neural decoding Professors and Supervisors | ApplySharif

Find Academic Supervisors

Find and connect with academic supervisors, professors, and scholars for your research journey.

Showing 46-60 of 2,144 supervisors

Profile data may be stale. It is sourced from public faculty pages.

Jonny KohlView profile

Researcher

Francis Crick InstituteUnited Kingdom

Jonny Kohl is a Group Leader at the Francis Crick Institute , where he established the State-dependent Neural Processing Laboratory in 2019. His work bridges neural circuits and internal physiological states (e.g., hunger, pregnancy) to decode instinctive behaviors like parenting and aggression in mice. PhD: MRC Laboratory of Molecular Biology, Cambridge (2013) Postdoc: Harvard University (2014–2019) with support from EMBO, HFSP, and Wellcome Trust fellowships Research Interests : Kohl investigates how hormonal and metabolic states dynamically rewire neural circuits to drive adaptive behaviors. His lab combines circuit neuroscience , molecular biology , and behavioral profiling to study: State-dependent neural processing in parental behavior Chemosensory dominance hierarchies in mice Plasticity mechanisms in aggression circuits Development of ultrafast tissue labeling and cryoanesthesia tools Scientific Trends : His recent publications highlight hormone-mediated synaptic remodeling (2023), cost-effective lab tools (2023), and brain-wide activity mapping (2016). Collaborative work spans computational biology , metabolism , and imaging disciplines. Honors: NARSAD Young Investigator Award (2019), ERC Starting Grant (2019), Wellcome Trust Discovery Award (2025) Grants: BBSRC Research Grant (2025), BBSRC Pioneer Grant (2023) His lab mentors PhD and MSc students and has developed open-source neuroscience tools (e.g., cryoanesthesia device, 2023). Kohl's research has been featured in Nature , Science , and Cell .

Official University Profile

Dirk Bernhardt-WaltherView profile

Professor

University of TorontoCanada

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

Official University Profile

Surya GanguliView profile

Associate Professor

Stanford UniversityUnited States

Surya Ganguli is an Associate Professor in the Department of Applied Physics at Stanford University, with courtesy appointments in Neurobiology and Electrical Engineering. He serves as Senior Fellow at the Stanford Institute for Human-Centered AI and is affiliated with the Stanford Neuroscience Institute , Bio-X , and Wu Tsai Neurosciences Institute . His research spans theoretical neuroscience, machine learning, and statistical mechanics. Ph.D. , UC Berkeley, Theoretical Physics (2004) M.A. , UC Berkeley, Physics (2000) M.A. , UC Berkeley, Mathematics (2004) M.Eng. , MIT, Electrical Engineering and Computer Science (1998) B.S. , MIT, Physics (1998) B.S. , MIT, Mathematics (1998) B.S. , MIT, Electrical Engineering and Computer Science (1998) His lab explores how higher-level cognitive phenomena emerge from neural network dynamics, focusing on perception, memory, attention, and decision-making . Research themes include statistical mechanics of learning , neural representational geometry , and biologically plausible learning rules . Current work examines nonlinear interactions in neural networks through the Schmidt Science Polymath Award (2023). Key article trends reveal expertise in neural coding limits (2022 Nature), synaptic plasticity models (2022 Neural Computation), and deep learning theory (2022 NeurIPS publications). His 2019 Annual Review chapter on statistical mechanics of deep learning established foundational insights into network criticality. Scientific Awards : NSF Career Award (2019) Simons Foundation Investigator (2016) McKnight Scholar Award (2015) James S. McDonnell Foundation Scholar (2014) Sloan Research Fellow (2013) As advisor, he mentors 10+ doctoral students across Applied Physics, Neurosciences, and Computer Science, including Vamshi Balanaga and Mason Kamb. His lab collaborates with experimental teams at Stanford and beyond, supported by grants from NSF , Simons Foundation , and Swartz Foundation . The Neural Dynamics & Computation Lab unites physicists, mathematicians, and neuroscientists to decode cognition through interdisciplinary methods.

Official University Profile

Azita EmamiView profile

Professor

California Institute of Technology (Caltech)United States

Azita Emami serves as the Andrew and Peggy Cherng Professor of Electrical Engineering and Medical Engineering at the California Institute of Technology (Caltech), where she concurrently holds leadership roles as Executive Officer for Electrical Engineering and Director of the Center for Sensing to Intelligence. Appointed to Caltech's faculty in 2007, she progressed from Assistant Professor to her current endowed professorship through demonstrated scholarly excellence. Her academic foundation includes: B.S. in Electrical Engineering from Sharif University of Technology (1996) M.S. in Electrical Engineering from Stanford University (1999) Ph.D. in Electrical Engineering from Stanford University (2004) Professor Emami pioneers mixed-mode integrated circuit systems that bridge theoretical innovation with practical applications. Her research emphasizes ultra-low power consumption and high reliability in scalable semiconductor technologies, targeting transformative solutions across multiple domains. Key thrusts include: Biomedical implantables for neural recording/stimulation and gastrointestinal monitoring Photonics-electronics co-design for energy-efficient optical interconnects Machine learning-enhanced signal processing for brain-computer interfaces Miniaturized magnetic sensors with unprecedented noise performance Her work consistently demonstrates how circuit-level innovations enable breakthrough capabilities in medical diagnostics and high-speed computing. Analysis of her 2021-2024 publications reveals a strategic convergence of biomedical sensing and intelligent signal processing . While maintaining strong contributions to optical interconnects (accounting for ~40% of recent output), her lab increasingly focuses on closed-loop medical systems where low-power analog neural networks interpret physiological signals. This evolution reflects growing NIH and industry interest in implantable/wearable health technologies, with her group leading in CMOS-based sensor miniaturization and energy efficiency. Her professional recognition includes: IEEE Solid-State Circuits Society Distinguished Lecturer appointment Mentorship excellence is evidenced by students receiving prestigious awards including the Jakob van Zyl Predoctoral Research Award (Saransh Sharma, Ryoto Sekine) and Charles Wilts Prize (Kuan-Chang Chen). Her research program leverages strategic partnerships with Heritage Medical Research Institute and industry collaborators, supported through center-based funding like the Center for Sensing to Intelligence. Administrative leadership spans departmental governance (as Executive Officer) and conference organization (ISSCC technical committees). She directs Caltech's Mixed-mode Integrated Circuits and Systems Lab (MICS) , which operates as a nexus for cross-disciplinary innovation between electrical engineering and medical applications. The lab's industry-collaborative framework accelerates translation of circuit concepts into real-world biomedical solutions through the Center for Sensing to Intelligence.

Official University Profile

Shinji WatanabeView profile

Associate Professor

Carnegie Mellon UniversityQatar

Shinji Watanabe is an Associate Professor at Carnegie Mellon University's Language Technologies Institute and a Courtesy Professor in the Electrical and Computer Engineering department. He holds a Ph.D. (Dr. Eng.) from Waseda University, Japan, and has held research roles at NTT Communication Science Laboratories, Mitsubishi Electric Research Laboratories (MERL), and Johns Hopkins University. His research focuses on automatic speech recognition, speech enhancement, and machine learning for speech processing. Watanabe has published over 300 peer-reviewed papers and received the Best Paper Award at IEEE ASRU 2019. His work emphasizes robust speech processing in challenging environments, multilingual models, and neural audio codecs. He leads the ESPnet toolkit development for end-to-end speech processing systems and contributes to technical committees like IEEE SLTC and APSIPA SLA. Recent research trends include streaming speech systems, universal speech enhancement (URGENT challenges), and fusion of discrete speech units with self-supervised representations. He explores scalable speech foundation models through benchmarks like ML-SUPERB 2.0 and investigates cross-modal audio-visual processing in challenges like MISP 2025. Education : B.S., M.S., Ph.D. (Waseda University) Affiliations : CMU Language Technologies Institute, CMU ECE, Former roles at MERL and Johns Hopkins Key Projects : ESPnet, OpenWhisper-Style Models, URGENT Challenge Frameworks

Official University Profile

Elisa DonatiView profile

Researcher

University of ZurichSwitzerland

Dr. Elisa Donati is a researcher and independent group leader at the Institute of Neuroinformatics , affiliated with both the University of Zurich and the Swiss Federal Institute of Technology Zurich . Her work bridges neuromorphic engineering, biomedical signal processing, and wearable healthcare technologies, with a focus on creating brain-inspired systems for neuroprosthetics and rehabilitation. Affiliation: Institute of Neuroinformatics, University of Zurich & ETH Zurich Email: elisa@ini.uzh.ch Elisa’s research emphasizes developing neuromorphic signal processing strategies for wearable and embedded systems, enabling real-time closed-loop interactions with the nervous system. She specializes in translating neuroscience insights into energy-efficient technologies for digital health applications, including neuroprosthetics and personalized biomedical devices using neuromorphic hardware. Her recent publications (2024–2025) highlight advancements in gesture recognition via EMG and event-based systems, neuromorphic heart rate monitoring , and spiking neural network architectures . These works span biomedical signal processing, low-power computing, and adaptive algorithms, reflecting her commitment to robust, real-time, and brain-inspired solutions for healthcare. Elisa’s contributions to neuromorphic computing are evident in her exploration of heterogeneous population encoding , event-driven processing , and ultra-low-power microcontrollers . Her projects often integrate wearable systems with neuroscience, aiming to improve prosthetic control and rehabilitation technologies .

Official University Profile

Gaetano ValenzaView profile

Associate Professor

University of PisaItaly

Gaetano Valenza is an Associate Professor of Bioengineering at the University of Pisa, Italy, where he leads the Neuro-Cardiovascular Intelligence Lab at the Enrico Piaggio Research Centre. He holds affiliations with the Neuroscience Statistics Research Laboratory at MIT and has served as a Research Fellow at Harvard Medical School and Massachusetts General Hospital. His academic work spans bioengineering, computational physiology, and affective computing. His research focuses on statistical and nonlinear biomedical signal and image processing , cardiovascular and neural modeling , and physiologically interpretable artificial intelligence . He develops wearable systems for physiological monitoring, with applications in autonomic nervous system assessment, brain-heart interactions, and mental health. His work has led to novel metrics such as the Sympathetic and Parasympathetic activity indices derived from ECG. The 15 most recent publications reflect a consistent trend in brain-heart interplay , complexity analysis of physiological signals , explainable AI in healthcare , and virtual reality applications in mental health . His work integrates advanced signal processing, nonlinear dynamics, and machine learning to decode emotional and cognitive states from physiological data. Dr. Valenza is a Senior Member of IEEE and serves on several technical committees. He is an active editorial leader, currently serving as Associate Editor for IEEE-EMBC , Plos One , Complexity , and Scientific Reports , and has guest-edited special issues in Philosophical Transactions of the Royal Society A and IEEE Journal of Biomedical and Health Informatics . He has led or participated in numerous international research projects, including FP7 and H2020 initiatives such as NEVERMIND and EXPERIENCE. He teaches courses in Biostatistics, Probability & Biostatistics, and Advanced Image Processing at the University of Pisa. As lab head and project coordinator, he leads a multidisciplinary team working on neuro-cardiovascular intelligence, wearable systems, and AI-driven mental health interventions.

Official University Profile

Ananth GramaView profile

Professor

Purdue UniversityUnited States

Ananth Grama is the Samuel D. Conte Distinguished Professor of Computer Science and Associate Director of the Center for Science of Information at Purdue University. He holds a faculty position in the Department of Computer Science, College of Science. His research focuses on parallel computing, distributed systems, machine learning, and their applications in complex systems such as materials modeling and clinical analytics. He teaches advanced courses like CS525 (Parallel Computing) and CS314 (Numerical Methods). Research interests span parallel algorithms, fault-tolerant learning, quantum machine learning, and data-driven healthcare analytics. Recent work addresses fundamental limits of generative models, online learning under noisy conditions, and clinical outcome predictions. His projects include DOE-funded research on critical element recovery and NIH grants for hearing assessment technologies. Notable contributions include over 50 peer-reviewed publications since 2022, with recent papers appearing at ICLR, NeurIPS, and ICML. Current postdocs include Changlong Wu (collaborating with Wojciech Szpankowski) and Luopin Wang (with Nadia Atallah). He advises seven graduate students and oversees multidisciplinary research teams.

Official University Profile

Naoki YoshinagaView profile

Associate Professor

The University of TokyoJapan

Naoki Yoshinaga is a tenured Associate Professor at the Institute of Industrial Science, The University of Tokyo, with extensive experience in natural language processing and computational linguistics. He has held academic positions since 2008 and currently leads research on pragmatic NLP models and multilingual systems. PhD in Computer Science, The University of Tokyo (2005-2008) MSc in Information Science (2000-2002) BSc in Information Science (1996-2000) His research focuses on mechanistic interpretability in NLP models, multilingual/multimodal NLP , and efficient model design using trie structures and conjunctive features. He also investigates knowledge acquisition from social data and evaluation metrics for language generation . Recent publications include work on neuron empirical gradient analysis (ACL-25), multilingual knowledge representation (EACL-24), and compact embedding methods (CoNLL-24). His research has been funded by multiple grants, including the University of Tokyo Excellent Young Researcher program and JSPS fellowships. Committee Special Award, Association for NLP (2023) JSAI SIG Research Award (2022) Best Interactive Award, DEIM Forum (2019, 2016) He developed widely-adopted NLP tools like pecco (fast classification library), RenTAL (LTAG-to-HPSG grammar converter), and J.DepP (Japanese dependency parser). His lab emphasizes strong equivalence in formalism comparisons and pragmatic model design .

Official University Profile

Hakwan LauView profile

Professor

University of California, Los AngelesUnited States

Hakwan Lau is a tenured Professor of Psychology at the University of California, Los Angeles (UCLA), where he is also a member of the Brain Research Institute. Additionally, he serves as Co-Director of the IBS Center for Neuroscience Imaging Research in South Korea, leading the Systems and Intervention Neuroscience (SIN) group. His work spans consciousness, metacognition, and perceptual reality monitoring, employing advanced neuroimaging and computational techniques. Education: Born and raised in Hong Kong, Hakwan Lau pursued graduate studies in England. He was previously an Associate Professor at Columbia University in New York before moving to UCLA, where he is now tenured. From 2017-2020, he was primarily based at the University of Hong Kong. Research Interests: Lau's lab investigates why we have subjective experiences , focusing on visual awareness, spatial attention, and metacognition. They use fMRI, decoded neurofeedback (DecNef), psychophysics, and computational modeling to explore how conscious perception arises and its clinical applications, such as treating phobias and PTSD. Research Trends: His recent work emphasizes decoded neurofeedback interventions for clinical conditions like phobias and PTSD, metacognitive mechanisms underlying confidence judgments, and neural correlates of subjective experience . The research bridges theoretical neuroscience with practical therapeutic applications, often using innovative techniques like closed-loop fMRI. Scientific Awards: While specific awards are not listed in the provided text, Lau has mentored 15 former trainees who are now independent PIs , reflecting his significant impact on the field. Advising and Grants: Lau actively advises numerous PhD students and postdocs across UCLA, the University of Hong Kong, and collaborative institutions. His lab supports projects ranging from AI-driven fMRI analysis to clinical neurofeedback trials . Labs and Teams: Lau leads the Metacognition and Consciousness Lab at UCLA, now relocated to the RIKEN Center for Brain Science near Tokyo. His team includes researchers from diverse backgrounds, focusing on interdisciplinary approaches to consciousness and metacognition.

Official University Profile

Ralf HaefnerView profile

Assistant Professor

University of RochesterUnited States

Ralf Haefner is an Assistant Professor in the Departments of Brain & Cognitive Sciences and Physics & Astronomy at the University of Rochester, holding this joint appointment since 2014. His interdisciplinary research bridges neuroscience and physics to investigate computational principles of perception and decision-making. Education and professional background: PhD, Oxford University, 1999 Visiting Research Fellow, Department of Neurobiology, Harvard Medical School Swartz Fellow, Sloan-Swartz Center for Theoretical Neurobiology, Brandeis University Haefner's research program centers on computational neuroscience , with primary focus on how the brain forms perceptual beliefs and uses them for decisions through Bayesian modeling . He employs machine learning tools to construct mathematical models explaining neural responses and behavior, particularly in the visual domain. His work addresses neural representation of uncertainty, causal inference mechanisms, and probabilistic computation in cortical circuits. Analysis of recent publications (2023-2025) reveals three dominant trends: (1) causal inference frameworks applied to motion perception and segmentation, (2) Bayesian modeling of perceptual biases and confidence computations, and (3) integration of generative and discriminative neural computations. His work extends beyond traditional neuroscience into scientific methodology through 'Generative Adversarial Collaborations' for improving research discourse. Honors and Awards: Swartz Fellowship, Sloan-Swartz Center for Theoretical Neurobiology NSF CAREER Award (2022) for 'Approximate inference at the intersection of neuroscience and machine learning' Haefner secured significant research funding through his NSF CAREER award, which supports foundational work on probabilistic inference at the neuroscience-ML interface. While specific students aren't listed, his active publication record and lab infrastructure suggest ongoing mentorship of graduate students and postdocs. His research has clinical relevance as shown by studies on perceptual abnormalities in autism spectrum disorder, indicating translational potential for understanding neurological conditions.

Official University Profile

Lee MillerView profile

Professor

University of California, DavisUnited States

Lee Miller is a Professor in the Department of Neurobiology, Physiology, and Behavior at the University of California, Davis, College of Biological Sciences. His research integrates neural engineering, physiology, and computational methods to develop communication restoration technologies and investigate sensory processing mechanisms. His primary research interests include neural engineering for speech neuroprosthetics, electrophysiological analysis of speech production, auditory neuroscience, and geometric approaches to neuromuscular signal decoding. He employs surface electromyography (EMG), electroencephalography (EEG), and computational modeling to study brain-machine interfaces for speech restoration and multisensory integration. Recent publications reveal a dominant focus on EMG-based speech neuroprostheses, with geometric and topological analysis of neuromuscular signals emerging as a key methodology. His lab has pioneered non-invasive approaches to speech articulation decoding, created standardized EMG databases, and investigated neural mechanisms of attention in speech-in-noise processing. This work bridges engineering innovation with fundamental neuroscience to address communication disorders. Professor Miller leads the Miller Lab at UC Davis, which specializes in neural engineering for communication restoration. The lab develops real-time speech synthesis systems from neural signals and investigates the physiological basis of speech production and perception using multimodal recording techniques.

Official University Profile

Xuezhe MaView profile

Assistant Professor

Carnegie Mellon UniversityQatar

Xuezhe Ma is an Assistant Professor in the Department of Computer Science at the University of Southern California's Viterbi School of Engineering. Previously, he was a Ph.D. student at Carnegie Mellon University's Language Technologies Institute, where he worked under the supervision of Professor Eduard Hovy. His academic journey includes a Master's degree from Shanghai Jiao Tong University's Center for Brain-like Computing and Machine Intelligence and a Bachelor's degree in Computer Science from the same institution. Ph.D. in Computer Science, Carnegie Mellon University (completed ~2020) M.S. in Brain-like Computing, Shanghai Jiao Tong University B.S. in Computer Science, Shanghai Jiao Tong University Dr. Ma's research spans multiple areas at the intersection of Natural Language Processing and Machine Learning, with particular focus on structured prediction, syntactic and semantic parsing, machine translation, language generation, and deep generative models. His recent work has expanded into vision-language models, large language model architectures, and applications across computer vision tasks. His research combines theoretical foundations with practical implementations, as evidenced by his development of tools like NeuroNLP2 and MaxParser. His publication record shows a clear trajectory from foundational NLP work during his PhD (including papers on dependency parsing and sequence labeling) to more recent contributions in generative models and large language systems. The 15 most recent publications reveal a strong focus on addressing fundamental challenges in generative modeling, context handling, and multimodal integration, with applications spanning literary translation, medical imaging, and news diffusion analysis. AI2 Outstanding Intern Award (2018) Dr. Ma has secured research funding supporting his work in generative models and language technologies, with projects focusing on improving the efficiency and capabilities of large language models. His research group at USC is actively working on next-generation language understanding and generation systems, with particular emphasis on context-aware modeling and multimodal integration. He has established collaborations with industry partners including the Allen Institute for AI and has contributed to open-source projects like Texar. At USC, Dr. Ma leads research in the Information Sciences Institute, directing projects on efficient large language model architectures and multimodal reasoning systems. His lab focuses on developing novel approaches to context handling, model efficiency, and multimodal integration, with applications across diverse domains including healthcare, literary analysis, and news media.

Official University Profile

Aysegul GunduzView profile

Professor

University of FloridaUnited States

Aysegul Gunduz, Ph.D., is a Professor and Fixel Brain Mapping Professor at the University of Florida's Herbert Wertheim College of Engineering, Department of Biomedical Engineering. She leads the Brain Mapping Laboratory, focusing on neural networks and clinical translation for neurological disorders. Her work integrates electrophysiology, bioimaging, and neuromodulation to develop diagnostic and therapeutic systems for conditions like Parkinson’s disease, epilepsy, movement disorders, and stroke. Education: B.S., Electrical Engineering, Middle East Technical University (2001) M.S., Electrical Engineering, North Carolina State University (2003) Ph.D., Electrical Engineering, University of Florida (2008) Post-doctoral Fellowship in Neurology, Albany Medical College (2011) Research interests include human brain mapping, closed-loop deep brain stimulation (DBS), neuromodulation strategies for movement disorders, and wearable sensor technologies for neurological monitoring. Her lab emphasizes translational research, bridging basic science with clinical applications to improve patient outcomes. Awards include the BMES Fellowship (2024), AIMBE Fellowship (2022), and PECASE (2019), reflecting her leadership in neural engineering. Her articles explore cutting-edge topics like DBS efficacy, neural network dynamics, and ethical considerations in neural device research. Grants and collaborations focus on advancing adaptive DBS and brain-computer interfaces. She mentors students in neuroengineering and advocates for equitable participation in clinical research. The Brain Mapping Laboratory actively engages in multidisciplinary projects with neurologists, surgeons, and industry partners. Future work includes optimizing closed-loop systems for Tourette syndrome and Parkinson’s disease, developing open-source neuroimaging tools, and expanding wearable sensor applications for real-time neurological monitoring.

Official University Profile

LEE Wee SunView profile

Professor

National University of SingaporeSingapore

LEE Wee Sun is a Professor in the Department of Computer Science at the National University of Singapore (NUS), where he previously served as Head of Department, Vice Dean of Undergraduate Studies, and Vice Dean of Research. His academic journey began with a B.Eng. in Computer Systems Engineering from the University of Queensland (1992) and a Ph.D. from the Australian National University (1996), followed by research roles at the Australian Defence Force Academy and MIT. Education: Ph.D., Australian National University, Canberra, Australia (1996) B.Eng. in Computer Systems Engineering, University of Queensland, Brisbane, Australia (1992) Research Focus: Professor Lee pioneers work in Machine Learning , Planning Under Uncertainty , and Approximate Inference , with emphasis on integrating AI subfields for holistic reasoning. His current projects include "Learning to Decompose for Reasoning and Planning" (enhancing LLMs via self-supervised problem decomposition) and "Learning to Reason with Visual-Linguistic Inputs" (unifying vision, language, and reasoning in single architectures). Publication Trends: Recent work (2023-2025) centers on bridging LLMs with classical AI techniques, featuring breakthroughs in uncertainty quantification, multi-task optimization, and graph-based reasoning. Key themes include sparsity-aware vehicle routing, epistemic uncertainty for reliable LLMs, and differentiable neural solvers for combinatorial problems. Awards: IJCAI-JAIR Best Paper Prize (2022) RSS Test of Time Award (2021) RoboCup Best Paper Award (IROS 2015) HRATC 1st Place (2015) IPPC POMDP Track 1st Place (2011, 2014) UAI Google Best Student Paper (2014) Semeval-1 1st/2nd Place (2007) J.G. Crawford Prize (ANU 1996) Leadership & Service: As steering committee chair for ACML and area chair for NeurIPS/ICML/AAAI/IJCAI, Professor Lee shapes global AI discourse. His administrative roles at NUS and collaborations with MIT/Singapore-MIT Alliance demonstrate commitment to advancing AI education and research infrastructure. While student advisees aren't listed, his leadership positions imply extensive mentoring. Research Ecosystem: His work drives NUS's AI initiatives including Knowledge@Computing projects on reasoning frontiers. Current efforts focus on making AI systems robust through uncertainty-aware planning and multi-modal integration, with applications in robotics, verification systems, and combinatorial optimization.

Official University Profile