جست‌وجوی استاد و پژوهشگر

Edgar Walkerمشاهده پروفایل

استادیار

• Computational Neuroscience
• Neural Coding
• Sensory Processing
• +۷ مورد دیگر

Edgar Walker, PhD, is an Assistant Professor in the Department of Physiology and Biophysics at the University of Washington School of Medicine and a faculty member of the Center for Neurobiology of Circuits and Cognition (CNC). He conducts interdisciplinary research combining experimental and computational approaches to study neural mechanisms of sensory representation and decision-making. Walker's research focuses on how neurons in the visual cortex encode not only what is seen but also the uncertainty associated with sensory observations. He integrates electrophysiology, 2-photon imaging, and machine learning to analyze neural population activity in primates during behavioral tasks. His work aims to bridge theoretical models of perception with empirical data, particularly testing long-standing theories about probabilistic neural coding. He previously trained at Baylor College of Medicine under Dr. Andreas Tolias and was a postdoctoral fellow in Dr. Fabian Sinz’s lab at the University of Tübingen, Germany. Walker emphasizes close collaboration with experimental labs, informed by his hands-on experience with primate neurophysiology and behavioral training. Walker was attracted to UW by the strength and focus of the CNC and the diverse academic environment that supports interdisciplinary research. He values the integration of engineering, medicine, and neuroscience in his work and looks forward to mentoring students with varied backgrounds. He can be reached at eywalker@uw.edu .

Michael Hicksمشاهده پروفایل

استاد

• Programming Languages
• Computer Security
• Quantum Computing
• +۷ مورد دیگر

Michael Hicks is a Professor in the Department of Computer and Information Science at the University of Pennsylvania and an Amazon Scholar. Previously, he was a Professor at the University of Maryland (2002-2021) and Senior Principal Scientist at Amazon Web Services (2022-2025). He co-founded the Programming Languages research lab (PLUM) at Maryland and was its director, as well as Director of the Maryland Cybersecurity Center (MC2). He is a Fellow of the Association of Computing Machinery (ACM), former Chair of ACM SIGPLAN, and Editor-in-Chief of Proceedings of the ACM on Programming Languages (PACMPL). His research focuses on programming languages and their application to software security , quantum programming , and formal verification . He has pioneered dynamic software updating techniques, developed tools like Checked C for memory safety, and co-designed the Cedar authorization policy language at AWS. In quantum computing, he works on verified compilers like VOQC and Qunity to ensure reliability and correctness. His recent work trends include quantum programming languages , secure multi-party computation (e.g., Wysteria , Symphony ), and fuzz testing methodologies . He has contributed to formal verification for quantum programs and language-based security frameworks. Scientific Awards include: ACM Fellow He has advised numerous PhD and Master’s students , including James Parker, Andrew Ruef, Kesha Hietala, and Aseem Rastogi, who now hold academic and industry positions. His lab, PLUM , has been instrumental in advancing programming languages research.

Hsin-Yi Linمشاهده پروفایل

استادیار

• Quantum Machine Learning
• Speech Enhancement
• Data Assimilation
• +۳ مورد دیگر

Hsin-Yi Lin is an Assistant Professor in the Department of Mathematics and Computer Science at Seton Hall University. Her research focuses on quantum machine learning, speech enhancement, and data assimilation with applications in computational physics and neural network interpretability. She explores interdisciplinary topics including quantum neural networks, adversarial robustness in speech models, and multi-scale geophysical dynamics modeling. Recent work emphasizes quantum measurement optimization, transfer learning in quantum circuits, and novel diffusion models. Her publications address challenges in speech quality assessment, domain adaptation, and scalable state estimation using recurrent networks. She has contributed to foundational mathematical analysis of transport equations and developed extensions to signal processing frameworks like SEMamba. Key research themes include hybrid quantum-classical systems, explainable AI for quantum models, and robust machine learning under adversarial conditions. Collaborations bridge theoretical mathematics with applied computational methods in earth system prediction and periodic migration modeling.

Ilias Zadikمشاهده پروفایل

استادیار

• High-Dimensional Statistics
• Machine Learning Theory
• Computational-Statistical Trade-offs
• +۵ مورد دیگر

Ilias Zadik is an Assistant Professor at Yale University's Department of Statistics and Data Science. His research focuses on computational-statistical trade-offs in modern machine learning, high-dimensional statistics, and probability theory. He has held postdoctoral positions at MIT (2021-2023) and NYU (2019-2021), and earned his PhD from MIT (2019), advised by David Gamarnik. He teaches courses like Stochastic Processes and has contributed to numerous conferences and workshops. His awards include MIT's Best Student Paper Honorable Mention (2017) and scholarships from Trinity College and the Onassis Foundation. Education: PhD in Operations Research, MIT (2019) MASt in Mathematics (Part III), University of Cambridge (2014) BA in Mathematics, University of Athens (2013) Internship at Microsoft Research New England (2017) Research Interests: Computational-statistical trade-offs, phase transitions in inference (e.g., All-or-Nothing phenomena), cryptographic methods in statistics, privacy-cost analysis, and algorithmic lower bounds. Awards: MIT Operations Research Best Student Paper Honorable Mention (2017) Trinity College Senior Scholarship (2014) Onassis Foundation Scholarship (2013-2014) SEEMOUS Gold Medal (2011), IMC First Prize (2011) Teaching & Service: Instructor for Yale's S&DS 351 (Stochastic Processes) and advanced courses on computational-statistical trade-offs. Co-organized the MaD+ seminar during the pandemic. Served on program committees for COLT, NeurIPS, and FOCS. Labs/Teams: Active in MIT's NSF/Simons Collaboration on Theoretical Foundations of Deep Learning and NYU's Math and Data group.

Peter Robin Hiesingerمشاهده پروفایل

استاد

• Neurogenetics
• Developmental Neurobiology
• Synaptic Plasticity
• +۳ مورد دیگر

Prof. Peter Robin Hiesinger is a Professor of Neurogenetics at the Institute of Biology, Freie Universität Berlin, leading the Hiesinger Group within the Neurobiology Department. He previously held positions at UT Southwestern Medical Center in Dallas (Assistant/Associate Professor with tenure) and completed postdoctoral work at Baylor College of Medicine. His research focuses on understanding neural circuit formation and synaptic connectivity in Drosophila, particularly mechanisms governing axon targeting, synapse development, and autophagy’s role in neuronal maintenance. His work integrates genetic, imaging, and computational approaches to study brain wiring principles and their evolutionary implications. Education: Undergraduate and graduate studies in genetics, computational biology, and philosophy at the University of Freiburg. Postdoctoral training at Baylor College of Medicine. Career path includes 15 years in Texas before relocating to Berlin in 2021. Research interests emphasize the interplay between stochastic processes and robust brain development, including studies on filopodial dynamics, Rab GTPase functions, and environmental influences on neural connectivity. Recent work highlights autophagy’s critical role in synaptic stability and circuit assembly. His lab also investigates neurodegenerative pathways linked to membrane trafficking defects, using Drosophila as a model. Publications from 2021–2025 reflect a focus on synaptic partner selection, axonal self-sorting, and probabilistic wiring rules in Drosophila visual systems. These studies reveal how neural networks balance flexibility and precision during development. His work bridges basic neuroscience with evolutionary biology, exploring how developmental mechanisms ensure functional connectivity despite environmental variability. Grants and advising details are not explicitly listed, but his lab’s activities suggest ongoing research into neurodegeneration and synaptic maintenance. Collaborations likely extend to systems biology and genetic engineering, given his work with Rab GTPases and microRNA profiling. Labs/Teams: Hiesinger Group at Freie Universität Berlin, specializing in live imaging of neural circuits and genetic dissection of developmental processes in Drosophila.

Ryan Williamsمشاهده پروفایل

استاد

• Algorithm Design
• Computational Complexity
• Theoretical Computer Science
• +۴ مورد دیگر

Ryan Williams is a Professor of Electrical Engineering and Computer Science at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Department of Electrical Engineering and Computer Science. Previously, he held a faculty position at Stanford University from 2011 to 2016. He obtained his PhD in Computer Science from Carnegie Mellon University under Manuel Blum and completed his undergraduate studies at Cornell University. His research focuses on computational complexity theory, exploring the boundaries of efficient computation and connections between algorithm design and complexity lower bounds. He teaches advanced courses such as Automata, Computability, and Complexity Theory at MIT. Education: PhD in Computer Science, Carnegie Mellon University (Advisor: Manuel Blum) Bachelor's Degree in Computer Science, Cornell University Research Interests: His work addresses fundamental questions in theoretical computer science, including the P vs. PSPACE problem, circuit lower bounds, and the development of algorithms with provable efficiency. He investigates connections between algorithmic techniques and complexity-theoretic limitations, aiming to establish barriers to solving computational problems efficiently. Publications and Trends: Ryan Williams' recent work spans topics like space-bounded computation, probabilistic polynomial sparsity, circuit lower bounds, and algorithms for compression and graph problems. His research often bridges theoretical insights with practical algorithm design, emphasizing the interplay between computational models and their limitations. Advising and Grants: Current advisees include Rahul Ilango, Ce Jin, and Ted Pyne. He has mentored numerous PhD students who have contributed to areas like fine-grained complexity and circuit analysis. While specific grants are not detailed, his research aligns with foundational studies in theoretical computer science. Labs and Teams: Williams is affiliated with MIT CSAIL, where he collaborates on projects exploring computational complexity and algorithmic foundations.

Jaijeet Roychowdhuryمشاهده پروفایل

استاد

• Machine Learning
• Cyber-Physical Systems
• Novel Computational Paradigms
• +۴ مورد دیگر

Jaijeet Roychowdhury is a Professor of Electrical Engineering and Computer Sciences (EECS) at the University of California, Berkeley. He specializes in machine learning, novel computational paradigms, and the analysis/simulation of cyber-physical, electronic, and biological systems. His research group pioneered self-sustaining oscillator-based Ising machines and contributed to oscillator phase macromodeling, nonlinear system reduction, and open-source prototyping platforms like MAPP. Education: B.Tech., Electrical Engineering, Indian Institute of Technology (IIT) Kanpur, 1987 M.S., Electrical Engineering & Computer Science, UC Berkeley, 1989 Ph.D., Electrical Engineering & Computer Science, UC Berkeley, 1993 Research Interests: Machine learning integration with physical systems Innovative computational hardware (e.g., oscillator-based Ising machines) Oscillator networks for unconventional computing Nonlinear dynamical systems analysis Analog circuit simulation and verification Awards & Recognition: 2019 Bell Labs Prize (with Tianshi Wang) 2009 IEEE Fellow 2019-2023 Bakar Foundation Awards Bell Labs' Extraordinary Achievement Award (1996) Advising & Industry: Notable advisee: Tianshi Wang (Bell Labs Prize co-winner) Cofounder of Berkeley Design Automation (acquired by Mentor Graphics) Leadership roles at AT&T Bell Labs, Bell Labs, and CeLight Inc. Lab & Tools: Leads the Roychowdhury Research Group, developing MAPP (Model and Algorithm Prototyping Platform) and PHLOGON phase-based logic frameworks.

• Integrated Circuits
• Micro/Nano Electro Mechanical Systems (MEMS)
• Computer Architecture & Engineering
• +۴ مورد دیگر

Vladimir Stojanović is an Adjunct Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. His research focuses on integrated electronic-photonic systems-on-chip, emerging technologies, and VLSI design. He has held positions at MIT (2005–2013) and Rambus, Inc. (2001–2004). Stojanović leads research initiatives in the Berkeley Deep Drive (BDD), Berkeley Emerging Technologies Research (BETR), and Center for Energy Efficient Electronics Science (E3S). Education: Ph.D. (2005, Stanford), M.S. (2000, Stanford), and Dipl. Ing. (1998, University of Belgrade). Awards include the IEEE Fellow (2024), NSF CAREER Award (2009), and multiple best-paper recognitions. He co-founded Ayar Labs, Numericcal, and MaxLinear (formerly NanoSemi). Key research areas include silicon photonics integration, nanoelectromechanical systems (NEMS), and energy-efficient computing architectures. His work spans optical interconnects, quantum photonics, and biomedical sensors, with emphasis on co-design of electronics and photonics in advanced CMOS processes.

• Synthetic Biology
• Systems Biology
• Bioinformatics
• +۷ مورد دیگر

Dr. Matthew Pocock is a Research Fellow in the Department of Computing Science at Newcastle University. His work focuses on synthetic biology, systems biology, and bioinformatics, with significant contributions to data standards (SBOL), workflow management (Taverna), and semantic integration tools (Saint, FuGE). He has collaborated extensively with Professor Anil Wipat and others on projects like BacillOndex and Microbase2.0. His research spans Developing standardized languages for biological design (SBOL, ShortBOL) Creating cloud-based frameworks for computationally intensive bioinformatics workflows Building ontologies and controlled vocabularies for systems biology Integrating experimental data through tools like GELI and Saint Key article trends include Evolution of SBOL standards across versions 1.0–2.3 Applications of probabilistic networks in drug repurposing Workflow environments for life sciences (Taverna, Microbase) Model validation through semantic constraints (SBML, SBO)

• Synthetic Biology
• Bioinformatics
• Computational Biology
• +۵ مورد دیگر

Professor Anil Wipat is a distinguished academic at Newcastle University with a prolific research career spanning over three decades. His work primarily focuses on synthetic biology, bioinformatics, and computational approaches to biological systems, with significant contributions to genetic circuit design, microbial genomics, and systems biology. His research has evolved from early microbial genome analysis to cutting-edge synthetic biology applications. Professor Wipat's research interests encompass Synthetic Biology , Bioinformatics , Computational Biology , Systems Biology , Genetic Circuit Design , and Microbial Genomics . His work bridges computational approaches with experimental biology, developing tools and frameworks for synthetic biological design. He has made significant contributions to the development of the Synthetic Biology Open Language (SBOL), creating standards for representing and exchanging synthetic biology designs. His recent publications demonstrate a strong focus on network-based approaches to biological problems, including network pharmacology, genetic circuit analysis, and multi-omics data integration. Professor Wipat's work shows a consistent trajectory from foundational microbial genomics to sophisticated computational synthetic biology applications, with particular emphasis on creating standardized frameworks for the field. Throughout his career, Professor Wipat has collaborated extensively with researchers across multiple disciplines, contributing to both theoretical frameworks and practical applications in synthetic biology. His work on tools like GENETTA and contributions to SBOL standards have helped shape the computational infrastructure of modern synthetic biology.

Remco Renkenمشاهده پروفایل

پژوهشگر

• Neuroimaging
• Functional MRI
• Parkinson's Disease
• +۳ مورد دیگر

Dr. Remco Renken is a neuroimaging researcher at the University of Groningen's Faculty of Medical Sciences , specializing in advanced MRI and PET scan analysis. His work bridges clinical neuroscience and computational methods , with a focus on movement disorders, visual processing, and neurodegenerative diseases. Department of Radiology and Nuclear Medicine (UMCG) Clinical Cognitive Neuropsychiatry Research Program (CCNP) Perceptual and Cognitive Neuroscience (PCN) group Research Interests : Using fMRI , PET , and machine learning to study brain connectivity , visual cortex plasticity , and neurodegenerative patterns in Parkinson’s, multiple sclerosis, and glaucoma. His innovations include algorithms for 3D motion perception tracking and neuroimaging harmonization . Notable Contributions : Co-developed IRMA (Machine learning harmonization for multicenter PET scans) Co-inventor of a 3D motion perception evaluation system Co-supervisor for PhD research on auditory cortex responses and schizophrenia hallucinations Collaborations span neurology, psychiatry, and biomedical engineering, with datasets shared via Mendeley and DataverseNL.

Stamatios Sotiropoulosمشاهده پروفایل

دانشیار

• Brain Imaging
• Diffusion MRI
• Tractography
• +۳ مورد دیگر

Dr. Stamatios Sotiropoulos is an Associate Professor at the Sir Peter Mansfield Imaging Centre , University of Nottingham , with prior affiliations as Principal Investigator and Postdoctoral Research Associate at the FMRIB Centre , University of Oxford . He leads the Computational Neuroimaging (CoNI) Laboratory , focusing on advanced MRI methodology for brain connectome mapping. PhD in Brain Image Analysis (University of Nottingham, 2010) MSc in Biomedical Engineering (University of Minnesota, 2005) BSc in Electronics & Computer Engineering (Technical University of Crete, 2003) His research spans diffusion MRI , tractography , and connectome modeling , with applications in brain aging , neurodegenerative diseases , and cross-species neuroanatomy . Recent work includes AI-driven QC , probabilistic inference , and cross-modal integration . Grants include major awards from the European Research Council (2020), Wellcome Trust , and EPSRC . The lab has developed widely-used tools like XTRACT , CUDIMOT , and NFacT within the FSL framework. 2021 Clarivate Highly Cited Researcher ISMRM 2022 Merit Awards Notable students include Shaun Warrington (PhD, 2021) and José-Pedro Manzano-Patrón (PhD, 2022). Collaborations span the Human Connectome Project , Developing Human Connectome Project , and Centre for Mesoscale Connectomics .

Robert Calderbankمشاهده پروفایل

استاد

• Information Theory
• Quantum Computing
• Wireless Communications
• +۶ مورد دیگر

Robert Calderbank is a distinguished academic and researcher at Duke University, holding professorships in Computer Science, Electrical and Computer Engineering, and Mathematics. He serves as Director of the Information Initiative at Duke and is affiliated with the Duke Quantum Center. His interdisciplinary work bridges information theory, quantum computing, and biomedical applications. Education: Ph.D. from California Institute of Technology (1980) Previous Institution: Princeton University (Distinguished Professor) Calderbank's research spans wireless communications, distributed storage systems, machine learning, and quantum information theory. Recent work focuses on two-dimensional magnetic recording, quantum error correction, and biomedical imaging with pump-probe microscopy. His publications demonstrate sustained innovation in constrained coding, matrix completion, and subspace classification. Scientific contributions include grants from the National Science Foundation and collaborative projects with the University of Maryland. Awards include Fellowships from the Royal Society, IEEE, and AAAS. His teaching and mentorship at Duke and Princeton have shaped next-generation signal processing and computer science research.

• Approximation Algorithms
• Probabilistically Checkable Proofs
• Hardness of Approximation
• +۳ مورد دیگر

Amey Bhangale is an Assistant Professor in the Department of Computer Science and Engineering at the University of California, Riverside. Prior to this, he held positions as a post-doctoral fellow at the Weizmann Institute of Science under Irit Dinur and a research fellowship at the Simons Institute. His research focuses on Approximation Algorithms , Probabilistically Checkable Proofs , Hardness of Approximation , and Analysis of Boolean Functions . Research Trends His recent work explores inapproximability bounds for constraint satisfaction problems, parallel repetition theorems, and additive combinatorics in finite fields. Notable collaborations include Subhash Khot, Dor Minzer, and Yang P. Liu. Teaching CS219: Advanced Algorithms (2025) CS141: Intermediate Data Structures and Algorithms (2024) CS218: Design and Analysis of Algorithms (2023) CS215: Theory of Computations (2021-2023)

• Computer Vision
• Sensor Fusion for Mobile Robotics
• Low Power Computing
• +۴ مورد دیگر

Jorge Lobo is an Assistant Professor at the Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Coimbra. His research focuses on computer vision, sensor fusion for mobile robotics, and low power computing, with recent emphasis on quantum computing and bio-inspired computational approaches. B.Sc/M.Sc in Electrical Engineering (University of Coimbra) Ph.D. in Electrical and Computer Engineering (2007, thesis: 'Integration of Vision and Inertial Sensing') Research Interests: Specializes in artificial perception systems combining computer vision and inertial sensing for robotics. Develops probabilistic models for sensor fusion and explores unconventional computing architectures like stochastic circuits and quantum computing for efficient Bayesian inference. Key applications include autonomous robotic grasping and disaster response systems. Scientific Leadership: Principal Investigator for Q-Bet: Bridging classical/quantum computing via HPC and bio-inspired methods Founding member of Quantum@UC interest group Coordinator of quantum computing specialization courses (collaboration with Physics & Computer Science departments) Key Projects: Contributed to European initiatives BACS (Bayesian Cognitive Systems), HANDLE (robotic dexterity), BAMBI (Bayesian inference hardware), and ECOBOTICS.SEA (marine ecosystem robotics). Academic Recognition: IEEE Senior Member and former president of the Portuguese IEEE RAS chapter. Developed innovative remote labs for stochastic computing education.