جست‌وجوی استاد و پژوهشگر

• Sparse signal processing
• Signal processing for machine learning
• Wireless communications
• +۴ مورد دیگر

Dr. Wei Dai is a Senior Lecturer (Associate Professor) in the Department of Electrical and Electronic Engineering at Imperial College London, part of the Faculty of Engineering. He holds affiliations with the EPSRC Centre for Maths of Precision Healthcare and the Communications and Signal Processing group. His research focuses on sparse signal processing, machine learning applications in signal processing, linear and bilinear inverse problems, wireless communications, and random matrix theory. Notably, he contributed to the first compressive sensing DNA microarray prototype and has a highly cited 2009 paper on compressive sensing reconstruction. Dr. Dai's educational background includes a Ph.D. in Electrical and Computer Engineering from the University of Colorado at Boulder (2007) and postdoctoral research at the University of Illinois at Urbana-Champaign (2007-2010). His work bridges theoretical signal processing with practical applications in sensing, communication systems, and biomedical signal analysis. He leads research initiatives in gridless DOA estimation, robust beamforming, and cortico-muscular coupling analysis using advanced optimization techniques. His research outputs span topics like spectral compressed sensing, Bayesian methods for integrated sensing-communication systems, and dictionary learning for causal discovery. Ongoing work emphasizes low-rank matrix recovery, distributed compressed sensing, and mathematical frameworks for super-resolution localization. Dr. Dai collaborates across disciplines, leveraging signal processing innovations for healthcare technology and next-generation wireless systems.

• Reinforcement Learning
• Neural Network
• Deep Reinforcement Learning
• +۵ مورد دیگر

Abhinav Verma is an Assistant Professor in Computer Science and Engineering, focusing on reinforcement learning, neural networks, and formal verification of control systems. His research bridges machine learning, program synthesis, and theoretical computer science to ensure interpretable and verifiable AI. Research Interests : Reinforcement learning, neurosymbolic AI, policy synthesis, formal guarantees in control systems. Key Contributions : Developed frameworks combining neural networks with formal methods for verifiable sequential decision-making, contributed to explainable AI through programmatic policy learning. Article Trends : Recent work emphasizes temporal logic constraints, counterfactual experience replay, and reduced-variance reinforcement learning. His publications highlight compositional approaches, differentiable programs, and neurosymbolic integration. Collaborations : Collaborates with institutions and researchers in computer science, with a focus on stochastic systems and interpretable machine learning.

Torbjörn Larssonمشاهده پروفایل

استاد

• Mathematical Optimization
• Operations Research
• Linear Programming
• +۱۰ مورد دیگر

Torbjörn Larsson is a Professor in the Department of Mathematics at Linköping University, affiliated with the Division of Applied Mathematics (TIMA). His work bridges theoretical and applied optimization with significant impact in healthcare, logistics, and finance. His research interests include Mathematical Optimization , Operations Research , Brachytherapy Treatment Planning , Vehicle Routing , and Portfolio Optimization . He develops advanced algorithms such as Lagrangian heuristics, metaheuristics, and feasible direction methods to solve complex decision problems. The recent publications indicate a strong focus on developing bounding techniques and heuristic frameworks for discrete and multi-objective optimization, with applications ranging from radiation therapy to transportation logistics. His work emphasizes both theoretical rigor and practical implementation. Scientific Contributions: Development of novel optimization methods for brachytherapy treatment planning Advancement of Lagrangian and metaheuristic frameworks Application of optimization in finance (portfolio selection) and scheduling He collaborates extensively on research projects involving mathematical modeling and algorithm design. While specific advising roles are not listed, his co-authorship with junior researchers suggests mentorship activity. He has contributed to projects on decision support systems for scheduling and large-scale optimization in finance. Laboratories and Research Groups: Applied Mathematics (TIMA), Department of Mathematics, Linköping University Research environment focused on optimization and its applications in medicine and logistics

R Raviمشاهده پروفایل

استاد

• Discrete Optimization
• Operations Research
• Computer Science
• +۴ مورد دیگر

R Ravi is the Vasantrao Dempo Professor of Operations Research and Computer Science at Carnegie Mellon University's Tepper School of Business. He holds a B.E. from IIT Madras and M.S./Ph.D. from Brown University. Active since 1995, he served as Associate Dean for Intellectual Strategy and Chair of the Future Educational Delivery Committee. He currently directs Analytics Strategy and leads the Center for Intelligent Business. His research focuses on discrete optimization, network optimization, and applications in supply chain logistics and online advertising, supported by NSF, Google, and others. He has advised over two dozen doctoral students and developed graduate courses like Network Optimization. He received the 30-year Test of Time Award (2023) and is an INFORMS Fellow. His academic roles include editorial leadership in Operations Research and FOCS chairmanship.

Martin Skovgaard Andersenمشاهده پروفایل

دانشیار

• Optimization
• Signal Processing
• Machine Learning
• +۱۲ مورد دیگر

Martin Skovgaard Andersen is an Associate Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU), where he specializes in Scientific Computing. His research integrates numerical methods, optimization, and machine learning to solve complex computational problems in engineering and data science. Education: Ph.D., University of California, Los Angeles (2006–2011) M.Sc., Aalborg Universitet (2001–2006) Postdoc, Linköping University, Sweden (2011–2012) His research interests include optimization (particularly conic and stochastic programming), numerical algorithms, signal processing, system identification, and fast solvers for integral equations. He works extensively on matrix analysis, regularization, and low-rank approximations, contributing to both theoretical and applied advancements in computational mathematics. The recent publications highlight a strong trend in developing efficient numerical algorithms for large-scale optimization and solving integral equations. His work emphasizes preconditioning, matrix truncation, and Bayesian inversion, with applications in electromagnetics, structural health monitoring, and network identification. There is a consistent focus on improving computational efficiency and scalability of solvers. Scientific Awards: No specific awards mentioned in the provided text. Andersen actively supervises PhD students and leads multiple research projects, including those on non-symmetric conic optimization, data-sparse models, and fast direct solvers. He is the Principal Investigator (PI) on projects related to physics-informed structural health assessment. His collaborative network spans Denmark and international institutions, particularly in computational mathematics and engineering. Labs and Research Teams: He is affiliated with the Scientific Computing section at DTU, which focuses on high-performance computing, numerical algorithms, and mathematical modeling. His work is embedded in a collaborative environment involving researchers in optimization, control theory, and computational electromagnetics.

Romain Raveauxمشاهده پروفایل

دانشیار

• Image Analysis
• Machine Learning
• Structural Pattern Recognition
• +۵ مورد دیگر

Romain Raveaux is an Associate Professor at the LIFAT Computer Science Laboratory, University of Tours, affiliated with Polytech Tours. His research focuses on Image Analysis, Machine Learning, Structural Pattern Recognition, Graph Matching, Graph Neural Networks, Discrete Optimization, Reinforcement Learning, and Transfer Learning . Email: romain.raveaux@gmail.com , romain.raveaux@laposte.net Address: 64 av. Jean Portalis, Tours, France, 37200 Phone: +33 (0)2 47 36 14 27 Research Interests Graph Matching and Neural Networks Discrete Optimization for Pattern Recognition Transfer Learning in Graph-Based Models Historical Document Analysis Scientific Trends His recent work bridges Graph Neural Networks with Mixed-Integer Programming , focusing on Image Semantic Segmentation and Graph Cycle Detection . Earlier studies emphasize Genetic Algorithms for graph classification and Graph Edit Distance optimization in pattern recognition.

• Convexification
• Quadratic Programming
• Mixed-Integer Programming
• +۳ مورد دیگر

Boshi Yang is an **Associate Professor** in the Department of Mathematical and Statistical Sciences at Clemson University. His research focuses on convexification, quadratic programming, mixed-integer programming, and conic programming applications. He holds a PhD from the University of Iowa (2015) and a BS from Zhejiang University (2010). Education: PhD, Applied Mathematical and Computational Sciences, University of Iowa, 2015 BS, Mathematics and Applied Mathematics, Zhejiang University, 2010 His research interests emphasize convexification techniques , quadratically constrained quadratic programming , and conic programming applications . Recent work explores data-driven optimization for energy systems and algorithmic improvements for combinatorial problems. His publications span journals like Mathematical Programming, Operations Research Letters, and IEEE Transactions on Power Systems. Key Awards: 2020 Faculty Teaching Award (Clemson School of Mathematical and Statistical Sciences) 2019 Outstanding Service to Graduate Students (Clemson School of Mathematical and Statistical Sciences) Teaching: Teaches courses including Linear Programming (MATH 4400/6400), Nonlinear Programming (MATH 8110), and Special Topics in Conic Programming (MATH 9880).

Aravindan Vijayaraghavanمشاهده پروفایل

دانشیار

• Theoretical Computer Science
• Machine Learning Theory
• Data Science
• +۷ مورد دیگر

Aravindan Vijayaraghavan is an Associate Professor in the Department of Computer Science at Northwestern University (affiliated with McCormick School of Engineering). He also holds courtesy appointments in the Industrial Engineering and Management Sciences (IEMS) department. Research interests include theoretical computer science , machine learning algorithms , quantum information , and combinatorial optimization under non-worst-case paradigms. He leads IDEAL (Institute for Data, Economics, Algorithms and Learning) as Site Director at Northwestern and former Institute Director (2023-24). Academic Background : PhD in Computer Science from Princeton University (advisor: Moses Charikar ) Bachelor's Degree in Computer Science from Indian Institute of Technology Madras Postdoctoral work at Courant Institute (NYU) and Carnegie Mellon University via Simons Collaboration grants Research Contributions : Developed smoothed analysis frameworks for random matrices with dependent entries Created sum-of-squares certificates for anti-concentration beyond Gaussian distributions Advanced quantum entanglement certification algorithms for subspaces Improved weak-to-strong generalization theory with data distribution expansion properties Designed error-tolerant e-discovery protocols for legal document classification Scientific Recognition : NSF CAREER Award NSF AITF Award (CCF-1637585, CCF-2154100) Google Research Scholar Program grant Amazon Research Awards program support Simons Postdoctoral Fellowship Academic Leadership : General Chair for FOCS 2024 Co-organizer of Junior Theory Workshop and Northwestern QTW series Active in program committees for COLT , ICML , NeurIPS , and STOC conferences Teaching Portfolio : CS262: Mathematical Foundations of CS (Continuous Mathematics for Computer Science) CS212: Mathematical Foundations of Computer Science (multiple offerings since 2015) CS496: Graduate Algorithms (since 2016) CS396/496: Quantum Computation & Information (co-taught with S. Rao) CS497: Machine Learning Theory (Spring 2025 offering)

Shaked Flurمشاهده پروفایل

پژوهشگر

• Formal Methods
• Computer Architecture
• Programming Languages
• +۳ مورد دیگر

Shaked Flur is a Researcher at the Department of Computer Science and Technology, University of Cambridge. His work focuses on formal methods, instruction-set semantics, and concurrency models in computer architectures. He contributes to the development of formal verification tools and specifications for ISA standards like ARMv8-A, RISC-V, and CHERI-MIPS. His research bridges academic formal models with practical software and hardware design, emphasizing the adoption of rigorous methods in real-world systems. Flur collaborates on projects such as the Sail specification language, enhancing clarity and precision in ISA documentation. His publications address challenges in memory models, concurrency semantics, and automated verification techniques.

• Applied mathematics
• Numerical and computational mathematics
• Advection-diffusion-reaction models
• +۷ مورد دیگر

Dr. Elliot Carr is a Senior Lecturer in the School of Mathematical Sciences at Queensland University of Technology (QUT), Faculty of Science. He holds a PhD in Mathematics from QUT and has been a faculty member since 2015, progressing from Lecturer to his current rank. His research and teaching focus on applied and computational mathematics, with strong interdisciplinary applications. Education: PhD in Mathematics, Queensland University of Technology, 2009–2012 Bachelor of Applied Science (Honours) in Mathematics, QUT, 2008 Bachelor of Mathematics, QUT, 2005–2007 Elliot Carr's research lies at the intersection of applied mathematics and real-world physical systems. His work centers on developing and analyzing mathematical models of advection, diffusion, and reaction processes, particularly in heterogeneous media. He employs both deterministic (PDE-based) and stochastic (random walk) frameworks, contributing to analytical solutions, multiscale modeling, surrogate models, and numerical methods such as finite volume and Newton-Krylov techniques. His research has been applied to diverse fields including groundwater contamination, drug delivery, heat transfer, and tumor spheroid modeling. The latest publications reflect a consistent focus on transport phenomena in complex geometries and heterogeneous environments. Key themes include dual-grid mapping for contaminant transport, analytical modeling of drug release from spherical capsules, thermal diffusivity in shell geometries, and stochastic models of biological systems. His methodological contributions span analytical, numerical, and statistical approaches, demonstrating versatility across applied mathematics. Scientific Awards and Recognitions: JH Michell Medal, ANZIAM (2022) ARC DECRA Fellowship (2015) QUT Outstanding Doctoral Thesis Award (2012) University Medal, QUT (2008) Dean’s Award for top graduate in both Honours and Bachelor programs Keynote and plenary speaker at major conferences including ANZIAM and Forum “Math-for-Industry” Dr. Carr actively supervises PhD and Masters students, with completed and ongoing projects on diffusive transport, tumor modeling, and sports analytics. He has secured competitive research funding, including an ARC Discovery Project on multiscale modeling. His teaching includes computational mathematics, linear algebra, and differential equations, with a focus on MATLAB-based implementation. He is a member of the Australian Mathematical Society (AustMS) and ANZIAM. Research Labs and Teams: While not explicitly tied to a named lab, Carr is part of the broader Applied Modelling and Computation research environment at QUT. He collaborates extensively with researchers such as Ian Turner, Matthew Simpson, and Chris Drovandi, contributing to interdisciplinary teams in mathematical biology, environmental modeling, and statistical computation.

Christopher Hojnyمشاهده پروفایل

استادیار

• Mixed-Integer Programming
• Symmetry Handling in Optimization
• Combinatorial Optimization
• +۳ مورد دیگر

Christopher Hojny is an Assistant Professor at the Department of Mathematics and Computer Science at Eindhoven University of Technology , specializing in combinatorial optimization. He contributes to the EAISI Foundational group and co-develops the academic solver SCIP . His research focuses on symmetry handling in mixed-integer programming , theoretical properties of integer programs, and algorithm development for combinatorial optimization. Recent work explores applications in graph neural network verification , clustering problems, and network coding through mixed-integer programming frameworks. Key publication trends show expertise in Symmetry detection and mitigation techniques Relaxation complexity theory Applications to machine learning robustness Decision diagram-based scheduling Scientific contributions include Proof systems for symmetry certification Topological bounds tightening in GNNs Stable set problem symmetry handling SCIP solver extensions He supervises PhD students Cédric Roy (NWO project on Local Symmetries) and Sten Wessel (co-supervised with Frits Spieksma), while Jasper van Doornmalen (2019-2023) investigated symmetry propagation algorithms.

Jerry Liمشاهده پروفایل

دانشیار

• learning theory
• quantum information theory
• large foundation models
• +۴ مورد دیگر

Jerry Li is an associate professor at the University of Washington's Paul G. Allen School of Computer Science & Engineering. Previously, he was a principal research scientist at Microsoft Research Redmond and was the VMware Research Fellow at the Simons Institute in Fall 2018. Li completed his Ph.D. at MIT under the supervision of Ankur Moitra and his master's degree at MIT under Nir Shavit. As an undergraduate, he also attended the University of Washington, where he worked on complexity of branching programs and hardness of learning problems in database theory and AI. His primary research interests focus on learning theory broadly defined, with specific expertise in quantum information theory, large foundation models, and high-dimensional statistics. He has a particular interest in applying analysis and analytic techniques to theoretical computer science problems. His recent work spans quantum computing, robust machine learning, and theoretical foundations of deep learning, showing a clear trend toward bridging quantum information theory with statistical learning theory. Li has made significant contributions to the fields of robust statistics, quantum computing, and theoretical machine learning, with numerous publications in top-tier conferences and journals including FOCS, STOC, NeurIPS, ICML, and Science. His work often bridges theoretical guarantees with practical applications in machine learning systems, particularly in the areas of robustness and quantum advantage. George M. Sprowls Award for outstanding Ph.D. theses in EECS at MIT Best Artifact Award at PPoPP 2015 for "The SprayList: A Scalable Relaxed Priority Queue" Communications of the ACM Research Highlights for "Robust Estimators in High Dimensions without the Computational Intractability" Invited to special issues of SIAM Journal on Computing for FOCS 2023 and STOC 2022 Spotlight Presentations at NeurIPS 2019 Notable top 5% paper at ICLR 2023 Li advises several Ph.D. students including Ziyun Chen (co-advised with Shayan Oveis Gharan) and numerous research interns. He has served on program committees for major conferences including STOC, SODA, and ITCS, and is co-organizing the FOCS 2024 Workshop on Recent Advances in Quantum Learning. His teaching includes courses such as CSE 422: Toolkit for Modern Algorithms and CSE 599-M: Robustness in Machine Learning, for which he has created publicly available video lectures.

Massimiliano Pontilمشاهده پروفایل

استاد

• Machine Learning
• Statistical Learning Theory
• Kernel Methods
• +۵ مورد دیگر

Massimiliano Pontil is a **part-time Professor of Computational Statistics and Machine Learning** in the Department of Computer Science at University College London (UCL) and a Senior Researcher at the **Istituto Italiano di Tecnologia (IIT)**, leading the **Computational Statistics and Machine Learning (CSML)** research group. He holds an MSc and PhD in Physics from the University of Genova (1994 and 1999) and has held postdoctoral positions at MIT and research roles at institutions like the University of Siena and City University of Hong Kong before joining UCL in 2003. **Research Interests**: Focuses on machine learning, including statistical learning theory, kernel methods, multitask and transfer learning, online learning, sparsity regularization, and applications in computer vision, bioinformatics, and user modeling. His work bridges theoretical foundations (e.g., regularization, optimization) with practical applications. **Publications**: Over 100 papers in top venues like NIPS/NeurIPS, ICML, COLT, and JMLR, covering topics such as structured sparsity, multitask learning, and optimization frameworks. Recent work emphasizes meta-learning, fair algorithms, and dynamical systems learning. **Grants & Awards**: EPSRC Advanced Research Fellowship. His research has been applied to diverse domains, including bioinformatics (e.g., protein-protein interface prediction) and social network analysis (e.g., mobility patterns). **Labs/Teams**: Leads the CSML group at IIT, collaborating on projects like Koopman operator regression and conditional meta-learning. Active in organizing workshops on multi-task learning and structured outputs (e.g., PASCAL thematic programs).

• Computational Mathematics
• Fluid Dynamics
• Traffic Flow Modeling
• +۳ مورد دیگر

Dr. Anargyros (Argiris) Delis is a Professor of Computational Mathematics at the School of Production Engineering & Management, Technical University of Crete. He holds a B.Sc. in Mathematics (1993, University of Crete), M.Sc. in Numerical Analysis and Computing (1994, University of Manchester/UMIST), and a Ph.D. in Applied and Computational Mathematics (1998, University of the West of England). His academic career includes roles as Lecturer (2003–2008), Assistant Professor (2008–2013), and Associate Professor (2013–2022) at Technical University of Crete, before becoming Full Professor in 2022. He has directed the MSc in Applied Mathematics program (2019–2023) and leads research in numerical methods for fluid dynamics, traffic flow modeling, and computational mathematics. Research Interests: Development of high-resolution finite volume schemes for shallow water equations, traffic flow dynamics, and fluid-structure interactions. Key domains include coastal engineering, tsunami modeling, and ACC/CACC vehicle systems. Active in labs like Turbomachines & Fluid Dynamics Laboratory and the Numerical Analysis Group at IACM-FORTH. Notable Projects: TRAMAN21 (traffic management), C-NORA (transport systems control), and contributions to flood simulation (e.g., Malpasset Dam Failure modeling). Publications focus on numerical methods, wave propagation, and traffic flow control, with over 50 peer-reviewed articles. Grants: Involved in EU-funded projects and Greek national initiatives. Collaborations with institutions like INRIA, ERCIM, and Global Power and Propulsion Society. Labs/Teams: Member of Applied Mathematics and Computers Laboratory (AMCL), Turbomachines Lab, and Coastal Information Research Unit (CIRUM).

Anna Huberمشاهده پروفایل

مدرس ارشد

• Algebra
• Graph Theory
• Randomized Methods
• +۳ مورد دیگر

Anna Huber is a Senior Lecturer in Mathematics and Computing at Teesside University's Department of Computing & Games Centre for Sustainable Engineering. She holds a PhD from Saarland University (2010) and completed postdoctoral research at Durham University (2010-2013) on EPSRC-funded submodular optimization projects. Her research focuses on algebraic structures, graph theory, and combinatorial algorithms with applications in discrete optimization. She has published influential work in SIAM journals on bisubmodular functions and valued constraint satisfaction problems. Education: PhD in Mathematics and Informatics, Saarland University & Max Planck Institute (2010) Previously affiliated with University of Derby as Lecturer in Mathematics Research Themes: Explores algebraic methods for solving combinatorial optimization problems, particularly through randomized algorithms and graph-based approaches. Current work emphasizes tractability analysis in constraint satisfaction frameworks and bisubmodularity applications. Grants & Projects: EPSRC Project: 'Submodular Optimization, Lattice Theory and Maximum Constraint Satisfaction Problems' (2010-2013) Labs/Teams: Active in Teesside University's Computing & Games research groups, collaborating on sustainable engineering computing applications.