Bo Zhu is an Assistant Professor in the School of Interactive Computing at Georgia Institute of Technology. His research focuses on computational approaches for complex physical systems, including fluid dynamics, topology optimization, and robotics control. He holds a Ph.D. from Stanford University and completed postdoctoral research at MIT CSAIL. He has been recognized with the NSF Career Award (2022) and multiple best paper awards at SIGGRAPH conferences. Education: B.E.-M.S., Software Engineering, Shanghai Jiao Tong University Ph.D., Computer Science, Stanford University Postdoc, EECS, MIT Research Interests: Develops numerical algorithms and machine learning techniques to simulate fluidic systems, soft materials, and multi-scale phenomena. His work emphasizes vorticity preservation, real-time simulation, and physics-based AI integration. Key Contributions: Pioneered Particle Flow Map (PFM) methods for fluid simulation, developed open-source libraries like SimpleX and PFM Hub, and contributed to projects like Genesis physics engine. Over 50 peer-reviewed publications in top venues (SIGGRAPH, NeurIPS, IEEE TVCG). Awards: NSF Career Award (2022) Best Paper Honorable Mention (SIGGRAPH 2025) Best Paper Award (SIGGRAPH Asia 2024) Grants & Projects: Leads NSF-funded research on Physical AI Design, collaborating with Sandia National Labs on real-time CFD solvers. Active in open-source software development for computational physics and graphics.
Parisa Hosseinzadeh is an Assistant Professor in the Department of Bioengineering at the University of Oregon. Her research focuses on computational protein design and structure-guided rational protein/peptide engineering, with applications in enzyme design, biosensors, and biomedical solutions. She holds a B.Sc. from the University of Tehran, a Ph.D. from the University of Illinois (advisor: Yi Lu), and a postdoc at the University of Washington in David Baker's lab. Her lab emphasizes interdisciplinary approaches at the intersection of computer science, chemistry, and biology, prioritizing diversity and inclusion in STEM. Key projects include designing cyclic peptides as enzyme inhibitors, developing methods for tuning redox potentials in metalloproteins, and creating tools to combat biomedical challenges. Lab members include postdocs, graduate students (e.g., Noora Azadvari, Andrew Powers), and undergraduates. Notable achievements include NSF grants, the Baxter Foundation Award, and the Hans Horse Meyer Award. The lab also emphasizes mentorship, collaborative culture, and outreach initiatives.
Avi Wigderson is the Herbert H. Maass Professor in the School of Mathematics at the Institute for Advanced Study, Princeton. He is a leading authority in theoretical computer science, particularly computational complexity theory. Wigderson organizes the Computer Science and Discrete Mathematics (CSDM) program at the Institute, fostering interdisciplinary research at the intersection of mathematics and computer science. Wigderson earned his Ph.D. (1983), M.A. (1982), and M.S.E. (1981) from Princeton University. Prior to his current position, he held appointments at The Hebrew University of Jerusalem (1986-2003), Princeton University (1990-1992), Mathematical Sciences Research Institute, Berkeley (1985-1986), IBM Research (1984-1985), and University of California, Berkeley (1983-1984). Wigderson's research spans computational complexity theory, randomness and computation, algorithms and optimization, circuit complexity, proof complexity, quantum computation and communication, and cryptography. His work explores fundamental questions like whether mathematical creativity can be automated (P vs NP problem), the security of electronic commerce, the role of randomness in computation, and the potential of quantum mechanics to enhance computation. He has made significant contributions to understanding the power and limitations of efficient computation. Analysis of Wigderson's recent publications reveals a strong focus on optimization, complexity theory, and their mathematical foundations. His work connects diverse areas including non-commutative algebra, geometric complexity, graph theory, and quantum computing. A recurring theme is exploring whether fundamental computational problems like P vs NP can be addressed through optimization techniques such as gradient descent. His research shows increasing interdisciplinary connections between theoretical computer science, mathematics, and physics. ACM A.M. Turing Award (2023) Abel Prize (2021) Donald E. Knuth Prize (2019) Gödel Prize (2009) American Mathematical Society's Levi L. Conant Prize (2008) Rolf Nevanlinna Prize (1994) Yoram Ben-Porat Presidential Prize for Outstanding Researcher (1994) Bergman Fellowship (1989) Member, American Academy of Arts and Sciences Member, National Academy of Sciences While specific details about Wigderson's students are not provided in the source material, his extensive lecture series, workshops, and program organization suggest significant mentorship activities. His book "Mathematics and Computation" published by Princeton University Press serves as an educational resource for students and researchers. Wigderson has organized major programs at the Institute for Advanced Study including "Lower Bounds in Computational Complexity" (2018) and "Pseudorandomness" (2017), creating research opportunities for numerous scholars. Wigderson leads the Computer Science and Discrete Mathematics (CSDM) program at the Institute for Advanced Study, which brings together researchers from mathematics and computer science to explore fundamental questions in computation. His work with collaborators across multiple institutions has established connections between theoretical computer science and diverse fields including quantum information theory, algebraic geometry, and optimization. Recent projects focus on non-commutative optimization and its applications to computational complexity problems.
Dr. Timothy H. Murphy is a Professor in the Department of Psychiatry at the University of British Columbia's Faculty of Medicine. He is also an Associate Member of the School for Biomedical Engineering and a Member of the Djavad Mowafaghian Centre for Brain Health. Dr. Murphy leads the Dynamic Brain Circuits in Health and Disease initiative and the Division of Neuroscience and Translational Psychiatry at UBC. Dr. Murphy received his Ph.D. from Johns Hopkins University in 1989 and his B.Sc. from Saint Mary's College Maryland in 1984. His research focuses on understanding brain circuit structure-function relationships in relation to stroke recovery, psychiatric disorders, and neurological diseases. He specializes in mesoscale imaging techniques to study cortical activity patterns and develop automated approaches for brain imaging and stimulation. His laboratory develops innovative tools including open-source hardware for automated mouse brain imaging, synthetic data generation for behavioral analysis, and chronic recording systems that enable simultaneous mesoscale cortical imaging with subcortical or peripheral nerve activity monitoring. Research from the Murphy Lab has significantly advanced our understanding of how brain circuits reorganize after stroke and in models of psychiatric disorders. Dr. Murphy's recent publications reveal trends in mesoscale cortical imaging, development of synthetic data for behavioral analysis, and exploration of circuit-level changes in neurological and psychiatric disease models. His work bridges basic neuroscience with potential clinical applications for stroke recovery and mental health treatments. Dr. Murphy has mentored numerous students and postdoctoral fellows who have gone on to successful careers in neuroscience and related fields. His laboratory has received funding to support innovative approaches to understanding brain circuit function and recovery mechanisms. The Murphy Lab maintains strong collaborative ties across UBC and develops open-source tools that are widely adopted by the neuroscience community. Their work on automated home-cage imaging systems, synthetic behavioral data generation, and chronic recording technologies represents significant methodological advances in the field.
Satoshi Miyazaki is a Professor at Waseda University's Graduate School of Japanese Applied Linguistics and affiliated with the Faculty of International Research and Education . With a Ph.D. from Monash University, his work bridges Japanese language education, second language acquisition, and cross-cultural communication. Education Background: Doctor of Philosophy (Japanese Applied Linguistics) - Monash University (1997) Graduate School of Japanese Applied Linguistics - Waseda University (1982) Research Interests focus on Japanese language pedagogy, natural acquisition mechanisms, and interactional strategies for non-native learners. His innovative use of Eye Mark Recorders (EMR) and Event-Related Potentials (ERP) has advanced understanding of cognitive processes in language learning. Recent Articles (2011-2001) reveal trends in Japanese language education reform, cross-cultural learning challenges, and technology-enhanced teaching methods. Key themes include correctional language programs for foreign inmates, immersion environments, and brain processing studies using advanced neuro-linguistic techniques. Research Projects have received continuous funding from Japan Society for the Promotion of Science, including prison language education programs, foreign care worker competence frameworks, and EMR-based lecture comprehension analysis. His work extends to international collaborations with institutions in Australia, the U.S., and Europe.
Dr. Pulin Gong is an Associate Professor in the School of Physics at the University of Sydney. His research focuses on understanding the self-organizing mechanisms of neural circuits' spatiotemporal dynamics and their computational principles. He investigates distributed dynamic computation via propagating neural waves, irregular neural activity variability, and coherent spatiotemporal patterns in large-scale neural data. His work combines experimental and computational approaches to unravel neural coding principles. Research interests include: Distributed dynamic computation (e.g., visual feature integration) Irregular neural dynamics and membrane potential fluctuations Coherent spatiotemporal wave patterns (e.g., spiral waves) Recent projects involve analyzing cortical wave patterns in mice and primates, fractional neural sampling, and Lévy walk dynamics in neural systems. Collaborators include institutions like Fudan University and Kyoto University. Current research student: Andrew LY, working on cortico-cortical loop dynamics and AI applications.
Marta Kutas is a Distinguished Professor in the Department of Cognitive Science at the University of California, San Diego (UCSD), where she has held roles including Department Chair (2007–2019), Director of the Center for Research in Language (2007–2022), and Chancellor's Associates Endowed Chair (2017–2022). Her research focuses on language processing, neuropsychology, and electrophysiological methods, with a particular emphasis on event-related potentials (ERPs). She has contributed significantly to understanding semantic integration, memory, and neural mechanisms underlying language comprehension. Education includes a B.A. from Oberlin College (1971) and a Ph.D. in Psychology from the University of Illinois (1977). She has held adjunct roles at San Diego State University and the UCSD Department of Neurosciences. Her awards include the Revelle Medal (2023), membership in the American Academy of Arts and Sciences (2018), and the Cognitive Neuroscience Society's Distinguished Career Award (2015). Her research interests span language comprehension/production, neuropsychology, and ERP methodologies. Key publications analyze semantic processing, memory-related brain potentials, and the impact of individual knowledge on word processing. She has collaborated on studies involving Alzheimer's, Parkinson's, and schizophrenia, using ERPs to explore cognitive deficits.
Dr. Aaron Schurger is an Assistant Professor in the Psychology Department at Chapman University’s Crean College of Health and Behavioral Sciences. He is also a member of the Institute for Interdisciplinary Brain and Behavioral Sciences. Schurger holds a BA from Indiana University, and MA and PhD from Princeton University. His research focuses on the neuroscience of volition, consciousness, and decision-making, particularly exploring the readiness potential (RP) and its implications for free will debates. His work challenges classical interpretations of the RP using computational models, suggesting it reflects stochastic neural processes rather than preconscious decisions. Recent contributions include studies on the origins of the RP in spiking neural networks, critiques of causal structure theories of consciousness, and interdisciplinary analyses of free will. His findings emphasize that the RP may not indicate preconscious decision-making but instead arise from natural neural fluctuations during decision thresholds. Schurger collaborates across neuroscience, philosophy, and cognitive science, contributing to debates on consciousness, action initiation, and neural correlates of subjective experience. His research also addresses methodological rigor in studying unconscious processing and integrates computational models with empirical data, as seen in studies on movement timing and neural stability during perception. While no specific grants or labs are explicitly listed, his affiliations suggest involvement in interdisciplinary projects at Chapman.
Daniel Hyde is an Associate Professor in the Department of Psychology at the University of Illinois Urbana-Champaign, affiliated with the College of Liberal Arts & Sciences and the Neuroscience Program. His research explores the nature and development of abstract conceptual knowledge through behavioral and neural measures. PhD in Psychology from Harvard University Hyde investigates cognitive development from infancy to adulthood, focusing on quantitative reasoning , spatial reasoning , and psychological reasoning using techniques like event-related brain potentials (ERPs) , functional near-infrared spectroscopy (fNIRS) , and behavioral assessments. His recent work examines how symbolic number knowledge builds on non-symbolic foundations and how neural sensitivity to mental states in infancy predicts later theory of mind abilities. Hyde's publications demonstrate a consistent focus on numerical cognition, multisensory integration, and developmental neuroscience. He leads the Brain and Cognitive Development Lab , participates in global initiatives like ManyNumbers and ManyBabies , and collaborates across disciplines to apply developmental insights to education and public health contexts.
Dr. Junfang Wu is a Professor in the Department of Anesthesiology and holds a secondary appointment in Neurobiology at the University of Maryland School of Medicine. She serves as Associate Director of UM-MIND (University of Maryland - Medicine Institute for Neuroscience Discovery), Director of the Anesthesiology Center for Neuroscience Research, and Vice-Chair for Translational Research in the Department of Anesthesiology. Her academic journey includes a BM in Medicine, MS in Pharmacology from Jiangxi Medical College, and PhD in Neuropharmacology from Nanjing Medical University, followed by postdoctoral training at China's Institute of Materia Medica and NIH. Research Focus: Neurotrauma, neuroinflammation, autophagy-lysosomal pathway, extracellular vesicles, Hv1/NOX2/ROS, TrkB.T1 Key Techniques: Rodent models of SCI/TBI, behavioral evaluations, EV characterization, quantitative imaging, molecular/cellular biology Dr. Wu's research explores the cellular and molecular mechanisms of neurological dysfunction following CNS trauma, with emphasis on autophagy-lysosomal disruption, microglial Hv1 channels, and EV-mediated neuroinflammatory signaling. Her 2025 Aging and disease study reveals age-dependent transcriptional changes post-anesthesia, while 2024 Brain, Behavior, and Immunity work demonstrates how SCI alters EV cargoes to drive brain neuroinflammation. Her team recently discovered that Hv1 proton channel ablation in microglia provides neuroprotection in SCI models. Her scientific contributions are recognized through the 2025 Matjasko Professorship in Anesthesiology Research. She has secured multiple NIH grants including: R01 NS145443 (2025-2030): cGAS signaling in brain trauma-induced neuroinflammation R01 AG077541 (2022-2027): TBI olfactory dysfunction and dementia progression 2RF1 NS094527 (2016-2027): Autophagy mechanisms in SCI R01 NS110825 (2020-2026): Hv1 channel in SCI/TBI Dr. Wu's laboratory personnel include Research Associates Yun Li and Zhuofan Lei, Post-doctoral Fellows Balaji Krishnamachary and Zihui Wang, Research Assistant Hui Li, and medical student Ruth Park. Her work spans from bench research on lysosomal damage to clinical implications for Alzheimer's disease-related dementia (AD/ADRD) and potential therapeutic strategies.
David J. Field is a Professor of Psychology at Cornell University, affiliated with the College of Arts and Sciences. His research focuses on theories of sensory coding, visual processing, and the relationship between natural environmental structure and sensory system representations. He is an active member of the Graduate Field of Psychological Sciences and Human Development. His work spans computational neuroscience and visual perception, with a particular emphasis on efficient coding principles and neural responses to natural scenes. Key research interests include the spatiotemporal dynamics of visual processing, sparse coding models, and the application of advanced imaging techniques like dynamic electrode-to-image (DETI) mapping. His studies often bridge neuroscience with computer science, exploring how neural systems encode visual information efficiently. Recent publications emphasize the role of behavioral goals in shaping neural coding and the statistical properties of natural scenes. Dr. Field teaches courses such as PSYCH 3420 (Human Perception: Application to Computer Graphics, Art, and Visual Display) and contributes to graduate training programs in psychological sciences. His work has been published in top journals, reflecting a strong focus on interdisciplinary approaches to understanding perception and neural representation.
Professor Karl Peter Giese holds the position of Professor of Neurobiology of Mental Health and Co-Head of the Basic & Clinical Neuroscience Department at King's College London's Institute of Psychiatry, Psychology & Neuroscience (IoPPN). His research focuses on memory mechanisms in health and disease, particularly Alzheimer's pathology, synaptic dysfunction, and aging effects. He leads projects funded by Alzheimer's Research UK and other institutions, investigating molecular and cellular bases of memory storage. His work bridges experimental models (e.g., mice) with translational insights for clinical applications. He has over 140 publications, including high-impact studies on CYFIP proteins in dementia and CaMKII in synaptic plasticity. Collaborations include researchers at King's College London and international partners. His lab explores mechanisms linking amyloid-beta, tau, and synaptic proteins to cognitive decline, with recent work applying computational methods to model aging brains. Education: PhD from ETH Zurich (1992), MSc Chemistry from Ruhr-University Bochum (1989). Current grants include Alzheimer's Research UK Network Centres and studies on MNK inhibition for Alzheimer's therapies. Projects span protein synthesis dysregulation, thalamic amyloid pathology, and intellectual disability genetics. His research has been featured in Nature Neuroscience , Brain , and Neuron . He advises on translational neuroscience initiatives and mentors early-career researchers.
Professor Tim Denison FREng holds a joint appointment in the Department of Engineering Science and Nuffield Department of Clinical Neurosciences at the University of Oxford, where he serves as the Royal Academy of Engineering Chair in Emerging Technologies and an MRC Investigator. His research focuses on the fundamentals of physiologic closed-loop systems and developing next-generation neural interface technologies for treating chronic neurological diseases. Professor Denison received his A.B. in Physics from The University of Chicago, followed by M.S. and Ph.D. degrees in Electrical Engineering from MIT. He later completed an MBA at The University of Chicago, where he was named a Wallman Scholar. His research spans neural engineering, closed-loop neuromodulation systems, and computational neuroscience, with particular emphasis on deep brain stimulation, neural oscillations, and adaptive neurostimulation techniques. His work integrates engineering principles with clinical neuroscience to develop innovative treatments for neurological disorders. Professor Denison's approach combines computational modeling with experimental validation to optimize brain stimulation parameters for individual patients. Professor Denison has received numerous prestigious awards, including membership in the Bakken Society (2012, Medtronic's highest technical honor), the Wallin leadership award (2014), election to the College of Fellows for the American Institute of Medical and Biological Engineering (2015), and recognition as a Fellow of the Royal Academy of Engineering (FREng). As a former Technical Fellow at Medtronic PLC and Vice President of Research & Core Technology for the Restorative Therapies Group, Professor Denison brings significant industry experience to his academic work. His research group focuses on developing advanced neurostimulation technologies that incorporate chronobiology principles and adaptive algorithms to improve treatment outcomes for neurological conditions.
Ankit Kariryaa is a Tenure Track Assistant Professor at the Department of Computer Science and Department of Geosciences and Natural Resource Management , University of Copenhagen. His work bridges Machine Learning and Environmental Informatics , focusing on remote sensing, geospatial analysis, and ecological modeling. University of Copenhagen, Denmark Machine Learning Section, Department of Computer Science Geography, Land, Environment and Society, Department of Geosciences Kariryaa specializes in applying deep learning and computer vision to environmental challenges. His research includes: Automated tree detection and biomass estimation via satellite imagery Multi-modal geospatial representation learning Monitoring farmland tree decline and carbon sequestration potential Agroforestry system mapping using AI Developing AI tools for climate policy and sustainability Recent work trends show a focus on quantum-inspired machine learning , environmental monitoring , and cross-cultural AI applications . His 15 most recent publications span topics in remote sensing , ecological modeling , and AI ethics , with methods ranging from neural networks to tensor-based learning. He collaborates across disciplines, notably with researchers in ecology , climate science , and quantum computing . His outreach includes seminars on AI in agroforestry and ecosystem management , while his team contributes to global tree resource databases like TreeSense.
Vivek Boominathan is an Assistant Research Professor in the Department of Electrical and Computer Engineering at Rice University. He is affiliated with the GLEE lab (Geometry, Light, & Imaging lab). His research focuses on computational imaging, combining computer vision, machine learning, applied optics, and nanofabrication to develop innovative imaging systems for applications such as robotics, medical sensing, and virtual/augmented reality. He has contributed to projects like PhlatCam (a lensless camera) and NeuWS (neural wavefront shaping). His work bridges optics, algorithms, and materials science to overcome traditional limitations in imaging systems. Boominathan's research interests include lensless imaging, optical meta-devices, turbulence mitigation, and bio-inspired imaging systems. He has developed systems like Foveated thermal imaging prototypes and real-time lensless microscopes. His lab emphasizes interdisciplinary approaches, integrating hardware design with machine learning. Key projects include: NeuWS: Neural wavefront shaping for imaging through scattering media CoIR: Compressive implicit radar for sensing applications FlatCam and PhlatCam: Ultra-thin lensless imaging devices Bioluminescence imaging in marine species His work has been published in top venues like Science Advances, Optica, and IEEE TPAMI. He collaborates with institutions like NASA JPL and industry partners on applied imaging solutions. Current research trends emphasize sensor-algorithm co-design and high-speed imaging systems for AR/VR applications. Boominathan holds a PhD in Electrical Engineering and has extensive postdoctoral experience in computational imaging. He advises projects in the GLEE lab and mentors students in hardware-software co-design for imaging systems. His lab focuses on translating theoretical innovations into practical devices with commercial potential.
