جست‌وجوی استاد و پژوهشگر

Chengxi Liمشاهده پروفایل

پژوهشگر

• Distributed Learning
• Federated Learning
• Signal Processing
• +۳ مورد دیگر

Chengxi Li is a Postdoctoral Researcher at the Division of Information Science and Engineering (ISE) within the School of Electrical Engineering and Computer Science at KTH Royal Institute of Technology, Stockholm. He works under Prof. Mikael Skoglund and Prof. Ming Xiao, with a visiting position at École Polytechnique Fédérale de Lausanne (EPFL) under Prof. Rachid Guerraoui. His research focuses on distributed learning, federated learning, signal processing, and information theory, with applications in sensor networks and wireless communication systems. Li holds a Ph.D. in Electronic Engineering from Tsinghua University (2022) and a B.S. in Information and Communication Engineering from the University of Electronic Science and Technology of China (2018). He has also completed visiting studies at the University of Wollongong and City University of Hong Kong. His work has been recognized through awards such as the Beijing Municipal Outstanding Doctoral Dissertation (2023) and the Excellent Doctoral Dissertation from the China Education Society of Electronics (2023). His research interests emphasize developing theoretical foundations and algorithms for distributed systems, including federated learning frameworks, secure signal processing, and efficient communication strategies in decentralized networks. Key contributions include innovations in gradient coding for straggler mitigation, robust federated learning under label quality disparities, and distributed detection in sensor networks with secrecy constraints. Li has secured grants as Principal Investigator, including the MSCA Postdoctoral Fellowship (2024–2026) and the Digital Futures Postdoc Fellowship (2023–2025). He mentors Master student Aiyang Yu and actively contributes to academic service, including reviewing for top journals like IEEE Transactions on Signal Processing and organizing workshops on secure federated learning. His work bridges foundational research and practical applications, with publications in prestigious venues such as IEEE Transactions on Signal Processing, IEEE Transactions on Communications, and IEEE Internet of Things Journal. Current projects explore communication-efficient learning algorithms and secure distributed systems for next-generation networks.

Omri Weinsteinمشاهده پروفایل

استادیار

• Theoretical Computer Science
• Data Structures
• Information Theory
• +۱۲ مورد دیگر

Omri Weinstein is an Assistant Professor in the Department of Computer Science at Columbia University. His research bridges Information Theory, Data Structures, and Optimization, focusing on dynamic data structures and dimensionality-reduction techniques to accelerate optimization and search. He received his PhD from Princeton University and was a Simons Society Junior Fellow at the Courant Institute (NYU). Education: PhD in Computer Science, Princeton University Simons Society Junior Fellowship, Courant Institute (NYU) His work explores the theoretical foundations of data structure lower bounds, communication complexity, and secure computation. Recent research includes advancements in dynamic matrix inversion for linear programming, oblivious near-neighbor search, and the interplay between matrix rigidity and data structure efficiency. Key trends in his publications include: Proving polynomial and super-logarithmic lower bounds for static and dynamic data structures Developing novel techniques in information complexity and protocol compression Applications in parallel algorithms, compressed data structures, and algorithmic game theory Scientific Awards: NSF CAREER Award Simons Society Junior Fellow Best Paper Award at CSR '13 Advising and Grants: Advised PhD students Hengjie Zhang and Shunhua Jiang MsC student Victor Lecomte and postdoc Alexander Golovnev Research funded by NSF CAREER Award on data structure lower bounds Labs and Teams: Omri is affiliated with the Theoretical Computer Science Group at Columbia and leads the Data-Structure Lower Bounds Reading Group.

Edgar Dobribanمشاهده پروفایل

دانشیار

• Statistics
• Machine Learning
• Artificial Intelligence
• +۴ مورد دیگر

Edgar Dobriban is an Associate Professor of Statistics and Data Science at the University of Pennsylvania's Wharton School, with a secondary appointment in Computer and Information Science. He leads a research group focused on problems at the interface of statistics, machine learning, and AI. Education PhD in Statistics, Stanford University (2017) BA in Mathematics, Princeton University (2012, Summa cum Laude/with Highest Honors) Research Focus His work spans uncertainty quantification, AI safety, robustness, high-dimensional asymptotic statistics, distributed learning, fairness, and COVID-19 testing methodologies. Current projects include developing conformal prediction methods, jailbreaking robustness benchmarks (JailbreakBench), and safety alignment techniques for large language models. Publication Trends Recent papers predominantly address AI safety and reliability, featuring novel methods for uncertainty quantification in language models (calibration, conformal prediction), adversarial robustness (jailbreaking defenses), and distribution shift adaptation. Theoretical foundations blend with practical applications in high-dimensional statistics. Awards and Honors Peter Gavin Hall IMS Early Career Prize (2024) Sloan Research Fellowship (2023) ICSA Outstanding Young Researcher Award (2023) NSF CAREER Award (2021) AFOSR/Army Research Office YIP Awards (2024, 2023) COPSS Emerging Leader Award (2023) Research Leadership He leads the Wharton Statistics and Data Science research group, recruiting PhD students through Statistics & Data Science, CIS, and AMCS programs. Current projects involve collaborations with Penn Medicine and the NSF-Simons Mathematical and Scientific Foundations of Deep Learning initiative. He co-founded the ASA StatsUpAI Special Interest Group and co-organized the Shenzhen Conference on Random Matrix Theory (2023).

Eugene Katsevichمشاهده پروفایل

استادیار

• Genetics
• Genomics
• High-dimensional Statistics
• +۳ مورد دیگر

Eugene Katsevich is an Assistant Professor at the University of Pennsylvania's Wharton Department of Statistics and Data Science. His research spans statistical theory, methodology, and applications in genomics and computational biology. Education: Ph.D. in Statistics (Stanford, 2019), A.B. in Mathematics (Princeton, 2014) His work focuses on: High-dimensional variable selection and multiple testing Conditional independence testing and computational efficiency Applications to genetics and genomics Development of statistical software for biological discovery Recent publications address: Robust differential expression analysis in single-cell CRISPR screens Theoretical advances in false discovery rate control Multi-resolution causal variant localization CRISPRi-based enhancer-gene mapping Scientific awards include: Wharton Undergraduate Teaching Excellence Award (2021) He teaches courses in modern data mining (STAT4710/5710) and statistical methodology (STAT9610), and leads the Katsevich Lab at UPenn, funded by NSF and Analytics at Wharton. His research develops methods like KnockoffZoom and SCEPTRE for genome-wide analysis and single-cell CRISPR data.

Tina Hecksherمشاهده پروفایل

دانشیار

• Condensed Matter Physics
• Glass Transition
• Rheology
• +۳ مورد دیگر

Tina Hecksher is an Associate Professor at the Department of Science and Environment at Roskilde University , specializing in Mathematics and Physics (IMFUFA) . Her work bridges experimental and theoretical physics, focusing on glass-forming liquids, rheology, and dielectric spectroscopy. Keywords: Physics, Physics didactics, Glass and Time Projects: Supervised 'Closing the Gap in Broadband Mechanical Spectroscopy' (2019–2022), participated in 'Matter', 'ROSE', and 'COOEE' projects. Research Interests: Shear mechanics, hydrogen-bonded systems, density scaling, and time-scale ordering in amorphous materials. Her studies often reveal connections between dielectric and mechanical responses. Collaborations: Works with J.C. Dyre, K. Niss, B. Jakobsen, and others on experimental techniques like RUSC (Roskilde University Shear Code). Press Coverage: Featured in Danish media (2024) for discovering a universal formula for animal movement rhythms and collaborations with Continental (2019). Email: tihe@ruc.dk

• Pattern Recognition
• Coding Theory
• Signals and Systems
• +۹ مورد دیگر

Jack Koplowitz is an Associate Professor in the Department of Electrical & Computer Engineering at Clarkson University's Coulter School of Engineering & Applied Sciences. His research focuses on pattern recognition, information theory, image/signal processing, computer vision, and communication theory. He holds a Ph.D. in Electrical Engineering from the University of Colorado (1973), an M.E. from Stanford University (1968), and a B.E. from City College of New York (1967). His teaching spans pattern recognition, coding theory, signals and systems, and communications. Research highlights include contributions to digital geometry (e.g., contour reconstruction, digital convex polygons) and applications of neural networks in power systems. He has published extensively since the 1970s, with notable work in subpixel accuracy, photovoltaic modeling, and hierarchical image representations. No scientific awards are explicitly listed. His academic contributions emphasize foundational work in digital image processing, signal theory, and algorithmic approaches to geometric problems.

• Computational Neuroscience
• Machine Learning
• Perceptual Decision Making
• +۳ مورد دیگر

Heiko SCHÜTT is an Associate Professor of Computational Science and Modelling at the University of Luxembourg's Faculty of Humanities, Education and Social Sciences. He is affiliated with the Department of Behavioural and Cognitive Sciences, where his research focuses on integrating computational models with cognitive and neural processes. His work spans machine learning, perceptual decision making, and visual perception, leveraging tools like deep neural networks and Bayesian analysis. Key research interests include analyzing neural representations through methods like representational similarity analysis, developing probabilistic models for feature segmentation, and investigating reward processing mechanisms in the brain. His publications highlight contributions to understanding how computational models can explain human cognition, particularly in areas like face recognition and color constancy. Recent work emphasizes the application of Bayesian experimental design to controversial stimuli and the optimization of loss functions in machine learning contexts. SCHÜTT collaborates across disciplines, bridging artificial intelligence with neuroscientific insights while maintaining a focus on experimental validation of computational hypotheses. He holds a strong academic profile with over 35 publications since 2016, addressing topics ranging from early visual processing to high-level cognitive functions. Despite his prolific output, no formal awards or grants are listed in the provided materials.

Karl Fristonمشاهده پروفایل

استاد

• Theoretical neurobiology
• Brain imaging
• Statistical parametric mapping
• +۱۱ مورد دیگر

Karl Friston FMedSci FRSB FRS is a Wellcome Principal Research Fellow and Scientific Director at the Wellcome Trust Centre for Neuroimaging, and Professor at the Institute of Neurology, University College London. He also serves as an Honorary Consultant at The National Hospital for Neurology and Neurosurgery, UK. Friston is a theoretical neuroscientist and authority on brain imaging who has made seminal contributions to neuroscience methodology and theory. Friston's research interests span theoretical neurobiology, computational neuroscience, and computational psychiatry. He invented statistical parametric mapping (SPM), voxel-based morphometry (VBM), and dynamic causal modelling (DCM). His most significant theoretical contribution is the free-energy principle for action and perception (active inference), which provides a unified framework for understanding brain function. His work integrates predictive coding, Bayesian inference, and information theory to explain perception, action, and learning. His research has profound implications for understanding schizophrenia through the dysconnection hypothesis, which frames psychosis as a failure of hierarchical predictive coding. Friston's publication record demonstrates consistent thematic development across decades, with recent work focusing on active inference, free-energy minimization, and computational psychiatry. His articles reveal a progression from methodological innovations in neuroimaging to comprehensive theoretical frameworks that unify perception, action, and learning under Bayesian principles. The recurring themes include hierarchical generative models, precision weighting, and the role of prediction errors in neural processing. Young Investigators Award in Human Brain Mapping (1996) Fellow of the Academy of Medical Sciences (1999) President of the international Organization of Human Brain Mapping (2000) Minerva Golden Brain Award (2003) Fellow of the Royal Society (2006) Medal, College de France (2008) Weldon Memorial prize and Medal (2013) Charles Branch Award for unparalleled breakthroughs in Brain Research (2016) Glass Brain Award - lifetime achievement award in human brain mapping (2016) As Scientific Director of the Wellcome Trust Centre for Neuroimaging, Friston leads one of the world's premier neuroimaging research centers. His theoretical work has generated numerous research programs across multiple institutions investigating active inference in perception, action, psychiatry, and even developmental biology. His frameworks have been applied to diverse areas including robotics, machine learning, and philosophical questions about consciousness and agency. Friston's work continues to shape the theoretical foundations of cognitive neuroscience through both his mathematical innovations and conceptual frameworks that unify previously disparate phenomena.

• Causal Inference
• Statistical Methodology
• Machine Learning
• +۳ مورد دیگر

Ying Jin is an Assistant Professor in the Department of Statistics and Data Science at the Wharton School, University of Pennsylvania. She completed her Ph.D. in Statistics at Stanford University (2019–2024) under the advisement of Emmanuel Candès and Dominik Rothenhäusler, and holds a B.S. in Mathematics and a B.A. in Economics (Finance) from Tsinghua University (2015–2019). Prior to Wharton, she served as a Wojcicki-Troper Postdoctoral Fellow at the Harvard Data Science Initiative, mentored by José Zubizarreta and collaborating with Marinka Zitnik. Her research focuses on causal inference, distribution shifts, and generalization across populations, with contributions to conformal prediction, replication studies, and reinforcement learning. She co-organizes the Online Causal Inference Seminar and has developed open-source tools like the repDiagnosis R package for replication diagnostics. Research Interests Causal inference and effect generalization across populations Statistical methodology for distribution shifts and covariate adjustment Conformal prediction and post-selection inference Machine learning applications in scientific replication and AI safety Her work bridges theoretical advancements and practical tools, addressing challenges in replicability and uncertainty quantification across disciplines.

Felix Leditzkyمشاهده پروفایل

استادیار

• Quantum Information Theory
• Quantum Channels
• Multipartite Entanglement
• +۳ مورد دیگر

Felix Leditzky is an Assistant Professor in the Department of Mathematics at the University of Illinois Urbana-Champaign, affiliated with the Illinois Quantum Information Science and Technology Center (IQUIST). He holds the Lincoln Excellence for Assistant Professors (LEAP) Scholar title and was a David H. Blackwell Scholar from 2023 to 2025. His research focuses on mathematical and computational aspects of quantum information theory, including quantum channels, entanglement, representation theory, and network information theory. He earned his PhD in Mathematics from the University of Cambridge (2017) and completed postdoctoral fellowships at the University of Waterloo’s Institute for Quantum Computing and JILA at the University of Colorado Boulder. His research explores quantum channel capacities, non-additivity problems, multipartite entanglement, and topological methods in quantum information. He has received an NSF CAREER grant (2025) for studying symmetries in noisy quantum systems. Felix teaches advanced courses such as representation-theoretic methods in quantum information and quantum channels. He also organizes conferences like the Rocky Mountain Summit on Quantum Information and serves on editorial boards of journals like Quantum and the Illinois Journal of Mathematics. Key contributions include work on port-based teleportation, quantum resource theories, and unitary designs. His research group includes graduate students in mathematics, physics, and computer science, focusing on quantum communication networks and entanglement quantification. Recent publications highlight advancements in asynchronous quantum processing, multipartite entanglement localization, and fidelity measures for quantum states.

Doug Altshulerمشاهده پروفایل

استاد

• Avian Flight Mechanics
• Biomechanics
• Neuroscience of Locomotion
• +۳ مورد دیگر

Dr. Doug Altshuler is a Professor in the Department of Zoology at the University of British Columbia's Faculty of Science. His research focuses on the neural and biomechanical mechanisms underlying avian flight, particularly in hummingbirds. His work integrates aerodynamics, physiology, and neuroscience to understand how visual information is translated into flight control. He leads the Altshuler Lab, which explores topics such as wing morphing, optic flow processing, and the evolutionary adaptations enabling complex flight maneuvers. Key research interests include: Visual guidance of flight and its neural substrates Mechanics of wing deformation and aerodynamic performance Evolutionary trade-offs in flight physiology and morphology Comparative studies across bird species to uncover universal flight principles Recent studies highlight breakthroughs like uncovering how wing morphing enhances gliding efficiency and how hummingbirds use optic flow to control hovering and forward flight. His collaborative projects span biomechanics, genomics, and robotics, with notable partnerships with institutions like the University of Michigan and Stanford University. Dr. Altshuler has mentored numerous graduate students and postdocs, many of whom have pursued academic and industry careers. His lab actively engages in outreach, including collaborations with museums and the development of innovative tracking technologies for flight analysis.

Daniela Wittenمشاهده پروفایل

استاد

• High-Dimensional Data Analysis
• Statistical Learning
• Genomics
• +۴ مورد دیگر

Daniela Witten is a Professor in the Departments of Biostatistics and Statistics at the University of Washington and holds the Dorothy Gilford Endowed Chair of Mathematical Statistics. Her research focuses on high-dimensional data analysis, statistical learning, genomics, and computational biology. She co-authored the influential textbook Introduction to Statistical Learning , bridging theory and application in data science. Education: BS in Biological Sciences and Mathematics (Stanford University) MS and PhD in Statistics (Stanford University) Research Interests: Her work spans statistical methods for biomedical data, including genomics, neuroscience, and clinical applications. She emphasizes interpretable machine learning and collaborates across disciplines to address complex biological questions. Recent projects include methods for calcium imaging data analysis and multi-omics integration. Awards and Recognition: 2022 COPSS Presidents’ Award (prestigious early/mid-career statistician honor) 2022 Elected Fellow of the Institute of Mathematical Statistics 2018 Simons Investigator Award 2019 Mortimer Spiegelman Award (best paper in public health statistics) Advancing the Field: She advocates for diversity in STEM, mentors over 30 PhD students, and actively contributes to statistical education and policy. Her leadership includes roles on the IMS Council and advocacy for equitable award nomination practices.

Najoung Kimمشاهده پروفایل

استادیار

• Natural Language Processing
• Cognitive Science
• Computational Linguistics
• +۳ مورد دیگر

Najoung Kim is an Assistant Professor in the Department of Linguistics and an affiliated faculty member in the Department of Computer Science at Boston University. She holds a PhD in Cognitive Science from Johns Hopkins University and an M.St. in General Linguistics from the University of Oxford. Her research bridges Natural Language Processing (NLP) and Cognitive Science, focusing on meaning and generalization in human and machine learners, with a particular emphasis on compositional generalization and neural network behavior. She has held roles as a Visiting Faculty Researcher at Google DeepMind and a Faculty Fellow at NYU's Center for Data Science. Her research employs computational and experimental methodologies to explore how humans and machines process linguistic structure and generalize beyond observed examples. Key contributions include the COGS benchmark for compositional generalization and work on evaluating large language models' deductive reasoning capabilities. She has also contributed to foundational NLP projects such as the (QA)^2 framework for identifying questionable assumptions in question-answering systems. Awards include a Best Paper Award at Genbench 2024 and an Area Chair Award at ACL 2023. Her work frequently appears in top venues like EMNLP, ACL, and NeurIPS. She currently supervises PhD students and postdoctoral researchers through Boston University's Linguistics, Computer Science, and Center for Data Science programs, emphasizing interdisciplinary approaches to NLP and cognitive science challenges.

Arie Beresteanuمشاهده پروفایل

دانشیار

• Econometrics
• Partial Identification
• Empirical Industrial Organization
• +۴ مورد دیگر

Arie Beresteanu is an Associate Professor and Associate Chair in the Department of Economics at the University of Pittsburgh. Previously, he was an Assistant Professor at Duke University. He holds a Ph.D. from Northwestern University and an undergraduate degree from the Hebrew University of Jerusalem (summa cum laude). His research focuses on micro-econometrics, partial identification in structural models, and applications to industrial organization and environmental economics. Notable contributions include work on observable consequences of incompleteness in economic behavior and methodologies for convex moment predictions. Education includes: Ph.D. in Economics, Northwestern University Bachelor's Degree, Hebrew University of Jerusalem (summa cum laude) Research interests emphasize econometric methods for identification and partial identification, nonparametric estimation, and discrete choice modeling. His work bridges theoretical econometrics with applied problems in industrial organization, environmental policy, and cost analysis in telecommunications. Recent advancements include contributions to Minkowski summation inverse operations and quantile regression with interval data. Teaching spans Ph.D. and undergraduate courses in econometrics and industrial organization. He developed a new data course for economics undergraduates. His coding projects include interactive econometric tools in Fortran, Julia, Python, and JavaScript, such as an OLS visualization app and a simulated annealing Sudoku solver. Athletic pursuits include running with notable personal records in 5K (19:58), Half Marathon (1:34:08), and Marathon (3:18:45). He maintains an extensive resource page on running strategies and training programs.

• Wireless Communications
• 5G/6G Technology
• Satellite Communications
• +۳ مورد دیگر

Rahim Tafazolli is the Regius Professor of Electronic Engineering and Director of the 5G/6G Innovation Centre (5GIC/6GIC) and the Institute for Communication Systems (ICS) at the University of Surrey. He has over 30 years of experience in digital communications research and has authored/co-authored over 1,000 publications. His work focuses on 5G/6G technologies, satellite communications, reconfigurable intelligent surfaces (RIS), and semantic communication systems. He leads major initiatives such as the 5GIC and 6GIC, collaborating with industry partners like InterDigital and A*STAR. His research has been recognized with prestigious awards including the KIA Laureate Award (2015) and Fellowships from the Royal Academy of Engineering (FREng) and the Wireless World Research Forum (WWRF). Key research areas include next-generation wireless networks, quantum-based communications, and AI-driven semantic systems. Recent advancements include frameworks like ResiTok for robust image transmission and SA-MIMO for quantum-native receivers. His work on distributed hybrid beamforming in satellite networks and resilient tokenization for low-rate communication highlights his contributions to practical wireless solutions. Collaborations with institutions such as NPL and international partners ensure his research bridges academia and industry. Scientific achievements include pioneering 5G testbeds and co-developing the first UK-made end-to-end 5G system. Awards reflect his global impact in advancing communication technologies. His leadership in initiatives like the £8M academic-industry center for secure AI-era networks underscores his role in shaping future network security and efficiency.