintaining a strong connection to societal impact, with publications appearing in top venues across AI, machine learning, and interdisciplinary applications. Scientific Awards: MBE for services to digital innovation (2022 Queen's Birthday Honours) Turing AI Fellowship for Trustworthy Machine Learning Weller actively supervises a large group of PhD students and postdocs, with current students including Juyeon Heo, Yanzhi Chen, Katie Collins, Isaac Reid, Yichao Liang, Herbie Bradley, and Shoaib Siddiqui. His former students have gone on to positions at leading institutions including Google DeepMind, ETH Zurich, NYU, and MPI-IS Tübingen. He has served on numerous advisory boards including the Centre for Data Ethics and Innovation, UNESCO's expert group on AI ethics, and the World Economic Forum's Global Future Council on AI. His research has been supported through his Turing AI Fellowship and various collaborative projects focused on safe and ethical AI development. Weller leads a vibrant research group focused on trustworthy machine learning, which actively organizes workshops and conferences including ICML 2024 (where he served as Program Chair), multiple workshops on responsible AI, and events through the ELLIS network. His group collaborates extensively across disciplines, working with researchers in computer science, social sciences, law, and policy to address the multifaceted challenges of developing beneficial AI systems.
Colin Raffel , currently an Associate Professor at the University of Toronto and Associate Research Director at the Vector Institute , is a leading researcher in machine learning and natural language processing . His career spans roles at Hugging Face (Faculty Researcher), Google Brain (Senior Research Scientist), and UNC Chapel Hill (Assistant Professor). Education: PhD in Electrical Engineering (Columbia), MA in Music/Science (Stanford), BA in Mathematics (Oberlin) Key affiliations: Google Brain (2016-2020), Hugging Face (2021-present), Vector Institute (2023-present) His research focuses on language model development , attention mechanisms , efficient machine learning , and music information retrieval . Recent work explores model merging , parameter-efficient fine-tuning , and data-constrained language models . Teaching : Has instructed courses at University of Toronto and UNC Chapel Hill on Neural Networks , Deep Learning , and Information Theory . Academic service includes organizing ICLR workshops and serving as Senior Area Chair for NeurIPS and EMNLP . Notable awards : NSF CAREER (2022), Caspar Bowden Award (2023), NeurIPS Outstanding Paper (2023) Key contributions : Core developer of WT5 , Git-Theta , and mir_eval software
Chris Donahue is an Assistant Professor in the Computer Science Department at Carnegie Mellon University . He also serves as a part-time Research Scientist at Google DeepMind on the Magenta team. His work focuses on leveraging generative AI to enhance human creativity, particularly in music. Education: PhD in Computer Science (UC San Diego), Postdoctoral Scholar (Stanford University) His research spans controllable generative modeling of music and audio , with a focus on real-time interactive systems. Projects like Piano Genie , Beat Sage , and Copilot Arena demonstrate his commitment to real-world deployment. His Generative Creativity Lab (G-CLef) explores AI applications beyond music, including programming and natural language. Recent publications highlight advancements in multimodal music evaluation , real-time adaptation , and AI-driven sound morphing . He co-developed Magenta RealTime , an open-weight real-time music generation model, and MusicFX DJ Mode . Scientific Awards: Best Paper Award (top 1) at NAACL Student Research Workshop 2025 Best Paper Award (top 1% of submissions) at CHI 2025 Best Paper Runner-up at ISMIR 2021 He co-advises PhD students like Wayne Chi (NDSEG Fellow) and mentors Irmak Bukey . His lab receives support from the AIxArts incubator fund at CMU .
Lerrel Pinto is an Assistant Professor of Computer Science at the Courant Institute of Mathematical Sciences at New York University (NYU), where he leads the General-purpose Robotics and AI Lab (GRAIL) as part of the CILVR research group. His work bridges the gap between theoretical machine learning and practical robotics applications, with a focus on enabling robots to generalize and adapt in real-world environments. Dr. Pinto received his undergraduate degree from IIT Guwahati, followed by a PhD from the Robotics Institute at Carnegie Mellon University (CMU). He then completed a postdoctoral fellowship at the University of California, Berkeley before joining NYU as faculty. His research program centers on robot learning and decision making, with several key thrusts that demonstrate his innovative approach to robotics. Pinto's work emphasizes large-scale learning techniques that leverage both extensive data and sophisticated model architectures. A significant portion of his research focuses on representation learning for sensory data, particularly developing methods that enable robots to make sense of visual, tactile, and auditory inputs. His lab has made notable contributions to reinforcement learning algorithms that allow robots to adapt to new scenarios with minimal retraining. Pinto also champions open-source robotics , developing affordable robot platforms that democratize access to robotics research. Analysis of Pinto's recent publications reveals a strong trend toward multimodal perception in robotics, integrating visual, tactile, and auditory information to create more robust robot systems. His work increasingly focuses on zero-shot and few-shot learning capabilities, enabling robots to handle novel situations without extensive retraining. There's also a clear progression toward general-purpose robotics , moving away from task-specific solutions toward more flexible systems that can handle diverse real-world challenges. Dr. Pinto's scientific contributions have been recognized with several prestigious awards: Sloan Research Fellowship (2025) NSF CAREER Award (2024) RAL Early Career Award (2024) Best Student Paper Award at ICRA (2016) Outstanding Paper Award at MFM-EAI workshop at ICML (2024) Best Paper Award at NGSM workshop at ICML (2024) Best Student Paper Award at RSS (2023) As an advisor, Pinto has mentored numerous students who have gone on to impactful careers in both academia and industry. His former PhD student Denis Yarats co-founded Perplexity.AI, while Mahi Shafiullah became a postdoc at UC Berkeley and Meta AI. Many of his Masters students have pursued PhDs at top institutions like CMU, MIT, and Stanford, or joined leading robotics companies including 1X, Fauna Robotics, and NVIDIA. Pinto's lab has secured significant research funding, including the NSF CAREER award and likely other grants supporting his robotics research program. The General-purpose Robotics and AI Lab (GRAIL) that Pinto leads brings together a diverse team of researchers working on cutting-edge robotics challenges. The lab maintains strong collaborations with industry partners and other academic institutions, facilitating technology transfer and real-world impact. GRAIL's research spans multiple robotics platforms and focuses on developing algorithms that enable robots to learn from diverse experiences and generalize across environments.
Dr. Hassan Qudrat-Ullah is a Professor at the School of Administrative Studies, York University, and Coordinator of the Certificate in Logistics Management. He holds a PhD in Decision Sciences from NUS Business School and completed a post-doctoral fellowship at Carnegie Mellon University. His research focuses on dynamic decision making, system dynamics modeling, energy planning, and interactive learning environments. He teaches courses on quantitative methods, logistics, and decision analysis, informed by global industry experience across 20+ countries. Research interests include sustainability, climate change, systems thinking, and educational applications of decision sciences. He serves as Editor-in-Chief of the International Journal of Complexity in Applied Science and Technology and is a member of IEEE, DSI, and the International System Dynamics Society. His work has been published in Energy , Decision Support Systems , and others. Key projects include studies on 'structured-debriefing in dynamic decision making' and renewable energy policies in Africa. His recent articles (2023–2025) address AI integration in energy governance, system dynamics for supply chain resilience, and education for sustainability. Hassan advocates for systems thinking in K-12 education and enjoys traveling (visited 129 countries) and bird-watching.
Michael Carbin is the Jamieson Career Development Assistant Professor of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology (MIT) and leads the MIT Programming Systems Group. His research focuses on programming systems that address system uncertainty to enhance performance, energy efficiency, and resilience, particularly in environments involving neural networks , approximate computing , and unreliable hardware . His work spans probabilistic programming , quantum computing , and machine learning systems . Articles highlight contributions in pruning neural networks , quantum data structures , and compiler optimization , reflecting trends in deep learning , formal verification , and language-driven systems . Scientific Awards : MIT Frank E. Perkins Award (2020) Sloan Research Fellowship (2020) Facebook Research Award (2019) NSF CAREER Award (2018) Best Paper Awards at OOPSLA (2013, 2014) He has advised numerous graduate students and postdocs including Eric Atkinson, Cambridge Yang, and Charles Yuan, and served on program committees for conferences like POPL, OOPSLA, and ICLR. His group collaborates with institutions such as MIT CSAIL and explores applications in quantum algorithms and probabilistic inference .
Harris H. Wang is an Associate Professor in the Department of Systems Biology and Department of Pathology and Cell Biology at Columbia University's Vagelos College of Physicians and Surgeons, where he also serves as Interim Chair of Systems Biology. He is affiliated with the Center for Computational Biology and Bioinformatics (C2B2) and the Integrated Program in Cellular, Molecular and Biomedical Studies (CMBS). B.S., Physics and Mathematics, MIT Ph.D., Biophysics, Harvard University Dr. Wang's research lies at the intersection of systems and synthetic biology, focusing on developing foundational technologies for genome engineering, microbiome manipulation, and synthetic genomics. His lab pioneers methods such as MAGE, MAGIC, CAST, and CAMII to enable high-throughput genetic manipulation, in situ microbiome engineering, and AI-driven microbial culturomics. Key research themes include understanding microbial community dynamics, engineering cellular memory systems, designing biocontained genetic circuits, and applying synthetic biology to human health challenges in personalized medicine and infectious disease. His recent publications reveal a strong trend in spatial and functional metagenomics, CRISPR-based microbiome editing, and synthetic biology tools for data storage and genetic stability. The articles span high-impact journals like Nature , Science , and Nature Biotechnology , reflecting his leadership in developing scalable, programmable biological systems. Scientific Awards: NIH Director’s Early Independence Award Forbes 30 Under 30 in Science Sloan Research Fellowship NSF CAREER Award ONR Young Investigator Award Burroughs Wellcome Fund PATH Award Schaefer Scholar Blavatnik National Award Vilcek Prize PECASE Dr. Wang has advised numerous PhD and postdoctoral researchers, many of whom have gone on to independent scientific careers. His lab is supported by major grants from NIH, NSF, DARPA, DOE, and foundations including the Bill & Melinda Gates Foundation and CZ Biohub NY. He is actively involved in educational initiatives, including organizing Columbia’s iGEM team and the Cold Spring Harbor Laboratory Synthetic Biology course. The Wang Lab is based at the Columbia University Irving Medical Center and is part of national consortia such as the Engineering Biology Research Consortium (EBRC) and the Genome Project-Write (GP-Write) initiative. The lab develops and applies cutting-edge technologies in automation, machine learning, and synthetic biology to engineer microbiomes for applications in medicine, global health, and climate change.
Tijana Gajić is a researcher at Singidunum University in Belgrade, Serbia. She holds a Ph.D. in Philology (2020), an MA in English Language and Literature (2009), and a BA in the same field (2007) from the University of Belgrade. Her work focuses on the intersection of language education, digital tools, and critical pedagogy. Doctoral Studies: Philology , University of Belgrade (2017-2020) Master Studies: English Language and Literature , University of Belgrade (2008-2009) Bachelor Studies: English Language and Literature , University of Belgrade (2003-2007) Dr. Gajić’s research explores the impact of digital technologies on language acquisition, critical thinking development, and the sociopolitical dimensions of English language learning. She has published extensively on mobile applications like Duolingo, HelloTalk, and WordUp, analyzing their efficacy in modern pedagogy. Her recent publications (2024-2025) examine digital tools’ influence on language education, critical pedagogy in Serbian classrooms, and AI-driven language learning innovations. Earlier works (2020-2016) established her expertise in tandem learning, vocabulary enhancement via apps, and affective factors in language acquisition. Notable collaborations include partnerships with researchers like N. Maenza , A. Gagić , and J. Nikolić . While no specific grants or awards are mentioned, her involvement in conferences such as Sinteza and ELTA Serbia underscores her active role in advancing language education. Her 2022 book, Nastava stranih jezika i razvoj kritičkog mišljenja u eri neoliberalizma , synthesizes her critical pedagogy framework. Current projects likely continue her focus on digital transformation in language teaching.
Bo Dai is an Assistant Professor at the School of Computational Science and Engineering, Georgia Institute of Technology, and a Staff Research Scientist at Google DeepMind. His research focuses on Agent AI, Generative Models, and Representation Learning, aiming to create decision-making agents through world modeling. He holds a Ph.D. from Georgia Tech (2013–2018) and previously worked at Google Brain. Dai has authored numerous influential papers in top conferences like NeurIPS, ICML, and ICLR, and received the AISTATS Best Paper Award (2016). Education: Ph.D., School of Computational Science and Engineering, Georgia Tech (2013–2018) Research Interests: Reinforcement Learning and Representation Learning for decision-making agents Generative Models and their integration with Agent AI Provable and scalable algorithms for real-world applications His work bridges theory and practice, emphasizing spectral representations and provable guarantees in complex systems. Key Article Trends: His recent work emphasizes scalable spectral methods for multi-agent systems, diffusion policies, and representation-based techniques in reinforcement learning. He also explores LLM alignment and sim-to-real transfer learning. Awards: AISTATS Best Paper Award (2016) NeurIPS Workshop Best Paper (2017) Ross Fellowship (2011–2012) Advising & Grants: Supervises 6 current Ph.D. and M.S. students. Active in organizing workshops on reinforcement learning and serves as an Area Chair for top conferences. Labs & Software: Leads development of Representation-based Reinforcement Learning and Repr-Control toolboxes for nonlinear control and stochastic systems.
Maja J Matarić is the Chan Soon-Shiong Chaired and Distinguished Professor of Computer Science at the University of Southern California's Viterbi School of Engineering, with courtesy appointments in Neuroscience and Pediatrics. She serves as founding director of the USC Robotics and Autonomous Systems Center, co-director of the USC Robotics Research Lab, and Principal Scientist at Google DeepMind. Previously, she held leadership roles as USC's interim Vice President of Research (2020-2021) and Vice Dean for Research (2006-2019). Her educational background includes: PhD in Computer Science and Artificial Intelligence from MIT (1994) MS in Computer Science from MIT (1990) BS in Computer Science from University of Kansas (1987) Matarić pioneers Socially Assistive Robotics (SAR) , a field her lab named, focusing on human-robot interaction that provides assistance through social rather than physical support. Her research targets critical health and wellness challenges including post-stroke rehabilitation, autism spectrum disorder therapy, cognitive exercises for Alzheimer's patients, ADHD academic support, and mental health interventions. She develops systems modeling user engagement, personality, and motivation, with extensive real-world deployments in schools, rehabilitation centers, and homes. Analysis of her recent publications reveals dominant themes in cognitive health robotics (2025 CHI paper on LLM-powered elder care), pediatric assistive technology (2025 IDC speech therapy review), and adaptive preference modeling (2025 HRI contrastive learning work). Her research consistently bridges machine learning with human-centered design for vulnerable populations. Major scientific recognition includes: ACM Athena Lecturer Award (2024) ACM Eugene L. Lawler Humanitarian Award (2024) ACM Fellow (2020) Presidential Mentoring Award (2011) Multiple society fellowships (AAAS, IEEE, AAAI) As a dedicated mentor, Matarić has championed underrepresented groups through CRA-W, placing numerous women in faculty positions. She leads USC Viterbi's K-12 STEM Outreach Program serving low-income Los Angeles schools and authored The Robotics Prime for student education. Her research has secured significant funding enabling real-world technology transfer, with documented impact in rehabilitation centers and homes through deployable SAR systems. Her Robotics Research Lab at USC drives innovation in embodied AI, with current projects spanning LLM-integrated elder care robots, ADHD academic companions, and autism therapy systems. The lab emphasizes co-design with end-users and rigorous real-world validation across diverse populations.
Olga Sorkine-Hornung is a Professor of Computer Science at ETH Zurich and head of the Institute of Visual Computing. She leads the Interactive Geometry Lab, focusing on theoretical and practical advancements in digital content creation, geometry processing, and shape modeling. Current position: ETH Zurich, Department of Computer Science Previous roles: Courant Institute (NYU), Technical University of Berlin Education: BSc and PhD from Tel Aviv University, postdoc at TU Berlin Her research spans shape representation, digital fabrication, computer animation, and fundamental geometry processing. Key contributions include Laplacian surface editing, as-rigid-as-possible deformation, and generalized winding numbers. She works on applications in VR, AR, and autonomous systems. Awards include Test of Time Awards (2024), ACM Fellow (2020), ERC Consolidator Grant (2020), and EUROGRAPHICS Young Researcher Award (2008). She has supervised numerous students and co-developed software libraries like libigl and Instant Meshes . Co-chair roles for SIGGRAPH, Eurographics, and Pacific Graphics Editorial board member for ACM Transactions on Graphics and other journals Keynote speaker at VMV, CVPR, and SIAM conferences Her work bridges mathematical rigor with practical implementation, advancing computer graphics and geometry processing through intuitive algorithms that maintain surface detail while enabling efficient computation.
Roberto Martin-Martin is an Assistant Professor of Computer Science at the University of Texas at Austin, where he leads the Robot Interactive Intelligence (RobIN) Lab. His research bridges robotics, computer vision, and machine learning to enable robots to operate autonomously in human-centric environments like homes and offices. Previously, he was a Postdoctoral Scholar at the Stanford Vision and Learning Lab working with Fei-Fei Li and Silvio Savarese, and an AI Researcher at Salesforce AI. Education: Ph.D. and M.Sc. in Robotics from Technische Universität Berlin (TUB), advised by Professor Oliver Brock B.Sc. from Universidad Politécnica de Madrid Dr. Martin-Martin's research focuses on developing AI algorithms that combine reinforcement learning and imitation learning with advanced planning and control to address core challenges in robot perception. His work spans mobile and whole-body manipulation, dexterous and contact-rich interactions, and long-horizon tasks in unstructured environments. He takes inspiration from human cognition through psychology and cognitive science to develop solutions for skills ranging from simple pick-and-place operations to complex tasks like cooking and furniture assembly. His recent publications demonstrate a strong trend toward enabling robots to learn from human demonstrations, particularly through video, and to safely adapt these demonstrations to their own morphology. There's significant emphasis on mobile manipulation, bimanual tasks, and developing hardware that supports robust robot learning through trial and error. His work shows increasing integration of large language models and vision-language models to enhance robot understanding and task execution. Scientific Awards: RSS Pioneer (2020) Winner of Amazon Picking Challenge (2015) RSS Best Systems Paper Award (2016) ICRA Best Paper Award IROS Best Mechanism Award Amazon Faculty Award AAAI Young Faculty IJCAI Early Faculty Nominated for Best Paper at IROS (2014, 2017) Dr. Martin-Martin advises PhD students including Arpit Bahety, who is working on mobile manipulation and learning. He serves as Chair of the IEEE Technical Committee on Mobile Manipulation and is a co-founder of QueerInRobotics. His research is supported by industry partnerships and academic funding sources that enable his lab to develop both hardware and software innovations in robotics. He directs the Robot Interactive Intelligence (RobIN) Lab at UT Austin, which takes a holistic approach to robot intelligence, developing both the hardware (like the BaRiFlex gripper) and software frameworks necessary for robots to learn from interaction. The lab's research addresses the full pipeline from perception to action, with particular emphasis on learning from human demonstrations, safe exploration, and adapting to novel objects and environments.
Peter Bühlmann is a Professor at ETH Zürich within the Seminar für Statistik , focusing on high-dimensional statistics, causal inference, and machine learning. His work bridges theoretical advancements with practical software implementations in R packages like pcalg , mboost , and glmmlasso , impacting fields such as genomics, proteomics, and intensive care analytics. Key Contributions : Causal structure learning, stability selection, anchor regression, and deconfounding. Software : Developed widely used R packages for statistical modeling and causal inference. Teaching : Courses on high-dimensional statistics at ETH Zürich and international institutions. Research Trends : Recent articles emphasize causal robustness, domain adaptation, and applications in medicine. His work addresses challenges in heterogeneous data, missing values, and covariate shifts using methods like spectral deconfounding and residual prediction tests. Scientific Recognition : Co-author of a paper designated as a New Hot Paper (Meinshausen and Bühlmann, 2006) by Essential Science Indicators, indicating significant impact in high-dimensional multiple testing.
