Find Academic Supervisors

Brent DoironView profile

Professor

• Computational Neuroscience
• Neural Networks
• Theoretical Neuroscience
• +2 more

Brent Doiron is a Professor at the University of Chicago, holding appointments in the Departments of Neurobiology and Statistics, and serving on the Committee on Computational and Applied Mathematics (CCAM). His research integrates nonlinear dynamics and statistical mechanics to study neural circuit variability, focusing on mechanisms underlying neural coding and network learning through collaborations with experimentalists in sensory systems. Education: PhD in Physics (University of Ottawa, 2004) Postdoc: Center for Neural Science at New York University (2017) Previous Roles: Mathematics Professor at University of Pittsburgh (2007-2020), Co-Director of Neural Computation Program at Carnegie Mellon Neuroscience Institute Research interests center on neuronal population dynamics, recurrent circuit mechanisms, and computational neuroscience. Current work investigates correlated variability in cortical networks, inter-areal communication, and stochastic spiking models. Recent publications emphasize cortical stability/gain modulation, asynchronous/synchronous activity balance, and Bayesian inference frameworks. Key themes include sensory processing, network plasticity, and dimensionality reduction in neural coding. Scientific Awards Alfred P. Sloan Research Fellowship in Neuroscience Vannevar Bush Faculty Fellowship Chancellor’s Distinguished Research Award (University of Pittsburgh) Active grants include NIH R01 and R90/T90 awards for neuronal dynamics research and computational neuroscience training programs.

Byron YuView profile

Professor

• Computational Neuroscience
• Neural Dynamics
• Motor Control
• +7 more

Byron Yu is a Professor in Electrical & Computer Engineering and Biomedical Engineering at Carnegie Mellon University, with affiliations to the Neuroscience Institute and Robotics Institute. He is a core faculty member of the Center for the Neural Basis of Cognition. Research focuses on computational neuroscience , neural dynamics , and brain-machine interfaces . Key contributions include dimensionality reduction techniques and neural population activity analysis. Recent publications explore topics such as neural dynamics during motor imagery, BCI optimization, and attentional processing. His work has appeared in Nature Neuroscience , Neuron , and eLife , often as cover articles. Awardees include the Gerard G. Elia Career Development Professorship and AIMBE Fellowship . His lab has mentored numerous PhD and postdoctoral researchers, many now in academic and industry leadership roles.

• Computational Neuroscience
• Artificial Intelligence
• Machine Learning
• +7 more

Raul Vicente Zafra is a Professor of Data Science at the University of Tartu, Faculty of Science and Technology, Institute of Computer Science, where he has been working since 2013. His research spans computational neuroscience, artificial intelligence, and data science, with a particular focus on bridging biological and artificial models of intelligence. Education: PhD in Physics (2001-2006), University of the Balearic Islands BSc in Physics (1997-2001) Professor Zafra's research interests center on computational neuroscience and artificial intelligence, with specific expertise in brain-computer interfaces, reinforcement learning, neural modeling, and explainable AI. His work bridges the gap between biological and artificial intelligence systems, exploring how neural principles can inform machine learning algorithms and vice versa. He has made significant contributions to understanding neural coherence, time interval learning in neural systems, and the application of information theory to brain-computer interfaces. His research often involves interdisciplinary collaboration between computer science, neuroscience, and medicine. Analysis of Zafra's recent publications reveals a strong focus on the intersection of artificial intelligence and neuroscience. His work spans explainable AI methods, brain-computer interfaces, reinforcement learning models that mimic cognitive processes, and neurophysiological studies of brain activity. A notable trend is his exploration of how biological principles of neural computation can inform and improve artificial intelligence systems, particularly in areas like time-based learning, consciousness modeling, and neural coherence. Scientific Awards: 2012: Attendee at the 62nd Lindau Nobel Laureate Meeting 2007: Quantum Electronics and Optics Division Prize of the European Physical Society for the best PhD Thesis in Applied Optics in Europe 2006: PhD Extraordinary Award of the Physics Department of the University of the Balearic Islands 2001: Physics Degree Extraordinary Award (First Class Honors, best GPA) 1997: Bronze Medal in the "8th Spanish Physics Olympiad" Professor Zafra has been principal investigator on numerous significant research projects including the Estonian Centre of Excellence in Artificial Intelligence, Cardiovascular Stress Impacts On Neuronal Function, and Bridging biological and artificial models of vision. His grant portfolio demonstrates strong funding support from the Estonian Research Council, European Commission, and other major funding bodies. He has supervised multiple PhD students and mentored early-career researchers in computational neuroscience and AI. His laboratory work focuses on developing computational models of neural systems and applying these insights to artificial intelligence. Current research directions include explainable AI methods, brain-computer interfaces, modeling of consciousness and cognitive processes, and the application of AI to healthcare challenges.

Mayank R. MehtaView profile

Professor

• Neurophysics
• Synaptic Plasticity
• Learning and Memory
• +3 more

Mayank R. Mehta is a Professor at the University of California, Los Angeles (UCLA), holding joint appointments in the Departments of Physics & Astronomy, Neurology, and Neurobiology. He is a member of the Brain Research Institute and the W. M. Keck Center for Neurophysics at UCLA. His research bridges experimental and theoretical neuroscience, focusing on how neuronal networks encode space-time, the role of brain rhythms in learning and memory, and the impact of sleep and virtual reality on neural dynamics. His recent publications highlight breakthroughs in understanding hippocampal spatiotemporal selectivity, dendritic activity during behavior, and the causal influence of visual cues on memory neurons. Notable findings include the discovery that dendrites generate ten times more spikes than neuronal cell bodies and the modulation of hippocampal theta rhythms in virtual reality. Research Themes: Neurophysics of spatial-temporal coding Dendritic contributions to learning Virtual reality and brain plasticity Neural oscillations in memory consolidation Key Collaborators: Bert Sakmann (Max Planck Florida Institute) Thomas Hahn (Bernstein Center Heidelberg/Mannheim) Maryam Ghorbani (UCLA) Mehta's lab at UCLA trains graduate and postdoctoral researchers in cutting-edge techniques combining hardware development, electrophysiological recordings, and biophysical modeling. His work has significant implications for treating learning and memory disorders like Alzheimer's disease.

Bernhard J. BergerView profile

Lecturer

• Software Security
• Static Code Analysis
• Machine Learning
• +4 more

Bernhard J. Berger is a Lecturer in the Department of Computer Engineering at the Institute of Embedded Systems, Hamburg University of Technology (TUHH). His research focuses on software security, static code analysis, machine learning, optimization, and research data management. He has held significant roles such as Program Committee member for ICPC 2025 and MSR 2025, and has received awards including the Best Reviewer Award (ICPC 2023) and Best Engineering Paper Award (SCAM 2019). His work spans interdisciplinary applications including maritime systems security, GPU-accelerated AI, and evolutionary algorithms. Recent studies emphasize AI-driven security tools (e.g., ML-SAST) and domain-specific language approaches to optimization (EvoAl). He has contributed to over 30 peer-reviewed publications, with notable work in IEEE Transactions on Software Engineering and Science of Computer Programming. Berger collaborates closely with industry through DAAD review committees and serves on artifact evaluation boards for ISSTA and ARES conferences. Education: Doctoral Thesis (2022), Diploma in Computer Science (2007) Key Projects: ArchSec tool suite, Threat Modeling Frameworks, Bauhaus static analysis methodology Lab Affiliation: Embedded Systems Design Group His advisory roles include Deputy of TUHH's Election Verification Committee and Session Chair at IEEE Congress on Evolutionary Computation 2023. Current research trends integrate machine learning with static analysis for automated vulnerability detection, while also exploring explainable AI techniques for neural network optimization.

Jeffrey SchallView profile

Professor

• Visual Neurophysiology
• Neuroscience
• Cognitive Neuroscience
• +5 more

Jeffrey Schall is a Full Professor of Biology and Program Director of the Visual Neurophysiology Centre at York University. He holds the Canada Research Chair in Translating Neuroscience. His research focuses on neural mechanisms underlying behavior, integrating neurophysiological and computational approaches across multiple scales. Schall is a core member of the Centre for Vision Research and the Canada First Research Excellence Fund Connected Minds initiative. Education: PhD in Anatomy (University of Utah School of Medicine, 1986), postdoctoral training at MIT. Awards include the Troland Research Award, Sloan Foundation Fellowship, and AAAS Fellowship. He served as Vision Science Society President in 2019. Research interests include visual attention, executive control, error monitoring, and translational neuroscience applications in law. His work bridges basic science with applied studies in clinical populations like schizophrenia patients. Collaborative projects involve EEG/MEG analysis, cortical microcircuitry modeling, and neuromodulation techniques. Teaching: YU_NRSC 2100 Systems, Behavioral, and Cognitive Neuroscience. Active in interdisciplinary initiatives linking neuroscience with legal systems through scholarship and policy engagement.

J. Anthony MovshonView profile

Professor

• Neuroscience
• Visual Neuroscience
• Cognitive Neuroscience
• +3 more

J. Anthony Movshon is a Professor at New York University (NYU) in the Department of Psychology and a key member of NYU's Center for Neural Science (CNS). His research focuses on the primate visual system, particularly the encoding and decoding of visual information in cortical areas like V1 and MT, and its role in behavior and perception. Education: Doctorate in Visual Neurophysiology and Psychophysics from Cambridge University Research Interests: Movshon investigates the functional architecture of the visual cortex, emphasizing motion, form, and color processing. His work explores how neural activity relates to perceptual decisions and motor behavior, using electrophysiological recordings, neuroimaging, and computational models. He also studies developmental disorders like amblyopia and their impact on visual system organization. Publications: His recent work spans visual texture selectivity in V2, contextual modulation in neural responses, motion processing in MT, and decoding mechanisms in visual cortex. These studies employ interdisciplinary approaches blending neurophysiology, computational neuroscience, and cognitive modeling. Labs & Collaborations: Movshon leads the Visual Neuroscience Laboratory at NYU, collaborating with researchers such as Michael Hawken, Lynne Kiorpes, and Eero Simoncelli.

John P. O'DohertyView profile

Professor

• Decision Neuroscience
• Reward-related Learning
• Computational Modeling
• +3 more

John P. O'Doherty serves as the Fletcher Jones Professor of Decision Neuroscience within Caltech's Division of Humanities and Social Sciences, holding continuous faculty appointments since 2004 (Assistant Professor 2004-07, Associate Professor 2007-09, Professor 2009-present, Fletcher Jones Professor 2021-present). He previously directed the Caltech Brain Imaging Center (2013-17) and maintains affiliations with the T&C Chen Center for Social and Decision Neuroscience. His educational background includes a B.A. from University of Dublin, Trinity College (1996) and D.Phil. from University of Oxford (2000). His research focuses on computational and neural mechanisms of reward-based learning and decision-making , employing fMRI, intracranial recordings, and mathematical modeling to investigate how the brain solves complex decision problems through evolutionarily conserved algorithms. Key areas include Reinforcement learning systems (model-based/model-free arbitration) Observational and social learning mechanisms Neural representation of value, risk, and uncertainty Computational phenotyping of mental disorders Temporal dynamics of goal persistence Analysis of his 2023-2025 publications reveals dominant trends in computational psychiatry (problem gambling, autism traits), hierarchical decision-making, and neuroeconomic modeling of social behavior. His work consistently integrates cross-species computational frameworks with human neuroimaging to identify transdiagnostic mechanisms. While specific awards beyond his endowed professorship aren't detailed, his leadership as Brain Imaging Center Director and prolific high-impact publications demonstrate significant recognition. Current advising includes graduate researcher Sneha Aenugu on goal-persistence projects, with administrative support from Mary A. Martin (mmartin@caltech.edu). His active research program continues to pioneer computational approaches to understanding decision pathologies.

• Computational Neuroscience
• Computational Psychiatry
• Neuroimaging
• +3 more

John D. Murray is the Gregg L. Engles Associate Professor of Psychological and Brain Sciences at Dartmouth College and an Adjunct Associate Professor of Psychiatry at Yale School of Medicine. He holds a PhD in Physics from Yale University (2013) and a BS in Physics and Mathematics from Yale (2006). His research focuses on computational neuroscience and computational psychiatry, with secondary appointments in Physics and Neuroscience at Yale until 2023. His work integrates computational modeling, neuroimaging, and systems neuroscience to study decision-making processes, cortical organization, and psychiatric disorders. Collaborators include prominent researchers like Dr. John Krystal and Dr. Anticevic. Research interests include hierarchical brain organization, neuroimaging analysis techniques, and pharmacological effects on neural circuits. His lab (Murray Lab) develops computational tools like PsychRNN for cognitive task modeling. Notable contributions include linking transcriptomic data to neuroimaging patterns and modeling LSD’s effects on brain topography. He has been featured in YaleNews and Nature Communications for innovations in mapping mental illness variability and neural circuit dynamics. Grants and collaborations span translational neuroscience, addiction, and PTSD research through partnerships with Yale’s Center for Biomedical Data Science and VA National Center for PTSD. His interdisciplinary approach bridges physics, computer science, and clinical psychiatry to advance understanding of brain function and dysfunction.

Lourdes Pena-CastilloView profile

Professor

• Bioinformatics
• Genomics
• Machine Learning
• +5 more

Dr. Lourdes Pena-Castillo is a Professor jointly appointed in the Departments of Computer Science and Biology at Memorial University of Newfoundland's Faculty of Science. Her research focuses on applying machine learning and bioinformatics to study bacterial gene regulation, with emphasis on transcriptomics, gene expression pathways, and microbiology. She leads the Bioinformatics Lab at MUN, developing computational tools like Promotech for promoter prediction and sRNARFTarget for sRNA target identification. Education: BSc in Information Systems Engineering, ITESM-Mexico MSc in Computer Science, University of Alberta PhD in Computer Science (Doktoringenieurin), Otto-von-Guericke Universität Magdeburg Postdoc in Bioinformatics, University of Toronto Research Interests: Bioinformatics, Genomics, Machine Learning, Artificial Intelligence, Transcriptomics, Gene Regulation, Microbiology Her work integrates computational methods with biological data to address challenges in molecular biology, including analyzing bacterial sRNA functions, promoter recognition, and disease diagnostics using machine learning. She has advised numerous graduate students, including PhD candidates Purvikalyan Pallegar and Bonita McCuaig, and MSc students like Ruben Chevez-Guardado and Kratika Naskulwar. Her lab focuses on translational research with applications in both basic science and clinical contexts. Publications span computational methods for bacterial gene regulation, bioinformatics tool development, and interdisciplinary projects in VR and healthcare informatics. Her research has contributed to understanding symbiotic relationships in marine organisms, inflammatory bowel disease diagnostics, and clavulanic acid production in Streptomyces. Grants & Collaborations: Works with interdisciplinary teams across computer science and biology, supported by grants enabling projects in bacterial genomics and computational tool development. Labs & Teams: Leads the Bioinformatics Lab at MUN, fostering collaborations with researchers in microbiology, computer science, and healthcare.

Johannes LarschView profile

Assistant Professor

• Neuroscience
• Social Behavior
• Neuronal Circuits
• +3 more

Johannes Larsch is a tenure-track Assistant Professor at the Center for Integrative Genomics within the Faculty of Biology and Medicine at Université de Lausanne (UNIL). His research focuses on understanding the neuronal mechanisms underlying social interactions, using larval zebrafish as a model organism. He employs advanced techniques like virtual reality and optical imaging to study how social signals are processed in the brain. Education: Bachelor of Biology, University of Konstanz, Germany PhD in Neurobiology, Rockefeller University, USA (lab of Cori Bargmann) Postdoc at Max Planck Institute of Neurobiology, Germany (with Herwig Baier) Research interests center on neuronal circuits driving group behavior, social recognition, and the interplay between genetics and environment in shaping behavior. His lab investigates how individual brains coordinate collective social behaviors through studies of neuronal activity and circuit dynamics. Current lab openings include fully funded PhD and PostDoc positions focused on social neuroscience and neurogenomics. Contact: johannes.larsch@unil.ch .

• Photonics
• Neuromorphic Engineering
• Optical Communications
• +5 more

Paul Prucnal is a Professor of Electrical and Computer Engineering at Princeton University, affiliated with the Princeton Materials Institute (PMI). He leads the Lightwave Communications Research Lab, focusing on ultrafast optical techniques for communication networks and neuromorphic photonics. His research spans optical security, CDMA networks, nonlinear signal processing, and photonic neurons. Education: Ph.D., Columbia University (1979) M.Phil., Columbia University (1978) M.S., Electrical Engineering, Columbia University (1976) A.B., Bowdoin College, summa cum laude (1974) Research Interests: Optical Network Security (eavesdropping/jamming countermeasures) Optical CDMA for broadband networks Silicon photonic neuromorphic computing RF interference cancellation in wireless systems Photonic spiking neurons mimicking biological organisms Awards: National Academy of Inventors Fellow (2017) 10+ teaching awards including Princeton's President's Award (2015) OSA/IEEE Fellowships (1992, 1997) Labs/Teams: Leads the Lightwave Communications Lab, collaborating with government/industry partners. Lab alumni like Prof. Bhavin Shastri have achieved international recognition. Grants/Publications: Over 350 journal papers, 22 U.S. patents. Authored/co-authored Neuromorphic Photonics (2017) and edited Optical Code Division Multiple Access (2019). Current projects include photonic tensor processors and real-time RF signal processing.

Jason RittView profile

Associate Professor

• Computational Neuroscience
• Data Science
• Neuroengineering
• +3 more

Jason Ritt is an Associate Professor of Brain Science (Research) and Scientific Director of Quantitative Neuroscience at the Robert J. and Nancy D. Carney Institute for Brain Science, Brown University. He holds affiliations with the Data Science Institute and collaborates across disciplines on quantitative research methods. Education : B.S., M.A., and Ph.D. in Neuroscience from Boston University (1997–2003). Research : Focuses on neural processing during active sensing and neuroengineering for neurostimulation. Combines electrophysiology, optogenetics, and theoretical approaches in rodent models. Develops closed-loop systems for studying sensory neural prosthetics and brain-machine interfaces. Key areas include synaptic diversity, neurocontrol algorithms, and sensory restoration. Teaching : Instructs NEUR 2100 NeuroPracticum, integrating hands-on neuroscience research training.

Kevin M. FranksView profile

Associate Professor

• Neurobiology
• Olfactory System
• Neural Circuits
• +3 more

Kevin M. Franks is an Associate Professor of Neurobiology at Duke University, where he investigates how the olfactory system forms neural representations of sensory environments. His work focuses on functional neural circuits in the olfactory bulb and piriform cortex, using techniques like in vivo recordings, optogenetics, and behavioral assays. His research explores Neural circuit dynamics and plasticity Odor coding mechanisms Role of recurrent circuitry Integration of sensory modalities Recent publications highlight his contributions to understanding cortical odor representations, developmental neural connectivity, and cross-modal interactions. Awards include the 2024 Don Tucker Finalist recognition. He teaches advanced neuroscience courses at Duke, including Neurobiology research and concepts in neuronal systems.

• Neural circuit mechanisms
• Adaptive learning
• State-dependent decision-making
• +4 more

Dr. Sabine Krabbe is a Group Leader at the German Center for Neurodegenerative Diseases (DZNE) in Bonn, Germany, where she leads research on neural circuit mechanisms underlying adaptive learning and state-dependent decision-making. Her work integrates neuroscience, molecular biology, and behavioral approaches to understand how internal states influence behavior and how these processes are disrupted in neurological disorders. Dr. Krabbe's research focuses on the interactions between midbrain circuits of the substantia nigra and ventral tegmental area with their output structures such as the striatum and amygdala. She investigates how these networks integrate internal states with environmental cues to produce appropriate behavioral responses. Her laboratory employs state-of-the-art techniques including deep-brain calcium imaging at single-cell resolution in mice, opto- and pharmacogenetic manipulations, anatomical tracings, and molecular approaches to characterize neural circuit elements in detail. Her recent publications reveal significant insights into amygdala interneuron plasticity during fear learning, brain-wide representational drift in memory consolidation, and the molecular mechanisms underlying Parkinson's disease progression. Her work demonstrates how activity patterns within specific neural circuits change in early stages of neurodegenerative diseases and how this dysfunction contributes to cognitive deficits and emotional disturbances. Dr. Krabbe is actively involved in the neuroscience community, organizing the BonnBrain Conference 2026 and sharing research through social media platforms. She has established herself as an emerging leader in the field of systems neuroscience with a particular focus on the neural basis of emotional states and decision-making processes.