جست‌وجوی استاد و پژوهشگر

Vivek F. Fariasمشاهده پروفایل

استاد

• Reinforcement Learning
• Dynamic Optimization
• Stochastic Systems
• +۴ مورد دیگر

Vivek F. Farias is the Patrick J. McGovern (1959) Professor at the MIT Sloan School of Management , where he is affiliated with the Operations Management group and the Operations Research Center (ORC) . His work bridges high-dimensional optimization, reinforcement learning, and stochastic modeling, with applications in commerce, healthcare, and biology. Education: Ph.D. in Electrical Engineering, Stanford University (Advisor: Prof. Benjamin Van Roy) Research Focus: Vivek’s research spans Reinforcement Learning , Dynamic Optimization , Approximation Algorithms , and Inference in Large-Scale Stochastic Systems . Recent applications include commerce platforms, protein networks, and healthcare operations. He has published extensively in top-tier venues like Management Science , Operations Research , NeurIPS , ICML , and Nature Communications . Editorial & Service Roles: Co-Editor, Data Science Area, Management Science Editorial Board, Operations Research Editorial Board, INFORMS Journal on Optimization Scientific Awards: 2022 RMP Jeff McGill Student Paper Award (First Prize) 2022 Applied Probability Society Student Paper Prize (First Prize) 2016 INFORMS MSOM Best Paper in Management Science 2015 INFORMS Revenue Management and Pricing Section Prize Best Simulation Publication Award, INFORMS Simulation Society (2014) George Nicholson Student Paper Competition, First Place (2017) Advising & Mentorship: Vivek has advised 14+ Ph.D. students and numerous MS students, many now faculty at top institutions (Columbia, NYU, CMU, INSEAD) or leaders in industry (Uber, Facebook, Lyft, McKinsey). His lab focuses on scalable algorithms for real-world decision-making under uncertainty. Industry & Entrepreneurship: He co-founded and served as CTO of Celect (acquired by Nike) and currently advises multiple technology startups in retail, healthcare, and finance.

Gregory Valiantمشاهده پروفایل

دانشیار

• Algorithms
• Machine Learning
• Statistics
• +۳ مورد دیگر

Gregory Valiant is an Associate Professor of Computer Science at Stanford University , specializing in Algorithms, Machine Learning, Statistics, and Information Theory. He holds a PhD from UC Berkeley and a BA in Mathematics from Harvard University. His research focuses on designing efficient algorithms for inferring information from limited data, addressing challenges in computation, memory, communication, and data quality. He advises multiple PhD students and collaborates with institutions like Microsoft Research New England. Educations: PhD, Computer Science, UC Berkeley (2012) BA, Mathematics, Harvard University (2006) Research Interests: Gregory’s work centers on the interplay between algorithms and statistical inference, particularly in high-dimensional settings. His lab explores topics like distribution learning, sample amplification, and adversarial robustness. Recent projects include developing algorithms for trace reconstruction, matrix completion, and transformer-based in-context learning. His methodologies often combine theoretical rigor with practical applications in machine learning and data science. Key Article Trends: His publications span theoretical foundations (e.g., sample amplification, convex optimization complexity) and applied machine learning (e.g., transformer capabilities, adversarial testing). Notable work includes contributions to distribution testing, statistical estimation under constraints, and algorithmic lower bounds. Advising: Current advisees include Annie Marsden, Steven Cao, and Chirag Pabbaraju. Former students have pursued roles at institutions like Harvard, Berkeley, and companies like Google, Waymo, and Facebook.

Oana Langمشاهده پروفایل

مدرس

• Stochastic Analysis
• Stochastic Partial Differential Equations (SPDEs)
• Nonlinear Filtering
• +۴ مورد دیگر

Oana Lang is a Lecturer in Mathematics at Babeş-Bolyai University and a former STUOD Research Associate at Imperial College London. Her research focuses on stochastic analysis, particularly nonlinear stochastic partial differential equations (SPDEs) and their applications in fluid dynamics and data assimilation. She holds a PhD from Imperial College London (2020), with a thesis on stochastic transport equations and data assimilation. Affiliations: Academic Women in Mathematics, Mathematics of Planet Earth, Stochastic Analysis Research Group at Imperial Education: PhD in Mathematics, Imperial College London (2016–2020) MRes in Mathematics of Planet Earth, Imperial College London (2015–2016) MSc in Applied Mathematics, University of Bucharest (2013–2015) BSc in Mathematics, University of Bucharest (2010–2013) Her research interests emphasize SPDEs driven by transport noise, particularly in ocean and climate modeling. She has developed analytical frameworks for stochastic Euler equations, rotating shallow water models, and their applications in data assimilation. Key contributions include proving well-posedness for transport SPDEs and advancing calibration methods for stochastic fluid models. She organizes the Stochastic Analysis Seminar at Imperial College and the STUOD SPDEs Seminar. Recent work includes invited sessions on stochastic models in fluid dynamics at international conferences and editorial roles for Emergent Scientist . Awards: MRes degree with distinction (Imperial College London, 2016), multiple conference organization roles, and active grant-funded research in stochastic fluid dynamics. Labs/Teams: STUOD research group, Mathematics of Planet Earth Centre for Doctoral Training (MPE CDT), and collaborations with institutions like Reading University and the University of Aachen.

Olga Mulaمشاهده پروفایل

پژوهشگر

• Optimal Transport
• Wasserstein Spaces
• Model Reduction
• +۴ مورد دیگر

Olga Mula is a researcher at Eindhoven University of Technology (TU Eindhoven) in the Netherlands specializing in optimal transport theory, Wasserstein spaces, and model reduction techniques for partial differential equations. Her work bridges theoretical mathematics with practical applications in state estimation and inverse problems. Her research interests focus on Optimal Transport , Wasserstein Spaces , Model Reduction , and Numerical Analysis of PDEs . She develops algorithms for state estimation in metric spaces, particularly focusing on the Wasserstein space of probability measures. Her work includes developing reduced models using barycentric approximation, analyzing convergence properties, and addressing challenges in sensor placement for optimal data acquisition. Her recent publications demonstrate significant contributions to understanding how to build efficient reduced models in Wasserstein spaces for both forward and inverse problems. She has developed theoretical frameworks for state estimation algorithms, analyzed their performance in terms of Kolmogorov widths, and created practical implementations using sparse Wasserstein barycenters. Her work spans pure mathematical theory to applications in image processing and PDE-constrained optimization. Her scientific contributions include: Development of piecewise-affine algorithms for state estimation Nonlinear model reduction on metric spaces for conservative PDEs Sparse approximation using Wasserstein barycenters Applications to shape reconstruction and line completion Theoretical analysis of approximation rates in Wasserstein spaces She collaborates extensively with leading researchers in the field including Cohen, Dahmen, Feydy, and Rai. Her work demonstrates both theoretical depth and practical relevance, connecting abstract mathematical concepts to real-world problems in data assimilation and inverse modeling.

Dipak Deyمشاهده پروفایل

استاد

• Bayesian analysis
• Bioinformatics
• Biostatistics
• +۸ مورد دیگر

Dipak Dey is a Professor in the Department of Statistics at the University of Connecticut . His work bridges theoretical and applied statistics, with a focus on Bayesian methodologies and computational statistics. Affiliations : University of Connecticut, Department of Statistics Contact : dipak.dey@uconn.edu , Office: AUST 327, Phone: (860) 486-4755 His research interests span Bayesian analysis , Biostatistics , Computational statistics , Statistical genetics , and Spatio-temporal modeling . He has pioneered techniques in spatial curvature processes and scalable Bayesian algorithms for large datasets. Recent publications highlight his contributions to Bayesian spatial modeling (blockNNGP, curvature processes), survival analysis (skew-t distributions, cure rate models), and computational statistics (variable selection in Gaussian processes, fast inference algorithms). Applications include insurance data , epidemiology , and environmental statistics . Scientific Awards : Board of Trustees Distinguished Professor (University of Connecticut) He actively collaborates on interdisciplinary projects and mentors researchers in advanced statistical methodologies for complex data structures.

Nitis Mukhopadhyayمشاهده پروفایل

استاد

• Sequential Analysis
• Statistical Inference
• Confidence Interval Estimation
• +۵ مورد دیگر

Nitis Mukhopadhyay is a Professor in the Department of Statistics at the University of Connecticut, with extensive research contributions in sequential analysis and statistical inference. His academic work focuses on developing innovative methodologies for confidence interval and point estimation problems, particularly in sequential and multistage sampling frameworks. His research has significant applications across various domains including environmental science, clinical trials, and survey methodology. Professor Mukhopadhyay's research interests center on sequential analysis, with particular emphasis on confidence interval estimation, point estimation, survey sampling, environmental sampling, clinical trials, and multivariate data analysis. His work bridges theoretical statistical developments with practical applications, developing methodologies that address real-world data challenges while maintaining rigorous mathematical foundations. His research often involves complex statistical problems requiring sophisticated solutions that balance accuracy with efficiency. Analysis of Professor Mukhopadhyay's recent publications reveals a strong focus on advanced sequential methodologies, particularly in minimum risk point estimation, fixed-width confidence interval problems, and multistage sampling strategies. His work demonstrates increasing attention to big data contexts and computational implementations, while maintaining rigorous theoretical foundations. The publications show consistent development of second-order asymptotic properties and practical implementations across various parametric families including normal, exponential, and gamma distributions. Professor Mukhopadhyay maintains active correspondence with the statistical community through his office at AUST 331 on the Storrs Campus of the University of Connecticut. His work continues to influence methodological developments in sequential analysis and statistical inference, with applications spanning environmental monitoring, clinical research, and various scientific domains requiring sophisticated sampling strategies.

• Automata Theory
• Logic in Computer Science
• Infinite-State Systems
• +۷ مورد دیگر

Wojciech Czerwiński is an Associate Professor at the Institute of Informatics, Faculty of Mathematics, Informatics and Mechanics, University of Warsaw. His research lies at the intersection of automata theory, logic, and computational complexity, with a focus on infinite-state systems such as vector addition systems (VASS) and Petri nets. His research interests include: Automata and logic Infinite-state systems Reachability and separability problems Complexity of computational models Formal verification and concurrency theory His recent work has significantly advanced the understanding of the complexity of reachability in VASS, proving it to be Ackermann-complete. This line of research has been published in top venues including FOCS, STOC, LICS, and CONCUR, where several papers received Best Paper Awards. His publications reflect a strong trend toward resolving long-standing open problems in decidability, complexity, and logical definability in infinite-state models. Scientific awards include: Best Paper Award, CONCUR 2022 Best Paper Award, STOC 2019 He leads an ERC-funded project and is actively recruiting PhD students and post-docs. He organizes the Friday Afternoon Seminar and co-organized the Infinite Automata Workshop (2024). He has also contributed to public understanding of science as an editor and author for the journal Delta from 2013 to 2022. His work involves collaboration with leading researchers such as Sławomir Lasota, Jérôme Leroux, Georg Zetzsche, and others. He has made foundational contributions to problems like bisimilarity, language equivalence, and regular separability in various automata models.

• Nonlinear Dynamics
• Applied Dynamics
• Mechanical Vibrations
• +۱۶ مورد دیگر

Professor Marian Wiercigroch is the Sixth Century Chair in Applied Dynamics and Director of the Centre for Applied Dynamics Research (CADR) at the School of Engineering, University of Aberdeen. He has held numerous leadership roles including Head of Engineering and Director of Internationalisation. His research spans theoretical and applied nonlinear dynamics with applications in drilling, energy harvesting, and mechanical systems. Research Interests: Nonlinear Dynamics and Chaos Applied Dynamics and Vibrations Rotary and Percussive Drilling (Resonance Enhanced Drilling) Energy Harvesting via Parametric Pendula Control of Smart Structures Underwater Acoustics and Fluid-Structure Interaction Machine Tool Dynamics and Precision Machining His recent publications (2022–2023) reveal a strong focus on vibro-impact systems, drilling dynamics, machining stability, and bio-inspired damping. The work integrates mathematical modeling, experimental validation, and control strategies, primarily published in journals such as International Journal of Mechanical Sciences , Communications in Nonlinear Science , and Physica D . Scientific Awards and Honors: Fellow of the Royal Society of Edinburgh (FRSE) Fellow of the Institution of Mechanical Engineers (FIMechE) Fellow of the Institute of Mathematics and Its Applications (FIMA) DSc honoris causa, Lodz University of Technology REF2021 and REF2014 Panel Member Senior Fulbright Fellow Scottish Champion of Knowledge Transfer Honorary and Distinguished Guest Professorships in China, Russia, and Argentina Advising and Grants: Professor Wiercigroch supervises multiple PhD students including Joseph Emans, Olusegun Ajibose, James Ing, and Richard Morrison. He has led major funded projects from EPSRC, Royal Society, BP, European Space Agency, and ITI Energy, including a £4.6M programme on Resonance Enhanced Drilling. His grants focus on nonlinear dynamics in engineering design, vibration control, and offshore systems. Labs and Teams: He leads the Centre for Applied Dynamics Research (CADR) at Aberdeen, which includes researchers like Dr. Ekaterina Pavlovskaia and Dr. David Wagg. The group collaborates internationally with institutions in Brazil, Poland, China, and the USA, and has developed experimental rigs for drilling and energy harvesting.

Биљана П. Стошићمشاهده پروفایل

دانشیار

• Telecommunications
• Signal Processing
• Filter Design
• +۲ مورد دیگر

Биљана П. Стошић serves as an Associate Professor in the Department of Telecommunications at the Faculty of Electronic Engineering, University of Niš, Serbia. Appointed to this position in 2025, she maintains active roles in research, teaching, and professional leadership within the telecommunications engineering community. Her academic credentials include a PhD (2008), Master's degree (2004), and Diploma (1999), all in Telecommunications from the Faculty of Electronic Engineering, University of Niš. This foundational education established her expertise in core telecommunications principles and advanced signal processing techniques. Dr. Stošić's research centers on Telecommunications and Signal Processing with specialized focus on Filter Design, Microwave Engineering, and Artificial Neural Networks applications. Her work bridges theoretical innovation with practical implementations in communication systems, particularly through novel filter architectures and teletraffic modeling. Significant contributions include the development of CIC/FIR filter functions, Chebyshev polynomial applications, and neural network solutions for Erlang B formula calculations. Analysis of her 10+ journal publications reveals consistent emphasis on improving signal processing efficiency in communication systems. Key trends include multirate filter design for reduced insertion loss, passband-droop compensation techniques, and computational methods for microwave circuit analysis. Her research demonstrates increasing integration of AI methods with traditional telecommunications engineering. She actively participates in 9 national and 5 international research projects, reflecting strong grant acquisition capabilities. Professional service includes editorial roles for Microwave Review (2014-2022) and extensive reviewing for IEEE Transactions, Electronics Letters, and other top-tier journals. Conference involvement spans technical co-editing for TELSIKS (2007-2023) and program committee roles for ICEST and TSP. As an IEEE Senior Member, she leads the Women in Engineering Affinity Group and MTT-S Chapter for Serbia and Montenegro. Her ERASMUS+ mobility experiences at Technical University of Sofia and Malta College of Arts, Science and Technology demonstrate international collaboration. Professional affiliations include IEEE Signal Processing Society, Microwave Theory and Techniques Society, and the Association for Microwave Technique, Technologies and Systems.

• Atmospheric Dynamics
• Orographic Gravity Waves
• Mountain Wave Drag
• +۹ مورد دیگر

Miguel Teixeira is a Professor in the Department of Meteorology at the University of Reading , within the School of Mathematical, Physical and Computational Sciences. He is an active researcher in atmospheric and geophysical fluid dynamics, with a long-standing focus on orographic gravity waves, turbulence, and boundary layer processes. Research Focus : His work centers on the dynamics of mountain waves, wave drag parametrization, turbulence in stratified flows, and air-sea interactions. He investigates both theoretical aspects and their implementation in numerical models such as WRF and MPAS. Key Themes : Orographic gravity wave drag, non-hydrostatic effects, wind profile shear and curvature, Langmuir circulation, turbulent kinetic energy dissipation, finite-sized particle dynamics, and wave reflection at critical levels. The recent articles highlight a strong trend in refining gravity wave drag parametrizations for numerical weather and climate models, particularly by incorporating non-hydrostatic effects, directional shear, and wind profile curvature. His research bridges theoretical fluid dynamics with practical modeling applications, aiming to improve the representation of subgrid-scale processes in large-scale simulations. A secondary but consistent theme involves turbulence dynamics, especially at turbulent/non-turbulent interfaces and in particle-laden flows. Collaborations and Impact : He frequently collaborates with institutions in China, Portugal, and the US. He co-edited a 2016 Frontiers volume on The Atmosphere over Mountainous Regions , indicating leadership in the field. Advising and Grants : While no specific students or grants are listed in the provided texts, his extensive publication record and editorial roles suggest active supervision and research funding. His work is cited in ongoing modeling studies, reflecting its relevance to the atmospheric science community. Labs and Teams : He is affiliated with the Department of Meteorology at Reading, a leading center in atmospheric research, likely participating in groups focused on dynamical meteorology and numerical modeling.

• Analysis on metric spaces
• Geometric measure theory
• Harmonic analysis

Scott Zimmerman is an Associate Professor of Mathematics at The Ohio State University at Marion. His research focuses on geometric measure theory, analysis in metric spaces (particularly Carnot groups like the Heisenberg group), and harmonic analysis. He explores extension problems, Whitney extension theorems, and Lusin approximation for curves in non-Euclidean settings. Research Areas: Analysis on metric spaces, Geometric measure theory, Harmonic analysis His work often addresses theoretical challenges in sub-Riemannian geometry, such as curve regularity, singular integrals, and Sobolev extensions. Recent contributions include advancements in Whitney extension theorems for horizontal curves in Heisenberg groups and studies of 1-rectifiable measures in Carnot groups. His articles highlight interdisciplinary connections between pure mathematics and applied fields like computer vision (e.g., object tracking in video data). Despite no listed awards, his research demonstrates sustained innovation in geometric analysis. No advising or grant details are provided, but his active publication record reflects ongoing academic engagement.

Omar Fawziمشاهده پروفایل

پژوهشگر

• Quantum Information Theory
• Theoretical Computer Science
• Quantum Algorithms
• +۴ مورد دیگر

Omar Fawzi is a Research Director (Directeur de Recherche) at Inria, heading the QInfo team at École Normale Supérieure de Lyon. His primary roles include leading research in quantum information theory and theoretical computer science, with a focus on quantum algorithms, error-correcting codes, and quantum cryptography. He has held academic positions since 2011, including teaching at McGill University and ENS Lyon. His research interests span quantum information theory, theoretical computer science, and their applications. He has contributed to foundational work on quantum channel capacities, entropy accumulation theorems, and quantum error correction codes like quantum expander codes. His work bridges theoretical insights with practical implementations, including fault-tolerant quantum computing and device-independent cryptography. Fawzi has advised numerous PhD students and postdoctoral researchers, with students such as Aadil Oufkir (now at RWTH Aachen) and Antoine Grospellier (teaching in France). He has led major grants including the ERC Starting Grant AlgoQIP and the ANR-18-CE47-0011 ACOM project. His research team focuses on advancing quantum communication protocols, quantum algorithms, and the mathematical foundations of quantum mechanics. Key contributions include the entropy accumulation theorem, variational bounds on quantum divergences, and efficient simulation methods for quantum systems. His work emphasizes interdisciplinary approaches, combining tools from computer science, mathematics, and physics to tackle fundamental quantum information challenges.

Pradeep Singhمشاهده پروفایل

استاد

• Statistics
• Machine Learning
• Predictive Modeling
• +۵ مورد دیگر

Pradeep Singh is a tenured Professor of Statistics and Interim Chair in the Department of Mathematics at Southeast Missouri State University , Cape Girardeau, Missouri. He earned his Ph.D. in Statistics from Missippi State University in 1999. With over 20 years of university-level teaching experience, he has taught courses ranging from college algebra to advanced graduate statistics and mentored multiple Master's thesis committees. Research Focus: Machine learning for predictive modeling, missing data imputation in categorical variables, statistical genetics, and medical/biostatistical applications. Key Trends: Publications span machine learning in public health, ecological modeling, critical care analytics, and infectious disease immunology, with recurring emphasis on statistical methodology and real-world data challenges. Committee Involvement: Active in departmental, college, and university governance. His current work explores machine learning applications in cancer outcome prediction and missing data solutions.

• Statistical Relational Artificial Intelligence
• Human-aware AI
• Explainable AI
• +۱۲ مورد دیگر

Tanya Braun is a Junior Professor in the Institute of Computer Science at the University of Münster, Department of Mathematics and Computer Science. She leads the Data Science research group, focusing on statistical-relational AI, human-aware AI, and text understanding. Her work bridges formal AI methods with real-world applications in healthcare, digital humanities, and public sector systems. Education: Bachelor's and Master's in Computational Informatics, Hamburg University of Technology Doctorate in Computer Science, University of Lübeck (2020), thesis: 'Rescued from a Sea of Queries - Exact Inference in Probabilistic Relational Models' Her research centers on probabilistic inference in relational domains , with a focus on lifted inference techniques that exploit symmetries to scale reasoning. She investigates human-aware AI , particularly how AI systems can reconcile learned models with human expectations to improve explainability and trust. Her work on text understanding addresses challenges in data-scarce settings such as digital humanities, where traditional large language models fail. She has developed methods for identifying and enriching subjective content descriptions, topic modeling in specialized domains, and feedback-driven model improvement. The 15 most recent publications highlight a strong trajectory in lifted inference, model compression, privacy-preserving AI, and explainability . Her work integrates formal AI foundations with practical concerns in high-stakes domains like healthcare. She frequently publishes in top venues such as AAAI, IJCAI, ECAI, and Artificial Intelligence, often in collaboration with Ralf Möller, Marcel Gehrke, and Jan Speller. Scientific Awards: No specific awards listed in the provided text. Tanya Braun actively advises students and leads the HAPPI project, which focuses on human-AI model reconciliation using lifted probabilistic inference. She has supervised multiple theses and mentored researchers including Jan Speller (PostDoc), Nazlı Nur Karabulut, and Sagad Hamid. She has secured funding from the Ministry of Culture and Science of North Rhine-Westphalia for her research. She is deeply involved in academic service: serving as program co-chair for KI 2025, guest-editing special issues in journals like Künstliche Intelligenz and Annals of Mathematics and Artificial Intelligence , and organizing major conferences including ICCS and KR. Labs and Teams: She leads the Data Science Group at the University of Münster, which conducts research in AI, probabilistic modeling, and data science. The group is actively involved in teaching and mentoring students in advanced AI topics.

Sebastian Ferrer Martinezمشاهده پروفایل

پژوهشگر

• Mechanics of Particles and Systems
• Dynamical Systems and Ergodic Theory
• Ordinary Differential Equations
• +۴ مورد دیگر

Sebastian Ferrer Martinez is a researcher at Universidad de Murcia specializing in Mechanics of Particles and Systems , Dynamical Systems , and Celestial Mechanics . He earned his Ph.D. from Universidad de Zaragoza (1979) with a thesis on three-body integration and Trojan asteroids. His work focuses on Hamiltonian systems, resonance dynamics, and symmetry reduction in galactic and orbital mechanics. Research group: Applied Mathematics in Science and Engineering Key collaborations: Francisco Crespo, Martin Lara, Jan-Cees van der Meer His research bridges theoretical mathematics and engineering, with notable contributions to integrable systems, bifurcation theory, and perturbed oscillators. Publications span 1980–2021 , emphasizing applications in satellite theory, rigid body dynamics, and galactic tidal models.