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.
Professor CHEN Wei (National University of Singapore) holds the Provost's Chair Professorship (2023-2026) and serves as Vice-Dean (Research) with joint appointments in the Departments of Chemistry and Physics. His research focuses on molecular-scale interface engineering for 2D materials-based devices and interface-controlled nanocatalysis in energy/environmental applications. PhD in Material Science, NUS (2004) Lee Kuan Yew Research Fellow (2006-2008) Established Surface and Interface Lab (2009) Director, NUS Research Institute (Fuzhou) His work on 2D optoelectronic memory (Nat. Comm. 2018), Kagome lattice design (Nano Lett. 2020), and single-atom catalysis (Nat. Comm. 2021) has been recognized by multiple high-impact publications and the Clarivate Highly Cited Researcher status (2017-2021). Awards include the NRF Investigatorship (2023) , Mitsui Chemicals-SNIC Industry Award (2020) , and Singapore Young Scientist Award (2012) . Grants from NUS, Singapore MOE, CREATE/CRP programs, and A*STAR support his exploration of interface engineering for neuromorphic computing and energy-efficient nanocatalysts . Current projects include monolayer blue phosphorus synthesis and solid electrolyte interphase engineering for lithium batteries.
Prof. Alois Christian Knoll is a full professor at the Technical University of Munich (TUM) in the School of Computation, Information and Technology. His academic career includes roles at Bielefeld University and leadership in major EU initiatives like the Human Brain Project and ECHORD++. He specializes in robotics, AI, and autonomous systems, with a focus on medical robotics, sensor-based systems, and neuromorphic engineering. Knoll has supervised over 100 doctoral theses and authored/co-authored over 1,000 publications. Education: Diploma in Electrical Engineering (University of Stuttgart, 1985); PhD in Computer Science (Technical University of Berlin, 1988); Habilitation (TU Berlin, 1993). He has been at TUM since 2001, leading the Robotics, AI, and Real-Time Systems department. Research interests span autonomous systems, neuro-IT integration, and traffic simulation. Key projects include fortiss (Bavarian State Institute for Computer Science) and TUM-CREATE (Singapore collaboration). Awards include IEEE Fellow, University of Tokyo Fellow, and the Carl-Ramsauer-Prize (1990). Current roles include editorships in robotics journals, leadership in EU flagship projects, and teaching across multiple programs. His work bridges computer science, neuroscience, and engineering, with applications in healthcare, automotive systems, and urban mobility.
Dr. Gregor Schuhknecht is a Researcher and incoming Max-Planck Research Group Leader at the Max Planck Institute for Brain Research in Frankfurt, Germany. Starting December 2025, he will lead the Brain Algorithms and Circuits Group, focusing on computational neuroscience and neuronal circuitry in larval zebrafish. His work bridges experimental and computational approaches to understand brain algorithms enabling flexible behaviors. Education PhD in Neuroscience (2014–2019), Institute for Neuroinformatics, University of Zurich and ETH Zurich MSc in Biology (Neuroscience) (2012–2019), ETH Zurich BSc in Biosciences (2009–2012), Heidelberg University His research investigates how synaptic circuitry implements computational algorithms for sensorimotor processing, evidence accumulation, and behavior. Experimental approaches include functional imaging, optogenetics, connectomics, and computational modeling. The group will explore neuronal circuits across scales, from synapse to behavior, with a focus on biophysical properties and dynamic environmental adaptation. Recent publications highlight correlative light/electron microscopy, developmental activity-free circuit formation, and neocortical synapse analysis. Scientific Awards Meselson Prize (2024) Aspirational Neuroscience Award (2023) Best Publication Award (2021) Swiss National Science Foundation Fellowships (2021, 2019, 2018) Gregor Schuhknecht's lab will welcome graduate and postdoctoral researchers starting December 2025, focusing on experimental and computational systems neuroscience. He previously collaborated with Harvard University's Department of Molecular and Cellular Biology and the Department of Moritz Helmstaedter for connectomic analyses.
Professor Martin Schrimpf is a Tenure Track Assistant Professor at EPFL, holding dual appointments in the School of Life Sciences (SV) and the School of Computer and Communication Sciences (IC). His research bridges computational neuroscience, deep learning, and cognitive science to model human natural intelligence in vision and language. He leads the NeuroAI Lab, focusing on aligning artificial neural networks with brain mechanisms and human behavior. Education: PhD in Brain and Cognitive Sciences from MIT (2017–2022), MSc in Software Engineering from TUM/LMU/UNA (2014–2017), and BSc in Information Systems from TUM (2011–2014). His work has been recognized with awards including the Neuro-Irv Open Science Prize, McGovern Fellowship, and Takeda AI+Health Fellowship. He co-founded Integreat, a social impact startup recognized with Google.org’s Impact Challenge and TUM’s Social Impact Award. Research interests include neuroAI, brain-like models, and clinical translation (e.g., visual prosthetics). He has published in top venues like Neuron, Nature Human Behavior, NeurIPS, and ICLR. Current projects involve developing topographic language models (TopoLM) and investigating causal language network interactions using LLMs. Teaching: Courses include Neuroscience Foundations for Engineers and Brain-like Computation and Intelligence . Supervised over 26 students, including PhD candidates Badr Alkhamissi, Ben Lönnqvist, and Yingtian Tang. Active in grants from SNSF, NeuroX, and EPFL’s AI Center. Labs/Teams: NeuroAI Lab at EPFL Neuro-X Institute. Future directions include advancing brain-inspired models for clinical applications and expanding interdisciplinary collaborations between neuroscience and AI.
Mayank R. Mehta is a Professor at the University of California, Los Angeles (UCLA), holding joint appointments in the Departments of Physics & Astronomy, Neurology, and Neurobiology. He is a member of the Brain Research Institute and the W. M. Keck Center for Neurophysics at UCLA. His research bridges experimental and theoretical neuroscience, focusing on how neuronal networks encode space-time, the role of brain rhythms in learning and memory, and the impact of sleep and virtual reality on neural dynamics. His recent publications highlight breakthroughs in understanding hippocampal spatiotemporal selectivity, dendritic activity during behavior, and the causal influence of visual cues on memory neurons. Notable findings include the discovery that dendrites generate ten times more spikes than neuronal cell bodies and the modulation of hippocampal theta rhythms in virtual reality. Research Themes: Neurophysics of spatial-temporal coding Dendritic contributions to learning Virtual reality and brain plasticity Neural oscillations in memory consolidation Key Collaborators: Bert Sakmann (Max Planck Florida Institute) Thomas Hahn (Bernstein Center Heidelberg/Mannheim) Maryam Ghorbani (UCLA) Mehta's lab at UCLA trains graduate and postdoctoral researchers in cutting-edge techniques combining hardware development, electrophysiological recordings, and biophysical modeling. His work has significant implications for treating learning and memory disorders like Alzheimer's disease.
Sumeet Kumar Gupta is an Associate Professor in the Department of Electrical and Computer Engineering at Purdue University. His academic career spans from his current role to a prior Assistant Professorship at Pennsylvania State University (2014-2017) and an engineering position at Qualcomm Inc. (2012-2014). He holds a PhD in Electrical and Computer Engineering from Purdue University (2012), an M.S. from the same institution (2008), and a B.Tech in Electrical Engineering from IIT Delhi (2006). B.Tech, Electrical Engineering, IIT Delhi (2006) M.S., Electrical and Computer Engineering, Purdue University (2008) PhD, Electrical and Computer Engineering, Purdue University (2012) Dr. Gupta's research focuses on neuromorphic computing, low power variation-aware VLSI design in emerging nanotechnologies, device-circuit co-design, and nano-scale device modeling/simulations. His work addresses challenges in ferroelectric materials, crossbar arrays for deep neural networks, and energy-efficient AI hardware. Recent publications (2025-2024) highlight trends in: Ferroelectric HfO2/HZO thin films Compute-in-memory architectures Variability/stochasticity analysis Machine learning for device optimization Interconnect resistance/temperature effects AI hardware fault tolerance Scientific Awards & Recognitions: DARPA Young Faculty Award (2016) Early Career Professorship, Penn State (2014) 6th TSMC Outstanding Student Research Bronze Award (2012) Magoon Award (Purdue) Outstanding Teaching Assistant Award (Purdue, 2007) Intel PhD Fellowship (2009) His professional journey includes academic appointments at Purdue University (2020-present, Associate Professor) and Pennsylvania State University (2014-2017, Assistant Professor) after industry experience at Qualcomm Inc. (2012-2014). He maintains IEEE and EDS membership while publishing over 100 refereed works.
Dr. Juan Alvaro Gallego is a Senior Lecturer (equivalent to Associate Professor) in the Department of Bioengineering at Imperial College London's Faculty of Engineering. He leads the Behaviour and Neural Dynamics Lab (Be.Neural), a multidisciplinary team focused on understanding neural mechanisms underlying motor control and spinal cord learning, with applications in developing neural interfaces to restore movement in conditions like Parkinson’s disease and paralysis. His research integrates behavioral experiments, neural recordings, data analysis, and computational models, funded by the ERC, EPSRC, ARIA, and industry partners like InBrain Neuroelectronics and Meta Reality Labs. Research interests include motor control, neural dynamics, and clinical applications of neural engineering. The lab collaborates across systems neuroscience and biomedical engineering, aiming to translate fundamental discoveries into therapeutic technologies. Key areas of focus include neural manifolds, synaptic plasticity in motor learning, and closed-loop neuroprosthetics for tremor management. Funding sources include the European Research Council, Engineering and Physical Sciences Research Council, and industry collaborations. The Be.Neural Lab’s work is showcased on their dedicated website (https://beneural.ic.ac.uk).
Giacomo Indiveri is a dual Professor at the Faculty of Science of the University of Zurich and the Department of Information Technology and Electrical Engineering of ETH Zurich . He serves as the Director of the Institute of Neuroinformatics at both institutions. Indiveri holds an M.Sc. in Electrical Engineering (1992) from the University of Genoa and a Ph.D. in Computer Science (2004) from the same university. Primary Affiliation: University of Zurich (Faculty of Science, Institute of Neuroinformatics) Secondary Affiliation: ETH Zurich (Department of Information Technology and Electrical Engineering) Indiveri's research bridges neuroscience , computer science , and machine learning to develop neuromorphic cognitive systems . His work focuses on spike-based learning , recurrent neural networks , and analog/digital circuit design for real-time sensory-motor systems . He integrates emerging memory technologies into fault-tolerant event-based architectures, enabling brain-inspired computing paradigms in applications like robotics and medical monitoring. His recent publications emphasize neuromorphic hardware for epileptic seizure detection , spiking neural networks in robotic painting , and scalable processors with on-chip learning . These works explore biologically plausible neurons , delay lines , and memory arrays for temporal processing, with applications in healthcare , edge computing , and adaptive control . Scientific Awards & Recognitions: 2021 IEEE Biomedical Circuits and Systems Best Paper Award Senior Member of IEEE Society ERC Fellow with three European Research Council grants Indiveri's group at the Institute of Neuroinformatics develops event-based systems for real-world validation of brain-inspired computing. His work includes multi-core processors , feedback optimizers , and dynamic routing architectures , supported by grants for advancing neuromorphic technologies .
Marc V Fuccillo is an Associate Professor of Neuroscience at the Perelman School of Medicine, University of Pennsylvania, where he leads a research laboratory focused on understanding the neural circuit mechanisms underlying behavioral control. His work bridges molecular, synaptic, and behavioral approaches to investigate how striatal circuits regulate mouse behavior from simple motor patterns to complex goal-directed actions. Fuccillo holds dual appointments in the Neuroscience and Cell and Molecular Biology Graduate Groups at Penn and maintains an active laboratory investigating the synaptic and circuit basis of neuropsychiatric disorders. Education: B.A. in Molecular and Cellular Biology and Music Performance (Violin) from Brown University (1998) Ph.D. in Developmental Genetics from New York University School of Medicine (2007) M.D. from New York University School of Medicine (2008) Fuccillo's research centers on the synaptic and circuit mechanisms of behavioral control, with particular emphasis on striatal circuits. His laboratory employs a range of technologies including mouse genetics, in vitro electrophysiology, in vivo imaging, and quantitative behavioral analysis to explore how neural circuits of the striatum regulate behavior and how disruptions in these circuits contribute to neuropsychiatric disorders. His work has particularly focused on autism-associated abnormalities in behavioral control, examining how synaptic adhesion molecules like neuroligins and neurexins shape circuit function and behavior, with significant findings regarding D1 dopamine receptor positive medium spiny neurons in the nucleus accumbens. Analysis of Fuccillo's recent publications reveals a strong focus on striatal circuit function across multiple dimensions. His work spans molecular neuroscience (examining synaptic adhesion molecules), cellular physiology (studying specific neuron types in striatal circuits), systems neuroscience (mapping circuit connectivity), and behavioral neuroscience (quantifying motor learning and decision-making). A unifying theme is how disruptions in specific molecular pathways lead to circuit-level abnormalities that manifest as behavioral phenotypes relevant to neuropsychiatric disorders, with particular attention to autism, OCD, and schizophrenia models. Scientific Recognition: Publications in high-impact journals including Nature Neuroscience, Current Biology, Cell Reports, and Neuron Research supported by multiple NIH grants including NIMH F32, NIMH K01, and HHMI Gilliam Fellowship awards for lab members Fuccillo actively mentors a diverse group of trainees including postdoctoral fellows, graduate students, and undergraduates. His laboratory has produced numerous successful alumni who have gone on to faculty positions, medical residencies, and graduate programs at prestigious institutions. His mentoring approach emphasizes technical skill development across multiple neuroscience disciplines while fostering independent scientific thinking. Current research in his lab is supported by NIH funding focused on understanding the molecular architecture of striatal circuits and their role in behavioral control, with three major research directions exploring molecular logic of striatal circuits, circuit mechanisms of behavioral control, and striatal dysfunction in neuropsychiatric disease models. The Fuccillo Laboratory operates within the Department of Neuroscience at the University of Pennsylvania, with access to state-of-the-art facilities for molecular, electrophysiological, imaging, and behavioral neuroscience research. The lab maintains active collaborations with other neuroscience research groups at Penn and beyond, creating a rich intellectual environment for studying the neural basis of behavior. Current research directions include investigating whether there is a molecular logic to striatal circuit composition, how striatal circuits shape behavioral control, and what mouse models of autism, schizophrenia, and OCD can reveal about striatal circuit dysfunction in disease pathophysiology.
Jason Ritt is an Associate Professor of Brain Science (Research) and Scientific Director of Quantitative Neuroscience at the Robert J. and Nancy D. Carney Institute for Brain Science, Brown University. He holds affiliations with the Data Science Institute and collaborates across disciplines on quantitative research methods. Education : B.S., M.A., and Ph.D. in Neuroscience from Boston University (1997–2003). Research : Focuses on neural processing during active sensing and neuroengineering for neurostimulation. Combines electrophysiology, optogenetics, and theoretical approaches in rodent models. Develops closed-loop systems for studying sensory neural prosthetics and brain-machine interfaces. Key areas include synaptic diversity, neurocontrol algorithms, and sensory restoration. Teaching : Instructs NEUR 2100 NeuroPracticum, integrating hands-on neuroscience research training.
Benjamin Machta is an Assistant Professor of Physics at Yale University, affiliated with the Department of Physics and the QBio Institute. He holds a BS from Brown University and a PhD from Cornell University, followed by a postdoctoral fellowship at Princeton University. His research focuses on applying theoretical physics to understand biological systems, particularly leveraging statistical physics and information theory to study biological membranes near critical points and the energetic constraints of biological signaling. Education: BS in Physics (Brown University), PhD in Physics (Cornell University), Postdoc at Princeton University (Lewis-Sigler Theory Fellow). Research Interests include: membrane criticality, phase transitions in biological systems, information-theoretic limits in organism function, and energy dissipation in biological processes. His work often bridges theoretical models with experimental data, such as collaborations with Sarah Veatch’s lab on membrane phase behavior. Publications highlight themes like membrane criticality, protein phase separation, and energy constraints in signaling. His group’s current projects explore cochlear mechanics, thermodynamic control in biological systems, and the role of criticality in sensory systems. Awards: 2019 Simons Investigator Award. Lab Affiliations: QBio Institute and Department of Physics at Yale, located in YSB-C164. Group members include postdocs Isabella Graf and Michael Abbott, and graduate students Asheesh Momi, Mason Rouches, and others.
Kevin M. Franks is an Associate Professor of Neurobiology at Duke University, where he investigates how the olfactory system forms neural representations of sensory environments. His work focuses on functional neural circuits in the olfactory bulb and piriform cortex, using techniques like in vivo recordings, optogenetics, and behavioral assays. His research explores Neural circuit dynamics and plasticity Odor coding mechanisms Role of recurrent circuitry Integration of sensory modalities Recent publications highlight his contributions to understanding cortical odor representations, developmental neural connectivity, and cross-modal interactions. Awards include the 2024 Don Tucker Finalist recognition. He teaches advanced neuroscience courses at Duke, including Neurobiology research and concepts in neuronal systems.
Professor Guy Williams is a leading academic at the University of Cambridge with a focus on imaging science and clinical neurosciences, affiliated with Downing College and the Wolfson Brain Imaging Centre . Holding a PhD in Physics from his initial Natural Sciences degree, he specializes in nuclear magnetic resonance (NMR) and MRI techniques for brain imaging. Education: BA, PhD in Physics His research centers on non-invasive imaging of brain structure and function, particularly in traumatic brain injury (TBI) and dementia. His work involves developing novel MRI pulse sequences and advanced data analysis algorithms, including AI-based diagnostic tools. He leads studies on white matter integrity post-trauma, longitudinal dementia assessment, and applications of MRI in disorders of consciousness and addiction. Recent publications highlight collaborations in traumatic brain injury outcomes, AI-guided dementia prediction, and neuroimaging of post-COVID cognitive deficits. His team's work on ultra-high field laminar fMRI and distortion correction methods has advanced clinical neuroscience applications. Key techniques include diffusion tensor imaging (DTI), 7 Tesla MRI, and positron emission tomography (PET/MR). His research spans from basic NMR physics to clinical translation, with a strong emphasis on multi-site studies and real-world diagnostic implementation.
Dr. Ting-Feng Lin is an Assistant Professor at the Cell Biology, Neurobiology and Biophysics department within the Faculty of Science at Utrecht University, Netherlands. His research focuses on understanding the mechanisms of learning and memory formation in the cerebellum, particularly how synaptic and intrinsic plasticity mechanisms coordinate to regulate neuronal signaling and behavior. He employs advanced microscopy, optogenetic, and chemogenetic techniques in transparent zebrafish models to study these processes in vivo, with implications for neurodevelopmental disorders like autism spectrum disorder (ASD) and schizophrenia. 2025: Assistant Professor, Utrecht University 2019-2025: Postdoctoral Researcher, University of Chicago 2015-2019: PhD in Neuroscience, Neuroscience Center Zurich (ZNZ) 2010-2014: MS in Physiology, National Taiwan University 2006-2010: BS in Sports Medicine, China Medical University His work investigates how sensory experiences shape cerebellar processing during development, focusing on climbing fiber pathways and their role in sensory prediction errors. His group also studies the interaction between synaptic, intrinsic, and structural plasticity mechanisms in neural circuits, using zebrafish models with genetic modifications (e.g., Grid2 knockout) to model human neurological conditions. Dr. Lin has received scientific recognition including the SfN Trainee Professional Development Award for his work on Purkinje cell plasticity and the JNS Meeting Award for research on parallel fiber ramping activity and LTD. His publications span topics from cerebellar plasticity to voltage-gated K+ channel dynamics, reflecting his interdisciplinary approach to neurobiology.
