جست‌وجوی استاد و پژوهشگر

Moritz Hardtمشاهده پروفایل

دانشیار مدعو

• Machine Learning
• Algorithmic Fairness
• Data Privacy
• +۳ مورد دیگر

Moritz Hardt is an Adjunct Associate Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley , and currently serves as Director at the Max Planck Institute for Intelligent Systems . He also holds an Honorary Professor affiliation with the University of Tübingen and contributes to the Tübingen AI Center and ELLIS Society . PhD in Computer Science from Princeton University (2011) Postdoctoral scholar at IBM Research Almaden Former research scientist at Google Research and Google Brain His research focuses on the scientific foundations of machine learning and algorithmic decision-making with emphasis on social questions , fairness criteria , and robustness . Key themes include: Algorithmic fairness frameworks (e.g., equalized odds, performative prediction) Generalization and stability in deep learning Privacy-preserving machine learning Explainability and saliency map evaluation Creation of benchmark datasets for fair ML His recent 10 publications (2010–2021) span fairness in supervised learning, differential privacy, and theoretical foundations of deep learning. Collaborations include seminal works with Solon Barocas and Arvind Narayanan on fairness benchmarks. Scientific Awards : Okawa Research Grant (2019) Sloan Research Fellow (2019) NSF CAREER Award (2018) He actively mentored 18 PhD students (current and graduated) and supervised postdocs like Celestine Mendler-Dünner and Ludwig Schmidt. His team at Max Planck Institute includes PhD students Nikhil Chandak , Mina Remeli , and Guanhua Zhang . Current affiliations include NeurIPS 2023 program co-chair , Cluster for "Machine Learning for Science" member , and JMLR Action Editor . Software projects include folktables , folktexts , and open-source libraries for private data analysis.

Jiawei Zhangمشاهده پروفایل

استاد

• Business Analytics
• Optimization
• Machine Learning
• +۷ مورد دیگر

Jiawei Zhang is the Michael Armellino Professor in Business and Professor of Information, Operations and Management Sciences at the Leonard N. Stern School of Business, New York University. He chairs the Department of Technology, Operations, and Statistics and serves as Academic Director of the Master of Science in Data Analytics & Business Computing program. He joined NYU Stern in September 2004 and held a joint position at NYU Shanghai from 2014 to 2017. PhD in Management Science and Engineering, Stanford University, 2004 MS in Operations Research, Tsinghua University, 1999 BS in Applied Mathematics, Tsinghua University, 1996 Professor Zhang's research centers on business analytics, optimization, and operations management. His work integrates machine learning, robust optimization, and stochastic modeling to solve complex problems in supply chain management, healthcare operations, pricing, and revenue management. He investigates decision-making under uncertainty, online learning, and algorithmic approaches to resource allocation. His recent publications reveal a strong focus on theoretical and applied aspects of stochastic optimization, prophet inequalities, assortment optimization, and process flexibility. These works appear in top journals such as Operations Research , Management Science , and Mathematics of Operations Research , reflecting deep contributions to both methodological innovation and practical applications in operations. His editorial service highlights scholarly leadership: Associate Editor, Management Science (2014–present) Associate Editor, Manufacturing & Service Operations Management (2021–present) Associate Editor, Mathematics of Operations Research (2009–present) Associate Editor, Operations Research (2006–present) Professor Zhang teaches a range of courses from undergraduate to PhD levels, including Decision Models and Analytics, Convex Optimization, and Dynamic Programming. He advises PhD students and contributes to executive education programs, including in Risk and Decision Analytics and FinTech. His research is supported by theoretical rigor and real-world applicability, with implications for technology, finance, and healthcare operations.

• Operations Research
• Robust Optimization
• Queueing Theory
• +۷ مورد دیگر

Chaithanya Bandi is an Associate Professor at the National University of Singapore (NUS) in the Analytics and Operations Department of the NUS Business School, with a joint appointment in the Department of Mathematics. His research focuses on decision-making under uncertainty, robust optimization, and their applications in operations management, healthcare, e-commerce, and energy systems. He develops robust optimization models for queueing control, risk optimization, and mechanism design. Key research areas include robust queue inference, two-stage distributionally robust optimization, and multi-item auction mechanisms. He has contributed to operational challenges in healthcare (e.g., patient re-entry scheduling), energy systems (electricity generation optimization), and e-commerce (price optimization for fashion products). His work integrates theoretical advancements with practical implementations in large-scale systems. Recent publications emphasize adversarial evaluation of large language models, dynamic scheduling algorithms, and robust policies for uncertain environments. His methodologies often involve novel optimization frameworks and scalable computational approaches. Dr. Bandi holds a PhD in Operations Research and has collaborated with industry leaders like Flipkart and healthcare providers to apply his models in real-world settings. His contributions bridge theoretical rigor and practical applicability in complex operational systems.

• Optimization Theory
• Numerical Optimization
• Newton-Type Methods
• +۵ مورد دیگر

Mikhail V. Solodov is a Researcher at the Institute for Pure and Applied Mathematics (IMPA) in Rio de Janeiro, Brazil, where he conducts cutting-edge research in optimization theory and algorithms. His work spans theoretical foundations and practical applications, with significant contributions to Newton-type methods, augmented Lagrangian techniques, and equilibrium problems. Dr. Solodov's educational background includes: Ph.D. in Optimization/Computer Sciences from University of Wisconsin-Madison (1995) M.S. in Computer Sciences from University of Wisconsin-Madison (1992) Diploma (with Honors) in Applied Mathematics from Moscow State University (1991) His research focuses on developing and analyzing optimization algorithms, particularly Newton and Newton-related methods for optimization and variational problems under weakened assumptions, augmented Lagrangian techniques, decomposition methods, and nonsmooth optimization (especially bundle methods). He has made significant contributions to understanding optimization problems with degenerate constraints and has developed relaxed regularity concepts for these challenging problems. Dr. Solodov's recent publications (2022-2025) show a strong trend toward applications in energy markets while maintaining theoretical rigor. His work bridges mathematical theory with practical implementation, demonstrating how optimization theory can solve real-world problems in energy systems and economic equilibrium modeling. The publications reveal increasing focus on equilibrium problems with applications to energy markets, while continuing to advance theoretical understanding of optimization algorithms. His recognition in the field is evident through his editorial appointments: Mathematical Programming, Series A (since 2005) Optimization Methods and Software (since 2002) SIAM Journal on Optimization (2009-2024) Dr. Solodov has also contributed to special journal issues dedicated to prominent figures in optimization, including a special issue on Hierarchical Optimization in Mathematical Programming (2023) and volumes dedicated to the memory of Prof. Naum Shor and Olvi Mangasarian.

Nicolas Garcia Trillosمشاهده پروفایل

دانشیار

• Applied Analysis
• Computational Probability
• Statistics
• +۵ مورد دیگر

Nicolas Garcia Trillos is an Associate Professor in the Department of Statistics at the University of Wisconsin-Madison, with research spanning applied analysis, computational probability, statistics, and machine learning. His work focuses on the intersection of calculus of variations, optimal transport, and partial differential equations in learning systems. His educational background includes: Bachelor's degree in Mathematics from Universidad de Los Andes, Bogotá, Colombia (2010) Ph.D. in Mathematics from Carnegie Mellon University (2015) Prager Assistant Professor (postdoctoral position) at Brown University (2015-2018) Garcia Trillos' research centers on the mathematical foundations of machine learning , developing tools in optimal transport and calculus of variations to analyze graph-based learning and continuum models. Key contributions address adversarial robustness , federated learning , and spectral clustering , providing theoretical guarantees for large-scale problems through PDE frameworks. Recent publications (2022-2025) demonstrate a cohesive focus on geometric approaches to adversarial robustness , using optimal transport to derive classification bounds and study solution existence. He has advanced federated learning via consensus-based bi-level optimization (CB2O) and developed Fermat distances for metric approximation, bridging pure mathematics with statistical learning theory. No scientific awards were mentioned in the provided text. There is no information available regarding student advising, research grants, or laboratory affiliations in the source material.