جست‌وجوی استاد و پژوهشگر

Chase Broederszمشاهده پروفایل

استاد

• Statistical Physics
• Biological Physics
• Soft Matter Physics
• +۴ مورد دیگر

Prof. Dr. Chase Broedersz is a tenured W2 Professor of Theoretical Physics – Statistical and Biological Physics at Ludwig-Maximilians-Universität München (LMU Munich) since 2015. He also holds a secondary affiliation as Associate Professor at VU Amsterdam since 2020. He is a member of the Young College of the Bavarian Academy of Science since 2017. Education Ph.D. in Theoretical Physics , VU Amsterdam (2011) MSc. in Physics , VU Amsterdam (2007) BSc. in Physics and Astronomy , VU Amsterdam (2005) Research Interests Prof. Broedersz leads a research program at the intersection of statistical physics and biological physics , with a focus on: Soft Matter Physics : mechanics of biopolymer networks, semiflexible filaments, and composite materials Active Matter : non-equilibrium dynamics in living systems, motor-driven networks, and active gels Cell Mechanics : confined cell migration, mechanosensing, and force transmission in tissues Chromosome Organization : bacterial chromosome structure, protein-DNA interactions, and SMC condensins His work combines theoretical modeling , computational simulations , and experimental collaborations to uncover the physical principles governing complex biological systems. Publication Trends Prof. Broedersz has authored over 50 peer-reviewed publications since 2010. His recent work (2019–2025) emphasizes: Cell migration in confined and soft environments, with studies in Nature Physics and PNAS Non-equilibrium dynamics in active biological systems, including broken detailed balance and emergent contractility Machine learning approaches to infer stochastic dynamics in biological data, published in Nature Communications and Physical Review Letters Chromosomal organization in bacteria, combining polymer physics with genomic data Labs and Teams He leads the Statistical and Biological Physics Group at LMU Munich, affiliated with the Arnold Sommerfeld Center for Theoretical Physics (ASC) . The group collaborates closely with experimental teams at LMU, Princeton, and other institutions, focusing on interdisciplinary biophysics research. Teaching Prof. Broedersz has taught advanced courses including: Advanced Statistical Physics Soft Matter Physics Stochastic Processes in Physics and Biology

Vinkle Srivastavمشاهده پروفایل

پژوهشگر

• Surgical Data Science
• 3D Medical Image Analysis
• Multi-modal Pre-training
• +۷ مورد دیگر

Vinkle Srivastav is a Research Scientist (Chargé de recherche R&D) at the CAMMA group, a collaborative research team between IHU Strasbourg and the University of Strasbourg, where he focuses on advancing surgical data science through novel computer vision and machine learning approaches. His work bridges the gap between clinical practice and artificial intelligence, developing methods for surgical video analysis, 3D medical imaging, and surgical workflow understanding. Education PhD in Computer Science (2018-2021) from University of Strasbourg, France. Thesis: "Unsupervised Domain Adaptation Approaches for Person Localization in the Operating Rooms." Master of Science in Computer Science (2014-2017) from Indian Institute of Technology, Delhi, India. Thesis: "Computerized evaluation of neurosurgery skills using image processing and computer vision techniques." Bachelor of Technology in Electronics and Communication (2007-2011) from Punjab Technical University, Jalandhar, India. Research Interests Vinkle's research spans surgical data science, with particular focus on multi-modal learning approaches for surgical computer vision. His work addresses fundamental challenges in medical AI including domain adaptation, self-supervised learning, and privacy preservation in clinical environments. He develops methods for 3D medical image analysis, multi-view human pose estimation in operating rooms, and surgical activity recognition. His recent work emphasizes multi-modal pretraining frameworks that leverage both visual and textual information to improve surgical workflow understanding. He also investigates scientific simulation techniques, particularly for therapeutic ultrasound applications, where physics-aware deep learning models can accelerate computational processes while maintaining accuracy. Publication Trends Vinkle's recent publications demonstrate a strong trajectory toward multi-modal surgical AI systems that integrate vision, language, and physics-based modeling. His work increasingly focuses on few-shot and zero-shot adaptation techniques to address the data scarcity problem in surgical AI. The publications reveal a progression from basic pose estimation to holistic surgical scene understanding, incorporating team communication analysis and surgical safety protocols. Scientific Awards IPCAI 2024 Best paper award (co-author) IPCAI 2019 Runner-up award in the bench-to-bedside category (co-author) Joint winner for the best paper award in the machine learning for CAI track, IPCAI 2025 Advising and Grants Vinkle actively mentors multiple PhD students and research interns at various levels, supervising thesis work on topics including large-scale multi-modality learning, holistic surgical scene analysis, and self-supervised video representation learning. He serves as Co-PI on two ITI-HealthTech projects: one focused on multi-modality learning for 3D medical imaging (2023), and another on physics-aware deep-learning approaches for therapeutic ultrasound simulation (2024). Laboratories and Teams Vinkle is a key member of the CAMMA research group at IHU Strasbourg, a collaborative team focused on computer-assisted medical modeling and analytics. He co-organizes the Surgical Data Science Summer School, an interdisciplinary program that brings together clinicians and computer scientists to develop AI-driven solutions with clinical impact. His work involves close collaboration with surgical teams at University Hospitals of Strasbourg and international partners including Johns Hopkins University and Technical University of Munich.

Giorgio Scorzelliمشاهده پروفایل

پژوهشگر

• Extreme Data Management
• Scientific Visualization
• Computational Topology
• +۶ مورد دیگر

Giorgio Scorzelli is a researcher at the University of Utah, serving as Director of Software Development for the Center for Extreme Data Management, Analysis, and Visualization (CEDMAV) and the National Science Data Fabric (NSDF) . He specializes in extreme data management, scientific visualization, and computational topology, with a focus on scalable solutions for climate science, materials science, and neuroscience datasets. His work emphasizes democratizing data access through platforms like OpenVisus , enabling efficient analysis of petascale and exascale data. Key contributions include orchestrating cyberinfrastructure, optimizing parallel I/O, and developing real-time visualization systems for heterogeneous resources. Notable scientific contributions include the NSF Grant #2127548 for NSDF development . His projects integrate cloud computing, geo-distributed storage, and FAIR digital objects to lower barriers to data democratization. Giorgio's research spans multi-resolution algorithms , computational topology , and 3D geometric modeling , with applications in infrastructure security, archaeological reconstruction, and biomedical imaging. His work bridges abstract mathematical frameworks (e.g., Boolean algebras, chain complexes) with practical software solutions.

• Systems Theory
• Mathematical Modeling
• Fuzzy Logic
• +۷ مورد دیگر

Konstantin Yuryevich Degtyarev is an Associate Professor at the Department of Software Engineering within the Faculty of Computer Science at the National Research University Higher School of Economics (HSE) in Moscow. He has been working at HSE since 2008 and has accumulated 29 years of scientific and teaching experience. His office is located at AUK "Pokrovsky Boulevard", Pokrovsky blvd, 11, office S919, where he holds consultations on Mondays, Thursdays, and Fridays during Module 1. 1995: Candidate of Technical Sciences from Moscow State University of Instrument Engineering and Computer Science, specialty 05.13.01 "System Analysis, Management and Information Processing" Postgraduate study at Moscow State University of Instrument Engineering and Computer Science (1986) Specialty: Moscow Institute of Electronic Engineering, Faculty of Applied Mathematics, specialty "Applied Mathematics", qualification "Engineer-mathematician" Professor Degtyarev's research focuses on systems theory and mathematical modeling, with particular emphasis on fuzzy logic and soft computing applications in systems analysis. His work explores cognitive approaches to structural analysis, verbal calculations, perceptions in systems analysis, F-geometry in modeling and decision making, interval calculations, Z-numbers (according to L.A. Zadeh), and fuzzy time series in forecasting. He also has significant expertise in programming in C/C++ and approaches to teaching programming courses. His research bridges theoretical foundations with practical applications in software engineering and decision support systems. Analysis of Professor Degtyarev's publication history reveals a consistent focus on fuzzy logic applications across multiple domains. His work demonstrates an evolutionary path from foundational theoretical work on polyhedral dynamics and Q-analysis to more applied research involving Z-numbers, fuzzy time series, and fuzzy logic applications in software engineering, e-commerce reputation systems, and metro positioning services. A notable trend is the increasing application of fuzzy methodologies to practical software engineering problems, particularly in the areas of linguistic summarization, decision support, and uncertainty modeling. Gratitude from HSE University (May 2024) Gratitude from the Department of Software Engineering, HSE (November 2023) Certificate of honor from the Faculty of Computer Science of HSE (September 2019) Gratitude from the First Vice-Rector of HSE (January 2018) Best Teacher award (2024, 2011-2020) Winner of the competition for the best article in the journal 'Proceedings of the ISP RAS' for 2019 Professor Degtyarev has supervised numerous bachelor's and master's theses, primarily focusing on fuzzy logic applications, Z-numbers, linguistic summarization, and decision support systems. His students have developed various software implementations related to fuzzy modeling, time series forecasting, and expert assessment aggregation. He has also contributed significantly to educational development, creating teaching materials for courses in programming, fuzzy systems, and applied system analysis. His involvement in curriculum development includes certified assessment materials for the discipline "Fundamentals of Informatics and Programming". As a member of IEEE (since 1999) and ACM (2005-2014), Professor Degtyarev participates in various professional activities including serving as a reviewer for prestigious journals such as "Fuzzy Sets and Systems" and "IEEE Transactions on Fuzzy Systems". He has also been involved in international conferences as a program committee member and section chair for several International Conferences on Soft Computing.

Arijit Bishnuمشاهده پروفایل

دانشیار

• Theoretical Computer Science
• Randomized Algorithms
• Approximation Algorithms
• +۶ مورد دیگر

Arijit Bishnu is an Associate Professor at the Indian Statistical Institute in the Advanced Computing and Microelectronics Unit (ACMU). He has taught courses such as Design and Analysis of Algorithms , Randomized Algorithms , Computational Geometry , and Algorithms for Big Data over multiple years (2008–2025), focusing on theoretical and applied aspects of computer science. Research Interests: His work spans Theoretical Computer Science , Randomized and Approximation Algorithms , Computational Geometry , and Combinatorics . He explores problems in sublinear algorithms, streaming computation, geometric data analysis, and complexity theory. Publications: Recent papers include contributions to STOC 2025 , RANDOM 2025 , and APPROX 2024 , covering topics like property testing, triangle counting complexity, and streaming algorithms. Collaborations with researchers like Sourav Chakraborty, Gopinath Mishra, and Sayantan Sen highlight his interdisciplinary approach. Academic Leadership: He has co-organized research courses such as Approximation Algorithms and Topics in Algorithms and Complexity , emphasizing mentorship and knowledge dissemination in theoretical computer science. His comprehensive work integrates algorithmic innovation with rigorous mathematical analysis, advancing computational techniques for large-scale and geometric data.

Volkert Paulsenمشاهده پروفایل

مدرس ارشد

• Financial Mathematics
• Stochastic Analysis
• Optimal Stopping
• +۵ مورد دیگر

Volkert Paulsen is a Senior Lecturer at the Institute of Mathematical Stochastics at the University of Münster. His career spans institutions including the University of Kiel, where he completed his Habilitation (2000), Dissertation (1994), and Diplomarbeit (1989). He has taught extensively in Financial Mathematics , Stochastic Analysis , and Mathematical Statistics , supervising over 50 Bachelor, Master, and Diploma theses on topics such as risk modeling, portfolio optimization, and derivative valuation. Research Interests: Paulsen's work focuses on Financial Mathematics (continuous-time models, American options, unit-linked insurance), Stochastic Analysis (optimal stopping, martingale methods), and Risk Modeling (credit risk, extreme value statistics). His publications include foundational studies on nonlinear observation costs in optimal stopping problems and stochastic approaches to portfolio management. Scientific Contributions: His research spans journal articles in Stochastic Processes and their Applications and Journal of Applied Probability , with recent seminar topics covering Lévy Processes , Copula Modeling , and Stochastic Volatility . He employs R for statistical applications and integrates mathematical theory with practical finance and insurance contexts. Contact: Email: Volkert.Paulsen@uni-muenster.de Room: 130.010, Orléans-Ring 10, 48149 Münster Phone: +49 251 83-33771

Charlotte Boccaraمشاهده پروفایل

پژوهشگر

• Developmental Neurobiology
• Systems Neuroscience
• Sleep Research
• +۶ مورد دیگر

Dr. Charlotte Boccara is a Group Leader at the University of Oslo, leading the Charlotte Boccara Group focused on Systems Neuroscience & Sleep. Her research addresses the neural mechanisms of sleep in cognitive development, funded by the European Union (ERC), and bridges Developmental Neurobiology, Systems Neuroscience, and Sleep Research. Education: PhD in Neurobiology (NTNU, 2014), MSc/BSc from Université Marie et Pierre Curie Research Experience: 2019–Present - Project Manager for SleepCog, 2018–2019 - Marie Curie Fellow, 2006–2012 - PhD Candidate at Kavli Institute Her work emphasizes computational methods to analyze neural representations in adult and developing brains, with key projects on SleepCog (2024–2029) mapping sleep oscillations in rat pups using in vivo recordings and optogenetics. Recent publications (2025–2008) span topics like: Autophagy and Sleep (2025) Parahippocampal Speed Cells (2022) Egocentric Boundary Representation (2019) Hippocampal Replay Mechanisms (2017) Scientific awards include: 2019 Young Research Talents (UiO) 2018 Marie Curie MSCA-IF Fellowship 2017 MRC CNDD Seed Funds 2006 NTNU Doctoral Fellowship She teaches physiology and comparative anatomy at the University of Oslo and collaborates with institutions like IST Austria, King’s College London, and SINTEF. Her lab includes researchers such as Alessandro Arena, Solomiia Korchynska, and Lina Okinina.

• Acoustic Signal Processing
• Blind Source Separation
• Digital Signal Processing
• +۶ مورد دیگر

Shoji Makino is a Professor at Waseda University's Graduate School of Information, Production and Systems. He has held academic and research positions at institutions such as the University of Tsukuba and NTT Communication Science Laboratories. His work spans acoustic signal processing, blind source separation, and adaptive filtering. Education: Ph.D., Tohoku University (1993.03) Mechanical Engineering, Tohoku University Graduate School of Engineering (1979.04–1981.03) Engineering, Tohoku University Faculty of Engineering (1975.04–1979.03) Research Interests: His research focuses on acoustic signal processing for speech and audio, including blind source separation (BSS) , beamforming , and adaptive filtering . He pioneered methods for solving permutation alignment in frequency-domain BSS and developed geometrically constrained ICA techniques. Scientific Awards: Hoko Award (2018.10, Hattori Hokokai Foundation) Outstanding Contribution Award of the Institute of Electronics, Information, and Communication Engineers (2018.06) IEEE Signal Processing Society Best Paper Award (2014.01) IEEE Fellow (2004.01) IEICE Achievement Award (1997.05) Committee Memberships: He has served as Chair of the IEEE CAS Society's Blind Signal Processing TC, General Chair of IEEE WASPAA2007, and Associate Editor of IEEE Trans. SAP. He is actively involved in EURASIP, APSIPA, and the Acoustical Society of Japan.

Reeta Vyasمشاهده پروفایل

استاد

• Quantum Optics
• Laser Physics
• Nonlinear Optics
• +۳ مورد دیگر

Reeta Vyas is a tenured Professor of Physics at the University of Arkansas , where she has been affiliated since 1989. She has held consecutive promotions from Assistant (1989–1994), Associate (1994–2002), to full Professor (2002–present). Her research focuses on quantum optics and laser physics, with a particular emphasis on light-matter interactions, beam propagation, and quantum coherence. Research Themes Quantum optics and photon statistics Nonlinear optical phenomena Orbital angular momentum of light Polarization properties of structured beams Her publications from 2017–2024 reveal a sustained focus on advanced optical phenomena including quantum light statistics , vortex beam diffraction , and nonlinear phase analysis . Notable achievements include the Faculty Gold Medal Award (2005) and Senior Membership in the Optical Society of America (2013) . While no student advising information is explicitly documented, her work demonstrates significant contributions to theoretical and experimental quantum optics.

• Biomedical Image Analysis
• Cryogenic Electron Microscopy (Cryo-EM)
• MRI and Ultrasound Image Segmentation
• +۵ مورد دیگر

Dr. Hemant Tagare is a Professor at Yale School of Medicine with primary appointments in the Radiology & Biomedical Imaging department and secondary appointments in Biomedical Engineering and Statistics . He is affiliated with multiple research centers, including the Bioimaging Sciences , Center for Brain & Mind Health , and Yale Biomedical Imaging Institute . Research Interests Cryogenic Electron Microscopy (Cryo-EM) for protein structure reconstruction MRI/ultrasound image segmentation using machine learning Non-rigid registration and shape theory Mathematical foundations of image analysis Articles Trends : Recent publications focus on cryo-EM particle picking consensus algorithms, T2-weighted MRI standardization, and IVIM perfusion measurement in peripheral artery disease. Earlier works emphasize segmentation techniques, non-rigid registration, and phantom validation. Scientific Awards Best Poster Award (2003) for ultrasound segmentation algorithm Francois Erbsmann Award (1993) for medical image processing National Science Talent Search Scholarship (1977) from India

• Mathematics of Data Science
• Stochastic Geometry
• Convex Geometry
• +۶ مورد دیگر

Eliza O'Reilly is an Assistant Professor in the Department of Applied Mathematics & Statistics at Johns Hopkins University. Her research focuses on the intersections of stochastic geometry , convex geometry , high-dimensional probability , and statistical learning theory . Her work explores: Nonconvex and convex regularizers in inverse problems Random tessellations and their machine learning applications Spectrahedral regression for convex function approximation Determinantal point processes for modeling repulsive interactions High-dimensional random convex sets and their asymptotic geometry Her research is supported by the National Science Foundation . Recent publications investigate gradient-based dimension reduction, oblique decision trees, and geometric properties of regularizers. She has received her PhD from the University of Texas at Austin and was a postdoctoral scholar at Caltech.

• Machine Learning
• Statistical Learning Theory
• Algorithmic Fairness
• +۱۲ مورد دیگر

Massi Pontil is a part-time Professor of Computational Statistics & Machine Learning in the Department of Computer Science at University College London (UCL). He joined UCL as a lecturer in 2003 and was promoted to Professor in 2010. Since 2016, his primary appointment has been at the Istituto Italiano di Tecnologia (IIT), where he leads the CSML research group. His work bridges theoretical machine learning with practical applications in physical sciences. His research interests span a wide range of topics in machine learning theory and algorithms: Machine Learning Theory and Statistical Learning Algorithmic Fairness and Ethical AI Kernel Methods and Reproducing Kernel Hilbert Spaces Transfer Learning, Multitask Learning, and Meta-Learning Operator Learning and Dynamical Systems Sparsity Regularization and Optimization Pontil's recent work focuses on the intersection of machine learning with numerical simulations of physical systems, particularly in molecular dynamics and climate science. His publications demonstrate a strong emphasis on theoretical foundations while addressing practical challenges in high-dimensional systems, symmetry-aware learning, and uncertainty quantification. Among his notable honors are: Best Paper Runner Up Award from ICML 2013 EPSRC Advanced Research Fellowship (2006-2011) Edoardo R. Caianiello Award for the Best Italian PhD Thesis on Connectionism (2002) Professor Pontil has served on program committees for major machine learning conferences (COLT, ICML, NeurIPS) and on editorial boards of prestigious journals including Machine Learning Journal, Statistics and Computing, and JMLR. He teaches Advanced Topics in Machine Learning at UCL, with a focus on convex optimization and statistical learning theory.

Yihong Wuمشاهده پروفایل

استاد

• High-dimensional statistics
• Information theory
• Statistical inference on graphs
• +۳ مورد دیگر

Yihong Wu is the James A. Attwood Professor of Statistics and Data Science at Yale University, where he also serves as Chair of the Department of Statistics and Data Science. His academic career spans prestigious institutions with a focus on theoretical and applied statistical methods. His research bridges information theory and statistics, with applications across multiple domains of data science. Professor Wu's research focuses on the theoretical foundations of high-dimensional statistics, information theory, and optimization. His work explores dimensionality reduction through both intrinsic low-dimensionality (sparsity, smoothness) and extrinsic low-dimensionality (functional estimation). He has made significant contributions to understanding statistical-computational tradeoffs in problems involving random graphs and combinatorial structures. His research has important applications in machine learning, network analysis, and signal processing. His recent publications reveal a strong focus on information-theoretic approaches to statistical problems, with particular emphasis on graph matching, empirical Bayes methods, and high-dimensional inference. Wu's work consistently addresses fundamental questions about the limits of statistical estimation and the computational feasibility of achieving those limits. His research spans theoretical foundations while maintaining relevance to practical data analysis challenges. Professor Wu actively contributes to academic education through multiple graduate-level courses including Information Theory, Statistical Inference on Graphs, and Topics in High-Dimensional Statistics and Information Theory. His teaching reflects his research interests, emphasizing mathematical rigor and theoretical foundations.

Kimin Leeمشاهده پروفایل

استادیار

• Safe and capable decision-making agents
• Physical AI
• Reinforcement Learning from Human Feedback
• +۹ مورد دیگر

Kimin Lee is an assistant professor at the Graduate School of AI at Korea Advanced Institute of Science and Technology (KAIST), where he focuses on developing safe and capable decision-making agents. His research spans multiple aspects of artificial intelligence with a strong emphasis on safety and reliability. Dr. Lee completed his educational journey at KAIST, earning a Ph.D. in Electrical Engineering with a focus on Machine/Deep Learning (2015-2020), advised by Professor Jinwoo Shin. He also holds a Master's degree in Electrical Engineering (Wireless Communication Networks, 2013-2015) and a Bachelor's degree in Electrical Engineering (2009-2013), both from KAIST. His primary research interests include: Physical AI - developing AI systems that can interact safely and effectively with the physical world Alignment - particularly reinforcement learning from human feedback (RLHF) and scalable oversight techniques Monitoring - safety evaluation frameworks and benchmarking for AI systems LLM Agents - enhancing the capabilities and safety of large language model-based agents Dr. Lee's recent publications reveal a strong trajectory toward addressing critical challenges in AI safety. His work consistently bridges theoretical advances with practical applications, particularly in the areas of reinforcement learning, computer vision, and natural language processing. A notable trend in his research is the development of methods to evaluate and enhance the safety of AI systems, especially large language models and diffusion models, while maintaining or improving their capabilities. As an active member of the academic community, Dr. Lee serves as an area chair for major conferences including NeurIPS, ICLR, and ICML, and regularly reviews for top-tier AI venues. He has also organized workshops focused on safe and trustworthy AI agents. Dr. Lee's research group at KAIST appears to focus on AI safety and decision-making, with research projects spanning from theoretical foundations to practical implementations of safe AI systems. His collaborative work with institutions like UC Berkeley and Google Research demonstrates the interdisciplinary nature of his research approach.

Tim Ruben Davidsonمشاهده پروفایل

پژوهشگر

• deep generative models
• agentic systems
• synthetic data
• +۱ مورد دیگر

Tim Ruben Davidson is a Researcher at the Digital Life Lab (DLAB) within the School of Computer and Communication Sciences at EPFL. His current role is Doctoral Assistant, pursuing a doctoral program in Computer and Communication Sciences. His research focuses on advanced machine learning topics including deep generative models, agentic systems, synthetic data applications, and representation learning. His work bridges theoretical advancements in AI with practical implications, particularly in understanding AI agency, synthetic data generation, and latent space structures. Key research trends in his articles include exploring AI self-awareness, evaluating AI-driven peer review systems, and optimizing generative models through geometric and topological approaches. He has contributed to foundational studies on hyperspherical VAEs and reparameterization techniques on Lie groups, advancing mathematical foundations of neural networks. His work is published in top-tier venues and reflects interdisciplinary engagement between computer science, mathematics, and ethical AI considerations.