جست‌وجوی استاد و پژوهشگر

Siyi Wangمشاهده پروفایل

پژوهشگر

• Value of information for control
• Stochastic control and optimization
• Online learning and control
• +۳ مورد دیگر

Siyi Wang is a Postdoctoral Researcher at the Division of Decision and Control Systems (DCS) , KTH Royal Institute of Technology, under the mentorship of Prof. Karl Henrik Johansson. She earned her Ph.D. from Technical University of Munich (TUM) under Prof. Sandra Hirche and holds B.Eng and M.Eng degrees from Harbin Institute of Technology under Prof. Ligang Wu. Education: B.Eng & M.Eng in Electrical Engineering, Harbin Institute of Technology Ph.D. in Electrical Engineering, Technical University of Munich (TUM) Research Focus: She investigates the value of information for control , stochastic control and optimization , and online learning and control , addressing challenges in networked control systems (delays, packet loss) and developing risk-averse reinforcement learning frameworks. Key Publications: Her work spans networked control systems under cyber-physical threats, sliding mode control for multi-agent systems, and distributional reinforcement learning for LQR optimization. Recent papers emphasize adaptive event-triggered scheduling, disturbance handling, and safety-critical control strategies. Academic Contributions: Advising: Supervised master's (2022), bachelor's (2021), and research internships (2021–2022) on topics like event-triggered learning and information value simulations Professional Service: Reviewer for ISA Transactions , IEEE JSAC , IFAC , and IEEE CDC Laboratory Affiliation: Division of Decision and Control Systems, KTH Royal Institute of Technology, focusing on networked systems, cyber-physical security, and learning-based control methodologies.

Ioannis Karatzasمشاهده پروفایل

استاد

• Stochastic Analysis
• Mathematical Finance
• Probability Theory
• +۳ مورد دیگر

Ioannis Karatzas is the Eugene Higgins Professor of Applied Probability at Columbia University, holding appointments in both the Department of Mathematics and Department of Statistics. His work bridges Stochastic Analysis , Mathematical Finance , and Optimization , with a focus on utility maximization, market equilibrium, and stochastic control problems. Research Interests: Stochastic processes, financial mathematics, optimal stopping, singular control, and portfolio optimization. His contributions include foundational work on martingale methods in incomplete markets and the development of stochastic portfolio theory. Books: Co-authored Brownian Motion and Stochastic Calculus and Methods of Mathematical Finance , both standard references in stochastic analysis and quantitative finance. Teaching: Taught advanced courses like Stochastic Methods in Finance and Probability , covering topics from arbitrage theory to optimal consumption. Doctoral Students: Advised 18 PhD students, including notable researchers in stochastic control, financial economics, and optimal stopping.

Grani Adiwena Hanasusantoمشاهده پروفایل

دانشیار

• Optimization Under Uncertainty
• Robust Optimization
• Stochastic Programming
• +۳ مورد دیگر

Grani Adiwena Hanasusanto is an Associate Professor in the Department of Industrial & Enterprise Systems Engineering at the University of Illinois Urbana-Champaign. His research focuses on robust optimization , stochastic programming , and fair machine learning , addressing decision-making under uncertainty in domains like transportation, healthcare, and energy systems. Recent work includes Finite-sample guarantees for robust system identification Scalable algorithms for neural network verification Fairness-aware policies for infectious disease mitigation NSF-funded projects on distributionally robust quadratic optimization for power systems Scientific contributions and recognition include Editorial board member of Operations Research Honoree on the List of Teachers Ranked as Excellent INFORMS Diversity, Equity, and Inclusion Ambassador His teaching portfolio includes courses on Optimization of Large Systems and Optimization Under Uncertainty at UIUC, previously taught at UT Austin. Advisees include PhD candidates exploring robust solution schemes for sequential decision-making, queue management, and classification problems.

Cheng Maoمشاهده پروفایل

استادیار

• statistical inference on random graphs
• mathematics of data science
• mathematical statistics
• +۲ مورد دیگر

Cheng Mao is an Assistant Professor in the School of Mathematics at the Georgia Institute of Technology (Georgia Tech), with office location in Skiles 102C and mailing address at 686 Cherry Street, Atlanta, GA 30332 USA. His position is active as of 2025, with no indication of part-time status or retirement. Education: B.S. and M.A. in Mathematics, University of California, Los Angeles (UCLA), 2013 Ph.D. in Mathematics and Statistics, Massachusetts Institute of Technology (MIT), 2018 (Advisor: Philippe Rigollet) Postdoctoral Researcher, Yale University, 2018-2019 (with Yihong Wu) Cheng Mao's research centers on the mathematics of data science, specifically statistical inference for random graphs. His work bridges mathematical statistics, applied probability, and theoretical computer science to address fundamental problems in graph matching, community detection, and matrix estimation under structural constraints like total positivity. He investigates algorithmic efficiency, information-theoretic limits, and the detection-recovery gap in planted models. His publication trends (2020-2025) reveal deep specialization in spectral methods for graph matching (e.g., Erdős-Rényi graphs, noise robustness) and combinatorial approaches to planted structures (dense cycles, subhypergraphs). Key themes include leveraging low-degree polynomials for detection, tree counting for network correlation, and method-of-moments techniques for permutation learning. Matrix estimation under Monge and total positivity constraints forms another consistent thread. Scientific Awards: No scientific awards mentioned in the provided text Cheng Mao advises three Ph.D. students at Georgia Tech: Jingyi (Joy) Zhang (co-advised with Debankur Mukherjee), Shenduo Zhang, and Abhishek Dhawan (Ph.D. 2024, now postdoc at UIUC, co-advised with Anton Bernshteyn). Timothy Wee joined as a postdoc in 2024. The text contains no references to research grants or external funding sources. No laboratories, research teams, or collaborative groups are specified in the source material.

Daniel Karapetyanمشاهده پروفایل

استادیار

• Combinatorial Optimization
• Artificial Intelligence
• Data Science
• +۱۷ مورد دیگر

Daniel Karapetyan is an Assistant Professor at the School of Computer Science , University of Nottingham, and a member of the Computational Optimisation and Learning Lab . His research spans Artificial Intelligence , Data Science , Operational Research , and Optimization , with applications in transportation , logistics , satellite mission planning , and access control . Education: PhD in Computer Science, Royal Holloway, University of London (2007–2010) BSc/MSc in Computer Science, Bauman Moscow State Technical University (2001–2007) His research interests focus on the automated design of heuristic algorithms , multi-objective optimization , and parameter tuning for machine learning and combinatorial problems. He integrates Large Language Models (LLMs) and hyperparameter optimization into practical systems, addressing real-world challenges in airport sequencing , TV ad scheduling , and electric vehicle range prediction . Notable scientific contributions include the CORS Practice Prize (2014) for ferry scheduling work and pioneering fixed-parameter algorithms in security-related optimization problems. His publications emphasize combinatorial optimization , Markov Chain methods , and automated algorithm configuration . Teaching roles include module convener for COMP3008: Knowledge Representation and Reasoning , personal tutoring for COMP1002 , and supervision of COMP2002: Software Engineering Group Project . He also offers PhD supervision in areas like AI for optimization , interpretability in ML , and emergency department coordination .

• Hamilton-Jacobi PDE
• Viscosity solutions
• Mean Field Games
• +۳ مورد دیگر

Claudio Marchi is an Associate Professor of Mathematical Analysis at the University of Padua's Department of Mathematics "Tullio Levi-Civita". He maintains an active teaching and research schedule, with courses in Mathematical Analysis 1 for Biomedical Engineering students and Introduction to Partial Differential Equations for Mathematics students scheduled for the 2025-26 academic year. Professor Marchi's research focuses on Hamilton-Jacobi PDEs and viscosity solutions , Mean Field Games , and multiscale problems in partial differential equations . His work frequently addresses analytical challenges in non-standard geometries including networks and the Heisenberg group. Recent publications demonstrate his expertise in convergence rates, homogenization, and singular perturbation methods for partial differential equations. His publication record shows consistent scholarly output with approximately 3-4 papers annually in recent years, appearing in top mathematics journals including SIAM Journal on Mathematical Analysis, Mathematische Annalen, and Calculus of Variations and Partial Differential Equations. This sustained research activity indicates an established academic career with significant contributions to the field of mathematical analysis. Professor Marchi collaborates extensively with researchers including Fabio Camilli, Paola Mannucci, and Nicoletta Tchou, among others. His international collaborations reflect the global relevance of his work in partial differential equations and game theory. As an educator, Professor Marchi teaches core mathematics courses for engineering and mathematics students, maintaining regular office hours and providing detailed course materials through the university's Elearning platform. His teaching schedule includes specific exam periods scheduled through 2026, demonstrating his ongoing commitment to academic instruction.

Nguyễn Kim Thắngمشاهده پروفایل

استاد

• Design and Analysis of Algorithms
• Optimization in Machine Learning
• Discrete Optimization
• +۲ مورد دیگر

Nguyễn Kim Thắng is a Full Professor affiliated with LIG , ENSIMAG , Grenoble INP , and University Grenoble-Alpes . He serves as Education Director of EFELIA-MIAI and member of the MIAI scientific board, while also participating in the scientific board of the GT Complexity and Algorithms and the pedagogical team at Ensimag's Fundamental Computer Science department. His research focuses on algorithm design and analysis, particularly in online algorithms, optimization in machine learning, discrete optimization, operations research, and algorithmic game theory. Nguyen works on problems related to resource allocation, scheduling, and decentralized optimization with applications to smart infrastructure. Nguyen co-chairs major conferences including the Conference on Frugal Machine Learning (2024) and STACS (2025), and has organized workshops on frugal learning, edge intelligence, and scheduling challenges. He serves on program committees for STACS, ACML, and selection committees for academic positions in artificial intelligence. His recent publications focus on primal-dual algorithms with predictions, decentralized optimization techniques, and applications to smart-building systems. Nguyen teaches courses ranging from foundational programming to advanced machine learning at Ensimag and has previously coordinated international academic programs.

Arka P Ghoshمشاهده پروفایل

استاد

• Stochastic Control
• Queueing Networks
• Stochastic Modeling
• +۴ مورد دیگر

Arka P Ghosh is a Professor at Iowa State University's Department of Statistics, with courtesy affiliations in the Department of Mathematics and the Department of Industrial and Manufacturing Systems Engineering. He holds a Ph.D. in Statistics from the University of North Carolina, Chapel Hill (2005), and a B-Stat and M-Stat from the Indian Statistical Institute, Kolkata, India (1998, 2000). Ph.D. in Statistics, University of North Carolina, Chapel Hill, 2005 M-Stat and B-Stat, Indian Statistical Institute, Kolkata, 1998-2000 His research focuses on Stochastic Control, Queueing Networks, Random Walks, and Random Graphs, with applications in statistical classification, clustering, and applied probability models. His work bridges theoretical analysis and practical optimization in stochastic systems. Arka teaches courses such as Stat 690: Introduction to Measure Theory Stat 641: Foundations of Probability Theory Stat 305: Engineering Statistics Stat 105: Introduction to Statistics for Engineers and coordinates online courses for the Statistics Department. He has mentored numerous Ph.D. and M.S. students, including Dong Dai, Subhomoy Ghosh, and Reza Rastegar, many of whom hold academic or industry positions in statistics, mathematics, and data analytics.

Randal Burnsمشاهده پروفایل

استاد

• Data Science
• Machine Learning
• Cloud Storage Systems
• +۴ مورد دیگر

Randal Burns is a Professor and the Bill and Lisa Stromberg Head of the Department of Computer Science in the Whiting School of Engineering at Johns Hopkins University. He is also affiliated with the Data Science and AI Institute and co-founder of NeuroData. His research focuses on scalable data systems for scientific applications, spanning storage technologies, cloud infrastructure, and graph/sparse-matrix engines for machine learning. Key themes include data-intensive science, neuroscience imaging analysis, and adaptive performance management. Recent publications highlight trends in edge-parallel graph encoders masked matrix operations for sparsity decentralized foundation model training storage optimization for evolving AI models batch-parallel data structures turbulence data analysis These reflect his work on scalable systems for machine learning and scientific computing. Scientific awards include NSF CAREER Award DOE Early Career Principal Investigator Kavli Fellowship IBM Outstanding Innovation Award Advising: Mentors PhD students in scalable data systems and machine learning. Grants: Supported by NSF, DOE, and DARPA initiatives. Labs: Core member of the Johns Hopkins Turbulence Database Group and NeuroData team, developing open platforms for neuroscience and turbulence research.

Tamir Hazanمشاهده پروفایل

دانشیار

• Machine Learning
• Theoretical Machine Learning
• Computer Vision
• +۶ مورد دیگر

Associate Professor Tamir Hazan is a faculty member at Technion - Israel Institute of Technology, where he joined in 2015. His research focuses on theoretical and practical aspects of machine learning, with applications spanning computer vision, natural language processing, and computational biology. His work bridges mathematical foundations with real-world problem solving in complex systems. Professor Hazan received his Ph.D. from the Hebrew University in 2009. His academic trajectory has established him as a leading researcher in machine learning theory and its applications, with a particular emphasis on developing mathematically rigorous approaches to modern AI challenges. Professor Hazan's research centers on mathematically founded solutions to problems demonstrating non-traditional statistical behavior. His work encompasses perturbation models for efficient learning of high-dimensional statistics, deep learning of infinite networks, and primal-dual optimization for high-dimensional inference problems. His research program spans three major interconnected areas: attention models that improve prediction interpretability, perturbation frameworks that integrate optimization and sampling through extreme value statistics, and convex duality approaches to message-passing in graphical models. His work demonstrates both theoretical depth and practical relevance across multiple domains. Analysis of Professor Hazan's recent publications reveals an evolving research trajectory with increasing emphasis on interpretable machine learning, causal modeling, and applications in medical imaging and behavioral science. His work consistently bridges theoretical foundations with practical implementations, with recent publications showing strong connections between perturbation theory, attention mechanisms, and optimization frameworks. The interdisciplinary nature of his research is evident in applications ranging from pedestrian navigation using smartphone sensors to video-text matching systems and medical image analysis. Professor Hazan has mentored numerous students throughout his career, including: Alex Schwing, now Assistant Professor at UIUC Alon Cohen, now Associate Professor at Tel Aviv University Idan Schwartz, currently Postdoc at Tel Aviv University Current Ph.D. students: Guy Lorberbom, Itai Gat, and Hedda Cohen Multiple M.Sc. students including Adi Manos, Ram Yazdi, and others Professor Hazan's research group maintains an active program with several key focus areas: Attention models for interpretable and improved prediction processes in visual question answering and multimodal applications Perturbation models that enable efficient statistical reasoning in complex systems with exponential configuration spaces Markov random fields, convex duality, and message-passing algorithms for structured prediction and distributed computing

• Machine Learning
• Support Vector Machines
• Evolution Strategies
• +۴ مورد دیگر

Christian Igel is a Full Professor at the Department of Computer Science (DIKU), University of Copenhagen. He serves as Director of the SCIENCE AI Centre and co-lead of the Pioneer Centre for Artificial Intelligence. Igel holds a Doctoral degree from Bielefeld University (2002) and a Habilitation from Ruhr-University Bochum (2010). Previously, he was a Juniorprofessor at Ruhr-University Bochum (2003-2010). His research focuses on fundamental and applied machine learning, including: kernel methods, evolutionary optimization, deep neural networks, PAC-Bayesian analysis, and applications supporting sustainable development goals. He maintains extensive collaborations across disciplines, particularly in environmental monitoring and medical informatics. Igel's recent publications demonstrate strong interdisciplinary focus with applications in: Environmental science (tree cover mapping, carbon stock quantification) Healthcare (sleep staging, mental health epidemiology) Core ML theory (monotonic networks, PAC-Bayesian bounds) His work consistently appears in high-impact venues including Nature, ICML, and NeurIPS. Awards and Honors: ELLIS Fellow (European Lab for Learning and Intelligent Systems) Editorial Roles: Editor, German Journal on Artificial Intelligence (KI) Associate Editor, Evolutionary Computation Journal (ECJ) Associate Editor, Artificial Intelligence Journal (AIJ)

David Ginsbourgerمشاهده پروفایل

استاد

• Statistics
• Machine Learning
• Uncertainty Quantification
• +۷ مورد دیگر

David Ginsbourger is a Professor and Head of Research Group at the Institute of Mathematical Statistics and Actuarial Science (IMSV) within the University of Bern, Switzerland. He maintains dual affiliations through his role at IMSV and as a member of the Multidisciplinary Center for Infectious Diseases (MCID), reflecting interdisciplinary engagement across statistical methodology and applied domains. His research program centers on advanced statistical methodologies with emphases on Gaussian process modeling, uncertainty quantification, and experimental design for computer experiments. Key contributions include novel kernel constructions for equivariant systems, sequential design strategies for excursion set estimation, and efficient computational frameworks for spatial distributional modeling. His work bridges theoretical statistics with practical applications in agriculture, chemoinformatics, environmental science, and risk assessment, demonstrating consistent innovation in handling complex prediction problems under uncertainty. Analysis of his 15 most recent publications (2024-2025) reveals persistent methodological development in Gaussian process theory alongside expanding application domains. Recurring themes include integration-free kernel design for structured data, rare event probability estimation, and multivariate forecast calibration. His research exhibits strong continuity in addressing computational challenges for large-scale inverse problems while increasingly incorporating domain-specific constraints from fields like molecular chemistry and agricultural science. Ginsbourger leads a dedicated research group at IMSV focused on advancing statistical frameworks for computer experiments and uncertainty quantification. The group maintains active collaborations across disciplines, particularly evident in recent work connecting statistical methodology to infectious disease modeling through MCID affiliations and agricultural optimization projects.

Marius Hofertمشاهده پروفایل

دانشیار

• Dependence Modeling
• Computational Statistics
• Quantitative Risk Management
• +۳ مورد دیگر

Marius Hofert serves as Associate Professor of Statistics in the Department of Statistics and Actuarial Science at The University of Hong Kong's School of Computing and Data Science, with office in Room 228 of the Run Run Shaw Building. His research fundamentally addresses statistical dependence structures and computational methodologies for risk assessment. His core research domains include: Copula theory and dependence modeling for complex stochastic systems Advanced Monte Carlo and quasi-random sampling techniques Quantitative risk management frameworks for financial and insurance applications Recent publications demonstrate increasing integration of machine learning with traditional statistical methods, particularly through generative neural networks for dependence modeling. His critical examinations of AI tools like ChatGPT in quantitative contexts reveal methodological pitfalls while exploring new computational frontiers. As Academic Adviser in Risk Management, he mentors PhD candidates with strong mathematical backgrounds. He actively develops open-source statistical software through R packages including copula , nvmix , and qrmtools , maintaining the Quantitative Risk Management tutorial website (qrmtutorial.org).

• Combinatorial Optimization
• Distributed and Parallel Computing
• Coding Theory
• +۴ مورد دیگر

Bernhard Haeupler is a Professor at INSAIT (Institute for Computer Science, Artificial Intelligence and Technology) under Sofia University "St. Kliment Ohridski" since 2024. He is also a Post-Professor and Researcher at ETH Zurich 's Computer Science Department since 2020 and an Adjunct Professor at Carnegie Mellon University 's Computer Science Department since 2022. His roles span theoretical computer science, algorithm design, and distributed/parallel systems. Research Interests : Haeupler focuses on algorithms for combinatorial optimization, distributed and parallel computing, coding theory, graph/network algorithms, and information theory. His work bridges theoretical foundations with practical applications in network resilience, synchronization, and quantum/parallel optimization. Article Trends : His 15 most recent articles (2023-2025) emphasize distributed algorithms (e.g., dynamic routing, fault-tolerant spanners), parallel computing (e.g., low-congestion shortcuts, near-linear work), and coding theory (e.g., synchronization strings, insertion-deletion codes). Topics include graph theory , network design , and quantum shortest paths . Scientific Awards : ERC Starting Grant (2020) Sloan Research Fellow (2019) NSF CAREER Award (2018) ACM-EATCS Doctoral Dissertation Award (2014) George Sprowls Award for MIT PhD Thesis Advising & Grants : Haeupler has advised eight PhD students, including Jason Li (CMU, now at Google Research Zurich) and Nicolas Resch (University of Amsterdam). He has secured multiple NSF grants for projects like Distributed Optimization Beyond Worst-Case Topologies and New Coding Techniques for Synchronization Errors .

Peter Manoharمشاهده پروفایل

پژوهشگر

• Theoretical Computer Science
• Algorithms
• Coding Theory
• +۴ مورد دیگر

Peter Manohar is a postdoctoral researcher in the Computer Science and Discrete Math group at the Institute for Advanced Study , focusing on Theoretical Computer Science with emphasis on algorithms, coding theory, and cryptography. His work explores spectral algorithms for semirandom and smoothed instances of NP-hard constraint satisfaction problems, linking these methods to coding theory, extremal combinatorics, and cryptography. Education: PhD in Computer Science from Carnegie Mellon University, advised by Venkatesan Guruswami and Pravesh K. Kothari B.S. in EECS from UC Berkeley, advised by Alessandro Chiesa and Ren Ng Research Trends: His recent publications highlight advancements in spectral refutation techniques, locally decodable/correctable codes, and connections between complexity theory and coding. Articles span venues like FOCS, STOC, APPROX, and arXiv, reflecting his interdisciplinary approach. Awards: He has received prestigious NSF and Cylab Presidential Fellowships, along with ARCS scholarships during his PhD. His work on quantum proofs (TCC 2019) and constraint satisfaction problems has been recognized in invited journal special issues. Teaching & Collaboration: Peter has taught courses at Carnegie Mellon, including Quantum Computing and Computer Graphics. He interned at TTIC in Summer 2023 and co-organized CMU's Theory Club, demonstrating active engagement in academic communities.