Find Academic Supervisors

Pieter AbbeelView profile

Professor

• Artificial Intelligence (AI)
• Robotics
• Machine Learning
• +5 more

Pieter Abbeel is a Professor in the Department of Electrical Engineering and Computer Sciences (EECS) at the University of California, Berkeley. He leads the Berkeley Robot Learning Lab and co-directs the Berkeley Artificial Intelligence Research (BAIR) Lab. His work focuses on advancing AI and robotics through deep reinforcement learning, imitation learning, and unsupervised learning, with applications in automation, healthcare, and education. Abbeel's research also explores the societal implications of AI and its potential to revolutionize other scientific and engineering fields. Education: Ph.D. in Computer Science, Stanford University (2008) M.S. in Electrical Engineering, KU Leuven, Belgium (2000) Research Interests: Robotics, AI, Machine Learning, Reinforcement Learning, Autonomous Systems, and Applications in Surgery, Manufacturing, and Education. Recent Article Trends: Focus on multimodal learning, robot manipulation, protein structure prediction, and scalable AI systems. Key areas include sim-to-real transfer, embodied AI, and foundation models for decision-making. Awards & Honors: IEEE Kiyo Tomiyasu Award (2022) ACM Prize in Computing (2021) IEEE Fellow (2018) MIT Tech Review TR35 (2011) Advising & Grants: Advises startups and has received grants from NSF, DARPA, and industry partnerships. Notable students include those advancing robotics, reinforcement learning, and bioAI. Labs & Initiatives: Berkeley Robot Learning Lab, BAIR Lab, and collaborations with the Center for Human-Compatible AI (CHAI). Founded companies include Gradescope, Covariant, and Berkeley Open Arms.

Peter N. BelhumeurView profile

Professor

• Computer Vision
• Machine Learning
• Species Identification Technology
• +3 more

Peter N. Belhumeur is a Professor in the Department of Computer Science at Columbia University and Director of the Laboratory for the Study of Visual Appearance (VAP LAB). He holds a Sc.B. from Brown University and a Ph.D. from Harvard University, followed by a postdoctoral fellowship at the University of Cambridge. His career includes roles at Yale University before joining Columbia in 2002. Education: Brown University (Sc.B., 1985), Harvard University (Ph.D., 1993) Postdoc: Isaac Newton Institute, University of Cambridge (1994) His research focuses on computer vision and machine learning, with applications in biodiversity and mobile technology. Notable projects include the Leafsnap, Birdsnap, and Dogsnap apps – pioneering species/breed identification tools using machine learning. He has received awards such as the PECASE, Helmholtz Prize, and EO Wilson Biodiversity Technology Pioneer Award. His work bridges academia and industry, demonstrated by collaborations with Dropbox and contributions to consumer-facing AI applications. The VAP LAB explores visual appearance modeling and computational photography.

Dirk Bernhardt-WaltherView profile

Professor

• Visual Perception
• Cognitive Neuroscience
• Scene Categorization
• +5 more

Professor Dirk Bernhardt-Walther is an academic at the University of Toronto, serving as Program Director of the Cognitive Science Program and Department of Psychology . He investigates neural and computational mechanisms underlying high-level sensory perception, focusing on real-world scenes, mid-level vision, and visual aesthetics. Education: PhD in Computation and Neural Systems (Caltech, 2006), M.Phil (University of Cambridge) Research Focus: His lab employs fMRI, MEG, EEG , and GAN-generated stimuli to study scene categorization, perceptual organization, and aesthetic processing. Recent work explores curvature perception, emotion representation in scenes, and neural dissociations between computational and subjective visual metrics. Laboratory Members: The Bernhardt-Walther Lab includes PhD students like Gaeun Son (scene perception), Charlotte Leferink (scene representation), and Dela Farzanfar (aesthetic processing), alongside postdocs and collaborators. Advising: Supervises graduate students in projects combining computational modeling, psychophysics, and neuroimaging, particularly those with backgrounds in computer science or cognitive neuroscience.

John SerencesView profile

Professor

• Cognitive Psychology
• Neuroscience
• Cognitive Neuroscience
• +4 more

John Serences is a Professor in the Department of Psychology at the University of California, San Diego (UCSD). He leads the Perception and Cognition Lab, which participates in the Neuroscience Graduate Program. His research focuses on how behavioral goals and attention influence perception, memory, and decision-making, employing techniques like psychophysics, computational modeling, EEG, and fMRI. Key projects explore serial dependence, neural adaptation in visual cortex, and the interplay between sensory processing and mnemonic storage. Recent work highlights mechanisms reconciling repulsive neuronal adaptation with attractive behavioral biases. Affiliations: Department of Psychology, UCSD; Neuroscience Graduate Program. Research Themes: Visual perception, working memory, decision-making, neuroimaging. His lab investigates neural dynamics underlying cognitive processes, with particular emphasis on how attentional modulations and stimulus history shape neural representations. Notable contributions include studies on adaptive sensory coding and the role of top-down signals in perceptual stability.

Oisin Mac AodhaView profile

Associate Professor

• Computer Vision
• Machine Learning
• 3D Understanding
• +3 more

Oisin Mac Aodha is a Reader (Associate Professor) in Machine Learning at the School of Informatics, University of Edinburgh. He is also an ELLIS Scholar and founder of the Turing interest group on biodiversity monitoring and forecasting, having previously served as a Turing Fellow from 2021-2025. Mac Aodha completed his undergraduate degree in electronic engineering from the University of Galway in Ireland, followed by his MSc and PhD at University College London (UCL). His academic journey includes postdoctoral positions at UCL (2013-2016) working with Prof. Gabriel Brostow and Prof. Kate Jones, and at Caltech (2016-2019) in Prof. Pietro Perona's Computational Vision Lab as part of the Visipedia team. His research centers on computer vision and machine learning with emphasis on 3D understanding, human-in-the-loop methods, and AI for conservation and biodiversity monitoring. He has made significant contributions to monocular depth estimation (including the influential Monodepth2 paper), fine-grained visual categorization, and biodiversity monitoring systems. His work bridges theoretical machine learning with practical ecological applications, developing tools for species identification, range estimation, and conservation efforts. Recent publications reveal a strong trend toward ecological applications while maintaining fundamental contributions to 3D vision and representation learning. His major scientific achievements include: Turing Fellow (2021-2025) ELLIS Scholar Founder of the Turing interest group on biodiversity monitoring and forecasting Co-organizer of the Fine-Grained Visual Categorization (FGVC) workshop series at major vision conferences Mac Aodha advises multiple PhD students and postdocs working on computer vision for biodiversity monitoring, 3D understanding, and human-in-the-loop learning. His team has developed practical tools like Whombat (an open-source annotation tool for bioacoustics) and contributed to field-deployed biodiversity monitoring systems. He has served as Area Chair for top conferences including NeurIPS, CVPR, ICCV, and ICML, demonstrating his standing in the computer vision community. His research group collaborates extensively with ecologists at University College London, particularly with Prof. Kate Jones' team, bridging machine learning expertise with ecological domain knowledge. The Vision at Edinburgh group he contributes to focuses on developing practical AI tools that address real-world conservation challenges while advancing fundamental computer vision research.

• Multisensory Perception
• Predictive Processing
• Speech Perception
• +3 more

Prof. Helen Blank is a Professor leading the Multisensory Perception Group and the Prediction in Communication Lab at the Institute for Systems Neuroscience, University Medical Center Hamburg-Eppendorf. Her work focuses on understanding how sensory information is integrated and predicted in contexts like speech perception and face recognition. She holds a Marie Curie Fellowship for her research on prior information's role in human communication. Fluent in German, English, and French, she contributes to experimental medicine and systems neuroscience. Her research spans predictive coding, neuroimaging, and clinical applications in Parkinson’s and developmental disorders. Education: Not explicitly stated in text, inferred as advanced degrees in neuroscience or related fields. Her research interests emphasize multisensory integration, predictive processing in speech and vision, and the neural bases of perception. Recent articles explore topics such as pupil responses to auditory surprise, face expectation hierarchies, and audio-visual speech processing. Awards include the Marie Curie Fellowship supporting her predictive communication work. She leads interdisciplinary teams within the Center for Experimental Medicine, advancing knowledge on perceptual mechanisms and their clinical implications.

Raymond J. MooneyView profile

Professor

• Artificial Intelligence
• Bioinformatics and Computational Biology
• Machine Learning
• +14 more

Raymond J. Mooney is a Professor in the Department of Computer Science at the University of Texas at Austin, where he has been a faculty member since 1987. He is the Director of the UT Artificial Intelligence Laboratory and affiliated with multiple research groups including the Machine Learning Research Group, UT Computational Linguistics Lab, and the UT Center for Computational Biology and Bioinformatics. He holds a B.S., M.S., and Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign, where his thesis was supervised by Gerald DeJong. His research spans diverse areas in artificial intelligence, machine learning, and natural language processing: Natural Language Learning Connecting Language and Perception Statistical Relational Learning Information Extraction Transfer and Active Learning Abductive Reasoning Text Mining and Clustering Recommender Systems Knowledge-Base Refinement Recent publications highlight trends in grounded language processing, human-robot interaction, and multimodal reasoning. He has been recognized with prestigious fellowships including ACL (2014), ACM (2010), and AAAI (2005). Scientific awards: Fellow of the Association for Computational Linguistics (2014) Fellow of the Association for Computing Machinery (2010) Fellow of the American Association for Artificial Intelligence (2005) Classic Paper Award (2019) Best Paper Awards (2007, 2004, 1996) He teaches graduate courses like CS 371R: Information Retrieval and Web Search (Fall 2025) and CS 395T: Grounded Natural Language Processing (Spring 2025). His research labs include: UT Artificial Intelligence Laboratory Machine Learning Research Group UT Computational Linguistics Lab UT Center for Computational Biology and Bioinformatics

Christian StilpView profile

Associate Professor

• Auditory perception
• Speech perception
• Music perception
• +5 more

Christian Stilp, PhD, is an Associate Professor in the Department of Psychological and Brain Sciences at the University of Louisville. His research focuses on auditory perception, speech perception, and music perception, with an emphasis on how acoustic context and natural signal statistics influence perceptual processes. He leads the Auditory Perception and Processing (APP) Lab, which investigates perceptual learning, efficient coding, and the role of variability in speech and music perception. Dr. Stilp earned his B.S. in Psychology from the University of Wisconsin in 2003 and his Ph.D. in Perceptual Psychology in 2011, also from the University of Wisconsin. He completed a postdoctoral fellowship at Case Western Reserve University before joining the University of Louisville faculty in 2012. His research interests include spectral contrast effects, talker adaptation, expertise in speech and music perception, and the application of these principles to hearing-impaired listeners. He teaches courses such as Sensation and Perception, Music Perception and Cognition, and Advanced Statistics. Dr. Stilp has advised numerous graduate and undergraduate students, many of whom have contributed to his lab’s publications. His lab collaborates with researchers at institutions including the University of Connecticut, University of Minnesota, and University of Zurich.

• Visual Perception
• Cognitive Psychology
• Developmental Science
• +6 more

Prof. Anna Franklin is a Professor of Visual Perception and Cognition at the University of Sussex's School of Psychology. She leads the Sussex Colour Group and Sussex Baby Lab , focusing on human color perception, development, and neural representation. Her research combines cognitive psychology, developmental science, and neuroscience to explore how color perception develops, influences aesthetics, and relates to conditions like autism. Key projects include ERC-funded initiatives studying environmental impact on color perception and developing the ColourSpot diagnostic app for childhood color vision deficiency. She also serves as Deputy Director of Research and Knowledge Exchange for the School of Psychology. Education includes a BA from the University of Nottingham and a PhD from the University of Surrey, followed by a postdoctoral fellowship. Her work spans 107+ publications, emphasizing interdisciplinary methods like hyperspectral imaging and fMRI. Collaborations include industry partnerships with ETTA LOVES and COSATTO to apply infant visual preference research in product design. Research grants include European Research Council awards (Starting Grant 2012-2017, Consolidator Grant 2018-2025) and a Proof of Concept grant for ColourSpot . Her labs investigate cross-cultural color categorization, infant aesthetics, and neural correlates of color processing. Future work focuses on calibrating visual systems to environmental statistics and improving early childhood color vision screening.

Olivia CheungView profile

Assistant Professor

• Cognitive Neuroscience
• Visual Cognition
• Perceptual Expertise
• +7 more

Olivia Cheung is an Assistant Professor of Psychology and Global Network Assistant Professor at New York University Abu Dhabi (NYUAD), affiliated with the Division of Science and the Department of Psychology. She leads the Objects and Knowledge Laboratory (OAK Lab), which is also associated with the Center for Brain and Health at NYUAD. Education: BSSc, Chinese University of Hong Kong PhD, Vanderbilt University Postdoctoral Training: Harvard Medical School, CIMeC (Trento, Italy), Harvard University Her research focuses on cognitive neuroscience and visual cognition, particularly how experience and learning shape perception. She investigates how visual and conceptual knowledge interact to influence representations of objects, faces, words, musical notations, and scenes. Her lab employs behavioral experiments, functional magnetic resonance imaging (fMRI), and computational modeling to explore perceptual expertise and category selectivity in the brain. Her recent publications (2022–2024) reveal a consistent focus on high-level vision, with studies on holistic face and word processing, neural and computational models of category recognition, ensemble perception of animacy, and social judgments from faces (e.g., election prediction). These works, presented at Vision Sciences Society (VSS), demonstrate interdisciplinary methods and collaborations with students and international researchers. Olivia Cheung teaches courses such as Capstone Projects in Computer Science and Psychology, and Concepts and Categories: How We Structure the World , reflecting her interdisciplinary approach. She mentors undergraduate researchers, many of whom have co-authored conference posters. Her lab, the OAK Lab, fosters research on the intersection of perception, knowledge, and expertise.

Pascal VincentView profile

Associate Professor

• Machine Learning
• Representation Learning
• Deep Learning
• +6 more

Pascal Vincent is an Associate Professor at the Department of Computer Science and Operational Research , University of Montreal, and a key member of the Montreal Institute for Learning Algorithms (MILA) . He holds a PhD in Computer Science from the University of Montreal and has been pivotal in advancing machine learning and artificial perception. Education: PhD in Computer Science (University of Montreal, 2003) His research spans machine learning , deep learning , representation learning , and neural networks , focusing on unsupervised methods and geometrically inspired algorithms. He explores how intelligent systems can autonomously build meaningful representations from raw data, driven by principles like the manifold hypothesis . Key projects include generative stochastic networks , contractive autoencoders , and high-dimensional sequence transduction . His work has resulted in 15+ recent publications in top venues like NIPS, ICML, and CVPR. Scientific Awards : Best student-paper award at ICML 2012 Honorable mention at NIPS 2011 Funded by FCI, FRQNT, CRSNG, CIFAR, and IBM Pascal has supervised 15+ doctoral and Master’s students , including Florian Bordes, Tom Bosc, and Nicolas Boulanger-Lewandowski, across topics like representation learning and generative models . He is also a co-founder of the UNIQUE (Union Neurosciences & Intelligence Artificielle Québec) research consortium.

Benjamin de HaasView profile

Researcher

• Visual Perception
• Face Processing
• Individual Differences
• +3 more

Dr. Benjamin de Haas is a vision scientist and faculty member at Justus Liebig University Giessen , Germany, within the Department of Psychology and Sports Science . He currently leads the ERC-funded Indivisual project and co-leads project C9 Factors influencing categorical face processing within the Collaborative Research Centre CRC/TRR 135. He is also a principal investigator in the NeurOscientific Workflow Assistance (NOWA) infrastructure project, dedicated to open, reproducible neuroscience. Research Focus Dr. de Haas pursues two intertwined questions: How do early and late stages of visual processing interact—from the initial registration of slanted edges to the recognition of faces? How and why do our perceptions differ from one person to the next? To answer these questions his group combines psychophysics, high-resolution eye-tracking, functional and quantitative MRI, and computational modelling, with a strong emphasis on face perception, individual differences, and naturalistic viewing conditions. Publications Overview Across more than 20 publications since 2016, Dr. de Haas has advanced understanding of individual differences in face processing, gaze control, and visual salience. His work repeatedly appears in Journal of Vision , Nature Communications , PNAS , and NeuroImage , highlighting a sustained focus on eye-movement behaviour, cortical representations of faces and scenes, and methodological best practices in neuroimaging. Current Supervision & Team Dr. de Haas currently supervises two PhD students: Elaheh Akbarifathkouhi Hilal Nizamoglu Together with Dr. Katharina Dobs (co-project leader) and affiliated post-docs and research technicians, the group forms the Indivisual laboratory at Giessen. Contact & Resources Email: Benjamin.de-Haas@psychol.uni-giessen.de Department of Psychology and Sports Science Otto-Behaghel-Str. 10F, 35394 Gießen, Germany

Marcus RogersView profile

Professor

• Digital Forensics
• Cyber Security
• Cyber Crime
• +4 more

Marcus Rogers is Professor and Associate Dean for Faculty at Purdue Polytechnic Institute, Purdue University, where he directs the Cyber Forensics Lab. He holds dual fellowships with the American Academy of Forensic Sciences (AAFS) and the Center for Education and Research in Information Assurance and Security (CERIAS). Previously, he served as Editor-in-Chief of the Journal of Digital Forensics Security & Law and Secretary of AAFS. Education includes: PhD in Forensic Psychology, University of Manitoba (2001) MA in Personality Psychology, University of Manitoba (1995) BA in Psychology/Criminology, University of Manitoba (1986) His research integrates behavioral science with digital forensics, focusing on: Psychological profiling of cybercriminals and online offenders Advanced cyber forensic methodologies including mobile/GPU forensics Predictive modeling of cyber threats and adversarial behavior IT insider threats and cyber terrorism dynamics This interdisciplinary approach advances evidence analytics and digital crime scene interpretation. His publications demonstrate consistent focus on cybercrime psychology, forensic methodologies, and digital evidence innovation, with recent work emphasizing behavioral analytics, adversarial modeling, and advanced data recovery techniques. Major scientific honors include: Paul H. Chapman Medal for justice innovation (2009) AAFS Digital Sciences Research/Case Study Awards (2012, 2013) Fellow distinctions from AAFS and CERIAS University Faculty Scholar (2008) He leads the Cyber Forensics Lab, developing novel investigative tools like SMIRK (SMS forensics) and TracHac (cell phone analysis). Secured multiple grants for digital evidence research from federal and industry partners. Extensive professional engagements include chairing NIST OSAC committees and FBI InfraGard membership.

Taylor H BlackView profile

Associate Professor

• 20th Century American Literature
• Popular Music Studies
• Queer Theory
• +3 more

Taylor H Black is an Associate Professor of English at Duke University, Trinity College of Arts & Sciences, specializing in 20th Century American Literature , Popular Music Studies , and Queer Theory . He is currently working on two book projects: Cult of Personality: An American Romance and Butterfly on the Wheel , a critical biography of Quentin Crisp and cultural history of New York's Downtown Arts scene in the 1980s. Education: Ph.D., Rutgers University (2015) B.A., CUNY Hunter College (2007) Black's research explores the intersections of literature, music, and queer identity , focusing on how style functions as a cultural force beyond social categorization. His work traces stylistic evolution in American arts and media, particularly through figures like Bob Dylan and Quentin Crisp. His scientific award includes the Langford Lectureship Award (2025) from Duke University. Black's teaching portfolio includes courses on American literature, film, and critical theory, such as America Dreams American Movies II and Criticism and the Arts .

• Machine Learning for Music
• Music Modeling and Generation
• Ethics of AI in Music
• +4 more

Bobby Lee Townsend Sturm JR is an Associate Professor at KTH Royal Institute of Technology, leading the MUSAiC project (ERC-2019-COG). He holds a PhD in Electrical and Computer Engineering from UC Santa Barbara (2009), followed by postdoctoral research at LAM, Paris 6, and academic roles at Aalborg University and Queen Mary University of London. His research focuses on AI ethics in music, generative AI for music, and folk music preservation. Current roles at KTH include teaching and supervising in Machine Learning, Music Informatics, and AI Ethics. He has pioneered AI music generation challenges (e.g., 2020 Double Jigs Challenge) and investigates societal impacts of AI on traditional music cultures. His work bridges technical innovation with cultural and ethical considerations, addressing issues like data colonialism, algorithmic bias, and human-AI collaboration in creative contexts. Education: PhD (UCSB, 2009), Postdoc (Paris 6), Academic appointments at Aalborg University (2010–2014) and Queen Mary University (2014–2018) Key Projects: MUSAiC (ERC), Virtual Session System for Irish Music, Traditional Music Dataset Analysis Teaching: Courses in Machine Learning, Music Acoustics, and ICT Innovation Publications span peer-reviewed journals and conferences, emphasizing ethical AI, music generation, and interdisciplinary research in MIR (Music Information Retrieval). He actively collaborates with musicians, anthropologists, and technologists to ensure culturally informed AI development.