جست‌وجوی استاد و پژوهشگر

Ran Zhuoمشاهده پروفایل

استادیار

• Economics of Innovation
• Technology
• Digitization
• +۶ مورد دیگر

Ran Zhuo is an Assistant Professor in the Technology and Operations department at the University of Michigan Ross School of Business. He holds a PhD in Business Economics from Harvard University (2023) and a B.A. from Dartmouth College (2017). His research focuses on the economics of innovation, technology, digitization, and industrial organization. Key areas include digital markets, privacy regulations, and resource allocation in R&D environments. He employs methods like structural estimation, machine learning, and reduced-form analysis. His work spans studies on software vulnerability management, vaccination behavior dynamics, and U.S.-China decoupling impacts on tech investments. Notable articles include analyses of GDPR effects on internet interconnection and low-price guarantees' anticompetitive outcomes. Current research explores innovation resource allocation and organizational tech adoption strategies. Publications appear in Research Policy , Journal of Industrial Economics , and Telecommunications Policy . Ongoing projects investigate semiconductor supply coordination and coevolution of vaccine beliefs. No specific awards listed, but active in academic networks including Google Scholar and WDI Publishing.

Yuan Zhouمشاهده پروفایل

دانشیار

• Optimization
• Integer Programming
• Real Algebraic Geometry
• +۳ مورد دیگر

Yuan Zhou is an Associate Professor at the University of Kentucky specializing in Mathematics. His research focuses on optimization, integer programming, and real algebraic geometry. He actively contributes to the Gomory-Johnson infinite group problem, exploring inverse semigroup theory, equivariant perturbations, and computational methods for cut-generating functions. His scholarly work spans theoretical advancements in polyhedral combinatorics and practical implementations in mathematical software. Recent publications highlight automated proofs, decomposition theorems, and novel approaches to mixed-integer optimization. Earlier work includes applications in stochastic bandit problems and health insurance policy analysis. Dr. Zhou’s research bridges abstract algebraic geometry with computational optimization, addressing both foundational and algorithmic challenges. He has developed tools for analyzing infinite-dimensional relaxations and extreme functions in integer programming.

Maurits Kapteinمشاهده پروفایل

استاد

• Statistics
• Statistical Learning
• Causality
• +۴ مورد دیگر

Prof. dr. Maurits Kaptein is a part-time Professor of Applied Causality at the Department of Mathematics and Computer Science, Eindhoven University of Technology (TU/e). He will deliver his inaugural lecture on February 6, 2025, titled 'Uncertainty Propagation in Causal Effects Estimation.' Current Role : Supervises PhD students, contributes to TRI-DSA research, and develops causal frameworks. Education & Career : MSc in Economic Psychology (Tilburg), PdEng (TU/e), PhD in Industrial Design (TU/e/Stanford), postdoc in Quantitative Marketing (Aalto), and former assistant/associate/full professor at Radboud University and Tilburg University. Research Interests : Focuses on causality, statistical learning, and methodology. Key subtopics include causal effect estimation, uncertainty propagation, Bayesian multivariate logistic regression, and real-world evidence validation. His work bridges theoretical statistics with applied healthcare and technology solutions. Recent Publications : His 2024-2025 articles address causal uncertainty frameworks, preterm birth prediction via machine learning, and real-world evidence validation in oncology. These works emphasize multivariate Bayesian approaches, heterogeneous treatment effects, and cross-disciplinary applications in medicine. Ancillary Activities : CEO of Scailable B.V., data advisor for Luscii/OMRON, and speaker at The Next Speaker Global.

Angelika Stegerمشاهده پروفایل

استاد

• Probabilistic Methods
• Randomized Algorithms
• Graph Theory
• +۳ مورد دیگر

Prof. Angelika Steger is a Full Professor in the Department of Computer Science at ETH Zurich, leading research in theoretical computer science since 2003. She holds a Master's in Applied Mathematics from Stony Brook University (1985) and a PhD from the University of Bonn (1990). Her career includes roles at Kiel, Duisburg, and TU München before joining ETH. She is a Leopoldina member (2007), ICM speaker (2014), and Collegium Helveticum Fellow (2009+). Research focuses on probabilistic methods, randomized algorithms, graph theory, and combinatorial optimization. She has contributed to understanding discrete structures, neural networks, and algorithmic resilience. Awards include recognition in both computer science and mathematics circles. Her work bridges theoretical foundations with applications in AI, neuroscience, and distributed systems.

Christine Herlihyمشاهده پروفایل

دانشیار مدعو

• Artificial Intelligence
• Machine Learning
• Health Informatics
• +۵ مورد دیگر

Christine Herlihy holds the academic rank of Adjunct Associate Professor in the Department of Computer Science at the University of Maryland. She is also a PhD Candidate advised by John Dickerson, located in IRB 2108. Her research focuses on algorithmic fairness, health informatics, and natural language processing with applications in socially consequential domains like public health and agriculture. She explores optimization techniques such as restless bandits and develops tools for scientific model augmentation, including a Julia package named SemanticModels.jl. Education: Christine is pursuing her PhD in Computer Science at the University of Maryland under the guidance of John Dickerson. Earlier academic qualifications are not detailed in the provided information. Research Interests: Christine’s work bridges computer science and societal impact. She investigates how AI systems can improve health literacy through personalized smartphone applications and ensure fairness in algorithmic decision-making. Her studies also address challenges in clinical NLP, resource allocation for small farmers, and longitudinal analysis of LLM data contamination. She advocates for transparent and equitable algorithmic frameworks in domains ranging from healthcare to policy evaluation. Advising & Grants: Christine is advised by John Dickerson in her doctoral studies. No grants or independently advised students are explicitly listed in the provided materials.

• Decentralized Finance
• Automated Market Makers
• Machine Learning in Finance
• +۴ مورد دیگر

Fayçal Drissi is a Postdoctoral Research Fellow at the Oxford-Man Institute, University of Oxford, within the Department of Engineering Science. His research bridges Mathematics and Machine Learning to address financial decision problems in both traditional and decentralized finance contexts, with strong industry experience in hedge fund research and development. Dr. Drissi's educational background includes: Ph.D. in Mathematics from Université Paris 1 Panthéon Sorbonne (2023) MSc in Quantitative Finance from Université Paris Dauphine - PSL (2014) His research centers on the economics and microstructure of decentralized financial markets, automated market makers (AMMs), and the integration of machine learning techniques like transfer learning and deep PDE solving for optimal execution and market making. He investigates how blockchain technology and asset digitization reshape financial systems, focusing on practical applications in trading strategies and market efficiency. Analysis of his 12 publications (2022-2025) reveals a dominant focus on decentralized finance and AMMs, with recurring themes in market microstructure, liquidity provision, and algorithmic trading. His work consistently combines stochastic control theory, machine learning (particularly reinforcement learning), and financial mathematics to develop novel frameworks for execution strategies, statistical arbitrage, and liquidity management across traditional and decentralized markets. Dr. Drissi has received significant recognition including: Research Grant from Uniswap (2025) for "Fixed for Floating Fee Swap in Decentralized Finance" PhD research grant from G-Research (2023) for "Unsupervised Learning for Algorithmic Trading" Best PhD Thesis prize from EWGCFM (2023) for "Decentralized Finance, Execution and Speculation" Research Grant from Chaire Fintech University Paris Dauphine ‑ PSL (2022) for "Decentralised finance and automated market making: Predictable loss and optimal liquidity provision" As an active academic contributor, he serves as Topic Editor for The Journal of FinTech and organizes major international events including the OMI Finance Seminar series (2023-2026), the Oxford-Harvard Conference on Decentralised Finance, and multiple workshops at SIAM and INFORMS conferences. His research is supported by substantial grants from leading finance and blockchain institutions. Dr. Drissi conducts his research within the Control Group and Machine Learning research groups at Oxford's Department of Engineering Science, operating from the Oxford-Man Institute—a premier interdisciplinary research center for quantitative finance that bridges engineering, mathematics, and financial applications.

• Artificial Intelligence
• Optimization
• Evolutionary Algorithms
• +۳ مورد دیگر

Dr. Ali Jamali is a Research Fellow at RMIT University's School of Engineering. He previously held roles as a visiting professor at Kyungpook National University (South Korea) and associate professor at the University of Guilan (Iran). His research focuses on artificial intelligence, optimization, evolutionary algorithms, control systems, and energy systems. He has supervised multiple doctoral and master's students and teaches courses in complex systems modeling, dynamics, automatic control, and advanced vibration. His teaching interests include soft computing, game theory, and evolutionary algorithms, reflecting his expertise in interdisciplinary applications of AI and optimization. He has contributed to projects like enhancing EV charging infrastructure and intelligent transportation systems. His work often integrates computational intelligence with real-world problems in engineering and automation. Dr. Jamali has published extensively in journals such as IEEE Transactions on Vehicular Technology and Engineering Applications of Artificial Intelligence , addressing topics like adaptive control systems, energy management, and machine learning applications in engineering. He currently collaborates on research projects at RMIT and maintains international academic connections.

• Machine Learning
• Statistics
• Probability Theory
• +۳ مورد دیگر

Dr. Steffen Grünewälder is a Senior Lecturer in Statistics at the University of York, Department of Statistics. Previously, he held roles as Senior Lecturer at Newcastle University (2022-2024) and Lecturer at Lancaster University (2014-2022). His research focuses on statistical learning theory, kernel methods, and reinforcement learning. He has contributed to advancements in Gaussian processes, Markov processes, and fairness in machine learning. His work spans theoretical developments and practical applications, including compressed empirical measures, stochastic processes, and algorithmic fairness. Key research interests include Bayesian methods, computational statistics, and optimization techniques in machine learning. Dr. Grünewälder’s recent publications explore topics such as deep Gaussian processes, Markov chain analysis, and reinforcement learning algorithms. He has collaborated on interdisciplinary projects, including ecological studies on animal behavior. He can be contacted via email at steffen.grunewalder@york.ac.uk .

• Machine Learning
• Human-Computer Interaction
• Dynamic Systems
• +۵ مورد دیگر

Professor Roderick Murray-Smith is a Professor of Computing Science at the University of Glasgow, affiliated with the School of Computing Science. He leads the Inference, Dynamics and Interaction Research Group and the Information, Data & Analysis Section . Previously, he held positions at institutions including MIT, Technical University of Denmark, and Daimler-Benz Research. His research focuses on Closed-loop Data Science , Machine Learning in Science , and Mobile Human-Computer Interaction . Notable projects include quantum imaging, music recommender systems, and collaborations with industry leaders like Nokia and Samsung. He served as Director of the SICSA research pool and founding Head of the Information, Data & Analysis Section. Key technical contributions span medical image segmentation , neural network architectures , and biomechanical VR simulations . His work bridges control theory, machine learning, and interactive systems, emphasizing real-world applications in healthcare, robotics, and mobile devices.

Stanton Hudjaمشاهده پروفایل

استادیار

• Experimental economics
• Behavioral economics
• Computational economics
• +۱ مورد دیگر

Stanton Hudja is an Assistant Professor of Business Analytics and Strategy at Illinois Institute of Technology. He holds a Ph.D. in Economics from Purdue University (2020), an M.S. in Economics from Johns Hopkins University, and dual undergraduate degrees in Economics and Mathematics from the University of Maryland. His research focuses on experimental economics, particularly exploration-exploitation tradeoffs and decision-making under uncertainty. Current projects investigate collective experimentation dynamics, information asymmetry effects, and legal decision-making processes. His academic work spans experimental methodologies applied to criminal justice systems, innovation contests, and behavioral policy design. Key themes include understanding how individuals balance exploration of new options versus exploitation of known strategies, and the implications for economic systems and legal frameworks. Research outputs include analyses of prosecutor discretion, jury decision thresholds, and gender effects in legal error tolerance. His recent work (2023–2025) emphasizes multi-agent experimentation models, public goods provision mechanisms, and algorithmic decision-making frameworks derived from experimental economics principles. Teaching responsibilities include courses on business analytics and strategy at the Mies (Main) Campus. Office hours for Fall 2025 combine on-campus and virtual availability.

Zhigen Zhaoمشاهده پروفایل

دانشیار

• Bayesian/empirical Bayesian statistics
• High-dimensional data analysis
• Multiple comparison
• +۵ مورد دیگر

Zhigen Zhao is an Associate Professor and Beury Research Fellow at the Fox School of Business and Management, Temple University, within the Department of Statistics, Operations, and Data Science. He holds a Ph.D. from Cornell University (2009) and specializes in Bayesian/empirical Bayesian statistics, high-dimensional data analysis, and bioinformatics. His research is supported by the National Science Foundation. Dr. Zhao’s research focuses on developing statistical methodologies for high-dimensional problems, including selective inference, multiple comparisons, and sufficient dimension reduction. His work bridges theoretical advancements with practical applications in healthcare, genomics, and predictive analytics. Key contributions include Bayesian hierarchical models for electronic health records and novel techniques for controlling false discovery rates in genomic studies. He teaches courses such as Intermediate Statistics, Regression and Predictive Analytics, and Statistical Methods for Business Research at both undergraduate and graduate levels. His recent publications appear in top-tier journals like the Journal of the American Statistical Association and Journal of the Royal Statistical Society, Series B. Dr. Zhao’s grants include NSF funding for high-dimensional statistical research. He advises on methodological challenges in data science and collaborates on projects involving healthcare analytics, genomics, and machine learning applications.

Yuhua Zhuمشاهده پروفایل

استادیار

• Machine Learning
• Reinforcement Learning
• Optimization Algorithms
• +۴ مورد دیگر

Yuhua Zhu is a tenure-track Assistant Professor in the Department of Statistics and Data Science at the University of California, Los Angeles (UCLA). Her research focuses on the intersection of partial differential equations (PDEs) and machine learning, with particular emphasis on using differential equations to analyze and design efficient reinforcement learning and optimization algorithms. Education: Ph.D. in Mathematics, University of Wisconsin-Madison (2019), advised by Shi Jin M.S. in Mathematics, University of Wisconsin-Madison (2015) B.S. in Mathematics, Shanghai Jiao Tong University (2014) Postdoc at Stanford University (2019–2022), mentored by Lexing Ying Research Interests: Her work spans sequential decision-making (e.g., continuous-time reinforcement learning, multi-armed bandits), optimization (gradient-free methods, asynchronous-SGD, convergence analysis), and uncertainty quantification in kinetic equations (sensitivity analysis, high-dimensional uncertainty). She develops PDE-based frameworks for model-free reinforcement learning and consensus-based optimization methods for distributed learning systems. Recent Work Trends: Her publications emphasize PDE-driven approaches to reinforcement learning, consensus-based optimization in federated learning, and uncertainty quantification in kinetic equations. Recent topics include addressing adversarial attacks in federated learning, high-order discretization in policy evaluation, and stabilization of kinetic equations with reflective boundary conditions. Awards & Grants: No specific awards mentioned in the text. Current recruiting focuses on graduate students in reinforcement learning and optimization for machine learning problems. Labs & Collaborations: Collaborations include work with Jose Carrillo, Shi Jin, Lexing Ying, and others on consensus-based methods and kinetic theory applications in machine learning.

• Consumer Privacy
• Recommendation Systems
• Social Media
• +۴ مورد دیگر

Guy Aridor is an Assistant Professor of Marketing at the Kellogg School of Management, Northwestern University. He holds a PhD in Economics from Columbia University. His research focuses on consumer privacy, recommendation systems, and social media policy, with emphasis on antitrust and regulatory issues in digital markets. He previously worked as a software engineer at HubSpot, specializing in growth engineering and marketing automation. Key research interests include privacy regulations' impact on e-commerce, competition dynamics in digital markets, and the societal implications of algorithmic systems. His work has been published in leading journals like the RAND Journal of Economics , Management Science , and Proceedings of the ACM Conference on Recommender Systems . Recent studies examine topics such as competing bandit algorithms' market effects, substitution patterns in the attention economy, and the welfare consequences of platforms acting as both content producers and recommenders. His research frequently combines experimental methods with real-world data from platforms like MovieLens. Awards: Best Short Paper at ACM Conference on Recommender Systems Teaching: Full-Time/Evening MBA course Omnichannel Experience Strategy focuses on modern retail strategy across digital and physical channels Industry Experience: Software engineering background in marketing automation tools

Chris Wigginsمشاهده پروفایل

دانشیار

• Data Science
• Machine Learning
• Computational Biology
• +۱۲ مورد دیگر

Chris Wiggins is an Associate Professor of Applied Mathematics and Systems Biology at Columbia University, with affiliations in the Department of Statistics. He is based in the School of Engineering and Applied Science and conducts interdisciplinary research at the intersection of data science, machine learning, and biological systems. His work bridges computational modeling with applications in genomics, single-molecule biophysics, digital health, and disinformation studies. His research interests include: Data Science and its societal implications Machine learning for biological networks and gene regulation Bayesian and statistical inference in single-molecule experiments Privacy-aware data analysis and algorithmic ethics Modeling in digital health, including menstrual cycle prediction and cancer drug screening Information-theoretic approaches to network structure and dynamics The trends in his recent publications reveal a growing focus on the ethical and societal dimensions of data science, as seen in works like How Data Happened and An Agenda for Disinformation Research , while maintaining strong technical contributions in biomedical modeling, particularly in cancer and women’s health. His work combines rigorous statistical methods with real-world data challenges. Notable scientific contributions include foundational work on ARACNE for gene regulatory network inference, Bayesian modeling of single-molecule dynamics, and methodological advances in dose-response modeling. His publications span top journals in bioinformatics, computational biology, and data science. He has advised doctoral students in applied mathematics and computational biology, though specific names are not listed in the provided text. His research has been supported by interdisciplinary grants, particularly in systems biology and data science initiatives. He is actively involved in shaping the philosophy and pedagogy of data science as a field. Chris Wiggins is associated with research centers at Columbia that focus on applied statistics, probability, and data science. He contributes to seminars and educational programs that promote quantitative reasoning across disciplines, particularly in the context of modern data-driven challenges.

Arman Sabbaghiمشاهده پروفایل

دانشیار

• Bayesian Data Analysis
• Causal Inference
• Clinical Drug Development
• +۸ مورد دیگر

Arman Sabbaghi is an Associate Professor of Statistics at Purdue University's Department of Statistics within the College of Science. His research focuses on Bayesian analysis, causal inference, and statistical methods in clinical trials and manufacturing. He holds a B.S. in Mathematical Statistics and Mathematics (with Honors) from Purdue (2009), an A.M. (2011), and Ph.D. (2014) in Statistics from Harvard University. He has advised PhD students including Raquel De Souza Borges Ferreira and Timothy Keaton. Sabbaghi's work spans interdisciplinary applications, including clinical drug development, machine learning, and quality control in additive manufacturing. He has received numerous accolades, such as the International Statistical Institute Elected Membership (2020) and the Department of Statistics Outstanding Teaching Award (2019). His research emphasizes causal inference methodologies, Bayesian modeling, and the design of efficient clinical trials. Notable contributions include developing prognostic covariate adjustment techniques and frameworks for handling interference in experiments. Sabbaghi's work bridges statistical theory with practical applications in healthcare, engineering, and manufacturing. He teaches courses on Bayesian data analysis, statistical consulting, and experimental design, emphasizing pedagogical strategies rooted in active learning and real-world problem-solving. His grants and collaborations include the Trask Innovation Fund Award and contributions to the CITIES clinical trial simulator project.