Find Academic Supervisors

Guillaume LajoieView profile

Associate Professor

• Artificial Intelligence
• Neuroscience
• Machine Learning
• +3 more

Guillaume Lajoie is an Associate Professor in the Department of Mathematics and Statistics at Université de Montréal and a Core Academic Member of Mila – Quebec Artificial Intelligence Institute. He holds a Canada CIFAR AI Research Chair and a Canada Research Chair in Neural Computation and Interfacing. His research focuses on the intersection of AI and neuroscience, particularly in understanding neural network dynamics and developing brain-machine interfaces for clinical and scientific applications. He is affiliated with the Centre de recherches mathématiques (CRM), the Interdisciplinary Center for Research on the Brain and Learning (CIRCA), and the UNIQUE initiative. Education: PhD in Applied Mathematics from the University of Washington (Seattle), postdoctoral fellowships at the Max Planck Institute for Dynamics and the University of Washington Institute for Neuroengineering. Awards include the FRQS Scholar designation and leadership roles in strategic research initiatives like UNIQUE and CIRCA. Research interests include neural computations, recurrent neural networks, neurotechnology, and responsible AI development. Supervised students include François Paugam (PhD), Giancarlo Kerg (PhD), and others. Key grants include projects on adaptive neuroprosthetics, neural decoding, and Canada Research Chairs funding.

Steve LuckView profile

Professor

• Attention
• Working Memory
• Cognitive Neuroscience
• +3 more

Steve Luck is a Distinguished Professor at the University of California, Davis, holding appointments in the Department of Psychology and the Center for Mind and Brain (CMB). He served as CMB Director from 2009–2019 and is affiliated with the UC Davis MIND Institute and the Center for Neuroscience. His research focuses on attention, working memory, and cognitive dysfunction in psychiatric disorders (e.g., schizophrenia), employing ERP recordings, eye tracking, and behavioral methods. He is a leading developer of ERP methodologies, including the ERPLAB Toolbox and global ERP Boot Camp workshops. Education: Ph.D., Neurosciences, UC San Diego, 1993 M.S., Neurosciences, UC San Diego, 1989 B.A., Psychology, Reed College, 1986 Research Interests: Dr. Luck explores mechanisms of cognitive control, with a focus on working memory's role in guiding attention. His lab investigates ERP correlates of attentional deficits in schizophrenia and develops standardized ERP protocols. Recent work emphasizes multivariate decoding of EEG signals and transdiagnostic neurocognitive biomarkers. Awards: Troland Award (2001) APA Distinguished Scientific Award (1998) McGuigan Young Investigator Prize (2004) Elected Fellow, Society of Experimental Psychologists and AAAS Teaching & Leadership: Professor Luck pioneered hybrid course formats in Cognitive Science and teaches advanced topics in perception and cognitive neuroscience. He co-founded the UC Davis Cognitive Science major and advocates for innovative undergraduate education models. Labs & Collaborations: The Luck Lab integrates clinical and basic research, collaborating globally on ERP method development and schizophrenia biomarker studies. Key projects include ERP Core resources and the CNTRACS consortium for neurocognitive reliability studies.

Angelina WangView profile

Assistant Professor

• Machine Learning Fairness
• Algorithmic Bias
• Responsible AI
• +3 more

Angelina Wang is an incoming Assistant Professor at Cornell Tech and the Department of Information Science at Cornell University, starting Fall 2025. Her research focuses on responsible AI, particularly machine learning fairness and algorithmic bias. She holds a Ph.D. in Computer Science from Princeton University and a B.S. in Electrical Engineering and Computer Science from UC Berkeley. Current postdoctoral work at Stanford’s HAI and RegLab explores sociotechnical challenges in AI deployment. Her research addresses fairness evaluation in generative AI, societal impacts of AI systems, and ethical trade-offs in algorithm design. Notable awards include the NSF GRFP, Siebel Scholarship, and Microsoft AI & Society Fellowship. Her work bridges technical and social dimensions of AI, emphasizing human-centered evaluation and interdisciplinary collaboration. Recent publications span medical AI applications (e.g., Alzheimer’s subphenotypes, corticosteroid treatment efficacy) and foundational fairness research. She advocates for proactive ethical considerations in technical work, citing examples like surveillance risks in facial recognition and dataset biases in computer vision. Angelina advises prospective PhD students in Cornell’s Information Science program and collaborates on projects like SciDaSynth for scientific knowledge synthesis. Her advocacy includes challenging fairness impossibility theorems and promoting algorithmic pluralism in auditing practices.

Peter ChinView profile

Professor

• Machine Learning
• Artificial Intelligence
• Differential Geometry
• +8 more

Professor Peter Chin is a Professor of Engineering at Dartmouth College and Director of the Learning, Intelligence + Signal Processing (LISP) Lab. He holds affiliations with the Thayer School of Engineering and serves as Associate Editor of IEEE Transactions on Computational Social Systems. His research bridges signal processing, machine learning, game theory, and differential geometry, with applications in cybersecurity, healthcare, and network analysis. Education: Bachelor of Science in Electrical Engineering, Computer Science, and Mathematics from Duke University (1993) Doctor of Philosophy in Mathematics from MIT (1998) Research Interests: Chin’s work focuses on fundamental questions at the intersection of machine learning, game theory, and signal processing. His lab explores topics like adversarial defense mechanisms, topological machine learning, and computational neuroscience. Recent projects include cybersecurity resilience modeling, medical imaging enhancements via GANs, and multi-agent reinforcement learning frameworks. Publications Trends: His most recent articles address cutting-edge challenges in cybersecurity (e.g., autonomous defense systems), medical AI (e.g., Alzheimer’s classification), and adversarial robustness. A notable 2025 focus is on quantitative resilience modeling for cyber defense, reflecting growing demand for AI-driven security solutions. Awards: Faculty Scholar Award, Duke University George Sherred III Award, Duke University Julia Dale Memorial Award, Duke University Grants & Leadership: Recipient of DARPA cybersecurity research grants Co-chair for SPIE/DSS Cyber Sensing Conference (2013–2020) Developed novel compressive sensing microscope for biological imaging LISP Lab: This interdisciplinary lab pioneers projects like nFlip (multiplayer security game models) and topological machine learning frameworks, emphasizing practical applications of theoretical advancements.

Richard NaudView profile

Assistant Professor

• Computational Neuroscience
• Neural Coding
• Statistical Methods in Neuroscience
• +5 more

Richard Naud is an Assistant Professor in the Department of Cellular and Molecular Medicine at the Faculty of Medicine, University of Ottawa, with a cross-appointment in Physics at the Faculty of Science. He holds dual research positions at the Center for Neural Dynamics (CND) and Brain and Mind Research Institute (BMRI), focusing on computational approaches to decode neural signaling mechanisms and develop brain-machine interfaces. His educational background includes: PhD in Neuroscience from École Polytechnique Fédérale de Lausanne (EPFL), 2011 MSc in Physics from McGill University, 2006 BSc in Physics from McGill University, 2004 Dr. Naud's research investigates how neurons encode information through spikes, bursts, and silences using mathematical models and statistical analysis of electrophysiology data. His lab develops computational protocols for synaptic dynamics analysis, studies dendritic computation in neurological diseases, and creates neuromorphic algorithms for spiking neural networks. Current work emphasizes serotonin system dynamics, burst coding mechanisms, and neural network simulations for demyelinating conditions. Analysis of his recent publications reveals three dominant research vectors: (1) Burst coding as an independent information channel beyond firing rates, (2) Serotonin-mediated value coding in decision systems, and (3) Neuromorphic implementation of biologically plausible learning rules. His work bridges theoretical neuroscience with clinical applications in stroke recovery and neurological disorders. Dr. Naud leads the Neural Coding Lab, which actively recruits postdoctoral fellows, graduate students, and undergraduates for projects in neural coding theory, computational psychiatry, and neuromorphic engineering. The lab maintains collaborations with experimental neuroscience groups for model validation and develops open-source tools like SRPlasticity for synaptic dynamics analysis.

• Biomedical Engineering
• Nanotechnology
• Microelectronics
• +5 more

Professor Omid Kavehei is a Professor of Intelligent Microsystems in the Faculty of Engineering at the University of Sydney, serving as Deputy Head of the School of Biomedical Engineering. Previously, he held roles as a Research Fellow at the University of Melbourne and Lecturer at RMIT University. His research focuses on biomedical microsystems, nanotechnology, and brain-inspired hardware for healthcare applications, particularly epilepsy monitoring and seizure prediction. Education: PhD (specific institution not explicitly stated) His work bridges nanoelectronics and healthcare, aiming to develop low-power, brain-inspired devices for sensory perception and medical diagnostics. Key interests include neuromorphic engineering, wearable sensors, and AI-driven medical systems. Awards include the Ramaciotti Biomedical Research Award (2021), Microsoft AI for Accessibility Grant (2019), and multiple teaching/research excellence awards from the University of Sydney. His recent work explores neuromorphic cytometry for cell analysis, edge AI for ECG/EEG diagnostics, and closed-loop neurostimulation systems. Collaborations include the Sydney Nano Institute, Brain and Mind Centre, and industry partners like Microsoft. Current research students are advancing topics like bio-inspired algorithms for seizure detection, hardware-friendly machine learning models, and flexible sensor systems for aquatic environments. Grants include funding for neurophysiology platforms, quantum sensors, and semiconductor design. Labs/teams: Involved in the Centre for Drug Discovery Innovation and collaborations across engineering, neuroscience, and clinical domains.

Ahsan AdeelView profile

Associate Professor

• Artificial Intelligence
• Computational Neuroscience
• Multisensory Integration
• +5 more

Dr. Ahsan Adeel is an Associate Professor of Cellular AI and Theoretical Neuroscience at the University of Stirling. He holds affiliations with multiple institutions including MIT, Oxford, and the Howard Brain Sciences Foundation. His research focuses on bridging cellular neurobiology with AI, particularly through the study of pyramidal Two-Point Neurons (TPNs) and their role in conscious processing. His work emphasizes developing biologically plausible AI models for healthcare, robotics, and neuromorphic computing. Education: B.Eng. in Electrical Engineering, MSc in Electronics, PhD in Cognitive Computing. Research interests include: Conscious Multisensory Integration (CMI), neuromorphic chip design, explainable AI (XAI), and applications in hearing aids, disaster management, and neurological disorders. His lab, the CMI Lab, explores how TPN mechanisms can overcome computational limitations of current AI. Key projects include a £4M EPSRC-funded hearing aid initiative, 5G-enabled environment planners for dementia patients, and collaborations with NASA/ESA on AI for space exploration. His work is featured in flagship journals like Frontiers in Computational Neuroscience and the €1.2B Human Brain Project. Awards include Fellowships from Howard Foundation, MIT, and Oxford, alongside recognition for contributions to neuromorphic computing and TPN research. He has over 30 publications and leads interdisciplinary teams across 10+ global institutions.

• NeuroAI
• Computational Neuroscience
• Artificial Intelligence
• +4 more

Shahab Bakhtiari is an Adjunct Professor in the Department of Psychology at the University of Montreal's Faculty of Arts and Sciences. His research focuses on NeuroAI, exploring the intersection of neuroscience and artificial intelligence, particularly in visual perception and learning mechanisms in biological systems and artificial neural networks. He holds a PhD in Neuroscience from McGill University and conducted postdoctoral research at Mila, Quebec AI Institute. Education: Bachelor's and Master's in Electrical Engineering, University of Tehran PhD in Neuroscience, McGill University His research interests include computational neuroscience, machine learning, visual system modeling, and energy-efficient predictive coding. He teaches courses on AI, cognitive neuroscience, and deep learning applications in psychology. Key grants include a CRSNG grant (2023–2029) for comparative visual system studies and the UNIQUE strategic initiative (2022–2029), co-led by 50+ researchers. He has supervised one Master's student, Hamza Abdelhedi, on AI-human face recognition comparisons. His work bridges AI and biological systems, leveraging neuroimaging and deep learning to model brain dynamics and improve AI's biological plausibility.

Ralph Etienne-CummingsView profile

Professor

• Neuromorphic Engineering
• Neural Prosthetics
• Biomedical Robotics
• +5 more

Ralph Etienne-Cummings is the Julian S. Smith Professor of Electrical and Computer Engineering at Johns Hopkins University (JHU), where he also serves as Vice Provost for Faculty Affairs. He holds secondary appointments in Computer Science and is affiliated with JHU's Applied Physics Lab. His work spans three decades, pioneering advancements in neuromorphic engineering, neural prosthetics, and biomorphic robotics. Etienne-Cummings leads the Computational Sensory Motor Systems Laboratory and has developed systems for closed-loop neural interfaces, prosthetics, and biomedical sensors. Education: BSc in Physics (1988), Lincoln University MSEE (1990) and PhD (1994) in Electrical Engineering, University of Pennsylvania Research Interests: His work focuses on neuromorphic systems, bio-inspired algorithms, and neural prosthetics. Key areas include spinal cord stimulation for mobility restoration, wearable health monitoring, and ultrasonic imaging for infertility treatment. He has contributed to silicon Central Pattern Generators (CPGs) for bipedal robotics and developed the first large-scale neural computer using VLSI chips. His lab explores organoid intelligence and biohybrid systems, blending neuroscience with engineering. Impact & Recognition: Named Fellow of AIMBE (2021) and IEEE (2012) Recipient of JHU Discovery Awards (2018–2019) and NSF CAREER Award (1996) Developed the 'Microbead'—a 0.009mm³ wireless neural stimulator Industry & Outreach: Served as founding director of JHU's Institute of Neuromorphic Engineering and advised firms like Panasonic and Avago. Testified in federal court on intellectual property disputes. Recognized as a 'ScienceMaker' in the HistoryMakers Archive for contributions to African American STEM leadership. Labs & Collaborations: Directs the Computational Sensory Motor Systems Lab. Collaborates with DARPA on prosthetics and the NIH on bioelectronic medicine. His work bridges academia and industry, emphasizing practical applications of neural engineering.

Konrad Paul KordingView profile

Professor

• Neuroscience
• Machine Learning
• Computational Neuroscience
• +6 more

Konrad Paul Kording is the Nathan Francis Mossell University Professor at the University of Pennsylvania's School of Engineering and Applied Science, holding primary appointments in Bioengineering and Neuroscience, and a secondary appointment in Computer and Information Science. His research focuses on computational neuroscience, machine learning, and their applications to understanding neural systems and human behavior. He leads a research group located in Room 404 Richards and maintains an active research website and personal academic page. His work explores topics such as neural decoding, causal inference, and the alignment of artificial neural networks with biological systems. He has pioneered methods for analyzing neural activity in macaques, developing large-scale facial analysis resources like PrimateFace. His interdisciplinary approach integrates computer vision, medical informatics, and robotics to address challenges in neurology and developmental disorders. Recent research highlights include advancing techniques for early prediction of cerebral palsy using motion tracking and automated segmentation of synchrotron-scanned fossils. His contributions to causal discovery methodologies and neural network interpretability have been influential in both neuroscience and machine learning communities.

• Neuroscience
• Synaptic Transmission
• Cellular Physiology
• +6 more

Professor Colin Akerman is a Governing Body Fellow at the University of Oxford and leads a neuroscience research group within the Department of Pharmacology. His work focuses on synaptic connections, activity-dependent processes, and their role in neurological disorders like epilepsy and dementia. He teaches undergraduate preclinical Medicine and graduate courses in Pharmacology and Neuroscience, and tutors at Corpus Christi College.

• Artificial Intelligence
• Machine Learning
• Graph Neural Networks
• +5 more

Gustavo Borges Moreno e Mello is an Associate Professor in the Department of Computer Science at Oslo Metropolitan University's Faculty of Technology, Art and Design. Based in office SG209 at Stensberggata 29, Oslo, he actively contributes to the Applied Artificial Intelligence research group with focus on cutting-edge computational methodologies. His research spans artificial intelligence with emphases on graph neural networks, multilingual NLP, reinforcement learning, and medical imaging. Key interests include developing propagation schemes for GNNs, cross-lingual BERT adaptations, rodent fMRI analysis, and empathy assessment in human-computer interaction systems. His work bridges theoretical AI with practical healthcare and communication applications. Analysis of his 2020-2024 publications reveals strong thematic continuity in neural architecture design, particularly HITS-GNN variants for graph analytics and time-representation models. Recent work increasingly integrates neuroscience principles with reinforcement learning while maintaining Nordic language processing applications, notably in English-Norwegian BERT alignment. No information regarding graduate student supervision or research grants was found in the provided materials. He maintains active collaboration within OsloMet's Applied Artificial Intelligence research group, focusing on interdisciplinary projects that connect computer vision, natural language processing, and neuro-inspired computing with real-world healthcare and communication challenges.

• Computational Neuroscience
• Neural Network Modeling
• Locomotion Control
• +3 more

Alexander Kozlov is a Researcher at KTH Royal Institute of Technology's Division of Computational Science and Technology. His work focuses on computational neuroscience, particularly modeling neural networks and locomotor control systems. He has contributed to understanding spinal cord networks, striatal connectivity, and neuromodulation mechanisms through in silico studies. Kozlov teaches courses in machine learning, artificial neural networks, and mathematical modeling of biological systems. His research integrates AI frameworks with biological data, emphasizing GPU-accelerated simulations and large-scale microcircuit analysis. Key contributions include studies on Parkinson's disease impact on neural connectivity and the role of sensory feedback in locomotion. He collaborates with the Science for Life Laboratory in Stockholm and maintains active roles in both peer-reviewed and conference publications. Education background and affiliations are not explicitly detailed in the provided text, but his extensive publication record reflects a deep engagement with interdisciplinary neuroscience and computational biology. Awards or grants are not listed here.

Randall O'ReillyView profile

Professor

• Computational Cognitive Neuroscience
• Neural Network Modeling
• Learning Mechanisms
• +3 more

Randall O'Reilly is a Professor of Psychology, Computer Science, and a member of the Center for Neuroscience at the University of California, Davis. He holds a B.A. from Harvard University, M.S. and Ph.D. from Carnegie Mellon University, and completed a postdoctoral fellowship at MIT. His research focuses on computational models of learning mechanisms in brain areas such as hippocampus, prefrontal cortex, and visual cortex, integrating biological plausibility with cognitive functions like memory, attention, and controlled processing. His work emphasizes the Complementary Learning Systems theory, predictive learning, and the Emergent neural network simulation environment. He has secured funding from agencies like ONR, NIH, and DARPA. O'Reilly leads the Computational Cognitive Neuroscience Lab (CCNL), exploring predictive learning in visual systems and cortico-thalamic interactions. His textbook Computational Cognitive Neuroscience is widely used, and his lab's software and research are openly accessible via GitHub. Education: A.B. Harvard (Psychology), M.S./Ph.D. Carnegie Mellon (Psychology), Postdoc MIT Labs: CCNL at UC Davis Center for Neuroscience Grants: ONR, NIH, NSF, IARPA, DARPA Software: Emergent Simulation Environment

Asieh Abolpour MofradView profile

Researcher

• Machine Learning
• Neural Networks
• Associative Memory
• +7 more

Asieh Abolpour Mofrad is a researcher at the Department of Informatics, University of Bergen (UiB), with a dual PhD background in behavioral sciences and informatics. She is currently engaged in the Retail Fresh project, leveraging machine learning to reduce food waste in the retail sector in collaboration with Link Retail. Previously, she contributed to the DRONE (Drug Repurposing for Neurological Diseases) project at the Department of Global Health and Public Health, UiB, where she applied machine learning to Norwegian health registry data to investigate Parkinson's disease treatments. Her academic credentials include two doctoral theses: one from OsloMet (2021) on the integration of behavior analysis and machine learning for modeling stimulus equivalence, and another from UiB (2021) on clique-based neural associative memories. Her research bridges artificial intelligence, cognitive modeling, neural networks, and public health applications. Her research interests span machine learning, neural associative memory, reinforcement learning, cognitive modeling, health informatics, and drug repurposing. She employs computational frameworks such as projective simulation and tournament-based neural networks to model complex cognitive and biological phenomena. The analysis of her recent publications reveals a consistent focus on machine learning applications in both cognitive science and healthcare. Her work includes modeling stimulus equivalence, designing neural memory architectures, solving stochastic optimization problems, and analyzing large-scale health data for neurological disease risk. These efforts reflect interdisciplinary innovation, combining theoretical computer science with real-world applications in psychology and medicine. Scientific Contributions: Developed the Enhanced Equivalence Projective Simulation (E-EPS) framework for modeling derived relations in behavior analysis. Designed tournament-based neural networks for efficient sequence storage and bidirectional retrieval. Applied machine learning to large-scale health registries to identify drug classes associated with Parkinson’s disease risk. Contributed to adaptive learning systems based on flow theory for educational optimization. She has advised no publicly listed students but collaborates extensively with researchers across institutions. Her work is supported by interdisciplinary research projects and she actively disseminates code via GitHub, particularly in Jupyter notebooks for reproducibility. She is affiliated with research teams in informatics and global health at UiB, contributing to both theoretical and applied machine learning initiatives. Laboratories and Research Teams: Department of Informatics, University of Bergen – Machine Learning and Neural Systems group. DRONE Project – Interdisciplinary team on drug repurposing using AI and health data. Retail Fresh Project – AI for sustainable retail, in collaboration with industry partners.