جست‌وجوی استاد و پژوهشگر

• Computational Statistics
• Machine Learning
• Applied Probability
• +۷ مورد دیگر

O. Deniz Akyildiz is an Assistant Professor in Statistics at the Department of Mathematics, Imperial College London. He holds a PhD in Signal Processing from Universidad Carlos III de Madrid (UC3M) and completed postdoctoral work at Warwick CS and The Alan Turing Institute. His research bridges computational statistics, machine learning, and applied probability, focusing on stochastic sampling methods, generative modeling, and optimization techniques for statistical inference and inverse problems. Education : BSc/MSc in Electronics and Communications Engineering (İTÜ), PhD in Signal Processing (UC3M). His work explores diffusion-based parameter estimation, score-energy-latent generative models, and overdamped/underdamped Langevin dynamics for probabilistic solutions to PDEs. He has developed adaptive importance samplers and contributed to high-dimensional stochastic filtering with real-world data. Recent efforts include lecture notes and code for stochastic simulation courses and organizing workshops on Gradient Flows for Sampling (Royal Statistical Society, 2023) and Accelerating Generative Models (Alan Turing Institute, 2022-2023). His publications emphasize error bounds in diffusion models, physics-informed neural processes, and convergence analysis of optimization-driven sampling algorithms.

Francisco Facchineiمشاهده پروفایل

استاد

• Nonlinear Optimization
• Nondifferentiable Optimization
• Complementarity Problems
• +۴ مورد دیگر

Francisco Facchinei is a Professor at Sapienza University of Rome, affiliated with the Department of Computer, Automatic and Management Engineering Antonio Ruberti within the College of Engineering. His research spans nonlinear and non-differentiable optimization, complementarity problems, variational inequalities, and game theory applications in telecommunications. His work focuses on developing algorithms for nonconvex optimization with ghost penalties, asynchronous distributed methods, and applications in healthcare and communications systems. Key Contributions: Foundational work in variational inequality theory, generalized Nash equilibrium problems, and optimization over dynamic networks. Recent Trends: Emphasis on asynchronous parallel algorithms, stochastic optimization, and non-invasive medical diagnostics via machine learning. He has held academic positions at Sapienza University since 1990, progressing from Ricercatore to Professore Ordinario. No scientific awards are explicitly mentioned in the provided texts.

Aryan Mokhtariمشاهده پروفایل

دانشیار

• Optimization
• Machine Learning
• Artificial Intelligence
• +۷ مورد دیگر

Aryan Mokhtari is an Associate Professor in the Department of Electrical and Computer Engineering at The University of Texas at Austin, holding the Jack Kilby/Texas Instruments Endowed Faculty Fellowship and William W. Hagerty Fellowship. He serves as a Core Member of two NSF AI Institutes: the Institute for Foundations of Machine Learning (IFML) and the Institute for Future Edge Networks & Distributed Intelligence (AI-EDGE). His academic journey includes a B.Sc. in Electrical Engineering from Sharif University of Technology (2011), followed by advanced degrees from the University of Pennsylvania: M.Sc. (2014), Ph.D. in Electrical and Systems Engineering (2017), and A.M. in Statistics from the Wharton School (2017). Post-PhD, he was a Research Fellow at UC Berkeley's Simons Institute and a Postdoctoral Associate at MIT's Laboratory for Information and Decision Systems. Professor Mokhtari's research pioneers optimization theory for machine learning, with dual focus on algorithmic innovation (convex/nonconvex, minimax, and bilevel methods) and AI foundations (multi-task learning, in-context learning, machine unlearning). His work provides theoretical guarantees for large-scale data science problems while addressing practical constraints in modern AI systems. His scientific achievements are recognized through: Penn’s Joseph and Rosaline Wolf Award for Best Doctoral Dissertation Simons-Berkeley Fellowship NSF CAREER Award (CCF-2338846) Google Research Scholar Award ARO Early Career Program Award Current research is supported by multiple high-impact grants including NSF CAREER, NSF Grants (CCF-2007668, ECCS-2127697), ARO ECP, Google Research, and leadership roles in NSF AI Institutes. This funding enables exploration of adaptive optimization frameworks and next-generation learning paradigms. Students benefit from his affiliation with UT Austin's Machine Learning Laboratory and NSF AI Institutes, gaining access to collaborative networks spanning academia, industry, and government labs. His group emphasizes mathematical rigor while tackling deployment challenges in edge computing and distributed intelligence through the AI-EDGE Institute.

Dominique Orbanمشاهده پروفایل

استاد

• Numerical Optimization
• Nonlinear Programming
• Numerical Linear Algebra
• +۴ مورد دیگر

Dominique Orban is a Full Professor in the Department of Mathematics and Industrial Engineering at Polytechnique Montréal. He holds a Ph.D. from FUNDP Namur and INP Toulouse and has established himself as a leading researcher in numerical optimization. His academic affiliations include the Institute for Data Valorization (IVADO) and the Decision Analysis Study and Research Group (GERAD). Professor Orban's research focuses on numerical mathematics, particularly continuous nonlinear optimization, nonlinear systems of equations, and numerical linear algebra. His work involves designing specialized numerical algorithms for optimization problems, with particular interest in degeneracy and ill-posed problems. His research spans theoretical development of algorithms, their implementation in software, and applications to real-world problems such as image reconstruction, optimal structure design, and optimization under differential constraints. Analysis of his recent publications (2021-2025) reveals a strong focus on developing practical optimization algorithms with theoretical guarantees. His work spans multiple areas including nonsmooth optimization, iterative methods for linear systems, regularization techniques, and software implementation in Julia. A notable trend is his increasing focus on developing open-source software tools that make advanced optimization methods accessible to practitioners. Over 160 publications including journal articles, conference papers, and technical reports Multiple publications in top optimization journals each year through 2025 Strong emphasis on both theoretical foundations and practical implementation Increasing focus on Julia-based optimization software development Professor Orban has successfully supervised 9 doctoral students and 13 master's students to completion, demonstrating his commitment to mentoring the next generation of researchers. His supervision style appears to balance theoretical depth with practical implementation skills, preparing students for both academic and industry careers. His research has been supported through various institutional and collaborative grants, enabling him to maintain an active research program with multiple ongoing projects. His contributions to the field include significant software developments such as Krylov.jl, JSOSuite.jl, and DCISolver.jl, which have made advanced optimization techniques more accessible to the broader scientific community. These tools reflect his philosophy of bridging theoretical optimization with practical computational implementation.

• Political Theory
• Public Policy
• Regulation and Governance
• +۴ مورد دیگر

John Wright is an Associate Professor at the Institute for Public Policy and Governance at the University of Technology Sydney (UTS), where he conducts research on political theory, public policy, regulation, and governance. His academic career spans multiple prestigious institutions including the London School of Economics and Political Science, London School of Hygiene and Tropical Medicine, and Australian National University. Wright's research focuses on political theory , public policy change , regulation and governance , and public health policy . His work examines critical issues such as the Australian Housing Crisis and British relations with the European Union, analyzing how policy regimes establish, consolidate, erode, and terminate meaning within policy objectives. He employs post-structuralist approaches to understand policy change as the transformation of meaning within discursive configurations. His recent publications reveal a strong focus on policy change analysis , particularly regarding European Union membership and Australian housing policy. The publications demonstrate interdisciplinary work spanning political science, health policy, regulatory studies, and economic evaluation. His research shows particular strength in analyzing how policy regimes function, how meaning is invested in policy objectives, and how these processes lead to policy continuity or change. Wright has secured research funding including the 2023 grant for Life Cycle Assessment of Hydrogen Storage Technologies for Circular and Sustainable Energy Systems . His scholarly impact is evident through numerous citations across his publications in political science, health policy, and regulatory studies. He actively engages with policy communities through external research impact activities related to the Australian Housing Crisis and British-EU relations.

• Statistical methods for high-dimensional data
• Cancer genomics
• Biomarker identification
• +۹ مورد دیگر

Dr. Yuan Huang is an Assistant Professor in the Department of Biostatistics at Yale School of Public Health. She holds multiple affiliations across Yale University, including the Yale Cancer Center, Yale Center for Analytical Sciences (YCAS), Cancer Prevention and Control program, and the Center for Brain & Mind Health. Her research focuses on developing statistical methods for high-dimensional data, addressing challenges in cancer genomics such as low reproducibility, nonlinearity, and heterogeneity. Dr. Huang specializes in biomarker identification, large-scale network structure estimation, and gene-environment analysis, with recent expansion into neurodegenerative diseases including Alzheimer's, Huntington's, and Parkinson's diseases. Dr. Huang's publication record demonstrates expertise in advanced statistical methodologies including Bayesian mixture models, precision matrix estimation, functional data analysis, and stagewise algorithms for nonconvex optimization. Her work consistently bridges theoretical statistical development with practical biomedical applications. As an active collaborator, Dr. Huang works on clinical trials, genetics, epidemiology, and other biomedical research projects, applying her methodological expertise to solve complex data analysis challenges in these fields.

• Process Systems Engineering
• Chemical Engineering
• Optimization
• +۶ مورد دیگر

Alexander Mitsos is a Professor at Forschungszentrum Jülich in Germany, where he leads research at the intersection of process systems engineering, chemical engineering, and computational methods. His work spans optimization theory, machine learning applications, and energy systems, with a focus on developing novel methodologies for complex engineering problems across multiple domains. Dr. Mitsos's research interests center on the application of advanced optimization techniques to chemical engineering problems. His primary areas of focus include: Process systems engineering and optimization Machine learning applications in chemical engineering Energy systems and hydrogen technologies Bioprocess engineering and control systems Ammonia energy storage and carbon capture His recent publications reveal a strong trend toward integrating machine learning with traditional chemical engineering approaches. He has pioneered work on graph neural networks for molecular property prediction, reinforcement learning for control systems, and bilevel optimization for energy systems. His research demonstrates a consistent focus on developing computationally efficient methods that bridge theoretical advances with practical engineering applications, particularly in sustainability-focused domains like hydrogen technologies and carbon emission reduction. The analysis of his 15 most recent publications shows a balanced portfolio between theoretical method development (e.g., optimization algorithms) and practical applications (e.g., cement production, hydrogen compression). Dr. Mitsos has mentored numerous graduate students and postdoctoral researchers, as evidenced by his extensive publication record with junior authors. His research has been supported by various grants focused on energy transition, process optimization, and sustainable chemical engineering solutions, with significant collaborations across European institutions. The funding landscape for his work appears to emphasize sustainability transitions and industrial decarbonization, particularly in energy-intensive sectors. His work appears to be conducted within a research group focused on process systems engineering, with strong connections to both computational mathematics and practical chemical engineering applications. The group maintains laboratories for experimental validation of computational models, particularly in bioprocess engineering and hydrogen technologies, while maintaining strong theoretical foundations in optimization and control theory.