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Giorgio AscoliView profile

Professor

• Computational Neuroanatomy
• Neuroinformatics
• Neuronal Morphology
• +5 more

Giorgio Ascoli is a University Professor in the Department of Bioengineering at George Mason University, where he has been since 1997. He is the Founding Director of the Center for Neural Informatics, Structures, & Plasticity (CN3) and Founding Editor-in-Chief of the journal Neuroinformatics . His affiliations span computational neuroanatomy, neuroinformatics, and hippocampal modeling. Education : PhD in Biochemistry and Neuroscience (1996), Scuola Normale Superiore; MS in Chemistry and Biochemistry (1993), Pisa University; BS in Chemistry and Physics (1991), Scuola Normale Superiore. Dr. Ascoli investigates the relationship between brain structure, activity, and function from cellular to circuit levels. His research focuses on anatomically plausible neural networks to model mammalian brains, particularly the hippocampus, with implications for understanding human memory and consciousness . He pioneered computational neuroanatomy, developing tools like L-Neuron for neuronal shape modeling and curating NeuroMorpho.Org , a central repository for digitally reconstructed neurons. His recent publications emphasize neuronal classification , connectome analysis , and biologically informed machine learning . Awards include the 2012 Outstanding Faculty Award (Virginia), 2022 AIMBE fellowship , and 2023 Presidential Faculty Excellence Awards . He has mentored over 20 graduate students and postdoctoral fellows, with funding from NIH, NSF, DARPA, and private foundations. Scientific Contributions : Over 137 peer-reviewed articles, 4 patents, 2 authored/edited books, and leadership in NeuroMorpho.Org and Hippocampome. Grants : $20M+ in cumulative funding, including NIH R01s, NSF BRAIN EAGERs, and Burroughs-Wellcome Trust support. Labs : Leads the Computational Neuroanatomy Group within CN3, focusing on hippocampal modeling, neuronal morphology, and consciousness theories.

Alexandra PapoutsakiView profile

Associate Professor

• Human-Computer Interaction
• Eye Tracking
• Shared Gaze for Remote Collaboration
• +5 more

Alexandra Papoutsaki is an Associate Professor of Computer Science at Pomona College since 2017. Her research focuses on Human-Computer Interaction , particularly in webcam-based eye tracking and shared gaze for remote collaboration . Ph.D. in Computer Science from Brown University M.Sc. in Computer Science from Brown University B.Sc. in Computer Science from Athens University of Economics and Business Her work spans personal informatics , crowdsourcing methodologies , and remote usability testing , with recent projects examining collaborative drawing interfaces, baby tracking reflection, and digital health communities. Earlier contributions include computational biology research in genome-wide survival analysis and pan-cancer mutation networks. Key trends in her publications include: Remote collaboration tools enhanced by eye tracking Personal data systems for health and creative domains Foundational work in crowdsourcing quality control Interdisciplinary approaches combining computer science with psychology and medicine Scientific recognition includes: NSF CRII Grant (2020-2022) Wig Distinguished Professor Award (2020) Best Paper at RECOMB (2013) She has developed multiple open-source platforms including WebGazer for scalable eye tracking and Remotion for mobile usability testing. Her research team has published extensively in top venues like CHI, IMWUT, and IJCAI.

Camillo J. TaylorView profile

Professor

• Computer Vision
• Robotics
• 3D Reconstruction
• +6 more

Camillo J. Taylor is the Raymond S. Markowitz President’s Distinguished Professor in the Department of Computer and Information Science at the University of Pennsylvania , where he has been a faculty member since 1997. He also serves as Associate Dean for Diversity, Equity, and Inclusion at the School of Engineering and Applied Science. His research focuses on Computer Vision and Robotics , particularly in 3D reconstruction, semantic mapping, and autonomous navigation. Education: A.B. in Electrical Computer and Systems Engineering, Harvard College (1988) M.S. and Ph.D. in Computer Science, Yale University (1990, 1994) Research Interests: Dr. Taylor’s work bridges Computer Vision and Robotics to enable autonomous systems to perceive and navigate complex environments. Key themes include semantic SLAM, event camera applications, and meta-learning for adaptive controllers. His projects often integrate vision, physics, and multi-agent collaboration, as seen in systems like EvMAPPER and OCCAM . Recent Article Trends: His 2024–2025 publications focus on semantic mapping , event-based vision , and multi-agent LLM systems , reflecting his lab’s emphasis on real-time perception, physics-informed reconstruction, and rational decision-making in robotics. These works span applications from solar eclipse imaging to wildfire analysis and natural hazard resilience. Awards: NSF CAREER Award (1998) Lindback Minority Junior Faculty Award (2001) IEEE WACV Best Paper Award (2012) Lindback Distinguished Teaching Award (2012) Advising and Service: Dr. Taylor has advised numerous PhD students, including Jason Hughes and Bowen Jiang. He has served as a Program Chair for CVPR (2006, 2017) and General Chair for ICCV (2021). His contributions to the GRASP Laboratory have advanced autonomous micro-UAVs and semantic SLAM.

Mehran SahamiView profile

Teaching Professor

• computer science education
• machine learning
• information retrieval
• +3 more

Mehran Sahami is the James and Ellenor Chesebrough Professor in the School of Engineering and Tencent Chair of the Computer Science Department at Stanford University. He holds the academic rank of Teaching Professor of Computer Science and is also a Senior Fellow by courtesy at the Freeman Spogli Institute for International Studies. As a Bass University Fellow in Undergraduate Education, he has made significant contributions to computer science education at Stanford. Dr. Sahami earned both his undergraduate and PhD degrees from Stanford University's Computer Science Department. After completing his PhD, he worked as a Senior Engineering Manager at Epiphany before joining Google as a Senior Research Scientist from 2002-2007, while also teaching as a Lecturer at Stanford. In 2007, he joined the Stanford faculty full-time, continuing to consult part-time at Google until 2010. Professor Sahami's primary research interests focus on computer science education, machine learning, and information retrieval on the Web. His work has significantly influenced how computer science is taught globally, particularly through his leadership in the ACM/IEEE-CS Joint Task Force on Computing Curricula 2013 (CS2013). He has pioneered approaches to teaching introductory programming and probability theory for computer scientists, with a particular emphasis on analyzing student performance trends as CS enrollments have grown dramatically. His recent publications demonstrate a strong shift toward educational research while maintaining connections to technical expertise in machine learning and data analysis. Bass University Fellow in Undergraduate Education Professor Sahami serves as the ACM Steering Committee Chair for the CS2013 effort to define international curricular guidelines for undergraduate computer science programs. He is also the founder and first Chair of the Symposium on Educational Advances in Artificial Intelligence (EAAI), an annual meeting for researchers and educators to discuss pedagogical issues in teaching AI. He has received significant grant funding through these initiatives and has been instrumental in shaping national and international computer science curriculum standards. At Stanford, he teaches CS106A: Programming Methodology and CS182: Ethics, Public Policy, and Technological Change, with his educational materials widely distributed through the Stanford Engineering Everywhere initiative. Professor Sahami maintains connections to the startup ecosystem through advisory board positions and has published a book on Text Mining with Ashok Srivastava. His career trajectory from industry researcher to academic educator gives him a unique perspective on practical applications of computer science education.

Gianni FranchiView profile

Assistant Professor

• Machine Learning
• Computer Vision
• Uncertainty Quantification
• +2 more

Gianni Franchi is an Assistant Professor at ENSTA Paris, part of Institut Polytechnique de Paris. His research focuses on robust computer vision, uncertainty quantification, and explainable AI (XAI). He has been teaching Deep Learning, Computer Vision, and Machine Learning courses since 2020 at ENSTA Paris and Télécom Paris. PhD in Fusion of Information, Machine Learning, and Image Processing (2016) from Mines de Paris Postdoctoral experience at Paris Saclay University (2018-2020) and Seigen University (2016-2018) Current PhD students: Rémi Kazmierczak, Olivier Laurent, Adrien Lafage, Mouïn Ben Ammar Alumni: Xuanlong Yu (2020-2023) Research interests include robust computer vision, anomaly detection, uncertainty quantification, out-of-distribution detection, certifiable AI, and explainable AI. He leads the development of the PyTorch library Torch Uncertainty for uncertainty quantification in deep learning. Recent publications span uncertainty quantification in foundation models, trajectory forecasting, vision-language adaptation, and explainability benchmarks. Gianni actively collaborates on multimodal autonomous driving datasets and uncertainty-aware systems for human-agent interaction.

Bernhard SchölkopfView profile

Professor

• Machine Learning
• Causal Inference
• Artificial Intelligence
• +4 more

Bernhard Schölkopf is a Director at the Max Planck Institute for Intelligent Systems and holds a Professorship at ETH Zurich. He is one of Europe's leading researchers in Artificial Intelligence and serves as co-founder and president of ELLIS (European Laboratory for Learning and Intelligent Systems). His scientific interests focus on machine learning and causal inference, with applications spanning biomedical problems, computational photography, and astronomy. Schölkopf has conducted research at prestigious institutions including AT&T Bell Labs, GMD FIRST in Berlin, and Microsoft Research Cambridge before joining the Max Planck Society in 2001. Schölkopf's recent work examines the tangible risks of AI systems rather than speculative existential threats, highlighting important challenges including algorithmic bias, lack of transparency, privacy violations, worker exploitation, carbon footprint of AI systems, and job displacement across multiple publications in different languages. His scientific achievements have been recognized with numerous prestigious awards: Academy Prize of the Berlin-Brandenburg Academy of Sciences and Humanities Royal Society Milner Award Leibniz Award Koerber European Science Prize BBVA Foundation Frontiers of Knowledge Award Allen Newell Award Fellow of the ACM Fellow of the CIFAR Program "Learning in Machines and Brains" Schölkopf is a member of the German Academy of Sciences (Leopoldina) and co-founded the series of Machine Learning Summer Schools. His work bridges theoretical advances in machine learning with practical applications across diverse domains, while advocating for responsible AI development that addresses real-world challenges rather than speculative existential threats.

Dhruv ShahView profile

Assistant Professor

• Robotics
• Machine Learning
• Reinforcement Learning
• +5 more

Dhruv Shah is an Incoming Assistant Professor of Electrical and Computer Engineering at Princeton University starting January 2026 and currently serves as a Senior Research Scientist at Google DeepMind. He is also an Associated Faculty member in Princeton's Center for Statistics and Machine Learning, focusing on the convergence of machine learning and robotics for real-world deployment. His academic credentials include a Ph.D. and M.S. in Electrical Engineering and Computer Sciences from the University of California, Berkeley (2024) and a B.Tech. (Honors) in Computer Science and Engineering from the Indian Institute of Technology, Bombay (2019). Shah's research pioneers foundation models for robotics, emphasizing large-scale robot learning, out-of-distribution generalization, and long-horizon reasoning. His group adopts a full-stack methodology spanning algorithmic innovation to system design, drawing from cognitive science to develop physical AI systems at the perception-learning-control interface. Key focus areas include reinforcement learning, human-robot interaction, and continual learning for challenging environments. Analysis of his 15 most recent publications reveals dominant trends in foundation models for visual navigation, language-conditioned policies, and multi-agent systems. His work increasingly integrates multimodal inputs (vision, language) while addressing generalization gaps in real-world settings, with strong emphasis on efficient data curation and scalable robot learning frameworks. His accolades feature the Microsoft Future Leaders in Robotics & AI Fellowship (2024), two IEEE ICRA Best Conference Paper Awards (2024), multiple ICRA finalist awards across cognitive robotics and manipulation categories, and the Berkeley Fellowship (2019-2024). Shah will recruit PhD students for Princeton's upcoming admissions cycle, establishing a research group dedicated to full-stack robotics development. While specific grant details aren't provided, his trajectory indicates significant funding for AI-robotics convergence projects, particularly in foundation model development and real-world deployment challenges. His laboratory at Princeton will integrate algorithmic innovation with system design, focusing on physical AI systems that bridge perception, learning, and control while maintaining strong ties to cognitive science principles for human-aligned robotic intelligence.

Zsolt KiraView profile

Assistant Professor

• Robotics
• Machine Learning
• Artificial Intelligence
• +6 more

Zsolt Kira serves as an Assistant Professor in the School of Interactive Computing at Georgia Institute of Technology's College of Computing, with additional affiliations at the Georgia Tech Research Institute and as Associate Director of ML@GT. He leads the Robotics Perception and Learning (RIPL) Lab, driving research at the intersection of machine learning and robotics. Dr. Kira earned his Ph.D. in 2010 under Professor Ron Arkin, establishing foundational expertise in robotics and AI before transitioning to his current academic role after industry experience at SRI International Sarnoff. His research pioneers beyond supervised learning through unsupervised, semi-supervised, self-supervised, and continual/lifelong learning frameworks, while advancing distributed perception via multi-modal fusion and cross-robot information integration. This dual focus addresses core challenges in robotic autonomy and sensor processing. Analysis of his 15 most recent publications reveals dominant trends in embodied AI systems, robust foundation model adaptation, and multimodal learning architectures. Key themes include neural radiance field applications, reinforcement learning for locomotion, and novel benchmarks for memory evaluation in agents. While specific advisees and grants aren't documented in the source material, his RIPL Lab leadership implies active graduate mentorship and research funding acquisition. The lab's work directly enables next-generation robotic systems through algorithmic innovation in perception and learning. The RIPL Lab operates as a hub for developing machine learning techniques that solve difficult perception problems in robotics, with particular emphasis on unsupervised learning paradigms and distributed multi-robot systems that push the boundaries of autonomous operation.

Nigel ShadboltView profile

Professor

• Artificial Intelligence
• Web Science
• Open Data
• +7 more

Professor Sir Nigel Shadbolt is Principal of Jesus College Oxford and Professor of Computing Science at the University of Oxford. He is Chairman and Co-founder of the Open Data Institute which he established with Sir Tim Berners-Lee. His career spans multiple prestigious institutions including the University of Southampton and University of Nottingham, where he held leadership roles in AI research. He has served in significant government advisory positions including as Information Advisor to the UK Prime Minister and member of the Public Sector Transparency Board. Nigel Shadbolt earned his undergraduate degree in Philosophy and Psychology from the University of Newcastle (1st Class Honours, 1978) followed by a PhD in Artificial Intelligence from the University of Edinburgh. His academic journey includes positions as the Allan Standen Professor of Intelligent Systems at Nottingham and leadership of the Web and Internet Science Group at Southampton before joining Oxford. Shadbolt's research spans the interdisciplinary field of Web Science, which he helped originate, focusing on human-centered AI, social machines, open data, and ethical considerations in technology. His work examines how humans and computers integrate at web scale to create novel applications, with recent emphasis on children's digital autonomy and privacy. He has published over 500 articles exploring topics from cognitive psychology to computational neuroscience, Artificial Intelligence to the Semantic Web. His notable contributions include the development of data.gov.uk and leadership in establishing Oxford's Institute of Ethics in AI. His 2018 book 'The Digital Ape: how to live (in peace) with smart machines' has been described as a 'landmark book' in the field. Knighted in 2013 for services to science and engineering Fellow of The Royal Society (FRS) Fellow of the Royal Academy of Engineering (FREng) Fellow of the British Computer Society (FBCS) Technology Pioneer status by the Davos World Economic Forum UK national BT Flagship IT Award As an advisor, Shadbolt has supervised numerous PhD students who have gone on to positions at Google DeepMind, the World Bank, and other prestigious institutions. His research has been supported by multiple grants including SOCIAM, KOALA, and ReTiPS projects. He co-founded Garlik Ltd, which was acquired by Experian, demonstrating his commitment to translating research into practical applications that benefit society. Shadbolt leads the Web and Internet Science research group at Oxford, focusing on social machines - applications that succeed at web scale by integrating humans and computers in novel ways. His work on the KOALA Hero Toolkit and CHAITok system addresses critical challenges in children's data autonomy on social media platforms, reflecting his commitment to ethical technology design.

Tracy Xiao LiuView profile

Professor

• Behavioral Market Design
• Economics
• Computer Science

Tracy Xiao Liu is a Professor at the Department of Economics, School of Economics and Management, Tsinghua University. Her research focuses on behavioral market design and the intersection of economics and computer science (Econ-CS), utilizing experimental methods to explore decision-making mechanisms, incentive structures, and behavioral spillovers. Her publications span journals such as Management Science , Games and Economic Behavior , and Journal of Economic Behavior & Organization . Key themes include behavioral economics, game theory, public goods, and experimental studies on market design, incentive contracts, and social preferences. Recent work examines AI-driven economic rationality (GPT models), energy policy impacts, and virtual red packet dynamics in online groups. Tracy collaborates with scholars across institutions, including her husband Baoping Liu (Peking University, harmonic analysis). Her experimental research bridges microeconomic theory with real-world applications in education, pensions, and crowdsourcing platforms.

Yon VisellView profile

Associate Professor

• Haptics
• Robotics
• Human-Computer Interaction
• +5 more

Prof. Yon Visell is an Associate Professor at the University of California, Santa Barbara (UCSB) with appointments in the Department of Bioengineering, Department of Electrical and Computer Engineering, and Department of Mechanical Engineering. He directs the RE Touch Lab, which focuses on haptics, robotics, and interactive technologies, including sensorimotor augmentation, soft robotics, and virtual reality applications. The lab is affiliated with the Media Arts and Technology Program, Communication and Signal Processing group (ECE), Dynamic Systems and Control group (ME), California NanoSystems Institute, Center for Polymers and Organic Solids, UCSB Research Center for Virtual Environments and Behavior, and UCSB Robotics Group. Education: PhD in Electrical and Computer Engineering from McGill University, MA in Physics from University of Texas, Austin, and BA in Physics from Wesleyan University His research spans robotics, haptics, biomechanics, and soft electronics, aiming to advance human-computer interaction and wearable technologies. Recent work includes light-driven tactile displays, biomechanical filtering in tactile encoding, and haptic systems for VR and healthcare. The lab has received numerous awards at IEEE Haptics Symposium, World Haptics Conference, and EuroHaptics Society events. 2025 Best Demonstration Awards at IEEE World Haptics Conference 2024 Best Paper at IEEE Haptics Symposium 2023 Best Journal Paper at IEEE Transactions on Haptics Visell's group has pioneered wave-based haptic rendering, tactile holography, and soft wearable robotics. They have developed tools like SkinSource for tactile biomechanics simulation and collaborated with institutions in North America, Europe, and Japan. Current projects involve photonics-driven tactile systems, soft robotics for therapy, and next-generation haptic interfaces while mentoring students like Gregory Reardon (2024 EuroHaptics Best Dissertation), Max Linnander (IEEE Haptics awards), and Neeli Tummala (SWE Intel Scholar). Research Grants: Funded by NSF, tech, and healthcare industries Lab: RE Touch Lab at California NanoSystems Institute Collaborations: with TU Dresden, Northwestern University, NC State, UCLA, and others

Soojin Oh ParkView profile

Assistant Professor

• Early Childhood Education
• Bilingual Education
• Family Literacy
• +7 more

Soojin Oh Park is an Assistant Professor in the Learning Sciences & Human Development department at the University of Washington College of Education. Her research focuses on equity-driven improvements in early childhood education, family literacy engagement with Dual Language Learners (DLLs), and culturally grounded parenting practices. Ed.D., Human Development and Education, Harvard University M.Ed., Educational Policy and Analysis, Harvard University M.S.Ed., Learning, Teaching, and Literacies, University of Pennsylvania B.A., Psychology, University of Pennsylvania Park’s interdisciplinary work combines qualitative, quantitative, and mixed-methods approaches to address gaps in early childhood education (ECE) and parenting, emphasizing: Family literacy engagement for DLLs’ language and literacy development Culturally sustaining pedagogy to amplify family strengths Equity-oriented reforms in ECE systems and policies Her publications span topics like heritage language maintenance, culturally responsive assessment, and the impact of immigration policy on child development, often utilizing advanced methodologies such as machine learning. Awards include the SRCD Federal Policy Fellowship and Best Article of the Year from the Journal of School Psychology. Outstanding Research Paper Award (Korean American Educational Research Association) Distinguished Paper Award (Washington Educational Research Association) Best Article of the Year Award (Journal of School Psychology) Emerging Scholar Fellowship (National Research Center on Hispanic Children and Families) Park leads projects such as FAMILY (Fathers And Mothers Investing in Learning of Young Children) and BASECAMP (Bolstering Asian Students in Early Childhood by Advancing Multilingual Pedagogies). She teaches courses on language and literacy learning, bilingual development, and parenting impacts on early education.

• Computer Vision
• Machine Learning
• Robotics
• +6 more

Dr. Feras Dayoub is a Senior Lecturer at the School of Computer and Mathematical Sciences (Faculty of Sciences, Engineering and Technology) at the University of Adelaide , specializing in Embodied AI and Robotic Vision within the Australian Institute for Machine Learning (AIML) . He co-directs the CROSSING French-Australian laboratory for human-autonomous agent teaming and holds an Adjunct position at the Queensland University of Technology (QUT) , serving as an Associate Investigator at its Centre for Robotics . Previously, he was a Chief Investigator at the ARC Centre of Excellence for Robotic Vision . His research focuses on advancing reliable deployment of computer vision and machine learning on mobile robots in real-world environments. Applied projects include agricultural automation , environmental conservation , and autonomous infrastructure monitoring . He has published extensively on topics like object detection , domain adaptation , 3D representation learning , and vision-language navigation , with a particular emphasis on robustness in dynamic and partially observed environments. Dr. Dayoub is also an educator specializing in programming , computer vision , and robotic perception . He contributes to open-source robotics research through tools like AARK (Autonomous Racing Toolkit) and has led teams developing solutions for precision agriculture (e.g., Deepfruits fruit detection system) and environmental monitoring (e.g., Crown-Of-Thorns starfish detection ). Key Collaborations : CROSSING Lab, QUT Centre for Robotics Research Themes : Embodied AI, Robust Perception, Domain Adaptation

Chenhao TanView profile

Associate Professor

• language and social dynamics
• human-centered machine learning
• multi-community engagement
• +3 more

Chenhao Tan serves as an Associate Professor in the Department of Computer Science and the UChicago Data Science Institute at the University of Chicago. His academic journey includes a PhD from Cornell University, followed by a postdoctoral position at the University of Washington, and previous affiliation with the University of Colorado Boulder. Dr. Tan's research focuses on language and social dynamics , human-centered machine learning , and multi-community engagement . He is broadly interested in computational social science, natural language processing, and artificial intelligence. His work explores the paradigm of 'machine-in-the-loop' systems where AI serves as an assistant rather than a replacement for human capabilities, particularly in complex tasks requiring human judgment. His publications demonstrate significant contributions across multiple domains including AI ethics, creative writing assistance, political language analysis, and healthcare applications. His research shows a clear trajectory toward developing AI systems that enhance human capabilities while addressing important ethical considerations in AI deployment. Sloan Research Fellowship Dr. Tan's research program examines how machine learning can be integrated into human workflows to augment human capabilities rather than replace them. His laboratory investigates applications across social media, healthcare, creative writing, and political discourse analysis, with current projects focusing on AI platform moderation, detection of subtle signals in professional communication, and computational analysis of social dynamics.

Pontus StenetorpView profile

Professor

• Natural Language Processing
• Artificial Intelligence
• Machine Learning
• +3 more

Pontus Stenetorp is a Professor of Natural Language Processing at University College London (UCL) and holds a Specially Appointed Professorship at the National Institute of Informatics (NII) in Japan. At UCL, he leads the NLP Group, serves as Deputy Director of the Centre for Artificial Intelligence, and directs the Facebook Artificial Intelligence/UCL PhD Programme. His research focuses on the intersection of human language and computational systems, emphasizing empirical approaches to advancing and evaluating language processing capabilities. He is affiliated with UCL's Department of Computer Science and NII's Large Language Model Research and Development Center. Stenetorp prioritizes in-person collaboration for internships and research visits due to institutional bureaucratic constraints at UCL. He actively supervises MSc theses in Machine Learning and Data Science programs but restricts undergraduate and industry-oriented projects to exceptional cases. Communication preferences include structured emails sent to role-specific addresses: p.stenetorp@cs.ucl.ac.uk (UCL), pontus@nii.ac.jp (NII), and pontus@stenetorp.se (general). His research group, the NLP Group at UCL, pursues cutting-edge projects in computational linguistics and AI, often involving collaborative efforts with students and industry partners. While no specific awards are listed, his dual institutional roles underscore his significant contributions to the field of NLP and AI research.