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.

Daniel W. BlissView profile

Professor

• Radar
• Wireless Communications
• Precision Positioning
• +7 more

Daniel W. Bliss is a Professor in the School of Electrical, Computer and Energy Engineering at Arizona State University and Director of ASU's Center for Wireless Information Systems and Computational Architectures (WISCA). With over $50 million in research funding as principal investigator from organizations including DARPA, ONR, Google, and Airbus, his work bridges theoretical foundations with practical implementations across multiple domains of wireless systems. Dr. Bliss received his educational foundation with a B.S.E.E. from Arizona State University (1989), followed by M.S. and Ph.D. degrees in Physics from the University of California-San Diego (1995, 1997). His academic journey includes significant industry experience at General Dynamics (1989-1993) and MIT Lincoln Laboratory (1997-2012) before joining ASU. His research program focuses on advanced wireless systems spanning radar, communications, precision positioning, computational architectures, and medical monitoring applications. Bliss employs information theory, estimation theory, and signal processing to develop novel system concepts with disruptive capabilities. Current research emphasizes RF convergence, integrated sensing and communications, and anticipatory medical analytics using wireless technologies, with particular focus on extracting physiological data from radar signals. Analysis of recent publications reveals a strong trend toward integrated sensing and communications systems, particularly utilizing mmWave and radar technologies for medical monitoring applications. His work increasingly bridges traditional communications and radar domains while expanding into physiological monitoring, demonstrating a clear trajectory toward convergence of wireless technologies for healthcare applications and remote vital sign detection. Dr. Bliss has received significant recognition for his contributions: Fellow of the IEEE (2015) 2021 IEEE Warren D. White Award for Excellence in Radar Engineering 2016-2017 Top 5% Teaching Award at ASU 2017 ASU Fulton Engineering Exemplar Faculty As a dedicated mentor, Dr. Bliss has supervised numerous graduate students through successful dissertation and thesis defenses across both PhD and Master's programs. His research portfolio includes substantial funding from diverse sources with over $50 million secured as principal investigator. Current projects include the $17M DARPA DASH project focused on advanced software-reconfigurable heterogeneous SoCs for next-generation RF systems, and multiple initiatives in contactless vital sign monitoring using radar technologies. Dr. Bliss leads the BLISS Lab and serves as director of WISCA, fostering interdisciplinary research in wireless systems. His team includes researchers working on distributed coherent systems, MIMO radar, RF convergence, and medical monitoring applications, with recent successes including the Making Waves team that tied for first place in the Air Force Spark Tank challenge. He has founded two startup companies: DASH Tech Integrated Circuits Company and the Big Little Sensor Company, focusing on high-performance embedded processing and small-scale radar physiological monitoring, respectively.

• Brain-computer interface
• Rehabilitation Robotics
• Machine Learning
• +5 more

Dr. Yi-Feng Chen is a Research Assistant Professor and Master's Supervisor in the Department of Biomedical Engineering at the Southern University of Science and Technology (SUSTech) in Shenzhen, China. He joined SUSTech as a postdoctoral fellow in November 2020 and was promoted to Research Assistant Professor in February 2023. His academic journey includes interdisciplinary training across engineering, neuroscience, and biomedical applications. Dr. Chen's educational background includes: Ph.D. in Engineering from Wuhan University of Technology (2014-2017), supervised by Professor Quan Liu M.Sc. from Wuhan University of Technology (2011-2014), supervised by Professor Zhou Zude B.Sc. from Wuhan University of Technology (2007-2011) He also participated in exchange programs at Yuan Ze University in Taiwan (2012) and the University of Auckland in New Zealand (2015). Dr. Chen's research spans the intersection of biomedical engineering, neuroscience, and artificial intelligence, with particular focus on brain-computer interfaces and rehabilitation technologies. His work combines advanced signal processing techniques with clinical applications, especially in decoding neural signals for movement intention and monitoring brain states during anesthesia. His research has significant implications for neurorehabilitation, assistive technologies, and intraoperative neurophysiological monitoring. His recent publications demonstrate a clear trajectory toward increasingly sophisticated neural decoding techniques, with a focus on coordinated limb movements and practical rehabilitation applications. The work shows progression from basic EEG signal processing to complex bimanual movement decoding and robot-assisted rehabilitation systems, reflecting a translational research approach from basic science to clinical applications. Dr. Chen has secured significant research funding as principal investigator and core contributor on multiple projects: National Natural Science Foundation of China Youth Science Fund Project (2024-2026) Guangdong Natural Science Foundation General Project (2024-2026) Ministry of Science and Technology National Key R&D Program Project (2023-2026) Shenzhen Science and Technology Innovation Commission Key Project (2022-2025) As a Master's Supervisor, Dr. Chen mentors graduate students in biomedical engineering with focus on neural engineering and rehabilitation robotics. His laboratory collaborates closely with clinical partners to ensure research relevance to real-world medical challenges, particularly in neurorehabilitation and intraoperative monitoring.

Mark KingView profile

Professor

• Sports Biomechanics
• Injury Prevention in Sports
• Athletic Performance Optimization
• +3 more

Professor Mark King is a leading academic in Sports Biomechanics at Loughborough University, affiliated with the School of Sport, Exercise and Health Sciences, where he serves as Lead for the Sport Performance Research Theme. He holds a BSc (1993) and PhD (1998) in Mathematics and Sports Science from Loughborough, with career progression from Lecturer (1999) to Senior Lecturer (2006), Reader (2012), and Professor (2019). He is also Warden of Royce Hall since 1999, overseeing 375 students' welfare. Education: BSc in Mathematics and Sports Science (1993), PhD in subject-specific computer simulation of dynamic jumping (1998). Affiliations: England and Wales Cricket Board (ECB), International Cricket Council (ICC), Badminton World Federation, Lawn Tennis Association. His research focuses on optimizing elite sport performance through biomechanical analysis, particularly in cricket and badminton. Key areas include injury prevention (e.g., ACL risks in badminton, lumbar stress injuries in cricket bowlers) and technique optimization. He has pioneered ICC-accredited testing for illegal bowling actions and explored machine learning applications in data collection. His work highlights trends in cricket fast bowling kinematics, racket sports smash mechanics, and gender-specific performance metrics. He actively collaborates with national and international sports organizations to translate research into practical guidelines for athletes and coaches. Awards: None explicitly mentioned in the text. Grants/Advising: No listed advisees, but extensive industry partnerships support his research. Labs/Teams: National Centre for Sport and Exercise Medicine (NCSEM), part of his research infrastructure.

Tara McAllisterView profile

Associate Professor

• Speech Learning
• Speech Sound Disorders
• Biofeedback Intervention
• +5 more

Tara McAllister is an Associate Professor and Director of the Doctoral Program in Communicative Sciences and Disorders at New York University’s Steinhardt School. She leads the Biofeedback Intervention Technology for Speech (BITS) Lab , focusing on speech learning mechanisms and biofeedback treatments for speech disorders. Her work emphasizes acoustic and ultrasound biofeedback efficacy in resolving residual speech sound disorders, particularly in children. McAllister directs development of the staRt iOS app, expanding access to biofeedback training. She holds degrees from Harvard, MIT, and Boston University, with clinical expertise in speech-language pathology. Education: A.B./A.M., Linguistics, Harvard University (2003) M.S., Communication Disorders, Boston University (2007) Ph.D., Linguistics, MIT (2009) Research Interests: Speech motor control, perception-production links, bilingual phonological development, and technology-driven interventions. Her NIH-funded studies investigate biofeedback applications for speech disorders and crowdsourcing methodologies for perceptual analysis. Grants & Labs: NIH/NIDCD-funded BITS Lab research staRt app development since 2014 Teaching: Courses include Critical Evaluation of Research and Speech Science Instrumentation , emphasizing evidence-based practices in communication sciences.

Jeff MoherView profile

Associate Professor

• Cognitive psychology
• Cognitive neuroscience
• Visual attention
• +1 more

Jeff Moher is an Associate Professor of Psychology and Co-Chair of the Department of Psychology at Connecticut College, where he has been teaching since 2017. He also serves as the Data, Information, and Society Pathway Co-Coordinator, demonstrating his leadership within the institution. His educational background includes a Ph.D. and M.A. from Johns Hopkins University and a B.S. from the University of Michigan. This strong academic foundation has prepared him for his research and teaching career in cognitive psychology. Moher's research focuses on cognitive psychology and cognitive neuroscience, particularly visual attention and cognition in action. His work investigates why distractions occur, when they are likely to arise, and what mechanisms humans can harness to avoid them. He has found that humans employ various cognitive mechanisms to minimize distractions based on explicit knowledge, task goals, object properties, and recent experience, though he has also discovered surprising limitations in attentional selection. His research is particularly relevant given that over 3,000 people are killed annually in the United States from distracted driving. His recent publications show a consistent trajectory examining attentional mechanisms, distraction, and visual processing across different contexts. His work frequently employs sophisticated methodologies to measure both cognitive and motor responses to distractions, revealing how seemingly minor distractions can accumulate to induce serious performance costs. Moher's research is currently funded by grants from the National Science Foundation and the National Institutes of Health, indicating the significance and quality of his work. He likely mentors numerous students through the department's research groups, contributing to their development as researchers. He directs the CAMELab (Center for Attention, Movement, and Embodied Learning), which utilizes multiple methodologies including eye-tracking, electroencephalography, three-dimensional reach-tracking, and psychophysics to approach questions about attention and distraction. Current projects in his lab explore why salient distractors cause people to give up quickly during visual search, the brain mechanisms involved in learning to ignore distractions, how internal distractions impact physical interactions with the world, and how hand movement paths reveal information about attentional processes.

Raquel FernándezView profile

Professor

• Computational Linguistics
• Dialogue Systems
• Cognitive Science
• +3 more

Raquel Fernández is Full Professor of Computational Linguistics and Dialogue Systems at the University of Amsterdam, where she leads the Dialogue Modelling Group at the Institute for Logic, Language & Computation (ILLC). As Vice-Director for Research at ILLC and a Fellow of the ELLIS Society, she bridges computational linguistics, cognitive science, and artificial intelligence through her research on language use in multimodal and conversational contexts. PhD in Computational Linguistics from King's College London Prior research positions at University of Potsdam and Stanford University's CSLI Her work explores how cognitive constraints, social interaction, and perception shape language use, with a focus on: Visually-grounded language processing Multimodal dialogue modeling Model uncertainty and calibration Language grounding in multimodal data Language learning and semantic change Dialogue reference resolution Recent publications analyze multimodal reasoning limitations, cross-lingual knowledge consistency, and uncertainty modeling in dialogue systems. She has received multiple accolades including an ERC Consolidator Grant , NWO VENI/VIDI/Aspasia fellowships , and EMNLP/GenBench awards . Outstanding Paper Award (EMNLP 2023) Best Data Award (GenBench Workshop 2023) ELLIS Society Fellow ERC Consolidator Grant #819455 recipient NWO VENI/VIDI/Aspasia awardee As a leader in academic service, she serves on the SIGDAT Executive Committee and chairs multiple conference committees. Her lab develops models for multimodal dialogue, visual storytelling, and grounded language understanding.

Satoshi TomiokaView profile

Professor

• Semantics
• Pragmatics
• Prosody and Intonation
• +7 more

Satoshi Tomioka is a Professor in the Department of Linguistics and Cognitive Science at the University of Delaware, affiliated with the College of Arts & Sciences. He holds a B.A. from International Christian University (Tokyo) and a Ph.D. from the University of Massachusetts Amherst. His expertise spans semantics, pragmatics, syntax, and prosody, with a focus on Japanese and comparative East Asian linguistics. Education: B.A. in Liberal Arts (1987), International Christian University; Ph.D. in Linguistics (1997), University of Massachusetts Amherst. Research interests include ellipsis, anaphora, wh-interrogatives, distributivity, and contrastiveness. He investigates how prosody interacts with syntax and semantics in Japanese, exploring topics like focus marking, intervention effects, and scalar implicatures. Recent work examines associative plurals and pragmatic disambiguation in embedded questions. Publications analyze theoretical and empirical questions in linguistic interfaces, such as Bare quotatives as embedded speech acts (2024) and Focus without pitch boost (2022). His work bridges formal semantics and experimental approaches, addressing challenges in cross-linguistic typology and cognitive aspects of language processing.

Jiaqi GongView profile

Associate Professor

• Artificial Intelligence
• Smart and Connected Health
• Cyber-Physical Systems
• +13 more

Dr. Jiaqi Gong serves as Associate Professor in Computer Science and Adjunct Associate Professor in Mechanical Engineering at The University of Alabama's College of Engineering, while directing the Alabama Center for the Advancement of Artificial Intelligence. His academic foundation includes: B.S. in Engineering, China University of Geoscience (2004) Ph.D. in Engineering, Huazhong University of Science and Technology (2010) Dr. Gong's research pioneers human-AI convergence through cyber-physical systems and smart health technologies, developing mobile/wearable platforms to enhance human perceptual, cognitive, and physical capabilities. His work spans artificial intelligence, machine learning, computer vision, and IoT with applications in healthcare, environmental monitoring, and education. The Sensor-Accelerated Intelligent Learning (SAIL) laboratory he founded drives innovation in behavior change interventions, human movement modeling, and educational data mining. Recent publications reveal strong interdisciplinary trends: healthcare AI dominates with medication adherence prediction and surgical classification systems, while environmental applications feature flood-risk communication and drought analysis. His work increasingly integrates generative AI and LLMs across domains, demonstrating methodological innovation in federated learning, knowledge graphs, and explainable storytelling frameworks. Notable recognitions include: Best Student Paper Award, IEEE/ACM Connected Health Conference (2022) Best Student Paper Award, Body Sensor Networks Conference (2019) Data Challenge Win, IEEE Biomedical Health Informatics (2018) Best Paper Award, Body Area Networks Conference (2014) Best Demonstration Award, IEEE Wireless Health Conference (2014) Dr. Gong leads significant funded projects including a $2M CDC/NIOSH grant for first responder safety and $3M NSF funding for hydrologic research. As SAIL laboratory director, he mentors students in developing clinically deployed technologies for multiple sclerosis, dementia, and mental health. Future work focuses on scaling AI applications in chronic disease management and climate resilience through the Alabama AI Center. The SAIL laboratory (founded 2017) operates as a multidisciplinary hub developing wearable/mobile systems for health applications, with active collaborations across medical clinics and engineering departments for real-world deployment of behavior change interventions and movement analysis tools.

• Embedded Machine Learning
• High-Performance Computing
• Computer Architecture
• +6 more

Holger Fröning is a full professor at Heidelberg University’s Institute of Computer Engineering (ZITI), where he leads the Hardware and Artificial Intelligence (HAWAII) Lab. His research focuses on embedded machine learning , high-performance computing , and hardware-software co-design , with emphasis on resource efficiency, power optimization, and emerging architectures like analog , photonic , and resistive memory systems. He has held leadership roles including Managing Director of ZITI (2023–present) and Dean of Studies for Computer Science (2019–2022) , and has collaborated with institutions such as NVIDIA Research, Chinese Academy of Sciences, and Graz University of Technology. Research Trends : His recent publications explore Bayesian neural networks , green machine learning , analog computing noise mitigation , and GPU/FPGA optimization . Articles highlight photonic computing for AI , memory-efficient training , and hardware-aware DNN compression . Scientific Awards : 2025 HiPEAC Paper Award (Nature Computational Science) 2014 Google Faculty Research Award Multiple Best Paper Awards (IPDPS, ICPP, ECML-PKDD workshops) Leadership & Service : Organized workshops (WEML, ITEM, F4HD), chaired tracks at EuroPar and ISC, and served on program committees for ICPR, ECAI, and FPL. Education & Affiliations : PhD and MSc from University of Mannheim (2007/2001). Sponsors include DFG, FWF, FFG, NVIDIA, SAP, and XILINX.

Amy R. WuView profile

Associate Professor

• Human-Robot Interaction
• Human Biomechanics
• Locomotion
• +4 more

Amy R. Wu serves as Associate Professor in Mechanical and Materials Engineering at Queen's University and holds the Mitchell Professorship in Bio-inspired Robotics. She leads the Biomechanics x Robotics Laboratory (BxRL) and contributes to the Ingenuity Labs Research Institute. Her academic credentials include: Ph.D. in Mechanical Engineering, University of Michigan Postdoctoral Research, Biorobotics Laboratory, EPFL, Switzerland Dr. Wu's research bridges biomechanics and robotics to enhance legged mobility through innovations in human-robot interaction , exoskeleton design , and gait assistance . Her work focuses on developing robotic systems that adapt to human movement patterns, particularly for spinal cord injury rehabilitation and stability augmentation during walking. Analysis of her 2019-2023 publications reveals consistent advancement in exoskeleton control algorithms, gait stability metrics, and human adaptation studies. Key trends include modular exoskeleton systems (Symbitron), neuromuscular controllers for ankle assistance, and haptic feedback integration for motor learning enhancement. No scientific awards are documented in the provided materials. Dr. Wu mentors graduate students through BxRL, where her team conducts human subject testing and robotic development. Research is supported by institutional resources at Ingenuity Labs, though specific grant details remain unspecified. The Biomechanics x Robotics Laboratory operates within Queen's Ingenuity Labs Research Institute, facilitating interdisciplinary collaboration on projects like outdoor stability assessment, winter walking adaptations, and teleoperation interfaces for drone control.

• Cognitive Robotics
• Developmental Robotics
• Human-Robot Interaction
• +5 more

Tetsuya Ogata is a Professor at the Faculty of Science and Engineering, School of Fundamental Science and Engineering at Waseda University. He also holds joint appointments as a Fellow at the Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology (AIST), and as a visiting professor at the National Institute of Informatics (NII). Professor Ogata serves as director of the Institute of AI and Robotics at Waseda University, chairperson of the AI Robot Association (AIRoA), and research supervisor of JST CREST "Fundamentals and Core Technologies for Embodied AI" since 2025. Professor Ogata received his B.S., M.S., and D.E. degrees in mechanical engineering from Waseda University in 1993, 1995, and 2000, respectively. He began his academic career as a Research Associate at Waseda University (1999-2001), followed by positions as a Research Scientist at RIKEN Brain Science Institute (2001-2003), Lecturer (2003-2005) and Associate Professor (2005-2012) at Kyoto University's Graduate School of Informatics, before becoming a Professor at Waseda University in 2012. He was also a JST PRESTO Researcher from 2009 to 2015. Professor Ogata's research focuses on cognitive developmental robotics, deep predictive learning, and the integration of vision, language, and action (VLA) for human-robot interaction. His work explores how robots can learn through experience and interaction with humans and environments, developing models that allow for flexible behavior generation and adaptation. His research has significant implications for creating robots that can understand human intentions, collaborate effectively in real-world settings, and continuously learn from their experiences. The integration of multimodal information processing with predictive models forms the core of his approach to embodied artificial intelligence. Analysis of Professor Ogata's recent publications reveals a strong focus on deep predictive learning frameworks that enable robots to anticipate future states and generate appropriate actions. His research spans multiple domains including soft object manipulation, bimanual coordination, transparent object grasping, and integration of large language models with robotic systems. A notable trend is the increasing emphasis on multimodal integration, combining visual, tactile, and linguistic information to create more robust and adaptable robotic systems. His recent work also shows growing interest in the application of foundation models to robotics, aiming to create more general-purpose robotic capabilities. Best Paper Award, Advanced Robotics, The Robotics Society of Japan (2024) Best paper award Nomination Finalist, IEEE/SICE International Symposium on System Integration (2024) Frontiers of Science Awards, The International Congress for Basic Science (2023) Minister of Education, Culture, Sports, Science and Technology Award (2023) Fellow, Japan Society of Mechanical Engineering (2023) Fellow, The Society of Instrument and Control Engineers (2022) Fellow, The Robotics Society of Japan (2022) IBM Academic Awards (2017) Professor Ogata has been instrumental in establishing collaborative research frameworks between academia and industry, serving in leadership roles for multiple professional organizations including the Robotics Society of Japan, Japanese Society for Artificial Intelligence, and Japan Deep Learning Association. His research has been supported by significant grants including JST CREST funding for "Fundamentals and Core Technologies for Embodied AI." He has mentored numerous researchers who have gone on to contribute to the field of robotics and AI, with many of his former students now holding positions in leading academic and industrial research institutions. Professor Ogata directs the Ogata Laboratory at Waseda University, which focuses on cognitive developmental robotics and deep predictive learning. The lab maintains strong collaborations with industry partners including major robotics companies and technology firms. In 2025, he played a key role in establishing the AI Robot Association (AIRoA), which brings together companies like Toyota, Nissan, and KDDI to advance the development of practical AI robotics technologies. His research group is known for its interdisciplinary approach, combining insights from neuroscience, cognitive science, and engineering to create more human-like robotic capabilities.

Qingguo LiView profile

Professor

• Biomechanical system design
• Energy harvesting
• Wearable sensors
• +4 more

Qingguo Li is a Professor and Associate Head at the Department of Mechanical and Materials Engineering , Queen's University , and a member of the Ingenuity Labs Research Institute . He specializes in biomechanical system design, energy harvesting, wearable sensors, gait analysis, and load carriage systems. His research integrates robotics, biomedical engineering, and sensor technology to develop human-centric devices and mobility aids. Current Roles : Professor, Associate Head, Queen's University Research Institute : Ingenuity Labs Research Institute Lab : Bio-Mechatronics and Robotics Laboratory His work focuses on biomechanical energy harvesting , IMU-based motion analysis , and assistive device development . Key applications include stroke rehabilitation, gait monitoring, and wearable power generation systems. Articles span cable-driven robots , smart walkers , and 3D printing mechanisms , emphasizing human-robot interaction and dynamic modeling . The lab explores sensor calibration , adaptive control algorithms , and human movement optimization . Areas of impact include rehabilitation engineering , load carriage stability , wearable sensor accuracy , and assistive robotics . His team develops solutions for gait asymmetry detection , post-stroke mobility , and low-cost energy systems , leveraging machine learning and kinetic modeling .

Sarah F. AnziaView profile

Professor

• American politics
• State and local government
• Elections
• +6 more

Dr. Sarah F. Anzia is the Chancellor's Professor of Public Policy and Political Science at the University of California, Berkeley, with a joint affiliation at the Goldman School of Public Policy. She is also a Senior Fellow at the Niskanen Center and a Senior Fellow (adjunct) at the Hoover Institution. Her research bridges American politics, public policy, and subnational governance. Her work focuses on state and local government election timing and voter turnout interest group influence public-sector labor dynamics gender in electoral politics pension policy The 15 most recent publications highlight her expertise in historical labor movements policy feedback theory local taxation public-sector pension economics gender dynamics in electoral outcomes subnational institutional analysis Her studies often employ quantitative datasets spanning multiple decades of local government financial records and electoral patterns. Scientific recognition includes National Research Data Access via Goldman School working papers Senior Fellowships at Niskanen Center and Hoover Institution Endowed Chancellor's Professorship at UC Berkeley Key research contributions examine the interplay between business groups and labor unions in local governance structural determinants of senior citizen policy influence pension fund governance mechanisms historical development of local electoral institutions She received her PhD in political science from Stanford University and holds an MPP from the Harris School at the University of Chicago.

Daniel WolpertView profile

Professor

• Neuroscience
• Computational Neuroscience
• Sensorimotor Control
• +4 more

Daniel Wolpert is a Professor of Neuroscience at Columbia University , where he is also Vice-Chair of the Department of Neuroscience and a key member of the Zuckerman Mind Brain and Behavior Institute . Additionally, he holds a part-time position as Director of Research at the Department of Engineering, University of Cambridge, and is a Fellow of the Royal Society and the Academy of Medical Sciences . Education: Medical Doctor (1989), D.Phil. in Physiology from the University of Oxford (1992) Previous Positions: Lecturer at Sobell Department of Motor Neuroscience (Institute of Neurology), Professor of Engineering at University of Cambridge (2005–2018) Wolpert is a world leader in sensorimotor control , combining computational neuroscience , Bayesian inference , and robotic/virtual reality technologies to reverse-engineer how the brain generates movements. His work emphasizes the brain's role in reducing sensorimotor uncertainty through predictive modeling and has implications for understanding disorders like autism and Parkinson’s disease. His awards include: Royal Society Ferrier Medal (2020) Minerva Foundation Golden Brain Award (2010) Royal Society Francis Crick Prize Lecture (2005) Daniel Wolpert actively contributes to public science communication, including a 2011 TED Talk on the computational role of the brain in movement, and leads the Wolpert Lab at Columbia, which investigates the neural basis of decision-making , motor learning , and reinforcement learning in both healthy and clinical populations.