Professor Hong Hao is a John Curtin Distinguished Professor at Curtin University, affiliated with the School of Civil and Mechanical Engineering and the Curtin Research Centre for Infrastructural Monitoring & Protection. His expertise spans Structural Dynamics, Earthquake Engineering, Blast and Impact Engineering, and Structural Health Monitoring. He holds prestigious roles like Fellow of ATSE, ISEAM, and ASCE, and has led organizations such as the International Association of Protective Structures and the Australian Earthquake Engineering Society. Education: BE (Tianjin University, 1982), MSc (UC Berkeley, 1985), PhD (UC Berkeley, 1989). Awards include the Tan Chin Tuan Fellowship and multiple Ko Medals. He has authored over 200 journal articles, with recent work focusing on blast-resistant materials, seismic fragility, and AI-driven structural health monitoring. His research emphasizes resilient infrastructure, including metaconcrete structures, corrosion-resistant materials, and sensor-based damage detection. Ongoing projects involve smart tunnel safety under BLEVE explosions and modular building systems.
Cong Shi, also known as Alex Shi, is a Professor of Management at the Miami Herbert Business School, University of Miami, since 2025. Previously, he served as Associate Professor at the University of Michigan (2019-2023) and Assistant Professor there (2012-2019). His academic journey began with a B.Sc. in Mathematics (First Class Honors) from the National University of Singapore (2007) and a Ph.D. in Operations Research from MIT (2012) under Professor Retsef Levi. Education : MIT (Ph.D.), NUS (B.Sc.) Current Role : Professor, Management, Miami Herbert Business School Prior Roles : Associate Professor (Tenured), University of Michigan; Assistant Professor, University of Michigan His research spans Revenue Management, Supply Chain Management, Healthcare Operations, Human-Robot Interaction, and Data-Driven Optimization. Recent publications focus on fairness-constrained inventory, sequential pricing, and trust-aware robotics. He has received prestigious awards including the Senior Research Award (2025) and Amazon Research Award (2021), alongside multiple INFORMS recognitions. The 15 most recent articles highlight advancements in inventory control with fairness constraints, sequential pricing algorithms, and trust propagation models in robotics. His work bridges theoretical rigor with practical applications in supply chains and human-robot collaboration. Scientific Awards : Senior Research Award, Miami Herbert Business School, 2025 Amazon Research Award, 2021 INFORMS Meritorious Service Awards (2018, 2019, 2021, 2023) IOE Graduate Course Professor of the Year, University of Michigan, 2019 He has advised 10 PhD students, many now in academia (e.g., UC Berkeley, Penn State) or tech roles (Meta, Amazon). Grants include NSF funding as PI and Co-PI.
Debmalya Panigrahi is a Professor and Associate Chair in the Department of Computer Science at Duke University. He holds a PhD in Theoretical Computer Science from MIT and has prior affiliations with Microsoft Research, Bell Labs, and the Simons Institute for Theory of Computing. His research focuses on algorithms, particularly graph algorithms, algorithms under uncertainty, and learning-augmented methods. He has received NSF CAREER and other awards, and his work spans peer-reviewed publications in top venues like STOC, FOCS, and SODA. He advises PhD students and mentors postdocs, emphasizing theoretical contributions with practical applications. His teaching includes courses on approximation algorithms, graph algorithms, and discrete mathematics. Education: PhD (MIT, advised by David Karger), MSc (Indian Institute of Science, advised by Ramesh Hariharan), BSc (Jadavpur University). Research highlights include fastest algorithms for graph connectivity, learning-augmented approximation methods, and online algorithms. Funded by NSF, ARO, Google, and others. Current projects explore network reliability, hypergraph algorithms, and algorithmic fairness. His lab collaborates across theory, AI/ML, and databases at Duke. Recent Grants: NSF CCF-2006512, CCF-1618286, CCF-1350537 Labs/Teams: Duke Algorithms Lab, Theory Group, Collaborations with CS-Econ and AI/ML groups Publications span 150+ papers, with 5+ journal articles in SIAM Journal of Computing and ACM Transactions. Recent focus on integrating machine learning into classical algorithms to improve worst-case performance bounds. Advised 10+ PhD students, many now in academia (e.g., UI Chicago, UT Dallas) and industry (Google, Microsoft).
Dr. Yongjia Song is an Associate Professor in the Department of Industrial Engineering at Clemson University's College of Engineering, Computing and Applied Sciences. His research focuses on optimization under uncertainty, stochastic programming, and network interdiction with applications in disaster logistics, energy systems, and humanitarian operations. BS in Computational Mathematics (2009), Peking University MS in Industrial Engineering (2012), University of Wisconsin-Madison MS in Computer Sciences (2012), University of Wisconsin-Madison PhD in Industrial Engineering (2013), University of Wisconsin-Madison His work addresses complex systems under uncertainty through: Stochastic and robust optimization frameworks Integer programming for discrete decision problems Applications in disaster response and transportation networks Evacuation planning and shelter management Human trafficking disruption modeling Recent publications demonstrate trends in: Multistage stochastic programming for dynamic disaster response Bayesian preference elicitation for complex design problems Network interdiction models for security and trafficking disruption Integration of logistics and evacuation planning under uncertainty Adaptive algorithms for large-scale optimization Professional affiliations include: Institute for Operations Research and the Management Sciences (INFORMS) Mathematical Optimization Society (MOS) Society for Industrial and Applied Mathematics (SIAM) He teaches graduate courses in risk modeling (IE 8090) and actively works on practical implementations of optimization techniques in real-world systems.
Georgia Perakis is the John C Head III Interim Dean of MIT Sloan School of Management and a Professor of Operations Management and Operations Research & Statistics. She has been on MIT Sloan's faculty since 1998, contributing extensively to research in analytics/AI, optimization, and machine learning applications in pricing, supply chains, healthcare, and energy. Recognized as a leading academic, she has won numerous awards, including the INFORMS Fellow and Distinguished MSOM Fellow, along with multiple best paper awards. Perakis has supervised 30 PhD and 59 master's students, fostering lifelong academic relationships. Her administrative roles include co-director of the Operations Research Center and Associate Dean for Social and Ethical Responsibilities of Computing. She currently serves as Editor-in-Chief of M&SOM and has held editorial leadership roles in top journals like Operations Research and Management Science. Her education includes a BS in Mathematics from the University of Athens and advanced degrees in applied mathematics from Brown University. Research Interests : Perakis focuses on solving complex problems at the intersection of optimization and machine learning, with applications in retail promotions, healthcare operations (e.g., emergency department management), and energy systems. Her work emphasizes practical solutions to real-world challenges, leveraging data-driven analytics and prescriptive models. Recent projects include optimizing patient placement in emergency departments and modeling demand for new products in retail. Grants & Awards : Her accolades include the NSF CAREER Award, PECASE Award, and over a dozen best-paper recognitions. Notable contributions include a finalist position in the JD.com Competition (2019) and winning first place for Johnson & Johnson’s demand-prediction work (2018). She has also pioneered methodologies for equitable resource allocation in healthcare. Education & Leadership : Perakis holds leadership roles in interdisciplinary initiatives like the MIT Initiative on the Digital Economy and the Food Supply Chain Analytics and Sensing Initiative. Her teaching excellence is underscored by awards such as the Jamieson Prize and Teacher of the Year (MIT Sloan). She has directed major programs like the MIT Leaders for Global Operations and the Executive MBA program. Labs & Teams : She is affiliated with the Operations Research Center (an interdepartmental PhD program) and collaborates with institutions like UMass Memorial Hospital on healthcare optimization projects. Her work integrates ethics into AI development, emphasizing fairness, bias mitigation, and societal impact.
Chaitanya Swamy is a Professor and University Research Chair in the Department of Combinatorics & Optimization at the University of Waterloo, Canada. His primary affiliation is within the Faculty of Mathematics, and he holds positions in both the Department of Combinatorics & Optimization and the School of Computer Science. He obtained his Ph.D. in Computer Science from Cornell University under the supervision of David Shmoys, followed by postdoctoral research at Caltech's Center for the Mathematics of Information. Swamy’s research focuses on algorithms, particularly in combinatorial optimization, approximation algorithms, algorithmic game theory, stochastic optimization, network design, scheduling, and online algorithms. His work spans theoretical contributions and practical applications, including algorithm design for facility location, network routing, and mechanism design. He has contributed to foundational results in approximation algorithms, such as the development of primal-dual methods and LP-rounding techniques. Swamy has held significant editorial roles, including as an associate editor for Discrete Optimization and SIAM Journal on Computing . He has organized major conferences like CanaDAM 2021 and sessions at ISMP 2018. His teaching record includes courses on combinatorial optimization, scheduling, and algorithmic game theory. He has advised numerous Ph.D. and Master’s students, many of whom have gone on to prestigious academic and industry positions. Swamy’s research has been recognized through awards for his students, including the University of Waterloo Alumni Gold Medal. He actively contributes to the academic community through committee work for conferences like STOC, APPROX, and SODA, and his publications reflect a deep engagement with both theoretical and applied aspects of algorithms and optimization.
Meng Wu is an Assistant Professor in the School of Electrical, Computer and Energy Engineering (ECEE) at Arizona State University, specializing in advanced optimization, control, and machine learning methods for integrating distributed energy resources (DERs) into power systems. Her work addresses critical challenges in power system planning, operations, stability, and electricity markets under high DER penetration. Education: Ph.D. in Electrical and Computer Engineering, Texas A&M University, 2017 M.Eng. in Electrical and Computer Engineering, Cornell University, 2011 B.Eng. in Electrical Engineering & Automation, Tianjin University, China, 2010 Research Interests: Dr. Wu's research focuses on DER integration through transmission-distribution coordination , spatio-temporal price forecasting , and optimal market participation strategies. Key areas include: Physics-guided machine learning for DER-penetrated distribution systems Computational algorithms for wholesale-distribution market coordination Dynamic modeling of DERs and composite loads for voltage stability Optimal bidding strategies for energy storage and DER aggregators Publication Trends: Her 2021-2024 publications reveal a concentrated effort on DER market integration using parametric programming and deep learning, with emphasis on real-time locational marginal price forecasting, transmission-distribution coordination, and degradation-aware energy storage operations. Scientific Awards: Best Paper Award, IEEE PES General Meeting (2021) Best Conference Paper Award, North American Power Symposium (2019) Invited Participant, US Frontiers of Engineering Symposium, NAE (2021) Advising and Grants: Dr. Wu mentors multiple PhD and Master's students, including recent graduates Zhongxia Zhang (PhD) and Sayyid Mohssen Sajjadi (MS). Her group secured PSERC funding for projects on DER aggregation and adaptive transmission-distribution modeling, with industry partnerships at ISO New England and Quanta Technology. Research Group: Leading an active research team at ASU, she collaborates with the Power Systems Engineering Research Center (PSERC) on DER integration challenges, advising students through FURI, MORE, and Barrett Honors College programs.
Alan Stocker is a Professor in the Department of Psychology at the University of Pennsylvania, affiliated with the Neuroscience Graduate Group, Bioengineering Graduate Group, and Computational Neuroscience Initiative. His research focuses on understanding how prior beliefs and expectations shape visual perception, combining theoretical models with psychophysical and physiological experiments. Key areas include optimal inference frameworks, neural coding efficiency, and decision-making under uncertainty. Education: PhD in Physics (ETH Zurich, 2002); MSc in Biomedical Engineering and Materials Science (ETH Zurich, 1995). Prior appointments include postdoctoral work at New York University and ETH Zurich. Research Interests: Behavioral Neuroscience, Cognitive Neuroscience, Sensation and Perception. Specific projects explore statistical properties of visual environments, subjective expectations from past decisions, and neural computations underlying perception. Advising: Current advisees include Cheng Qiu (Postdoc), Ansh Soni, and Mengting Fang (Graduate Students). Courses taught: Perception, Visual Neuroscience, and Probabilistic Models in Perception and Cognition. Labs/Teams: Leads the Computational Perception and Cognition (CPC) Laboratory, emphasizing theory-driven experimentation. Collaborates widely across disciplines including bioengineering and electrical systems engineering.
Howie Choset is a Professor of Robotics at the Robotics Institute, Carnegie Mellon University. He directs the Undergraduate Robotics Minor and leads the Biorobotics Laboratory, where his research focuses on snake robots, motion planning, and medical robotics. He is also affiliated with the Manufacturing Futures Institute. Ph.D., Mechanical Engineering, California Institute of Technology (1996) M.S., Mechanical Engineering, California Institute of Technology (1995) B.S.E., Computer Science and Engineering, University of Pennsylvania (1990) B.S., Economics, The Wharton School of Business (1990) Choset's research centers on robotics for confined and complex environments, particularly through the development of snake robots. His work integrates mechanism design, path and motion planning, and estimation to enable applications in surgery, manufacturing, infrastructure inspection, and search and rescue. He is a pioneer in medical robotics and has co-founded Medrobotics to commercialize minimally invasive surgical robots. His recent publications highlight a strong focus on ergodic exploration, multi-agent systems, motion planning under uncertainty, and medical robotics. Themes include optimizing robot trajectories for information gathering, solving complex path planning problems with dynamic obstacles, and advancing autonomous systems for disaster response and space exploration (e.g., the EELS robot for Enceladus). MIT Technology Review Top 100 Innovators under 35 (2002) Best Paper Award, RIA (1999) Best Paper Award, ICRA (2003) Best Paper, IEEE Bio Rob (2006) Best Video, ICRA (2011) Nominations for best papers at ICRA, IROS, and CLAWAR Choset has advised numerous students, many of whom have won top awards. His lab has received significant funding for robotics research, including projects in surgical robotics, additive manufacturing, and autonomous exploration. He is the lead author of the textbook Principles of Robot Motion and is actively involved in educational innovation through custom robotics labs. He leads the Biorobotics Laboratory at CMU, which develops advanced robotic systems like snake robots and the EELS (Exobiology Extant Life Surveyor) robot for NASA missions. The lab collaborates with industry and government agencies on applications ranging from surgery to space exploration.
Salim ROSTAMI is an Associate Professor at the IÉSEG School of Management in France, specializing in Operations Management. He holds a Ph.D. in Economics and Mathematics Sciences from KU Leuven (2019) and a Master’s in Engineering from KU Leuven (2013), alongside a Bachelor’s in Industrial Engineering from Ferdowsi University of Mashhad (2012). His research focuses on scheduling under uncertainty, project planning, combinatorial optimization, and healthcare logistics. Notable achievements include the 2016 2nd Best Conference Paper Award from the University of Valencia. Education: Ph.D., Economics and Mathematics Sciences, Operations Management, KU Leuven, Belgium (2019) Master, Engineering, Operations Research, KU Leuven, Belgium (2013) Bachelor, Engineering, Industrial Engineering, Ferdowsi University of Mashhad, Iran (2012) His work spans stochastic resource-constrained project scheduling, sequential testing of systems, and chemotherapy appointment scheduling. He has published widely in journals like the European Journal of Operational Research and Flexible Services and Manufacturing Journal. Teaching roles include courses on operations management and project management across undergraduate and graduate programs. Awards: 2016: 2nd Best Conference Paper Award, University of Valencia His research emphasizes practical applications in healthcare and project management, leveraging dynamic programming and metaheuristic algorithms. Collaborations include work with institutions like École des Mines de Saint-Étienne and KU Leuven.
Lars Rohwedder is an Associate Professor in the Algorithms Group at the University of Southern Denmark (SDU) in Odense. He previously held positions as an Assistant Professor at Maastricht University (Netherlands) and postdoc researcher at EPFL, Lausanne (Switzerland). He earned his Ph.D. in Computer Science from CAU Kiel (Germany), advised by Klaus Jansen, and is a recipient of the 2019 PhD of the year award from Förderverein der TF of Kiel University. His research focuses on algorithms for combinatorial optimization, including approximation algorithms, online algorithms, parameterized algorithms, and integer programming. He has contributed to solving scheduling problems, resource allocation, and optimization under uncertainty. Rohwedder has served on program committees for conferences like MAPSP, SODA, STACS, and ICALP. He is funded by NWO's Open Competition M1 project on quasi-polynomial time algorithms. His teaching includes courses on advanced algorithms, operations management, and optimization at SDU and Maastricht University. Key achievements include a quasi-polynomial approximation for the restricted assignment problem, FPT algorithms for scheduling, and contributions to the Submodular Santa Claus problem. His work bridges theoretical foundations and practical applications, with a focus on algorithmic efficiency and robustness.
Sharan Vaswani is an Assistant Professor in the School of Computing Science at Simon Fraser University (SFU). His research focuses on designing algorithms for sequential decision-making under uncertainty, stochastic optimization, and their interplay with machine learning generalization. He holds a PhD from the University of British Columbia (2019) and postdoctoral experiences at the University of Alberta and Mila. His academic journey includes MSc (UBC, 2015) and BTech (BITS Pilani, 2012) degrees. Education: PhD (UBC, 2019), MSc (UBC, 2015), BTech (BITS Pilani, 2012) Postdoctoral Work: University of Alberta (2020-2021), Mila (2019-2020) Teaching includes courses on Probability and Computing (CMPT 210), Optimization for Machine Learning (CMPT 409/981), and Theoretical Foundations of Reinforcement Learning (CMPT 419/983). His research group focuses on developing scalable optimization algorithms with theoretical guarantees. He advises multiple PhD and MSc students, contributing to areas like constrained MDPs, adaptive learning rates, and reinforcement learning theory. Research Highlights: Contributions to bandit algorithms, stochastic gradient methods, and reinforcement learning theory. Notable work includes global convergence analysis of policy gradients and variance-reduced optimization frameworks.
Matthias Feurer is a Thomas Bayes Fellow and interim professor at the Chair of Statistical Learning and Data Science, funded by the Munich Center for Machine Learning (MCML) at Ludwig Maximilian University of Munich. He is a member of the Department of Statistics at LMU Munich, working under Prof. Dr. Bernd Bischl. His academic background includes: PhD in Computer Science from Albert-Ludwigs-Universität Freiburg, supervised by Prof. Dr. Frank Hutter M.Sc. in Computer Science from the University of Freiburg B.Sc. in Computer Science and Media from the Media University Stuttgart Feurer's research focuses on simplifying machine learning usage through Automated Machine Learning (AutoML). His work encompasses hyperparameter optimization, meta-learning, and model selection, with increasing emphasis on multi-objective AutoML that considers factors beyond predictive performance such as interpretability, deployability, and fairness. He actively develops open-source tools to advance the field. His recent publications demonstrate a strong trajectory in practical AutoML systems, with growing attention to tabular machine learning, foundation models integration, and addressing real-world constraints in optimization. His work consistently bridges theoretical advances with practical implementations through several widely-used open-source projects. Notable achievements include: 1st place in the warmstarting-friendly leaderboard of the BBO NeurIPS challenge Winner of the 2nd AutoML challenge Winner of the kdnuggets blog contest on AutoML Feurer is actively mentoring and teaching, having advertised PhD positions focused on AutoML, optimization, and benchmarking. He co-founded the Open Machine Learning Foundation supporting OpenML.org. His upcoming move to TU Dortmund as an assistant professor in AutoML and Optimization signals continued growth in his academic career while maintaining his research focus on making machine learning more accessible and rigorous.
François-Xavier Briol is an Associate Professor in the Department of Statistical Science at University College London (UCL). He co-leads the Fundamentals of Statistical Machine Learning research group and holds roles including theme lead for Computational Statistics and Machine Learning (CSML), co-director of the UCL CDT in Data-Intensive Science, and ELLIS scholar. His research focuses on merging scientific models with data through robust statistical and machine learning methods, emphasizing computational efficiency and model misspecification robustness. Education: BSc/MMORSE from the University of Warwick, PhD from the joint Warwick-Oxford CDT in Statistics. Postdoctoral roles at Imperial College London (Mathematics) and University of Cambridge (Engineering), followed by a Group Leader position at The Alan Turing Institute (2020–2023). Research interests include probabilistic numerical methods, Bayesian inference for intractable models, Stein’s method, and kernel-based approaches. His work has been recognized via awards like the AISTATS Best Paper Award and Blackwell-Rosenbluth Award, with funding from EPSRC and Amazon. Advising: Supervised PhD students in areas like Bayesian quadrature, robust inference, and Gaussian processes. Current students focus on topics such as Bayesian filtering and sensitivity analysis. Grants include EPSRC funding and an Amazon Research Award. Labs/Teams: Active in UCL’s ELLIS unit, CSML, and Turing Institute collaborations. Editorships include SIAM/ASA Journal on Uncertainty Quantification and Bayesian Analysis.
Volodymyr Kuleshov is an Assistant Professor at Cornell Tech and Cornell University's Department of Computer Science. His research focuses on machine learning, particularly generative models, probabilistic methods, and applications in health and sustainability. He co-founded Afresh, an AI startup reducing food waste, and has commercialized genome sequencing work via Moleculo (now part of Illumina). Kuleshov earned his PhD from Stanford University, advised by prominent figures like Stefano Ermon and Serafim Batzoglou. He teaches courses like CS 5785 (Applied Machine Learning) and CS 6785 (Advanced Topics in Machine Learning). His awards include the NSF CAREER Award and Arthur Samuel Best Thesis Award. Education: PhD in Computer Science from Stanford University (2018), advised by Stefano Ermon, Serafim Batzoglou, Michael Snyder, Christopher Re, and Percy Liang. Research Interests: Core ML (generative models, approximate inference), health tech (genome sequencing, clinical decision support), sustainability (AI-driven food waste reduction). Notable projects include Caduceus for DNA sequence modeling and Diffusion Duality theory. Awards: Google Research Scholar Award (2025), Outstanding Paper Award (EMNLP 2023), NIH MIRA Award (2023). Students/Advising: Over 20 advisees across PhD, Master’s, and undergraduate programs, including Edgar Marroquin (PhD) and Charlie Marx (Stanford). Alumni include Allan Bishop (Bloomberg) and Yong Huang (UCI PhD). Labs/Teams: Leads research groups at Cornell Tech focusing on generative AI and its real-world applications. Collaborates with institutions like MILA (Montreal) and DeepMind.
