جست‌وجوی استاد و پژوهشگر

• Information Theory
• Quantum Communication
• Wireless Networks
• +۳ مورد دیگر

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 .

Mark Oskinمشاهده پروفایل

استاد مدعو

• Computer Architecture
• Parallel Computing
• Graph Processing
• +۱۲ مورد دیگر

Mark Oskin is an Adjunct Professor at the School of Computer Science and Engineering , University of Washington , focusing on Software & Hardware Systems . He leads the Sampa Group and collaborates on projects like HammerBlade and BlackParrot. University: University of Washington School: School of Computer Science and Engineering Department: Department of Electrical & Computer Engineering His research spans Computer Architecture , Parallel Computing , and Graph Processing , with additional expertise in Quantum Computing , Open Source Hardware , and Distributed Shared Memory . Ongoing work includes custom manycore devices for graph execution and open-source RISC-V designs. Past projects like Grappa and WaveScalar advanced distributed memory and dataflow execution. Recent publications include BlackParrot: An Agile Open Source RISC-V Multicore for Accelerator SoCs (IEEE Micro 2020) and Perceptual Compression of Video Storage and Processing Systems (SoCC 2019), reflecting trends in hardware-software co-design, quantum systems, and energy-efficient video processing. Best Paper Award , USENIX ATC 2015 IEEE Micro Top Picks , 2009 Mark has advised numerous students, including Amrita Mazumdar (IoT video compression startup), Brandon Lucia (CMU), and Steve Swanson (UC San Diego). He co-founded Corensic, a startup exploring deterministic multithreaded execution.

David V. Smithمشاهده پروفایل

دانشیار

• Neuroeconomics
• Decision Neuroscience
• Social Neuroscience
• +۴ مورد دیگر

David V. Smith is an Associate Professor of Psychology and Neuroscience at Temple University's College of Liberal Arts, directing the Neuroeconomics Laboratory. His work bridges neuroscience, psychology, and economics to investigate neural mechanisms underlying social and economic decision-making. Department: Psychology and Neuroscience Email: david.v.smith@temple.edu Research interests focus on neuroeconomics , reward processing , social neuroscience , and brain connectivity . Key questions include how humans compare rewards, build trust in group settings, and why decision-making varies across individuals. Recent publications highlight reward circuit abnormalities in depression , corticostriatal interactions , and age-related changes in neural trust mechanisms . His interdisciplinary approach integrates neuroimaging (fMRI), behavioral economics, and computational modeling. The lab actively recruits graduate students (PhD in Cognition & Neuroscience or Social Psychology) and supports undergraduate researchers. Opportunities include participation in studies with monetary compensation and collaboration via GitHub, Twitter, and Open Science Framework platforms.

• Neuroscience
• Genetic Access Tools Development
• Neural Circuit Mapping
• +۲ مورد دیگر

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 Gerosaمشاهده پروفایل

عضو هیئت علمی

• Human Aspects of Software Engineering
• Software Engineering Education
• Computer Supported Cooperative Work (CSCW)
• +۷ مورد دیگر

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.

Emily Firstمشاهده پروفایل

استادیار

• Artificial Intelligence
• Theorem Proving
• Machine Learning
• +۳ مورد دیگر

Emily First is an incoming Assistant Professor in Computer Science at Rutgers University New Brunswick starting Fall 2025. She previously served as a postdoctoral researcher at UC San Diego under Sorin Lerner and earned her PhD in Computer Science at UMass Amherst under Yuriy Brun in the Laboratory for Software Engineering Research (LASER). Her research focuses on leveraging AI for theorem proving, working at the intersection of machine learning, software engineering, and programming languages. She specializes in creating tools for automated proof generation in proof assistants like Coq, Isabelle/HOL, and Lean. Her work explores how AI can enhance human reasoning across domains, with applications in software verification and formal logic systems. Recent publications show trends in neuro-symbolic AI, software verification, and LLM integration for formal methods. Her team's research has been recognized with ACM SIGSOFT Distinguished Paper Awards at ICSE, ACL, and ESEC/FSE conferences, along with workshop presentations at AI and Theorem Proving conferences. ACM SIGSOFT Distinguished Paper Award (ICSE 2025) ACM SIGSOFT Distinguished Paper Award (ACL Main 2024) ACM SIGSOFT Distinguished Paper Award (ESEC/FSE 2023) ACM SIGSOFT Distinguished Paper Award (ICSE 2022) Contact: emfirst@ucsd.edu (new email coming soon).

Haining Wangمشاهده پروفایل

استاد

• Cybersecurity
• Networking Systems
• Cloud Computing
• +۵ مورد دیگر

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
• +۴ مورد دیگر

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 Morrisonمشاهده پروفایل

استاد

• Mathematical Neuroscience
• Neural Networks
• Neural Coding
• +۳ مورد دیگر

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 Javadzadehمشاهده پروفایل

پژوهشگر ارشد

• Neuroscience
• Computational Neuroscience
• Neural Networks
• +۳ مورد دیگر

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 DeWeeseمشاهده پروفایل

دانشیار

• Nonequilibrium Statistical Mechanics
• Machine Learning Theory
• Systems Neuroscience
• +۳ مورد دیگر

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 Dunbarمشاهده پروفایل

استادیار

• Computational Linguistics
• Speech Perception
• Unsupervised Learning
• +۳ مورد دیگر

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 Raginskyمشاهده پروفایل

استاد

• Probability and Stochastic Processes
• Control Theory
• Machine Learning
• +۵ مورد دیگر

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

André Longtinمشاهده پروفایل

استاد

• Nonlinear Dynamics
• Stochastic Dynamics
• Theoretical Biophysics
• +۶ مورد دیگر

André Longtin is a Full Professor in the Department of Physics at the University of Ottawa, affiliated with the Faculty of Science. He co-directs the Centre for Neural Dynamics & Artificial Intelligence and holds cross-appointments in Cellular and Molecular Medicine, Mathematics and Statistics. His research focuses on nonlinear dynamics, stochastic systems, and computational neuroscience, with applications to sensory processing, neural coding, and thermodynamic principles in biological systems. Education: B.Sc. (Honours Physics, Université de Montréal), M.Sc. (Physics, Université de Montréal), Ph.D. (Physics, McGill University, 1989). Postdoctoral training at Los Alamos National Laboratory. Research Interests: Theoretical biophysics, neural modeling, entropy production in biological systems, delayed dynamical systems, and interdisciplinary applications of nonlinear science. His group explores computations in hippocampus and zebrafish pallium, epilepsy mechanisms, neural coding in electrosensory systems, and autonomous stochastic rhythms. Recent Work: Articles highlight entropy dynamics in humans, myelin plasticity effects on neural networks, and reservoir computing with delayed loops. Awards include APS Fellowship (2003) and the NSERC Brockhouse Prize (2017). Editorial Roles: Biological Cybernetics, Frontiers in Computational Neuroscience, and others. Collaborations span systems neuroscience, clinical specialties, and engineering.

Kaspar Riesenمشاهده پروفایل

پژوهشگر

• Pattern Recognition
• Machine Learning
• Computer Vision
• +۴ مورد دیگر

PD Dr. Kaspar Riesen is the Head of the Pattern Recognition Group at the Institute of Computer Science, University of Bern. His research focuses on graph-based methods for pattern recognition, with applications in document analysis, environmental modeling, and healthcare. Key interests include graph matching, neural networks, and spatio-temporal modeling. His work spans structural pattern recognition, graph embeddings, and keyword spotting in historical documents. Recent projects involve river network analysis using graph regression and hypoglycemia prediction via LSTM-GNN hybrid models. Publications emphasize graph theory advancements, such as normalized graph compression and geometric similarity learning. Collaborations include developing specialized algorithms for automated error detection and improving decision-making in simulated sports. Labs/Teams: Pattern Recognition Group (PRG) at the University of Bern.