Find Academic Supervisors

Saba GharaeiView profile

Research Fellow

• Neuroscience
• Multisensory Integration
• Neural Coding
• +3 more

Dr. Saba Gharaei is a Research Fellow at the Australian National University (ANU), affiliated with the Division of Neuroscience (Eccles Institute of Neuroscience) within the John Curtin School of Medical Research (JCSMR). She is part of the Arabzadeh Group, focusing on neural coding and multisensory integration. Her research spans visual and somatosensory systems, with emphasis on how the brain processes sensory information across modalities. Key areas include the role of the superior colliculus in modulating cortical responses and the integration of visual/whisker signals in rodents. Education: PhD in Neuroscience (specific degree details not explicitly stated). Affiliations: ANU Eccles Institute of Neuroscience, Arabzadeh Group. Research Interests: Her work investigates multisensory integration mechanisms, neural pathways in sensory processing, and behavioral neuroscience. Specific topics include classical conditioning, reinforcement learning, and visual motion perception in primates. Recent studies highlight the superior colliculus’s role in somatosensory-cortical interactions and cross-modal signal integration. Publications: Dr. Gharaei has contributed to seminal studies on sensory integration, including work on rat visual-whisker signal integration (2018) and marmoset visual motion processing (2013). Her 2020 paper on somatosensory-cortical modulation in the superior colliculus advanced understanding of neural coding pathways. Grants/Labs: Active in collaborative projects within the Arabzadeh Group, focusing on neural coding and neurophysiological techniques. No explicit grant details provided in texts.

Susumu TonegawaView profile

Professor

• Neuroscience
• Learning and Memory
• Molecular Biology
• +3 more

Susumu Tonegawa is the Picower Professor of Biology and Neuroscience at MIT and Director of the RIKEN-MIT Center for Neural Circuit Genetics. He holds positions at the Howard Hughes Medical Institute and has led multiple research centers, including the Picower Institute for Learning and Memory. His work focuses on unraveling the molecular, cellular, and neural circuit mechanisms underlying learning and memory, with implications for neurologic and psychiatric disorders. Education: PhD in Biology from the University of California, San Diego (UCSD). Postdoctoral training at the Salk Institute and Basel Institute for Immunology. Research Interests: Tonegawa’s research explores memory formation, neural circuitry, and mechanisms of disorders like schizophrenia and Alzheimer’s. Key contributions include identifying memory engram cells and demonstrating their role in memory storage and retrieval. His lab investigates reward systems, fear extinction, and the neural basis of social memory. Awards: Nobel Prize in Physiology or Medicine (1987), David M. Bonner Lifetime Achievement Award (2010), Gold Medal from Spain’s National Research Council (2007), and numerous honorary degrees. His work spans immunology, neuroscience, and neurobiology, with over 50 years of impactful research. Affiliations: MIT, RIKEN Brain Science Institute, Howard Hughes Medical Institute, and advisory roles at institutions like the Okinawa Institute of Science and Technology.

• Neuroscience
• Cognitive Science
• Psychology
• +3 more

Amrita Lamba is a Postdoctoral Fellow in the Department of Brain and Cognitive Sciences at the Massachusetts Institute of Technology (MIT). Her research focuses on understanding how anxiety, uncertainty, and moral motivations influence decision-making processes and social learning. She investigates neural mechanisms underlying credit assignment, policy adaptation, and cooperative behavior using experimental and computational approaches. Her work bridges neuroscience and psychology, exploring topics such as prefrontal cortex function, emotional regulation, and the interplay between cognitive states and adaptive behavior. She employs Bayesian modeling and entropy analysis to study cooperation dynamics and moral decision-making under uncertainty. Selected publications highlight her contributions to understanding how anxiety disrupts adaptive learning, the role of prefrontal cortex states in credit assignment, and the effects of uncertainty on cooperative heuristics. While no awards or grants are explicitly mentioned, her research demonstrates impactful interdisciplinary investigations into human cognition.

Mingbo CaiView profile

Assistant Professor

• Cognitive Neuroscience
• Brain-Inspired AI
• Reinforcement Learning
• +5 more

Mingbo Cai is an Assistant Professor in the Department of Psychology within the College of Arts and Sciences at the University of Miami. His research focuses on the intersection of human cognition and machine intelligence, leveraging computational models and experiments to study learning, decision making, and spontaneous thoughts. He develops tools like the Brain Imaging Analysis Kit (BrainIAK) to advance neuroimaging research. Key interests include reinforcement learning, Bayesian inference, and computational psychiatry. His lab explores how the brain constructs cognitive maps and processes spatial navigation through fMRI studies, emphasizing the role of predictive learning principles. Recent work includes studies on 3D object perception, unsupervised learning inspired by infant cognition, and decoding spontaneous thoughts via neuroimaging. Cai has published extensively on neural representation analysis, fMRI simulation, and transfer learning, with contributions to both theoretical neuroscience and practical machine learning applications. Current opportunities include PhD admissions (fall 2025) and a postdoc position focusing on fMRI studies of spontaneous thoughts. Collaborations leverage interdisciplinary approaches to bridge cognitive models with AI advancements, aiming to understand how neural systems achieve adaptive behavior through structured computations.

Venugopal V. VeeravalliView profile

Professor

• Statistical Inference
• Machine Learning
• Information Theory
• +9 more

Venugopal V. Veeravalli is the Henry Magnuski Professor of Electrical and Computer Engineering at the University of Illinois Urbana-Champaign (UIUC), affiliated with the Grainger College of Engineering, Coordinated Science Laboratory (CSL), Department of Statistics, and Discovery Partners Institute (DPI). He holds a Ph.D. from UIUC (1992) and has been on the faculty since 2000, previously at Cornell University. His research spans statistical inference, machine learning, information theory, and their applications in wireless communications, sensor networks, and cyberphysical systems. He has authored/co-authored influential books like Statistical Inference for Engineers and Data Scientists and Interference Management in Wireless Networks , and serves as Editor-in-Chief of the IEEE Transactions on Information Theory. Notable awards include the IEEE Fellow (2006), PECASE (1998), and twice the Abraham Wald Prize in Sequential Analysis (2016, 2023). He advises students on topics like robust learning, distributed algorithms, and adaptive sensing. Current research focuses on sequential change detection, non-stationary environments, and IoT resilience. Education: B.Tech. (IIT Bombay, 1985), M.S. (Carnegie Mellon, 1987), Ph.D. (UIUC, 1992) Professional Roles: Editor-in-Chief (IEEE Transactions on Information Theory), NSF Program Director (2003–2005), Fulbright-Nokia Chair (2023) Research Interests: His work bridges theoretical foundations and practical applications, including quickest change detection, robust machine learning, and distributed systems. Recent projects involve anomaly detection in power grids, pandemic monitoring via wastewater data, and adaptive algorithms for wireless networks. Grants & Collaborations: Active in NSF-funded projects on robust learning and IoT resilience. Collaborates internationally, including with Aalto University (Finland) under the Fulbright-Nokia Chair. Teaching: Teaches courses on probability, optimization, communication theory, and data science. Recognized with the Michael Tien Excellence in Teaching Award (1999).

• Neural Prosthetics
• Brain-Machine Interfaces (BMIs)
• Motor Control Neuroscience
• +3 more

Krishna V. Shenoy is the Hong Seh and Vivian W. M. Lim Professor of Engineering at Stanford University, holding appointments in the Departments of Electrical Engineering and, by courtesy, Bioengineering, Neurobiology, and Neurosurgery. He is an HHMI Investigator and co-director of the Neural Prosthetics Translational Lab. His work focuses on neural prosthetics, brain-machine interfaces, and motor control neuroscience, aiming to restore mobility to paralyzed individuals. Shenoy earned his BS from UC Irvine (1990) and PhD from MIT (1995), followed by postdoctoral research at Caltech. He joined Stanford in 2001, advancing from Assistant to Full Professor and later an endowed chair. Research interests include decoding neural signals for prosthetic control, understanding motor cortex dynamics, and developing clinical translation strategies for BMIs. His labs (Neural Prosthetic Systems Lab and NPTL) pioneer high-performance neural interfaces, including wireless systems and handwriting-based communication. Notable achievements include the BrainGate clinical trial and the Andrew Carnegie Prize (2018). Awards include IEEE Fellowship (2022), membership in the National Academy of Medicine (2022), and NIH awards. He advises on multiple ventures like Neuralink and sits on scientific boards for MIND-X and Inscopix. Shenoy’s work bridges engineering, neuroscience, and clinical application, emphasizing translational research to enhance human mobility.

Franziska KnolleView profile

Researcher

• Computational Neuroscience
• Cognitive Neuroscience
• Clinical Neuroscience
• +15 more

Dr. Franziska Knolle is a Researcher and Principal Investigator at the Department of Neuroradiology, School of Medicine and Health, Technical University of Munich (TUM) . She leads the KnolleLab , focusing on computational and clinical neuroscience. She completed her Habilitation in Neuroscience in June 2024 and holds a PhD from the Max Planck Institute of Cognitive and Brain Sciences. Education and Training: PhD, Max Planck Institute of Cognitive and Brain Sciences, Leipzig Habilitation in Neuroscience, Technical University of Munich (2024) Medical undergraduate studies (Physikum), TU Dresden Marie Skłodowska-Curie Fellow Research Interests: Dr. Knolle uses computational modeling, fMRI, EEG, and machine learning to study cognitive mechanisms in health and psychiatric disorders, especially psychosis. Her work is grounded in the framework of predictive processing and Bayesian inference , exploring how prior beliefs influence perception, decision-making, and language. She investigates whether computational parameters can predict psychiatric risk or functional outcomes, with a focus on schizotypy, autism traits, and glutamate neurochemistry. Publication Trends: Her recent publications span computational psychiatry, neuroimaging, and cognitive modeling. Key themes include predictive processing in psychosis, reward prediction errors, semantic prior weighting, neural circuitry of salience, and multimodal imaging (fMRI, EEG, MRS). Her work increasingly integrates AI and machine learning for digital phenotyping and biomarker discovery. Scientific Awards: Marie Skłodowska-Curie Fellow Advising and Grants: Dr. Knolle supervises multiple PhD and medical doctorate students, including Elisabeth Sterner, Pritha Sen, and Onur Icin. She mentors students in computational modeling, neuroimaging, and clinical research. Her lab is supported by funding from competitive sources including DFG, DAAD, Marie Curie, and Leopoldina. She collaborates widely with institutions in the UK, Germany, and internationally. Labs and Teams: She leads the KnolleLab at TUM, which includes PhD students, medical researchers, and interns. The lab focuses on integrating computational models with multimodal brain data. She collaborates with researchers at the University of Cambridge, King’s College London, MRC CBU, and the University of Melbourne.

• Next-generation wireless transmissions
• Quantum communications and detection
• Channel coding and modulation
• +7 more

Professor Michael Ng is an academic at the University of Southampton's School of Electronics and Computer Science. He holds a professorship in Next Generation Communications and actively supervises PhD students in fields related to wireless technologies and quantum communications. Education: B.Eng. in Electronic Engineering (First Class), University of Southampton (1999) Ph.D. in Telecommunications, University of Southampton (2002) Research Interests: His work spans next-generation wireless transmissions, quantum communications, channel coding, MIMO systems, cooperative communications, and machine learning applications in communications. He leads cutting-edge research in quantum error correction and hybrid wireless-optical systems. Scientific Contributions: Principal Investigator for EPSRC-funded Cooperative Classical and Quantum Communications Systems Co-Investigator in multiple European projects (SCOUT, NEWCOM, PHOENIX, OPTIMIX, CONCERTO) Co-authored over 260 papers and two IEEE Press books Awards: Dean’s Award for outstanding contributions in scientific publications (2013) Leadership Roles: He has served as EEE Programme Leader (2018-2021) and currently directs the ECS Doctoral Programme. He also chairs the IEEE Quantum Communications & Information Technology Emerging Technical Subcommittee.

Kristin ScaplenView profile

Assistant Professor

• Neuroscience
• Psychology
• Learning and Memory
• +4 more

Kristin Scaplen serves as an Assistant Professor of Psychology within Bryant University's College of Arts and Sciences, where she investigates neural circuit mechanisms underlying learning and memory disruption in addiction contexts using Drosophila models. Her research focuses on how pathologic memories guiding maladaptive reward-seeking behaviors are established and modified through experience, particularly in alcohol use scenarios. Her academic foundation includes: Ph.D. in Neuroscience from Brown University B.S. in Biology from the University of Connecticut B.S. in Psychology from the University of Connecticut Dr. Scaplen's research integrates neurogenetics, behavioral analysis, and circuit mapping to study memory persistence in substance abuse models. She examines how neural circuits for alcohol-associated memories form and evolve, aiming to translate findings from Drosophila to mammalian brain function. Her work bridges molecular mechanisms with behavioral outcomes in addiction neuroscience. Analysis of her 15 most recent publications reveals a consistent focus on Drosophila neurogenetics applied to addiction research, with increasing emphasis on circuit-level analysis of alcohol preference behaviors. Key thematic trends include neural encoding of reward pathways, behavioral flexibility mechanisms, and comparative approaches to memory circuit function across species. Her recognition includes: Bryant University Convocation Address honor (2024) Research Engagement Day Director's Award (2024) Excellence in Teaching award (2023) Women's Basketball Honorary Coach designation (2023) Mention of Merit distinctions (2022, 2024) Dr. Scaplen actively mentors undergraduate researchers who gain comprehensive neuroscience experience in her lab, presenting findings at regional and international conferences including Society for Neuroscience. Her students have secured travel awards and won presentation competitions for their work on Drosophila memory circuits. The Scaplen Lab operates as a collaborative undergraduate research hub focused on fruit fly models of memory, emphasizing open resource sharing and neuroscience outreach to local communities while advancing fundamental understanding of neural circuit plasticity.

Ryunosuke AmoView profile

Assistant Professor

• Neuroscience
• Dopamine Signaling
• Computational Neuroscience
• +7 more

Ryunosuke Amo, Ph.D., is an Assistant Professor at the Fralin Biomedical Research Institute at VTC and the School of Neuroscience within the College of Science at Virginia Tech. He leads the Amo Lab, part of the Center for Neurobiology Research, where his team investigates the neural mechanisms of dopamine signaling and its role in behavior and disease. Research Interests: Dr. Amo's research integrates anatomy, physiology, behavior, computational modeling, and molecular tools to understand how dopamine neurons generate diverse signals related to reward, aversion, motivation, and learning. His work explores how opioid exposure disrupts these signals and how dopamine systems have evolved across vertebrates. Key areas include prediction error coding, habenula function, and the neural basis of psychiatric and neurological disorders such as addiction, depression, and Parkinson’s disease. Publication Trends: His recent publications in high-impact journals like Neuron and Nature Neuroscience reflect a strong focus on dopamine neuron dynamics, particularly in relation to reinforcement learning models. His work bridges experimental neuroscience with computational approaches, revealing deep parallels between neural activity and machine learning algorithms like temporal difference learning. Society for Neuroscience, Trainee Professional Development Award (2023) Japan Neuroscience Society, Young Investigator Award (2023) Comprehensive Brain Network, Summer Workshop 2011, Best Presentation Award (2011) Advising and Grants: While specific students are not listed, Dr. Amo leads an active research lab with open positions, indicating ongoing mentorship of graduate students, postdoctoral associates, and research coordinators. His prior support from prestigious institutions like Harvard and RIKEN suggests a strong history of competitive funding, though current grants are not detailed in the text. Labs and Teams: Dr. Amo directs the Amo Lab at the Fralin Biomedical Research Institute, which is embedded within the Center for Neurobiology Research. His team employs cutting-edge techniques to dissect neural circuits involved in dopamine and serotonin signaling, with a focus on translational relevance to brain disorders.

Luke Remage-HealeyView profile

Professor

• Behavioral Neuroscience
• Neuroendocrinology
• Neural Plasticity
• +7 more

Luke Remage-Healey is a Professor in the Department of Psychological and Brain Sciences at the University of Massachusetts Amherst, where he is also affiliated with the Neuroscience & Behavior Graduate Program and the Center for Neuroendocrine Studies. He leads the Healey Lab, which investigates the rapid actions of neurosteroids in the brain, particularly in songbirds as a model for vocal learning and neural plasticity. His research focuses on behavioral neuroscience, neuroendocrinology, and the modulation of neural circuits by locally synthesized estrogens. Using techniques such as in vivo microdialysis, electrophysiology, and neuropharmacology, his lab explores how neuroestrogens influence auditory processing, song learning, and social behaviors. His work bridges molecular mechanisms with system-level functions, emphasizing the dynamic role of steroids in brain function. The recent publications from his lab highlight a strong trend in understanding how neuroestrogens regulate neural plasticity, sensory processing, and learning. Key themes include the rapid, localized actions of estrogens in the auditory forebrain, the role of neurosteroids in social and vocal behaviors, and the evolutionary conservation of neuromodulatory mechanisms. His studies often involve songbirds like zebra finches, leveraging their well-mapped neural circuits for vocal communication. Luke Remage-Healey has received several scientific awards, including: Frank A. Beach Award, Society for Behavioral Neuroendocrinology (2012) Early Investigator Award, Endocrine Society (2015) Outstanding Research Award, UMass College of Natural Sciences (2019) He has mentored numerous graduate students and postdoctoral researchers, many of whom have gone on to independent academic careers. His lab has been supported by grants from the National Institutes of Health (NIH) and the National Science Foundation (NSF), reflecting the significance and impact of his research. The Healey Lab also engages in outreach and collaborative science, contributing to both fundamental neuroscience and broader scientific discourse. His lab is involved in studying neural circuits for vocal communication, with a focus on how neuromodulators like estrogens and dopamine shape neural activity and behavior. The Healey Lab uses songbirds to model human vocal learning, aiming to uncover general principles of brain function and plasticity.

• Neuroscience
• Cognitive Neuroscience
• Decision Making
• +6 more

Becket Ebitz is an Associate Professor in the Department of Neuroscience at Université de Montréal's Faculty of Medicine. He leads the noiseLab , focusing on how internal cognitive states (goals, beliefs, arousal) shape perception and behavior. His work integrates behavioral experiments, neural recordings, and computational modeling, often leveraging tools from AI and robotics. Teaching responsibilities include courses like Neurosciences: Travaux Pratiques and Colloque en Neurosciences . He advises programs such as the BSc in Biomedical Sciences and the Doctorate in Neuroscience. Ebitz holds a Canada Research Chair in the Dynamics of Cognition and leads major grants including the Neuroscience of Exploration (8.8M$ funded project) and UNIQUE (a Quebec AI-neuroscience initiative). His research explores neural mechanisms underlying decision-making, uncertainty processing, and cognitive flexibility. Notable contributions include studies on exploration-exploitation trade-offs, sex differences in learning, and the role of neurotransmitters like dopamine in behavioral strategies. He has been recognized with awards like the Jacobs Foundation Fellowship and FRQS Junior 1 Award. Labs/Teams: noiseLab (exploration dynamics), EthoLab (natural behavior studies), and collaborations in UNIQUE. His work bridges neuroscience with AI through computational models of neural population dynamics.

Yonatan LoewensteinView profile

Professor

• Decision-making
• Reinforcement Learning
• Neurobiology
• +3 more

Prof. Yonatan Loewenstein is a Professor in the Department of Neurobiology at the Hebrew University of Jerusalem. His research focuses on computational neuroscience and cognition, particularly the neural mechanisms underlying decision-making, reinforcement learning, and sensory processing. He leads an interdisciplinary laboratory exploring how learning principles govern behaviors in both biological and artificial systems. Key contributions include studies on somatosensory cortex organization, neuronal homeostasis, and human-machine synergy in decision-making. He co-authored the book Computational Models in Cognition , blending theoretical frameworks with empirical findings. Education details are not explicitly provided in the text, but his affiliations suggest advanced training in neurobiology and computational sciences. Research interests span decision-making biases, reinforcement learning dynamics, and the interplay between brain structure and function. His recent work addresses topics like idiosyncratic choice stability, value modulation in impulsivity, and abstract reasoning in neural networks. Publications highlight collaborations in fields ranging from cognitive dissonance to schizophrenia diagnostics. While no specific awards are listed, his involvement in high-impact journals and interdisciplinary projects underscores his academic contributions. The lab’s work is housed in the Goodman Faculty building, with active group members and experimental facilities.

Ron KeiflinView profile

Assistant Professor

• Neuroscience
• Neural Circuits
• Reward Prediction Learning
• +6 more

Ron Keiflin is an Assistant Professor in the Department of Psychological & Brain Sciences at the University of California, Santa Barbara (UCSB). He received his B.S. in Neuroscience from the University of Strasbourg, France, and his Ph.D. in Neuroscience from the University of Bordeaux, France. His postdoctoral research at UC San Francisco and Johns Hopkins University focused on the causal role of phasic dopamine signals in reward prediction learning. His lab at UCSB investigates neural mechanisms underlying associative learning, decision-making, and their dysfunctions in psychiatric disorders such as addiction and schizophrenia. Research Interests: Dr. Keiflin’s work combines advanced behavioral tasks in rodents with techniques like fiberphotometry, optogenetics, and chemogenetics to study neural circuits involved in reward processing, particularly dopamine pathways influencing striatal and prefrontal networks. His lab explores questions such as how organisms learn predictive relationships between events, exploit memories for adaptive decisions, and how these processes are disrupted in neuropsychiatric conditions. Advising & Grants: While specific grant details are not provided, his research aligns with cutting-edge neuroscience methodologies. His lab focuses on defining neural circuit dynamics and information processing principles governing behavior. Labs/Teams: His primary lab at UCSB integrates computational neuroscience approaches with molecular and cellular techniques to study behavior and neural circuits.

Eran DayanView profile

Associate Professor

• Neuroimaging
• Machine Learning
• Network Neuroscience
• +5 more

Eran Dayan, PhD, is an Associate Professor in the Department of Radiology at the University of North Carolina at Chapel Hill (UNC-Chapel Hill) and a faculty member at the Biomedical Research Imaging Center (BRIC). His lab focuses on age-associated neurological and neuropsychiatric dysfunction using computational approaches like machine learning and network analysis. Research emphasizes brain resilience mechanisms, Alzheimer’s disease progression, and clinical translation of neuroimaging biomarkers. Education: PhD from the Weizmann Institute of Science (Israel), followed by postdoctoral training at the National Institute of Neurological Disorders and Stroke (NINDS). Affiliations include the Department of Radiology and BRIC at UNC-Chapel Hill. Research interests span computational modeling of neuroimaging data, network neuroscience, and translational applications in neurodegenerative diseases. His team analyzes large datasets to identify resilience factors (e.g., redundancy in brain networks) and develops diagnostic/prognostic tools for diseases like Parkinson’s and Alzheimer’s. Lab collaborations include work with clinical partners and institutions like Dartmouth College (Peter Mucha). Advising includes mentoring PhD students (e.g., William Stanford) and undergraduate researchers in computer science and biostatistics. Lab alumni have pursued roles in academia and clinical research. Labs/Teams: Dayan Lab (founded 2016) and BRIC, leveraging advanced imaging technologies and computational infrastructure.