جست‌وجوی استاد و پژوهشگر

• Mathematical Finance
• Stochastic Analysis
• Model Uncertainty
• +۷ مورد دیگر

Max Nendel is a Junior Professor (Assistant Professor) at Bielefeld University, specifically affiliated with the Institute of Mathematical Economics within the Faculty of Business Administration and Economics. He is actively involved in the Collaborative Research Center 1283 (CRC 1283) "Taming uncertainty and profiting from randomness and low regularity in analysis, stochastics and their applications" as subproject manager for C7: "Markovian dynamics under model uncertainty". Additionally, he is a member of the Bielefeld Graduate School in Theoretical Sciences and an expert for the Center for Uncertainty Studies (CeUS) with expertise in Financial markets and Risk management. Dr. Nendel's research focuses on the intersection of mathematics, probability theory, and financial applications, with a particular emphasis on model uncertainty and its implications for financial markets. His work spans mathematical finance, stochastic analysis, nonlinear expectations, and risk management. He approaches these fields through rigorous mathematical frameworks, developing theoretical foundations that have practical applications in financial risk assessment and decision-making under uncertainty. His publication record reveals a strong trend toward addressing model uncertainty in mathematical finance, with recent works exploring risk measures based on weak optimal transport, convex semigroups, and Markov processes under nonlinear expectations. These publications demonstrate his expertise in bridging abstract mathematical concepts with concrete financial applications, particularly in the areas of risk management and financial modeling. Dr. Nendel serves as Principal Investigator for multiple significant research projects, including the Bielefeld University Research Training Group 2865 "Coping with Uncertainty in Dynamic Economies" (CUDE) and Project C7 of CRC 1283. His research has been presented at numerous international conferences and seminars across Europe, North America, and Australia, demonstrating the global recognition of his work in mathematical finance and uncertainty quantification. As an educator, Dr. Nendel has supervised numerous Ph.D. and Master's students, with several currently in progress. His teaching portfolio includes advanced finance courses with specific focus on stochastic control and model uncertainty in economics and finance. He has also organized and participated in various academic events related to risk measures, uncertainty in insurance, and robust finance.

• Combinatorial Optimization
• Algorithm Design and Analysis
• Game Theory and Social Choice
• +۴ مورد دیگر

Jannik Matuschke serves as Associate Professor in the Department of Decision Sciences and Information Management at KU Leuven's Faculty of Economics and Business (FEB). He is an active member of both the KU Leuven Institute for Artificial Intelligence (Leuven.AI) and the KU Leuven Institute for Mobility (LIM), holding office at Hogenheuvelcollege (HOGC) 04.123 in Leuven, Belgium. His academic career spans multiple prestigious institutions across Europe, reflecting his international recognition in operations research and theoretical computer science. Dr. Matuschke completed his PhD in Mathematics in 2013 at Technische Universität Berlin under Martin Skutella and Britta Peis. His academic journey included a postdoctoral position at Universidad de Chile (2014), a DAAD P.R.I.M.E. fellowship at the University of Rome 'Tor Vergata' (2015), and a junior group leader/lecturer position at Technische Universität München (2016-2018) before joining KU Leuven in 2019. His research focuses on the intersection of discrete mathematics, theoretical computer science, and operations research, with specific expertise in combinatorial optimization, algorithm design and analysis, game theory and social choice, robustness and uncertainty, and logistics applications. Dr. Matuschke has made significant contributions to network flow theory, security games, and robust optimization frameworks, developing both theoretical foundations and practical applications. Analysis of his recent publications reveals a strong emphasis on robustness under uncertainty, with increasing focus on security applications and theoretical aspects of scheduling problems. His work demonstrates consistent innovation in decomposing complex optimization problems and developing approximation algorithms for challenging combinatorial settings. 2024 Meritorious Service Reward of the journal Operations Research Dr. Matuschke actively mentors several PhD students and postdocs, including Yanfei Chen, Léonie Gallois, Phablo Moura, Felix Rauh, and Fei Wu. He leads significant research projects such as 'Rigidity and Flexibility: Structures and Algorithms' (starting 2025), 'Designing resilient and recoverable infrastructures via multi-level and multi-stage optimization' (2022), and 'Optimization and analytics for stochastic and robust project scheduling' (2020), with funding from diverse sources including KU Leuven start-up grants, FWO research projects, and DAAD fellowships. As an integral member of the academic community, Dr. Matuschke serves as Associate Editor for Omega (since 2023), Operations Research Letters (since 2024), and OR Spectrum (since 2019). He co-chairs the program committee for WAOA 2025 and has participated in numerous prestigious conference committees. At KU Leuven, he coordinates the 'Master's Thesis in Production and Logistics' program and serves on the Teaching Portfolio Peer Review Committee.

Eric Balkanskiمشاهده پروفایل

استادیار

• Algorithms
• Machine Learning
• Combinatorial Optimization
• +۳ مورد دیگر

Eric Balkanski serves as Assistant Professor of Industrial Engineering and Operations Research at Columbia Engineering, Columbia University, and is an Affiliated Member of the Foundations of Data Science Institute. His academic home integrates theoretical computer science with operations research methodologies. Balkanski earned his PhD in Computer Science from Harvard University, establishing foundational expertise in algorithmic theory before joining Columbia's faculty. His research pioneers algorithms with predictions —a transformative paradigm blending machine learning insights with classical optimization. Key thrusts include exponentially faster submodular optimization for data summarization and recommendation systems, strategyproof mechanism design incorporating predictive advice, and fairness-aware online algorithms . This work bridges theoretical guarantees with real-world applications in network analysis and decision-making under uncertainty, often yielding breakthroughs in computational efficiency. Recent publications (2022-2025) reveal a dominant trend toward prediction-augmented frameworks across scheduling, correlation clustering, and facility location. His submodular optimization advances enable orders-of-magnitude speedups, while fairness-oriented work introduces novel cost-free fairness models for online settings. The consistent focus on theoretical foundations of learning-augmented algorithms positions him at the forefront of this emerging field. His accolades demonstrate exceptional scholarly impact: ACM SIGecom Doctoral Dissertation Honorable Mention Award Google PhD Fellowship Smith Family Graduate Science and Engineering Fellowship Best Paper Award at CIAA 2013 Andrew Carnegie Society Scholar Balkanski co-founded Robust Intelligence (an AI security startup), translating theoretical work into practical cybersecurity applications. His NSF-funded collaborative research on 'Mechanisms with Predictions' indicates active grant leadership, though specific student advising details remain unpublicized. The absence of formal lab descriptions suggests integration within Columbia's broader data science and operations research ecosystems. As an early-career researcher, Balkanski demonstrates remarkable productivity with 15+ high-impact publications since 2022, primarily in top-tier venues like STOC, NeurIPS, and EC. His trajectory suggests continued leadership in bridging algorithmic theory with machine learning applications.

Ignacio Rios Uribeمشاهده پروفایل

استادیار

• Behavioral Operations Management
• Mechanism Design
• Matching Markets
• +۷ مورد دیگر

Ignacio Rios Uribe is an Assistant Professor at the Naveen Jindal School of Management (University of Texas at Dallas) specializing in Operations Management . He holds a PhD in Operations, Information and Technology (2020) from Stanford University, with additional MA in Economics from Stanford and MS/BS in Operations Research and Industrial Engineering from Universidad de Chile. PhD in Operations, Information and Technology (2020) - Stanford University MA in Economics (2020) - Stanford University MS in Operations Management (2014) - Universidad de Chile BS in Industrial Engineering (2014) - Universidad de Chile His research focuses on Behavioral Operations Management , particularly in matching markets , college admissions systems, and online platform optimization . Current projects examine mechanism design for dating apps, strategic behavior in college applications, team-building incentives, and charity donation systems. He employs mathematical modeling, field experiments, and structural estimation techniques. Recent publications analyze platform design for curated dating markets (M&SOM, 2023), stable matching with contingent priorities (Management Science, 2023), and capacity planning in school choice (Operations Research, 2022). His work combines theoretical analysis with empirical validation using real-world data from Chilean education systems and industry partners. Awards include First Place in Doing Good with Good OR (2018) and BOM Best Working Paper Competition (2023) Published in top journals like Management Science, M&SOM, Operations Research Professional affiliations with INFORMS and Manufacturing & Service Operations Management Society

Nate Veldtمشاهده پروفایل

استادیار

• Graph algorithms
• Network science
• Matrix computations
• +۳ مورد دیگر

Nate Veldt is an Assistant Professor in the Department of Computer Science and Engineering at Texas A&M University. His research focuses on combinatorial algorithms and computational methods for data analysis, particularly in graph and network modeling. He holds a Ph.D. in Mathematics with a concentration in Computational Science from Purdue University (2019), an M.S. in Mathematics from Purdue (2017), and a B.S. in Mathematics from Wheaton College (2013). Research Interests: - Graph algorithms and network science - Matrix computations - Computational optimization - Data science and machine learning applications in network analysis. His work bridges theoretical computer science, discrete mathematics, and practical data science challenges. Notable Awards: - AFOSR Young Investigator Program Award (2025) - Dimitris N. Chorafas Foundation Award (2019) - NSF East Asia and Pacific Summer Institute Award (2016) Recent Grants and Roles: - Course development grant for a new 'Computational Methods for Network Science' course (TAMIDS, 2025) - Co-chair of SIAM Symposium on Algorithm Engineering and Experiments (ALENEX26, 2026) - Secretary of SIAM Activity Group on Applied and Computational Discrete Algorithms (since 2025) Research Contributions: Develops algorithms for hypergraph clustering, cut improvement, and network analysis. His work emphasizes practical scalability and theoretical guarantees in combinatorial optimization problems. Active in interdisciplinary collaborations, including with Lawrence Livermore National Laboratory and the University of Utah.

• Machine Learning
• Computer Vision
• Natural Language Processing
• +۶ مورد دیگر

Rishabh Iyer is an Assistant Professor at the University of Texas at Dallas (UT Dallas) and a Visiting Assistant Professor at the Indian Institute of Technology Bombay (IIT Bombay). He leads the CARAML Lab at UT Dallas, focusing on machine learning, computer vision, and natural language processing. His research emphasizes data-efficient learning, subset selection, and combinatorial optimization techniques such as submodular functions. Education: Ph.D. and M.S. from the University of Washington (2011-2015), B.Tech from IIT Bombay (2011). He has held roles including Senior Research Scientist at Microsoft (2016–2019) and Postdoctoral Researcher at University of Washington (2015–2016). Research Interests: Combinatorial loss functions, compute-efficient learning via subset selection, robust deep learning, weak supervision, and continuous learning. Key projects include GLISTER, GRADMATCH, and SMILE frameworks for subset selection and active learning. Awards: NSF Medium Grant (2021), Adobe and Amazon Research Awards (2022), Best Paper Awards at ICML/NeurIPS (2013), Microsoft Research Fellowship (2014). Grants: Supported by NSF, Adobe, Google, Amazon, and UT Dallas startup funds. Active in teaching courses like Machine Learning (CS 6375) and Optimization in Machine Learning (CS 7301). Labs/Teams: CARAML Lab, collaborating on subset selection, active learning, and submodular optimization. Open-source tools include CORDS, DISTIL, and submodlib.

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.

Lalit Jainمشاهده پروفایل

استادیار

• Machine Learning
• Data Science
• Artificial Intelligence
• +۹ مورد دیگر

Lalit Jain is an Assistant Professor at the Foster School of Business , University of Washington. His research bridges theoretical mathematics with practical machine learning systems, focusing on adaptive data collection under budget constraints, applied to marketing, cognitive psychology, and humor detection. Research Interests : Adaptive machine learning algorithms Bandit optimization and experimental design False discovery rate control Human-AI interaction Applications in marketing and cognitive science Advising : Justin Weltz (Neurips 2023) Zhaoqi (AISTATS) Romain (AISTATS) Zhihan (Amazon collaboration) Jennifer Brennan (ICML 2022 workshop) Projects : Co-developer of the New Yorker Caption Contest voting system Collaboration with Amazon on adaptive pricing systems

• Algorithmic Game Theory
• Fair Division
• Computational Economics
• +۳ مورد دیگر

Dr. Georgios Birmpas is a researcher specializing in algorithmic game theory, computational economics, and fair division. He currently serves as Module Co-ordinator for Algorithmic Game Theory (COMP559) and Computational Game Theory and Mechanism Design (COMP326) at an institution not explicitly named. His research focuses on fairness and efficiency in resource allocation problems, often intersecting computer science, economics, and multiagent systems. Key research areas: Algorithmic Fairness Submodular Optimization Strategic Agent Modeling Distortion in Voting Systems Recent contributions include: Fair multiobjective submodular function maximization Existence of approximate equilibria in resource allocation Two-query distortion analysis in matching problems Interdependent value models in fair division His work has appeared in venues such as the International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS) and journals like Mathematics of Operations Research and SIAM Journal on Discrete Mathematics . Collaborators include researchers from institutions across Europe, focusing on theoretical and applied aspects of algorithmic fairness.

• Algorithms
• Complexity Theory
• Probability
• +۵ مورد دیگر

James R. Lee is a Professor in the Department of Computer Science at the University of Washington, with a focus on algorithms, complexity, and the theory of computation. He is affiliated with the UW Theory Group and currently on leave at Microsoft Research, which may delay responses to UW emails. Research Interests: Algorithms, complexity, geometry/discrete-continuous interfaces, probability, stochastic processes, metric embeddings, spectral graph theory, convex optimization. Recent Work Trends: Sparsification of generalized linear models and norms, spectral hypergraph methods, entropic regularization for metrical task systems, and analysis of scaling exponents in random graphs. His papers address sparsifier existence, lower bounds for SDP/LP relaxations, and geometric random walk properties. Scientific Awards: Best Paper Award at STOC 2015. Students: Farzam Ebrahimnejad, Ewin Tang, Yichuan Deng (co-advised with Shayan Oveis-Gharan, Shirshendu Ganguly, and others). Email: jrl@cs.washington.edu

• Task-Oriented Knowledge Acquisition
• Online Learning and Control
• Human-Robot Interaction
• +۴ مورد دیگر

Mahsa Ghasemi is an Assistant Professor at the Elmore Family School of Electrical and Computer Engineering, Purdue University, located in West Lafayette. She holds a B.Sc. in Mechanical Engineering from Sharif University of Technology (2014), an M.S.E. in Mechanical Engineering from The University of Texas at Austin (2017), and a Ph.D. in Electrical and Computer Engineering from The University of Texas at Austin (2021). Her research focuses on task-oriented knowledge acquisition, online learning and control, human-robot interaction, trustworthy AI, and socially beneficial autonomy. She is affiliated with the Materials and Electrical Engineering Building at Purdue. Education: B.Sc., Mechanical Engineering, Sharif University of Technology (2014) M.S.E., Mechanical Engineering, UT Austin (2017) Ph.D., Electrical and Computer Engineering, UT Austin (2021) Her research interests span interdisciplinary areas including reinforcement learning, causal inference, control systems, and human-autonomy collaboration. Recent work emphasizes resilient cyber-physical systems, privacy-preserving multi-agent learning, and causal discovery in decision-making frameworks. Her articles address challenges in sensor selection, no-regret learning in bandits, and formal methods for autonomous systems. Publications highlight contributions to submodular optimization in hypothesis testing, robust sensor scheduling in intrusion detection, and adaptive experimental design for causal discovery. Her work bridges theoretical foundations with practical applications in robotics, cybersecurity, and AI ethics. No scientific awards or grants are explicitly listed in the provided data. She advises no listed students but collaborates on projects involving diverse planning and decision-making in constrained environments.

Anna Seigalمشاهده پروفایل

استادیار

• Applied Algebraic Geometry
• Algebraic Statistics
• Tensors and Multilinear Algebra
• +۵ مورد دیگر

Anna Seigal is an Assistant Professor of Applied Mathematics at Harvard University's School of Engineering and Applied Sciences (SEAS), with an affiliation in the Department of Statistics. Her research focuses on applied algebraic geometry, tensors, multilinear algebra, and algebraic statistics, particularly in the context of data science. She explores algebraic approaches to data analysis, including matrix/tensor factorizations, parameter estimation, causal inference, and optimization, with applications to physical and biological systems. Her work is supported by the Sloan Foundation and Harvard's Dean’s Competitive Fund. Research Interests: Algebraic statistics, tensors and multilinear algebra, applied algebraic geometry, and the mathematics of data science. She investigates group symmetries in models, dimensionality reduction techniques, and machine learning algorithms. Current projects include causal disentanglement via cumulants and invariant theory applications to maximum likelihood estimation. Her academic contributions span theoretical advancements and interdisciplinary applications, such as genomic template analysis and COVID-19 molecular phenotyping. She collaborates with postdocs and students on research projects and teaches courses like Applied Math 210. Awards and Funding: Supported by the Alfred P. Sloan Foundation and Harvard's internal grants. No explicit named awards listed, but her research is institutionally recognized. Labs/Teams: Leads a research group focused on applied algebra and geometry in data science. Collaborates across departments in SEAS and the Statistics Department.

• Algorithmic Sequential Decision-Making
• Robust AI
• Safe AI
• +۱۱ مورد دیگر

Ilija Bogunovic is an Assistant Professor (UK Lecturer) in the Department of Electronic and Electrical Engineering at University College London (UCL), Faculty of Engineering Sciences. His research focuses on algorithmic sequential decision-making for robust, reliable, and safe artificial intelligence, with applications in large language model alignment, reinforcement learning, and human feedback integration. His research interests lie at the intersection of machine learning, optimization, and decision theory. He investigates robustness in Bayesian optimization, bandits, and reinforcement learning under adversarial attacks, model misspecification, and distributional shifts. His work spans both theoretical foundations and real-world applications in energy systems, mobility, and AI safety. A central theme is developing algorithms that are provably robust and efficient in uncertain and potentially corrupted environments. The recent publications highlight a strong trend in robust decision-making, particularly in reinforcement learning and preference optimization. Key themes include adversarial robustness in large models, distributional robustness in RL, safe multi-agent systems, and robust Bayesian optimization. The work frequently involves theoretical analysis of regret and sample complexity, combined with practical algorithm design for high-impact applications. Scientific Awards: Google Research Scholar Program Award (Machine Learning & Data Mining), 2023 EPSRC New Investigator Award, 2023 Ilija Bogunovic actively supervises PhD and MSc students, with several of his advisees leading papers accepted to top venues like NeurIPS and ICLR. He has secured competitive research grants, including the EPSRC New Investigator Award, to support his work on robust decision-making. He is building a research team focused on robust and safe AI. He leads a research team and has initiated an online reading group on modern adaptive experimental design and active learning, fostering a collaborative research environment.

Caterina VIOLAمشاهده پروفایل

استادیار

• Quantum Algorithms
• Computational Complexity
• Constraint Satisfaction Problems
• +۳ مورد دیگر

Caterina VIOLA is a theoretical computer scientist and mathematician currently serving as a Fixed-term Assistant Professor (RTDA) at the Department of Mathematics and Computer Science of the University of Catania . Her research focuses on quantum algorithms for text processing, computational complexity, and constraint satisfaction problems within the National Center for HPC, Big Data and Quantum Computing project. Education: Master's in Mathematics from University of Catania (Erasmus exchange at Universidad de Granada); Doctor rerum naturalium in Mathematics from Technische Universitaet Dresden Her research bridges theoretical computer science and mathematics, with a particular emphasis on approximation algorithms, inapproximability, and numerical semigroups. She has previously worked at the University of Oxford as a post-doctoral researcher and tutor, followed by a post-doc position at Charles University in Prague. Recent publications highlight her work on linear programming relaxations for constraint satisfaction problems, submodular functions, and numerical semigroup analysis. Current teaching responsibilities include Laboratorio di Algoritmi for Computer Science undergraduates and Informatica for Communication Sciences and Languages students. As a Senior Associate Post-doctoral Researcher at Oxford and post-doc at Charles University in Prague, she has developed expertise in mathematical foundations of computational optimization and their practical implementations.

Yair Zickمشاهده پروفایل

استادیار

• Theoretical Computer Science
• Artificial Intelligence
• Algorithmic Game Theory
• +۳ مورد دیگر

Yair Zick is an Assistant Professor at the Manning College of Information Systems and Computer Sciences (CICS), University of Massachusetts Amherst. Previously, he held positions at the National University of Singapore (NUS School of Computing) and Carnegie Mellon University as a postdoc. His research focuses on algorithmic fairness, game theory, fair division, and explainable AI. He earned his PhD in Mathematics from Nanyang Technological University (2014) and a BSc (Amirim Honors Program) from the Hebrew University of Jerusalem. Educations: PhD in Mathematics, Nanyang Technological University, Singapore (2014) BSc in Mathematics (Amirim Honors Program), Hebrew University of Jerusalem, Israel (2009) His research interests include computational aspects of game theory, fair division, algorithmic transparency, and applying machine learning to economic problems. He teaches courses such as Algorithms, Game Theory, and Fairness, Artificial Intelligence, and Algorithmic Fairness. Key Awards: 2021 IJCAI Early Career Spotlight Award 2020 AAAI Outstanding Senior Program Committee Member Award 2017 Singapore NRF Fellowship 2016 ACM EC Best Paper Award 2014 Victor Lesser IFAAMAS Distinguished Dissertation Award His work bridges theoretical foundations (e.g., Nash equilibria, mechanism design) with practical applications in fair resource allocation and transparent AI systems. He actively contributes to conferences like AAAI, AAMAS, and NeurIPS, and collaborates with institutions globally.