Invited scholars

F. Baccelli is a senior researcher at INRIA-ENS and an invited professor at Telecom Paris. His research is at the interface between applied mathematics and communication networks. His work on applied mathematics is focused on point processes, max plus algebras, network dynamics, queuing networks, random graphs, and stochastic geometry. His main contributions to communications are centered on congestion control, information theory, and wireless networks. He is a member of the French Academy of Sciences. He was the Math+X Simons chair in mathematics and ECE at UT Austin between 2012 and 2021. He is currently in charge of an interdisciplinary ERC advanced NEMO project on communications and network mathematics.

Sid Banerjee is an associate professor in the School of Operations Research at Cornell, working on topics at the intersection of data-driven decision-making, network algorithms and market design. His research is supported by grants from the NSF (including an NSF CAREER award), ARO, and AFOSR, and has received multiple awards including the INFORMS Applied Probability Society Best Publication award in 2021 and the Erlang Prize in 2022. He completed his PhD from the ECE Department at UT Austin, and was a postdoctoral researcher in the Social Algorithms Lab at Stanford. He also served as a technical consultant with the research science group at Lyft from 2014-18.

Richard Combes is currently an associate professor in Supelec. He received the Engineering Degree from Telecom Paristech (2008), the Master Degree in Mathematics from university of Paris VII (2009) and the Ph.D. degree in Mathematics from university of Paris VI (2013). He was a visiting scientist at INRIA (2012) and a post-doc in KTH (2013). He received the best paper award at SIGMETRICS 2019 and CNSM 2011. His current research interests are machine learning, networks and probability.

Jim Dai is the Leon C. Welch Professor of Engineering in the School of Operations Research and Information Engineering. He also serves as the Dean of School of Data Science at The Chinese University of Hong Kong, Shenzhen. Prior joining Cornell, he held the Chandler Family Chair Professorship in the School of Industrial and Systems Engineering at Georgia Institute of Technology, where he was a faculty member from 1990 to 2012.

Jim Dai received his BA and MA in mathematics from Nanjing University, and his PhD in mathematics from Stanford University. He is an elected fellow of Institute of Mathematical Statistics and an elected fellow of Institute for Operations Research and the Management Sciences (INFORMS). He has received a number of awards for research contributions including the Best Publication Award twice, in 1997 and 2017, The Erlang Prize in 1998, all from the Applied Probability Society of INFORMS, and the ACM SIGMETRICS Achievement Award in 2018. He served as the Editor-In-Chief for Mathematics of Operations Research (MOR) from 2012 to 2019.

Jim Dai's research interests include stochastic processing networks, fluid and diffusion models of queueing networks, reflecting Brownian motions, Stein's method, customer contact center management, hospital inpatient flow management, semiconductor wafer manufacturing, and airline revenue management.

Maria Deijfen is a professor of mathematics at Stockholm University. She is known for her research on random graphs and stochastic processes on graphs, including the Reed–Frost model of epidemics. Maria received her PhD in 2004. After completing her doctorate, she became a postdoctoral researcher at the Mittag-Leffler Institute, Vrije Universiteit Amsterdam, Chalmers University, and Delft University of Technology before returning to Stockholm as a junior faculty member in 2006. Prof. Deijfen was one of the 2018 recipients of the Paul R. Halmos – Lester R. Ford Award of the Mathematical Association of America for her paper with Alexander E. Holroyd and James B. Martin, "Friendly Frogs, Stable Marriage, and the Magic of Invariance", using combinatorial game theory to analyze the stable marriage problem.

Dr. Souvik Dhara is an Assistant Professor at the School of Industrial Engineering and Math department by courtesy at Purdue University. Previously, he has been a Schramm Fellow with a joint appointment between MIT Mathematics and Microsoft Research, Simons-Berkeley Fellow at the Simons Institute, UC Berkeley, and a Postdoctoral Research Associate at Brown University. He received his PhD in 2018 from the Department of Mathematics and Computer Science at Eindhoven University of Technology. Dr. Dhara’s research interest lies at the intersection of applied probability and network science. His primary focus is to develop theoretical foundations for stochastic processes and algorithms on large-scale networks. For instance, his interests include epidemics on networks, community detection, graph representation learning. Dr. Dhara was awarded the Stieltjes Prize for his PhD thesis at the Dutch Mathematical Congress, 2019.

Niao He is an Assistant Professor in the Department of Computer Science at ETH Zurich, where she leads the Optimization and Decision Intelligence (ODI) Group. She is also an ELLIS Scholar and a core faculty member of ETH AI Center and ETH-Max Planck Center of Learning Systems. Previously, she was an assistant professor at the University of Illinois at Urbana-Champaign from 2016 to 2020. Before that, she received her Ph.D. degree in Operations Research from Georgia Institute of Technology in 2015. Her research interests lie in large-scale optimization and reinforcement learning, with a primary focus on theoretical and algorithmic foundations for principled, scalable, and trustworthy decision intelligence. She is a recipient of AISTATS Best Paper Award, NSF CRII Award, SNSF Starting Grant, etc.

Emma is currently Assistant Professor of Probability at the University of Warwick, specialising in branching processes, interacting particle systems, Monte Carlo methods, and their applications to radiation transport. Prior to this position Emma was chargée de recherché with Inria in Bordeaux, during which time she spent seven months on secondment at the University of Melbourne.

PhD 1977 (Mathematics) and 1984 (Mathematical statistics). Professor in mathematics at Uppsala University. Random networks (graphs) and related topics, including both graph limits and other random combinatorial structures, has been my main research area for many years.

Junghyun Lee is a 2nd-year PhD student at the Kim Jaechul Graduate School of AI at KAIST, where he is jointly advised by Prof. Se-Young Yun and Prof. Chulhee Yun. His research focuses on mathematical and theoretical AI, with a recent emphasis on achieving tight statistical guarantees—such as regret and sample complexity—for interactive machine learning, encompassing sequential decision-making (bandits, online learning), reinforcement learning (MDPs), and active learning. He is also passionate about exploring fundamental concepts in statistics and probability theory, algorithmic fairness, and deep learning theory from both optimization and statistical perspectives. Essentially, he is intrigued by any machine learning challenge that requires rigorous mathematical analysis. His research has been published in international AI conferences, including AISTATS, NeurIPS, ICLR, and AAAI. He received the Best Student Paper Award at OPODIS 2023. Junghyun holds an MSc from the same school and a BSc in Mathematical Sciences and Computer Science (double major) from KAIST.

Shuangping Li is a Stein Fellow in the Department of Statistics at Stanford University. She received her PhD degree in Applied and Computational Mathematics in 2022 from Princeton University, where she was jointly advised by Professors Allan Sly and Emmanuel Abbé. Shuangping’s research interests span probability theory, statistics, and machine learning.

Laurent Massoulié is research scientist at Inria, scientific director of the Paris Inria Centre and professor at the Applied Maths Centre of Ecole Polytechnique. His research interests are in machine learning, probabilistic modelling and algorithms for networks. He has held research scientist positions at: France Telecom, Microsoft Research, Thomson-Technicolor, where he headed the Paris Research Lab. He obtained best paper awards at IEEE INFOCOM 1999, ACM SIGMETRICS 2005, ACM CoNEXT 2007, NeurIPS 2018, NeurIPS 2021, was elected "Technicolor Fellow" in 2011, received the "Grand Prix Scientifique" of the Del Duca Foundation delivered by the French Academy of Science in 2017, is a Fellow of the “Prairie” Institute and received the ACM Sigmetrics achievement award in 2023.

Mariana Olvera-Cravioto is a Professor in the Department of Statistics and Operations Research at UNC Chapel Hill. Her research interests include random graphs, heavy-tailed phenomena, and stochastic processes on random graphs, in particular, opinion dynamics. She holds a BA in Applied Mathematics from the “Instituto Tecnológico Autónomo de México (ITAM)”, a MS in Statistics from Stanford University, and a PhD in Management Science & Engineering, also from Stanford University. Prior to joining UNC Chapel Hill, she was a faculty member in the Industrial Engineering and Operations Research Department at Columbia University, and also briefly a visiting faculty in the Industrial Engineering and Operations Research Department at UC Berkeley.

Cynthia Rush is an Associate Professor of Statistics at Columbia University. She received a Ph.D. and M.A. in Statistics from Yale University in 2016 and 2011, respectively, and she completed her undergraduate coursework at the University of North Carolina at Chapel Hill where she obtained a B.S. in Mathematics in 2010. Her research focuses on message passing algorithms, statistical robustness, and information theory.

Clara Stegehuis is an associate professor at the university of Twente. She works at the intersection of probability theory, graph theory and stochastic networks, with an emphasis on asymptotic analysis, stochastic process limits, and randomized algorithms. Problems she investigates are often inspired by applications in network science, physics and computer science.

Subhabrata Sen is an Assistant Professor of Statistics at Harvard University. Prior to Harvard, he completed his PhD from Stanford Statistics, and was subsequently a Schramm postdoctoral fellow at Microsoft Research New England and MIT. His current research focuses on high-dimensional statistics, inference on networks and spin glasses. Subhabrata has received the NSF CAREER Award, an AMS Simons Travel grant and Bernoulli Society New Researcher Award (Hon. Mention).

Rajesh Sundaresan is a Professor of Electrical Communication Engineering, an Associate Faculty at the Robert Bosch Centre for Cyber-Physical Systems, and the current Dean of the Division of Electrical, Electronics, and Computer Sciences, at the Indian Institute of Science. His research interests include decision theory, communication, computation, and control over networks, cyber-social systems, and data-driven decision frameworks for public health responses.

Peter Taylor received a BSc(Hons) and a PhD in Applied Mathematics from the University of Adelaide in 1980 and 1987 respectively. In between, he spent time working for the Australian Public Service in Canberra. After periods at the Universities of Western Australia and Adelaide, he moved at the beginning of 2002 to the University of Melbourne. In January 2003, he took up a position as the inaugural Professor of Operations Research. He was Head of the Department of Mathematics and Statistics from 2005 until 2010. Peter's research interests lie in the fields of stochastic modelling and applied probability, with particular emphasis on applications in queueing, telecommunications, biological modelling, economics, healthcare and disaster management. Peter is the Editor-in-Chief of {\it The Journal of Applied Probability} and {\it Advances in Applied Probability}. In 2017, he was awarded the Ren Potts Medal by the Australian Society for Operations Research, in 2018 the George Szekeres Medal by the Australian Mathematical Society and in 2019 the ANZIAM Medal. He is a fellow of the Australian Academy of Science and the Australian Mathematical Society.

Siva Theja Maguluri is Fouts Family Early Career Professor and Associate Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech. He obtained his Ph.D. and MS in ECE as well as MS in Applied Math from UIUC, and B.Tech in Electrical Engineering from IIT Madras. His research interests span the areas of Control, Optimization, Algorithms and Applied Probability and include Reinforcement Learning theory and Stochastic Networks. His research and teaching are recognized through several awards including the Best Publication in Applied Probability award, NSF CAREER award, second place award at INFORMS JFIG best paper competition, Student best paper award at IFIP Performance, CTL/BP Junior Faculty Teaching Excellence Award, and Student Recognition of Excellence in Teaching: Class of 1934 CIOS Award.

Ruo-Chun is a KTH doctoral student, working with Prof. Aristides Gionis on signed graph mining and with Prof. Alexandre Proutiere on graph bandits. For the former, she is working with explict pattern mining. For the latter, she is focusing on the computational aspect of combiantorial bandits.

Cesar A. Uribe is the Louis Owen Assistant Professor at the Department of Electrical and Computer Engineering at Rice University. He received the M.Sc. degrees in systems and control from the Delft University of Technology in The Netherlands and in applied mathematics from the University of Illinois at Urbana-Champaign in 2013 and 2016, respectively. He also received the Ph.D. degree in electrical and computer engineering at the University of Illinois at Urbana-Champaign in 2018. He was a Postdoctoral Associate in the Laboratory for Information and Decision Systems-LIDS at the Massachusetts Institute of Technology-MIT until 2020. He is the recipient of the Ralph E. Powe Junior Faculty Enhancement Award, 100k Strong in the Americas Innovation Award, and the Google Research Scholar Award. He held a visiting professor position at the Moscow Institute of Physics and Technology until 2022. His research interests include distributed learning and optimization, decentralized control, algorithm analysis, and computational optimal transport.

Po-An is a fourth-year PhD student at KTH Royal Institute of Technology, working under the guidance of Prof. Alexandre Proutiere. In June 2023, he embarked on an enriching internship journey with CyberAgent in Japan. Prior to his academic pursuit at KTH, he gained valuable research experience as an assistant in Chi-Jen Lu’s lab at Academia Sinica.

Amy R. Ward is the Rothman Family Professor of Operations Management at the University of Chicago Booth School of Business. She received her Ph.D. degree from Stanford University in 2001. She is a fellow of the INFORMS Manufacturing and Service Operations Management (M&SOM) Society (elected June, 2023). She is the Editor-in-Chief for the journal Operations Research (term began 1/1/2024). Earlier, she held the position of Chair of the INFORMS Applied Probability Society (term 11/2016-11/2018).

Adam Wierman is the Carl F Braun Professor in the Department of Computing and Mathematical Sciences at Caltech. He received his Ph.D., M.Sc., and B.Sc. in Computer Science from Carnegie Mellon University. Adam’s research strives to make the networked systems that govern our world sustainable and resilient. He is best known for his work spearheading the design of algorithms for sustainable data centers, which as seen significant industry adoption (e.g. through the startup Verrus), and his work on heavy-tails, including co-authoring a book on “The Fundamentals of Heavy Tails”. He is a recipient of multiple awards, including the ACM Sigmetrics Rising Star award, the ACM Sigmetrics Test of Time award, the IEEE INFOCOM Test of Time award, the IEEE Communications Society William R. Bennett Prize, the Caltech IDEA Advocate award, multiple teaching awards, and is a co-author of papers that have received “best paper” awards at a wide variety of conferences across computer science, power engineering, and operations research.

Ruth Williams is a Distinguished Professor of the Graduate Division at the University of California, San Diego. She is a mathematician who works in probability theory, especially on stochastic processes and their applications. Her current research includes the study of stochastic models of complex networks, for example, those arising in Internet congestion control and systems biology. Williams earned her B.Sc. (Hons) and M.Sc degrees from the University of Melbourne, Australia, and her Ph.D. from Stanford University. She has been awarded honorary Doctor of Science degrees by La Trobe University and the University of Melbourne. She is an elected member of the US National Academy of Sciences, an elected fellow of the American Academy of Arts and Sciences, a corresponding member of the Australian Academy of Science, as well as being a fellow of multiple scientific societies. In 2007, Williams received the Best Publication Award of the INFORMS Applied Probability Society, jointly with Amber Puha and H. Christian Gromoll. In 2016, she was awarded the John von Neumann Theory Prize by the Institute for Operations Research and the Management Sciences, jointly with Martin I. Reiman, and was awarded the 2017 Award for the Advancement of Women in Operations Research and the Management Sciences. In 2012, Williams served as President of the Institute of Mathematical Statistics, a major international professional society for the development and dissemination of the theory and applications of probability and statistics.

Jiaming Xu is an associate professor at the Fuqua School of Business at Duke University. He received a Ph.D. degree from UIUC in 2014, an M.S. degree from UT-Austin in 2011, and a B.E. degree from Tsinghua University in 2009, all in Electrical and Computer Engineering. His research interests include high-dimensional statistics, networks, information theory, convex and non-convex optimization, and queueing theory. He received a Simons-Berkeley Fellowship in 2016 and an NSF Career Award in 2022.

Kuang Xu (Chinese: 许匡) is a Tenured Associate Professor at Stanford Graduate School of Business. He is an expert in Operations Research, AI and Data Science innovation, supply chains and logistics, and data-driven decision-making. He is a Co-Creator of AI and Data Science Strategy, the first Stanford course focusing on the strategy, management and entrepreneurship of AI and Data Science. He is a Co-Director of the Stanford GSB Value Chain Innovation Initiative.

Kuang’s research focuses on decision-making under uncertainty, leveraging tools from operations research, statistics and machine learning. His work has been published in leading academic journals including Operations Research and Management Science, and has received a number of prestigious awards, including the George E. Nicholson Prize from the Institute for Operations Research and the Management Sciences (INFORMS), the Best Paper Award as well as Outstanding Student Paper Award from the Association for Computing Machinery (ACM), Special Interest Group on Measurement and Evaluation (SIGMETRICS), and an ACM SIGMETRICS Rising Star Research Award. He serves as an Associate Editor for Management Scienceand Operations Research in Data Science and Stochastic Modeling. His research and writing have been featured in a variety of media outlets including the NPR, PBS, NBC and USA Today.

Kuang advises companies and investment funds on how to build core AI and Data Science capabilities and strategic moats. He has served as the Chief Data Science Advisor for Shipt Inc., Senior Advisor to Uber Inc., and scientific advisors to a number of startups as well as venture capital and private equity funds. 

Kuang received his Ph.D. degree in Electrical Engineering and Computer Science from MIT (2019), and the Bachelor of Science degree from the University of Illinois at Urbana-Champaign (2009). He is a native of Suzhou, China. 

Bert Zwart is a professor at Eindhoven University of Technology (TU/e). Bert’s research expertise is in applied probability and stochastic networks, in particular rare event analysis and simulation, scaling limits, scheduling under uncertainty, dynamic pricing and applications in communication and energy networks. The outcome of Bert’s research makes it possible to analyze complex systems and procedures, in which traditional methods of analysis are unusable, due to the number of factors and variables involved.

The scope of his activities extends to mathematical models for computer and communication networks, call centers and production processes, which are all frequently highly dimensional and, therefore, difficult to analyze. His research aims to reduce complexity of such models by focusing on their macroscopic behavior. In addition, a new application area for stochastic operations research is emerging - energy networks, especially electricity grids - which brings new and urgent research questions.