Find Academic Supervisors

• Distributed computing
• Real-time computing
• Algorithms and lower bounds
• +3 more

Nancy A. Lynch is the NEC Professor of Software Science and Engineering and Professor of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology, where she heads the Theory of Distributed Systems (TDS) group within CSAIL. Research Interests Distributed computing algorithms and lower bounds Real-time and fault-tolerant systems Formal modelling and verification Wireless network algorithms Biological distributed algorithms Neural computation and spiking networks Across her work, Lynch blends rigorous theoretical analysis with practical relevance, tackling problems ranging from consensus and leader election in unreliable networks to modelling decision-making circuits in the brain. Publications & Trends Since 2020 she has published extensively on distributed algorithms , swarm robotics , neuromorphic architectures , and biologically-inspired computation . Notable recent directions include hierarchical concept learning in spiking neural networks, nanobot locomotion modelling for cancer detection, and superconducting nanowire platforms for energy-efficient neural hardware. Scientific Awards & Honors Best Paper Award, OPODIS 2018 Best Paper Award, IEEE NCA 2014 Highlight Paper, Neuromorphic Computing and Engineering 2022 Teaching & Advising Lynch teaches core graduate and undergraduate subjects at MIT including 6.042J Mathematics for Computer Science , 6.852J/18.437 Distributed Algorithms , and 6.885/6.006 Algorithms . She has supervised dozens of PhD students and post-docs whose names are listed on her Past Students page. Laboratory & Teams She leads the Theory of Distributed Systems (TDS) Group , a vibrant research team within MIT CSAIL . TDS is part of the larger Theory of Computation group and hosts weekly seminars, reading groups, and collaborative projects with partners across MIT and worldwide.

James MillonigView profile

Associate Professor

• Neuroscience
• Developmental Biology
• Genetics
• +5 more

James Millonig is an Associate Professor in the Department of Neuroscience and Cell Biology at Rutgers University, where he also serves as the Senior Associate Dean for the Rutgers School of Graduate Studies. He is affiliated with the Center for Advanced Biotechnology and Medicine (CABM) and Robert Wood Johnson Medical School. His research focuses on understanding dorsal central nervous system (CNS) development, particularly through the integration of mouse genetics with neuroanatomy. His lab investigates how signaling centers like the roof plate coordinate CNS development through secreted factors such as Bone Morphogenetic Proteins (BMPs). A significant portion of his work examines the role of transcription factors like Lmx1a and Engrailed-2 in neural development and their potential connections to autism spectrum disorder (ASD). Dr. Millonig's recent publications demonstrate a continued focus on neurodevelopmental disorders, particularly autism spectrum disorder. His work spans from basic developmental neuroscience examining dorsal CNS formation to translational research using patient-derived neural progenitor cells. His laboratory has made significant contributions to understanding how genetic variations affect early brain development in ASD, with recent studies analyzing brain stem cells from ASD patients to identify irregularities in very early brain development. Autism Research Program (ARP) Idea Development Award from Department of Defense (2022) Standing appointment as a member of the National Institute of General Medical Sciences (NIGMS) Training and Workforce Development Subcommittee C (2020) Dr. Millonig has secured multiple federal grants, including an ongoing project titled "Investigation of Professional Coaching as an Intervention to Support the Success of URG Biomedical Ph.D. Students" (2023-2026) funded by the National Institute of General Medical Sciences. He has served as Principal Investigator for projects including "A Mouse Knock-In Model for ENGRAILED 2 Autism Susceptibility" funded by the National Institute of Mental Health and has been Co-Principal Investigator on the "IMSD at Rutgers - New Brunswick" project. The Millonig Lab maintains an active research program with several graduate students and research associates. Current lab members include Stuart Cattel (Graduate Student) and Arif S W Kusuma (Graduate Fellow), with Paul Matteson serving as Research Associate I. The lab has previously trained Marcos Ayala-Rivera, Tomas Kasza, and Monal Mehta, demonstrating a strong commitment to mentoring the next generation of scientists in neuroscience and developmental biology.

• Neuroscience
• Auditory Neuroscience
• Neuronal Microcircuits
• +3 more

Dr. Albert Berrebi is a Professor and Vice-Chair for Research in the Department of Neuroscience at the West Virginia University School of Medicine . He also holds a faculty position at the Rockefeller Neuroscience Institute and is a member of the WVU Cancer Institute Research Programs . Research Interests: The Berrebi lab investigates the organization of neuronal microcircuits in the central auditory pathway, focusing on chemical phenotypes via immunocytochemical techniques. Their work employs: Brain microinjections for tract-tracing Electron microscopy for ultrastructural analysis In vivo extracellular recordings of auditory neurons Pharmacological receptor blocking to study inhibition Key research areas span the superior olive nuclei (MNTB and SPON), synaptic connectivity, and auditory prosthetics. Recent publications highlight studies on forward masking, postinhibitory rebound spiking, and ketamine effects. Techniques: The lab integrates anatomical, physiological, and cytological methods, including light/electron microscopy and electrophysiology. Their long-term goal is to advance understanding of auditory neural mechanisms and improve cochlear prostheses for hearing impairment.

Heyuan ShiView profile

Associate Professor

• Software Quality Assurance
• Kernel Fuzz Testing
• Operating System Security
• +3 more

Heyuan Shi is an Associate Professor at the School of Electronic Information, Central South University since 2023. He earned his B.S. (2015) and Ph.D. (2020) from Central South University and Tsinghua University respectively. His research focuses on software quality assurance with emphasis on kernel fuzz testing , open source software security , and AI application security . Presided over 10+ projects including NSFC General Program (No.62472448) and National Key R&D Sub-Project Published 30+ CCF-A/B papers across software security, machine learning, and quantum testing Supervised 15+ graduate students in software quality assurance areas His recent 2024-2025 publications demonstrate expertise in: LLM-enhanced patch classification Quantum neural network verification Hypergraph adversarial attacks RTOS fuzzing techniques Scientific recognition includes: 2024 Beijing Science & Technology Progress Award (First Prize) Hunan Province Xiaohe Sci-Tech Talent (2023) China Association for Science & Technology Young Talent (2025-2027) Active in academic service as PC member for FM2024 and reviewer for IEEE Transactions journals. Leads industry collaborations with Alibaba and Beijing Institute of Aerospace Metrology.

Jan G. BjaalieView profile

Professor

• Neuroinformatics
• Digital brain atlasing
• Brain architecture and connectivity
• +4 more

Jan G. Bjaalie is Professor of Neuroinformatics and Dean of Research and Innovation at the University of Oslo Faculty of Medicine . Since 2023 he heads the faculty’s research and innovation strategy, while directing the Neural Systems and Graphics Computing Laboratory at the Institute of Basic Medical Sciences. Education: 1990 Ph.D. in Neuroanatomy, University of Oslo 1986 M.D., University of Oslo Research interests revolve around collaborative and open neuroscience, digital brain atlasing, and the cyber-infrastructures that enable data sharing. He leads efforts to build next-generation atlases that integrate multi-scale brain architecture and connectivity data, and to develop ontologies and FAIR-compliant platforms for global neuroscience. Recent work emphasizes in silico integration of rodent and human imaging datasets, leveraging machine-learning registration tools and cloud-based services such as EBRAINS. The goal is to transform how brain data are stored, visualised and reused across laboratories worldwide. Scientific output & impact: A scan of publications from 2023-2025 reveals a strong focus on digital atlas frameworks, automated image registration (DeepSlice, DeMBA), open data standards (AtOM ontology), and large-scale analyses of genetic influences on brain structure in Alzheimer’s models. These works collectively advance reproducible, high-throughput neuroanatomy and cross-species translation. Grants & leadership roles: Coordinator/Partner in EU Flagships EBRAINS 2.0, BRAIN Health, Human Brain Project (2013-2026) Infrastructure Director, Human Brain Project (2018-2023) Leader of Neuroinformatics Platform & EBRAINS Data Services (2017-2023) Head of Institute of Basic Medical Sciences (2009-2016) Executive Director, International Neuroinformatics Coordinating Facility (INCF) (2006-2008) Chair, International Brain Initiative (2021-) Editorial & governance service: Founding Chief Editor Frontiers in Neuroinformatics (2007-), Section Editor Brain Structure and Function (2002-2020), member of the INCF Governing Board and EBRAINS AISBL Management Board, and numerous international advisory panels on data governance and ethics. His laboratory hosts the Norwegian Neuroinformatics Node and collaborates closely with global consortia to deliver open-access atlases, software pipelines and FAIR data standards that underpin modern neuroscience.

• Information Theory
• Quantum Communication
• Wireless Networks
• +3 more

Pau Colomer is a Researcher at the Technical University of Munich , affiliated with the Theoretical Information Technology department. His work focuses on quantum communication, information theory, and wireless networks, contributing to foundational research in quantum channel identification and network information processing. His research interests span quantum information theory , neural networks , and signal processing , with recent publications addressing deterministic identification over quantum channels, coherence theory in quantum systems, and multi-user quantum communication. These works highlight advancements in quantum channel coding , decentralized processing , and security in wireless networks . Publications demonstrate a trend toward quantum communication and network information theory , emphasizing challenges in interference modeling , entanglement transmission , and information-theoretic security . Key subfields include superlinear rates in finite output channels , decoupling mechanisms , and quantum-enhanced sensing .

• Neuroscience
• Genetic Access Tools Development
• Neural Circuit Mapping
• +2 more

David Ng, PhD serves as Director of Genetic Access Tools and Associate Research Scientist at Columbia University's Zuckerman Institute. His work bridges molecular genetics and neuroscience through the development of precision tools for brain circuit investigation. Research focuses on neural circuit mapping and genetic access tool development , with emphasis on sensory-motor reflex assembly, synaptic organization, and disease-related pathways. His methodologies enable selective manipulation of specific brain cell types based on genetic identity. Key publications reveal expertise in: Spinal sensory-motor circuit organization (2019-2020) NMDA receptor regulation in schizophrenia (2011) Retinal cell development mechanisms (2015) Synaptic plasticity proteins (2009) Scientific recognition includes: Charles Epstein Trainee Research Award (Post-doctoral, 2007) Charles Epstein Trainee Research Award (Pre-doctoral, 2002) As Director of Custom Genetic Access Services, Ng leads a platform providing researchers with one-of-a-kind molecular tools for brain cell manipulation. The lab collaborates across Columbia's neuroscience ecosystem through advanced instrumentation, cellular imaging, and virology platforms, with recent work including pandemic-related diagnostic development.

Marco Aurélio GerosaView profile

Academic

• Human Aspects of Software Engineering
• Software Engineering Education
• Computer Supported Cooperative Work (CSCW)
• +7 more

Marco Aurélio Gerosa is a Professor at Northern Arizona University and was previously an Associate Professor at the University of São Paulo (USP), Brazil . He is affiliated with the School of Informatics, Computing, and Cyber Systems (SICCS) at NAU and the Department of Computer Science at USP. His research focuses on the Human Aspects of Software Engineering , including Software Engineering Education , Computer Supported Cooperative Work (CSCW) , and AI-Assisted Software Engineering . He has published extensively on topics such as Open Source Software development, Bots and Chatbots in software engineering, and Mining Software Repositories techniques. His recent work explores Using Large Language Models (LLMs) for educational purposes in programming, data science, and software engineering Developing chatbots to facilitate newcomer onboarding to OSS projects Investigating the evolution of Integrated Development Environments (IDEs) Assessing the impact of software bots on projects Understanding how to design effective chatbot languages Dr. Gerosa has received numerous scientific awards, including ACM SIGSOFT Distinguished Paper Award Best paper awards at ICSE and International Symposium on Open Collaboration IEEE Computer Society TCSE Distinguished Paper and Service Awards Productivity grants from CNPq (Brazilian Council for Scientific and Technological Development) He has graduated numerous PhD students who are now researchers in top institutions worldwide and has been a mentor to many more at various levels. His research projects have secured over USD 1 million in funding. Dr. Gerosa is also involved in the development of tools and environments for software engineering, including MetricMiner for repository analysis and various gamification platforms to enhance developer engagement. He brings over 25 years of teaching experience across multiple universities, teaching courses ranging from Introduction to Programming to Advanced Topics on Web Development and Collaborative Systems Development.

Haining WangView profile

Professor

• Cybersecurity
• Networking Systems
• Cloud Computing
• +5 more

Haining Wang is a Professor in the Bradley Department of Electrical and Computer Engineering at Virginia Tech. His research focuses on cybersecurity, networking systems, cloud computing, and cyber-physical systems. He holds a Ph.D. from the University of Michigan (2003). His work addresses critical challenges in network security, IoT device fingerprinting, drone navigation security, and 5G/6G infrastructure vulnerabilities. Notable contributions include developing frameworks for detecting deceptive reviews, securing industrial IoT devices, and enhancing geofencing systems with 6G technologies. Wang's IEEE Fellow award (2020) recognizes his contributions to network and cloud security. His research also explores cloud gaming security, data center thermal vulnerabilities, and DNS privacy risks. He actively publishes on topics like container registry typosquatting, acoustic indoor localization, and encrypted DNS censorship analysis. Education: Ph.D., University of Michigan, 2003 Awards: IEEE Fellow (2020) Key Research Areas: Cybersecurity, Network Measurement, IoT Security, 5G/6G Systems Wang's recent work emphasizes securing emerging technologies like drone navigation systems and optimizing sensor placements in indoor environments. His projects often bridge theoretical frameworks with practical implementations in real-world networks and cloud infrastructures.

• Computational Neuroscience
• Neural Networks
• Deep Learning
• +4 more

Prof. Sander M. Bohte holds a part-time appointment as a Professor of Computational Neuroscience at the Swammerdam Institute for Life Sciences (SILS), University of Amsterdam, and is a researcher at the CWI Machine Learning group. His research focuses on computational models of neural information processing, emphasizing spiking neural networks, predictive coding, and reinforcement learning. He bridges computational neuroscience and machine learning, exploring how biological insights can improve neural network designs and vice versa. Key collaborations include work with Cyriel Pennartz (UvA), Pieter Roelfsema (NIN), and Steven Scholte (B&C). His applied research spans scientific machine learning applications in finance and genomics. He actively supervises MSc thesis students, prioritizing those from UvA, with projects ranging from biologically inspired neural architectures to efficient spiking network simulations. Research highlights include developing biologically plausible learning rules for deep networks, predictive coding models for sensory data, and spiking network models for working memory tasks. His work also addresses challenges in temporal dynamics and scalable neural computation, leveraging both theoretical and applied perspectives.

Katie MorrisonView profile

Professor

• Mathematical Neuroscience
• Neural Networks
• Neural Coding
• +3 more

Katie Morrison is an Associate Chair and Professor in the Department of Mathematical Sciences at the University of Northern Colorado (UNC), part of the College of Natural and Health Sciences. Her research focuses on mathematical neuroscience, neural networks, and algebraic coding theory, with a particular emphasis on applying matrix codes and graph-based algorithms to understand neural dynamics. She has been funded by NSF DMS-1951599 and NIH R01 EB022862 grants. Education: Ph.D. in Mathematics (University of Nebraska-Lincoln, 2012), M.S. in Mathematics (2008), B.A. in Mathematics and Psychology (Swarthmore College, 2005), and studies at Budapest Semesters in Mathematics. Her work bridges abstract mathematics with applied neuroscience, including projects like the CTLN initiative. Research trends in her publications emphasize graph-theoretic approaches to neural network dynamics, pattern completion in threshold-linear networks, and algebraic analysis of neural codes. Collaborations with computational topologists and neuroscientists highlight interdisciplinary strengths. Grants and advising: Active in securing federal research funding and mentoring students in interdisciplinary projects. Her lab explores connections between network connectivity and emergent behaviors, with applications to both theoretical and applied neuroscience.

Mitra JavadzadehView profile

Research Fellow

• Neuroscience
• Computational Neuroscience
• Neural Networks
• +3 more

Mitra Javadzadeh is a CSHL Fellow at Cold Spring Harbor Laboratory , where she leads the Javadzadeh Lab . Her research focuses on understanding how distributed neural population dynamics in the neocortex underpin flexible perception, employing a combined experimental and computational approach involving multi-region electrophysiology, optogenetics, and dynamical systems analysis. Education : Ph.D. in Neuroscience from University College London (2021) Research Interests : High-dimensional neural activity during visual perception Role of long-range cortico-cortical and transthalamic pathways in sensory integration Dynamical systems principles in cortical network interactions Excitatory-inhibitory balance and multi-area coordination Publications Trends : Her work spans neuroscience and computational modeling , with a focus on visual cortex , optogenetics , and cross-areal communication . Earlier research (2011) also intersects with computer science in wireless sensor networks. Contact : Email: javadzadeh@cshl.edu

Michael DeWeeseView profile

Associate Professor

• Nonequilibrium Statistical Mechanics
• Machine Learning Theory
• Systems Neuroscience
• +3 more

Michael DeWeese is an Associate Professor of Physics and Neuroscience at the University of California, Berkeley. His research spans nonequilibrium statistical mechanics, machine learning theory, and systems neuroscience. He holds a BA in Physics from UC Santa Cruz (1988) and a PhD in Physics from Princeton (1995). Before joining UC Berkeley in 2007, he held postdoctoral positions at the Salk Institute and Cold Spring Harbor Laboratory. His work integrates principles from physics, neuroscience, and machine learning to address fundamental questions in theoretical and experimental biology, computation, and statistical mechanics. DeWeese Lab Website provides further details on ongoing projects and collaborations. Education: BA in Physics, UC Santa Cruz (1988) PhD in Physics, Princeton University (1995) Research Interests: Nonequilibrium Statistical Mechanics: Focuses on thermodynamic optimization, active matter, and non-equilibrium processes. Machine Learning Theory: Develops first-principles models to explain neural network performance and efficient algorithms for probabilistic models. Systems Neuroscience: Uses biologically inspired models to understand neural coding, sensory processing, and computational roles of neural networks. Advising & Grants: While no formal student advisees are listed, his lab actively collaborates across disciplines. Funding sources are not explicitly mentioned but likely involve NSF, NIH, or DOE grants based on research themes. His work on quantum control and neural networks suggests potential ties to interdisciplinary funding initiatives. Labs & Teams: Directs the DeWeese Lab, which bridges physics, neuroscience, and machine learning. Collaborations include institutions like the Helen Wills Neuroscience Institute (UC Berkeley).

Ewan DunbarView profile

Assistant Professor

• Computational Linguistics
• Speech Perception
• Unsupervised Learning
• +3 more

Ewan Dunbar is an Assistant Professor of Computational Linguistics at the University of Toronto (downtown campus), affiliated with St. Michael's College. He holds appointments in the Department of French, Department of Linguistics, and Department of Computer Science. His research explores human speech perception, technological advancements in speech processing, and related areas such as unsupervised learning and language acquisition. Educational Background: While specific degrees aren't listed, his research and appointments indicate advanced training in linguistics and computational sciences. Research Interests: Dunbar focuses on how humans perceive speech, developing computational models of speech perception, and advancing unsupervised learning techniques. He leads the Perceptimat Research Group and organizes the Zero Resource Speech Challenge, a machine learning competition promoting unsupervised approaches to speech processing. Awards: He has received notable grants including the 2028 NSERC Discovery Grant, 2028 NSERC Discovery Launch Supplement, and 2023 Connaught New Researcher Award, all supporting his work on computational psycholinguistics and speech perception models. Advising & Grants: While specific student names aren't listed, his grants indicate involvement in mentoring undergraduates (e.g., 2024 NSERC Undergrad Studentship Grant). His labs focus on creating speech perception tools and benchmarks like Perceptimatic. Labs/Teams: Director of the Perceptimat Research Group, organizer of the Zero Resource Speech Challenge.

Maxim RaginskyView profile

Professor

• Probability and Stochastic Processes
• Control Theory
• Machine Learning
• +5 more

Maxim Raginsky is a Professor at the University of Illinois at Urbana-Champaign, holding appointments in the Department of Electrical and Computer Engineering, Coordinated Science Laboratory, and a courtesy appointment in Computer Science. His work bridges probability, stochastic processes, control theory, machine learning, optimization, and information theory , focusing on modeling, learning, and simulation of nonlinear dynamical systems with applications to advanced electronics, autonomy, and artificial intelligence. Research Interests Nonlinear dynamical systems in machine learning and control Statistical machine learning theory Information-theoretic methods in learning Stochastic control and filtering Scientific Contributions Co-author of foundational monographs on concentration inequalities and generalization bounds Recipient of the NSF CAREER Award (2013) , IEEE Fellow (2025) , and Roberto Tempo Best CDC Paper Award (2024) Editorial roles in Foundations and Trends in Machine Learning , Journal of Machine Learning Research , and SIAM Journal on Mathematics of Data Science Academic Leadership Advising 15+ graduate students and postdocs including Joshua Hanson, Belinda Tzen, and Tanya Veeravalli Teaching core graduate courses: Control of Stochastic Systems , Statistical Learning Theory , Optimization by Vector Space Methods