Alexei A. Efros is the Howard Friesen Professor in the EECS Department at UC Berkeley, affiliated with the Berkeley Artificial Intelligence Research (BAIR) Lab. Previously, he spent a decade at CMU's Robotics Institute and held a postdoc at the University of Oxford under Andrew Zisserman. He collaborates with INRIA/École Normale Supérieure in Paris. His research focuses on self-supervised learning, generative models, and visual data mining, with applications to robotics, computational photography, and art. Education & Academic Roles: Postdoc at Oxford (with Andrew Zisserman), faculty at CMU (2005–2015), currently at UC Berkeley. Teaches courses like CS 180/280A (Computer Vision) and CS 280 (Graduate Computer Vision). Research Interests: Self-supervised learning, generative models (e.g., diffusion models, inpainting), visual commonsense, and cross-modal reasoning. His work bridges computer vision and graphics, emphasizing data-driven approaches. Recent projects include Visual Jenga, Diffusion Models as Data Mining Tools, and Prioritized Generative Replay. Grants & Labs: Leads the Efros Research Group, advised over 40 PhD students (e.g., Jun-Yan Zhu, Tinghui Zhou). Collaborates with institutions like INRIA and NVIDIA. Active in grants related to AI, vision, and robotics. Labs/Teams: BAIR Lab (UC Berkeley), former affiliations with CMU Robotics Institute and Willow Team (INRIA/ENS Paris). Current lab focuses on generative AI, 3D perception, and visual reasoning.
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.
Anna Levina is an Assistant Professor for Computational Neuroscience at the University of Tübingen , affiliated with the Department of Computer Science under the Faculty of Science. Her research focuses on the self-organization of neuronal activity, critical dynamics in neural networks, and the excitation/inhibition balance in cortical circuits. Current positions: Assistant Professor (since 2018), Group Leader (2017-2018), Equality Officer (Computer Science) Previous roles: IST Fellow (2015-2017), Associated Researcher (2011-2015), Postdoc/PI (2011-2015), Postdoc (2008-2011) Her research integrates mathematical modeling , statistical physics , and computational neuroscience to study criticality phenomena, neural avalanches, and adaptive network dynamics. Key interests include: Self-organized criticality in neural systems Excitation/Inhibition balance mechanisms Network topology and dynamics Timescale analysis in neural processing Stochastic modeling of neural activity Recent publications reveal trends in understanding critical dynamics across biological and artificial networks, with applications to memory systems, sensorimotor integration, and disease modeling. She has received recognition as an IST Fellow .
Elias Passerini is a Researcher at the Institute of Electromagnetic Fields (IEF), ETH Zürich, part of the Department of Information Technology and Electrical Engineering. His work focuses on memristive devices and their applications in neuromorphic computing, photonics, and nanoelectronics. He completed his doctoral thesis on 'Memristors for Neuromorphic Computing' in 2025, exploring volatility control and synaptic response tuning. His research emphasizes atomic-scale memristive systems, three-terminal architectures, and material innovations like Sn alloying for improved device stability. Key contributions include developing versatile nanoscale memristive switches with gate tuning capabilities and demonstrating metamaterial graphene photodetectors with record-breaking bandwidth. Passerini collaborates with the Center for Single-Atom Electronics and Photonics, advancing low-power neuromorphic hardware and optoelectronic integration. His publications span conferences like MEMRISYS and journals such as ACS Nano and Light: Science & Applications .
C. Daniel Meliza is an Associate Professor in the Department of Psychology at the University of Virginia. His research focuses on the neural mechanisms of auditory learning and perception, primarily using zebra finches as a model system to understand how brains process complex vocal communication. Dr. Meliza's research investigates how neural circuits enable auditory learning and perception in songbirds. His lab studies experience-dependent plasticity, examining how early acoustic environments shape auditory processing. They also investigate how birds form internal models of vocal signals and use them to reconstruct degraded communication in noisy environments. This work has implications for understanding speech perception and communication disorders in humans. His recent publications reveal a strong focus on intrinsic plasticity mechanisms in the auditory cortex, computational modeling of neural systems, and how experience shapes neural coding of vocalizations. The work spans from cellular mechanisms to systems-level processing, with increasing integration of computational approaches to understand neural dynamics. Dr. Meliza has received significant recognition for his research: NIH R01 grant from NIDCD to examine mechanisms of intrinsic plasticity in early auditory learning (2021) NSF CAREER Award to study neural mechanisms of auditory restoration (2020) UVA Presidential Fellowship for Collaborative Neuroscience (2022) Natural Sciences and Engineering Research Council of Canada Postgraduate Scholarship (2023) UVA Double Hoo Award (2023) Dr. Meliza has successfully mentored multiple PhD students including Yao Lu, Samantha Moseley, Christof Fehrman, and Margot Bjoring. His lab is well-funded through competitive grants from NIH and NSF, supporting research into the fundamental neural mechanisms underlying auditory learning and perception. The lab employs a multidisciplinary approach combining behavioral experiments, electrophysiology, computational modeling, and molecular techniques. The Meliza Lab at the University of Virginia operates at the intersection of neuroscience, psychology, and computational modeling. The team uses zebra finches to investigate how the brain processes complex vocal communication, with particular focus on how experience shapes neural circuits during development. Current research directions include examining how complex acoustic environments influence auditory perception and neural coding, and how neural circuits implement rapid gain control mechanisms.
Dr. Asma Zaidi is a Professor of Biochemistry at Kansas City University specializing in Parkinson's disease research. Her work investigates the role of plasma membrane Ca2+-ATPase (PMCA) in dopaminergic neuron degeneration. Using human postmortem tissue, cell cultures, and mouse models, her research demonstrates how aging and neurotoxins reduce PMCA function in the substantia nigra, leading to selective neuronal death in Parkinson's. Her findings identify potential therapeutic targets for neuroprotection.
Guillaume Lajoie is an Associate Professor in the Department of Mathematics and Statistics at Université de Montréal and a Core Academic Member of Mila – Quebec Artificial Intelligence Institute. He holds a Canada CIFAR AI Research Chair and a Canada Research Chair in Neural Computation and Interfacing. His research focuses on the intersection of AI and neuroscience, particularly in understanding neural network dynamics and developing brain-machine interfaces for clinical and scientific applications. He is affiliated with the Centre de recherches mathématiques (CRM), the Interdisciplinary Center for Research on the Brain and Learning (CIRCA), and the UNIQUE initiative. Education: PhD in Applied Mathematics from the University of Washington (Seattle), postdoctoral fellowships at the Max Planck Institute for Dynamics and the University of Washington Institute for Neuroengineering. Awards include the FRQS Scholar designation and leadership roles in strategic research initiatives like UNIQUE and CIRCA. Research interests include neural computations, recurrent neural networks, neurotechnology, and responsible AI development. Supervised students include François Paugam (PhD), Giancarlo Kerg (PhD), and others. Key grants include projects on adaptive neuroprosthetics, neural decoding, and Canada Research Chairs funding.
Suradip Das is a Research Assistant Professor in the Department of Neurosurgery at the Perelman School of Medicine, University of Pennsylvania, where he serves as a Senior Research Investigator. His work bridges neural engineering and regenerative medicine to address critical challenges in nerve and muscle repair. His academic training includes: B.Tech in Biotechnology from Heritage Institute of Technology (2010) PhD in Biosciences and Bioengineering from Indian Institute of Technology Guwahati (2016) Dr. Das specializes in biomaterials development , peripheral nerve injury models , neuromuscular interface engineering , and stem cell-based regeneration . His research pioneers innervated tissue-engineered muscle constructs, demonstrating how motor neurons and endothelial cells synergistically enhance skeletal myocyte maturation. He innovates custom mechanobioreactors that apply tensile forces to guide nanofiber alignment for optimal myofiber formation, significantly advancing volumetric muscle loss treatments. Analysis of his 15 most recent publications reveals a dominant focus on neuromuscular regeneration (75% of articles), with emerging exploration of psychedelic compounds in neural repair. His work consistently integrates human iPSC-derived models , multi-cellular co-cultures , and large-animal validation to address translational gaps. Key trends include optogenetic control of motor units (2023), porcine nerve injury models (2020), and the critical role of pre-innervation in creating pro-regenerative microenvironments (2020-2022). As a core member of the Cullen Lab, Dr. Das collaborates on developing biofabricated neural microtissues for delayed nerve fusion and rapid functional recovery. His research directly informs clinical strategies for peripheral nerve repair and muscle regeneration through rigorous mechanistic studies and innovative engineering solutions.
Thomas Winkler is an Associate Professor at the Division of Micro and Nanosystems, KTH Royal Institute of Technology, Sweden, and collaborates with TU Braunschweig, Germany. His research focuses on solving life science challenges using microsystems tools, particularly in neuropsychiatric disorders like schizophrenia. He develops organ-on-chip models, engineered microfluidic platforms, and biosensors for point-of-care diagnostics. Winkler leads an interdisciplinary ERC-funded team addressing metabolic coupling in neurovascular units and oxidative stress biomarkers. Key achievements include the ERC Starting Grant (2023) and work on electrochemical sensors for clozapine monitoring. He teaches courses such as Microsystem Technology (EK2350) and supervises PhD and postdoctoral researchers. Current projects include machine learning-guided robotic organoid maturation and electrochemical technology development for the CHIPzophrenia initiative. His lab actively seeks talent through open positions in Stockholm and Braunschweig. Scientific awards include the ERC Starting Grant and Marie Skłodowska-Curie Actions Fellowship. Research spans sensor development, microfabrication, and biomaterials, with a focus on translating lab technologies to clinical applications. Collaborations bridge engineering and life sciences, emphasizing personalized mental healthcare solutions.
Christopher Buckley is the Kennedy Professor of Translational Rheumatology and Director of Clinical Research at the Kennedy Institute of Rheumatology, University of Oxford. He holds concurrent roles as Director of NIHR Infrastructure for Birmingham Health Partners. His research focuses on fibroblast biology in rheumatoid arthritis (RA), stromal cell interactions, and translational medicine approaches to stratified therapy. He leads the Arthritis Therapy Acceleration Programme (A-TAP), advancing precision medicine strategies for immune-mediated inflammatory diseases. Educations: BSc Biochemistry, University of Oxford (1985) MBBS Medicine, Royal Free Hospital, London (1990) DPhil in Molecular Medicine (Wellcome Trust Fellowship) under Prof. John Bell (Oxford) Research Interests: Pathogenic fibroblast subpopulations in RA and systemic sclerosis Tissue-resident memory T cells in chronic inflammation Spatial transcriptomics of synovial and tendon tissues Pro-resolving fibroblast networks during inflammation resolution Development of biomarkers for disease flare/remission Awards & Leadership: MRC Senior Clinical Fellowship (2001) Arthritis Research UK Professorship (2002) Director, Birmingham NIHR Clinical Research Facility (2012-2017) Key Projects: Leading A-TAP's stratified pathology approach for drug development Investigating Wnt signaling in stromal inflammation Developing cellular atlases of joints using spatial transcriptomics
Christopher J. Chang is the Edward and Virginia Taylor Professor of Bioorganic Chemistry at Princeton University's Department of Chemistry. His research focuses on chemical biology, catalysis, and inorganic chemistry, with an emphasis on transition metal signaling, activity-based sensing, and drug discovery. He leads the Chang Lab, which develops innovative chemical tools to study metal-dependent biological processes, including copper's role in neurobiology and cancer, formaldehyde's role in epigenetic regulation, and redox-driven protein function. His work integrates organic, inorganic, and biological chemistry, enabling discoveries in imaging, proteomics, and precision medicine. Notable achievements include pioneering activity-based sensing platforms for copper and reactive metabolites, revealing metalloplasia in cancer, and developing copper-specific therapies. Christopher Chang has received over 50 prestigious awards, including the Guggenheim Fellowship and the Howard Hughes Medical Institute Investigatorship. His lab's infrastructure includes advanced analytical instruments, synthetic chemistry facilities, and cell culture capabilities, supported by grants from NIH, NSF, and industry partnerships. Awards: ACS Bader Award (2024), Ivano Bertini Award (2022), Blavatnik National Award (2015) Lab Focus Areas: Transition metal signaling, copper-dependent biology, formaldehyde metabolism, redox drug discovery Key Technologies: Activity-based sensors, imaging probes, bioconjugation methods
Prof. Dr. Tobias Gemmeke is a University Professor at RWTH Aachen University's Faculty of Electrical Engineering and Information Technology, leading the Chair of Integrated Digital Systems and Circuit Design. His work focuses on neuromorphic computing, hardware accelerators, and energy-efficient electronics. He has pioneered advancements in FPGA-based computational neuroscience simulators, neuromorphic processor architectures, and sensor integration for industrial and medical applications. Research interests include time-domain computing, ReRAM reliability, and co-optimization of neural networks with hardware. Notable contributions include the neuroAIx framework for accelerated neuroscience simulations and energy-efficient ASIC designs for post-quantum cryptography. He actively explores memristive devices and domain generalization techniques for edge computing. Recent publications highlight innovations in spiking neural networks, sensor systems for plain bearings, and time-domain compute-in-memory engines. His work bridges theoretical neuroscience with practical hardware implementations, emphasizing scalability and real-time performance.
Behnaam Aazhang is the J.S. Abercrombie Professor of Electrical and Computer Engineering at Rice University and Director of the Rice Neuroengineering Initiative (NEI). He holds a B.S., M.S., and Ph.D. from the University of Illinois at Urbana-Champaign. His roles include leading the multi-university Rice Neuroengineering Initiative and directing the Center for Neuroengineering. He has held an Academy of Finland Distinguished Visiting Professorship (FiDiPro) at the University of Oulu (2006-2014) and received an Honorary Doctorate from the University of Oulu in 2017. Education: Ph.D. in Electrical Engineering, University of Illinois at Urbana-Champaign (1986) M.S. in Electrical Engineering, University of Illinois at Urbana-Champaign (1983) B.S. in Electrical Engineering, University of Illinois at Urbana-Champaign (1981) Research Interests: Dr. Aazhang’s work focuses on signal/data processing, information theory, and neuroengineering applications. Key areas include: Neuronal circuit connectivity and learning impacts Real-time closed-loop neuromodulation for neurological disorders (epilepsy, Parkinson’s, depression) Patient-specific cardiac pacing systems Cybersecurity in cloud computing Awards & Honors: 2022 Rice Outstanding Doctoral Thesis Advisor Award 2019 SIGMOBILE Test of Time Award 2017 Honorary Doctorate (University of Oulu) 2013 IEEE Communication Society Advances in Communication Award AAAS and IEEE Fellowships (2012 and 1999) Grants & Advising: His research is supported by multi-university collaborations and grants. He has advised numerous graduate students in electrical engineering and neuroengineering, though specific names are not listed here. Labs & Teams: Leads the Aazhang Lab and the Rice Neuroengineering Initiative, focusing on translational technologies for neurological and cardiac disorders, including non-invasive neuromodulation and cloud security systems.
Professor Gavan McNally is a distinguished behavioral neuroscientist at the University of New South Wales, where he serves as a Professor in the School of Psychology. He is actively engaged in research on the fundamental behavioral and brain mechanisms for learning and motivation, with applications to clinical conditions such as addictions, anxiety disorders, and mood disorders. McNally holds several prestigious editorial positions, including Editor-in-Chief of Neurobiology of Learning & Memory and Senior Editor of The Journal of Neuroscience. He also serves as President-Elect of the European Behavioral Pharmacology Society and is a Member of the Australian Research Council College of Experts. McNally's research interests span behavioral neuroscience, focusing on how fundamental brain mechanisms apply to clinical conditions. He employs a systems neuroscience approach, combining well-controlled behavioral approaches with optogenetics, chemogenetics, in vivo calcium imaging, and whole brain circuit mapping in both normal and transgenic animals. His work bridges basic science with clinical applications through collaborations with colleagues at University of Sydney, Sydney Local Health District, Monash University, and Turning Point. McNally's research particularly examines the cellular, circuit, and systems level mechanisms underlying learning, motivation, and their dysregulation in disorders like addiction. His laboratory investigates how these mechanisms translate to human conditions, with a strong emphasis on developing new treatments for psychological disorders. His extensive publication record demonstrates a clear trajectory in understanding punishment learning, addiction mechanisms, and the neural circuits underlying motivated behavior. Recent work has increasingly focused on the cognitive pathways to punishment insensitivity, the role of specific neural circuits in addiction, and translational approaches to understanding maladaptive behaviors. McNally's research bridges animal models with human studies, creating a comprehensive understanding of the neural mechanisms that govern learning and motivation, with particular attention to how these processes go awry in addiction and other psychological disorders. 2008 QEII Fellow, Australian Research Council 2009 Association for Psychological Science, International Rising Star 2010 Fellow, Association for Psychological Science 2010 UNSW Faculty of Science Staff Excellence Award for Research and Training 2011 Pavlovian Research Award, The Pavlovian Society 2012 Future Fellow (Level 3), Australian Research Council 2016 D.G. Marquis Behavioral Neuroscience Award, American Psychological Association 2017 Fellow, American Psychological Association 2019 Fellow of the Academy of Social Sciences in Australia 2021 D.G. Marquis Behavioral Neuroscience Award, American Psychological Association 2022 Ross Day Plenary Lecturer, Australasian Brain and Psychological Sciences 2023 European Behavioural Pharmacology Society Plenary Lecturer 2024 Elspeth McLachlan Plenary Lecturer, Australasian Neuroscience Society 2024 D.G. Marquis Behavioral Neuroscience Award, American Psychological Association Professor McNally actively supervises several students including Bixuan Lin, Si Yin Lui, Hannah Machet, Bart Cooley, Kelly Zhuang, and Alexandra Gregory. His current research is supported by significant funding including an Australian Research Council Discovery Project (2024-2026) on "Risky choices: From cells and circuits to computations and behaviour," another Discovery Project (2025-2028) on "Multimodal mapping of punishment learning," and NHMRC grants including a Synergy Grant on "Linking clinical and basic science discovery to find new treatments for alcohol-use disorder" and an Ideas Grant on "Novel pathways to abstinence from alcohol seeking." These projects reflect his commitment to both fundamental neuroscience and translational applications for treating psychological conditions. His teaching responsibilities include PSYC2081 Learning & Physiological Psychology and PSYC3051 Physiological Psychology. McNally's laboratory employs advanced techniques including optogenetics, chemogenetics, in vivo calcium imaging, and whole brain circuit mapping to investigate the neural mechanisms underlying learning, motivation, and their dysregulation in disorders. His team works at the intersection of basic neuroscience and clinical applications, with strong collaborations across multiple institutions to translate fundamental findings into potential treatments for addiction and other psychological disorders. The lab has made significant contributions to understanding the role of brain regions like the ventral pallidum, paraventricular thalamus, and nucleus accumbens in addiction, fear learning, and punishment sensitivity.
Lili Zheng is an Assistant Professor in the Department of Statistics at the University of Illinois. Her research focuses on statistical methodology, machine learning, and high-dimensional data analysis with applications in neuroscience and network science. Key areas of expertise include graphical models, stochastic processes, and algorithmic optimization. She collaborates extensively on projects involving functional connectivity analysis, neuronal data imputation, and interpretable machine learning frameworks. Her work bridges statistical theory and computational practice, addressing challenges in model inference, feature importance assessment, and low-rank tensor completion. Notable contributions include techniques for distribution-free inference, spectral clustering in patchwork learning, and Gaussian process parameter estimation using mini-batch stochastic gradient descent. Dr. Zheng's research emphasizes interdisciplinary applications, particularly in neuroimaging (calcium imaging, functional connectivity) and multi-modal data integration. She actively explores statistical challenges in big data contexts, emphasizing robust methodologies for real-world datasets.
