Professor Tommy Chan is Chair in Civil Engineering at Queensland University of Technology's School of Civil and Environmental Engineering. With over $10M in research funding, his work focuses on structural health monitoring of bridges and infrastructure systems. His research group develops cutting-edge methods for assessing structural integrity using vibration analysis, optical sensors, and machine learning. Professor Chan leads major projects including the ARC-funded 'Next Generation Bridge Monitoring' initiative developing real-time monitoring systems for prestressed concrete bridges. His team's innovations include GNSS-based settlement monitoring and synergic identification methods for prestress force evaluation. Current research explores vehicle-bridge interactions, damage detection algorithms, and novel materials for impact protection. He has received numerous honors including the Vice Chancellors' Leadership Award and Top Supervisor Award. Professor Chan founded the Australian Network of Structural Health Monitoring and serves on editorial boards for multiple journals in structural engineering.
Chen Xu is an Associate Professor in the Department of Mathematics and Statistics at the University of Ottawa. He holds an M.A. from York University and a PhD from the University of British Columbia. His research focuses on sparse modeling, statistical learning, and big data processing, with an emphasis on both theoretical and computational advancements. Dr. Xu is affiliated with the Faculty of Science and contributes to editorial roles for journals such as the Journal of the American Statistical Association and Electronic Journal of Statistics. Education: M.A., York University PhD, University of British Columbia Research interests include feature selection, regularization methods, kernel methods, and high-dimensional regression. His work addresses computational challenges in big data, proposing efficient algorithms for tasks like singular value decomposition, clustering, and distributed feature screening. Recent publications highlight advancements in multiview PCA, low-tubal-rank tensor recovery, and model-free regression techniques. Publications span prestigious journals like the Journal of the American Statistical Association and IEEE Transactions series, focusing on statistical methodology, machine learning applications, and scalable computational solutions for complex data problems. Editorial Service: Associate Editor, Journal of American Statistical Association-T&M (2023–present) Associate Editor, Electronic Journal of Statistics (2023–present) Former Associate Editor, The Canadian Journal of Statistics (2019–2021) His research groups are Statistics and Biostatistics, and Data Science, Machine Learning, and Artificial Intelligence. He has no listed awards but maintains active editorial and academic collaborations in computational statistics and machine learning.
Holden Lee is an Assistant Professor in the Department of Applied Mathematics and Statistics at Johns Hopkins University, where he joined in 2022. His research focuses on the theoretical foundations of machine learning, probability, and their intersections with theoretical computer science. He explores probabilistic methods in modern machine learning, including deep learning-based generative models and convergence guarantees for sampling algorithms like Markov Chain Monte Carlo. Prior to JHU, he was a postdoc at Duke University and a Simons Fellow at UC Berkeley. He holds a PhD in Mathematics from Princeton University and degrees from MIT and the University of Cambridge. Education: PhD in Mathematics, Princeton University, 2019 MASt in Pure Mathematics, University of Cambridge, 2014 BSc in Mathematics, MIT, 2013 Research Interests: Machine Learning Theory Probabilistic Sampling Methods Generative Models Statistical Learning Theory His work emphasizes theoretical rigor, particularly in understanding the success and limitations of deep learning algorithms and designing efficient sampling techniques beyond classical log-concave settings. Articles Trends: Lee’s recent publications (2022–2024) focus on advancing sampling algorithms for complex distributions, analyzing generative models, and improving convergence guarantees for methods like MCMC and score-based diffusion. His work bridges theory and practice, with applications in multimodal data, text generation, and dynamical systems. Awards: Simons Fellow at UC Berkeley (2021) Advising & Grants: Lee has contributed to projects at NeurIPS and collaborates on research in AI efficiency and theoretical guarantees. He teaches courses on probability and applied mathematics at JHU and Duke. Labs/Teams: His research group focuses on theoretical machine learning and probabilistic methods, with ongoing projects on scalable sampling algorithms and generative model analysis.
Soledad Villar is an Assistant Professor in the Department of Applied Mathematics and Statistics and a member of the Mathematical Institute for Data Science at Johns Hopkins University. She also contributes to the Data Science and AI Institute . Her research focuses on computational methods for extracting information from data, emphasizing optimization for data science, machine learning, equivariant representation learning, and graph neural networks. Dr. Villar holds a PhD in Mathematics from the University of Texas at Austin and has been a research fellow at New York University and the Simons Institute at UC Berkeley. Her work bridges theoretical foundations with practical applications in fields like scientific computing and political analysis. Awards include the National Science Foundation CAREER Award (2024). Her research has addressed topics such as gerrymandering detection, fluid dynamics modeling, and graph representation learning. She collaborates on interdisciplinary projects and organizes academic events like the One World MINDS Seminar and the Cibercoloquio Latinoamericano de Matemáticas . Her research interests span computational methods, equivariant machine learning frameworks, and graph neural networks, with applications in physics, engineering, and data-driven decision-making. She actively engages in advancing machine learning techniques for scientific and engineering challenges.
Assoc Prof Xiaohui Bei is an Associate Professor at Nanyang Technological University (NTU), holding dual appointments in the School of Physical & Mathematical Sciences (Division of Mathematical Sciences) and the College of Computing & Data Science. His research focuses on algorithmic game theory, fair division, auction design, and computational economics. He earned his Ph.D. from Tsinghua University in 2012 under Prof. Andrew Yao and held postdoctoral positions at NTU and the Max Planck Institute for Informatics. His work bridges theoretical computer science and economics, addressing challenges in fair resource allocation, truthful mechanisms, and market design. Key interests include cake-cutting protocols, auction optimization, and voting systems with mixed goods. He has contributed to foundational studies on Nash welfare, submodular valuations, and connectivity constraints in fair division. Prof. Bei’s research is supported by grants exploring topics such as optimal auction mechanisms and dynamic resource allocation. His publications often emphasize practical algorithmic solutions to complex economic challenges, ensuring both efficiency and fairness.
Dr. Jianqiang Cheng is an Associate Professor in the Department of Systems and Industrial Engineering at the University of Arizona, College of Engineering. He is also a member of the Graduate Faculty and affiliated with the Applied Mathematics and Statistics Graduate Interdisciplinary Programs. His research is centered on optimization under uncertainty with applications in energy systems and logistics. Research Interests: His primary research areas include stochastic programming, robust optimization, distributionally robust optimization, semidefinite programming, and chance-constrained optimization. He applies these methodologies to challenges in power systems, renewable energy integration, microgrid design, and resilient supply chains. The recent publications (2020–2022) reflect a strong trend toward data-driven and computationally efficient methods in optimization. Key themes include distributionally robust optimization under moment and Wasserstein ambiguity, chance-constrained AC optimal power flow, and resilient supply chain modeling under disruptions such as the COVID-19 pandemic. His work frequently appears in top journals like INFORMS Journal on Computing , IEEE Transactions on Power Systems , and European Journal of Operational Research . Scientific Awards: Best Short Paper Award, INFORMS Workshop on Data Science (Fall 2022) NSF CAREER Award, National Science Foundation (Spring 2022) Science Foundation Arizona's 2017 Bisgrove Scholar (Spring 2017) Dr. Cheng has secured significant research funding, including the NSF CAREER Award, supporting his work in data-driven optimization. He collaborates extensively with researchers in energy systems and operations research, including K. Pan, M. Cheramin, A. M. Fathabad, and A. Lisser. While specific advisees are not listed, his role as a member of the Graduate Faculty indicates active supervision of graduate students in systems engineering, applied mathematics, and statistics. His research contributes to the development of advanced optimization models for real-world systems affected by uncertainty, particularly in energy and logistics. Though no specific lab is mentioned, his work implies involvement in computational optimization and energy systems modeling research groups within the College of Engineering.
Xiucai Ding is a tenured Associate Professor in the Department of Statistics at the University of California, Davis, starting in 2025. He is also affiliated with the Graduate Group in Applied Mathematics (GGAM) at UC Davis. Previously, he was an Assistant Professor in the same department from 2020 to 2025 and a Research Associate at Duke University from 2018 to 2020. PhD in Statistics, University of Toronto (2014–2018), advised by Jeremy Quastel Research Associate, Duke University (2018–2020), with Hau-Tieng Wu Assistant Professor, UC Davis (2020–2025) Associate Professor (tenured), UC Davis (starting 2025) His research focuses on mathematical statistics and statistical learning theory, particularly applied random matrix theory, high-dimensional statistics, non-stationary and functional time series analysis, statistical optimal transport, and the statistical foundations of machine learning algorithms. His methodological work emphasizes nonparametric and sieve-based estimation, inference under complex dependencies, and applications to noisy, high-dimensional data. The recent publications and software tools (such as RMT4DS, Sie2nts, SIMle) reflect a consistent trend in developing theoretically grounded, computationally feasible tools for analyzing complex time series and high-dimensional covariance structures. His work bridges theoretical statistics with practical data science. His research has been supported by the National Science Foundation (NSF). Estimation and inference for precision matrices of nonstationary time series (2020) Auto-regressive approximations to non-stationary time series (2021) On the partial autocorrelation function for locally stationary time series (2022) He advises students and researchers through his role in the Department of Statistics and GGAM. He has taught courses such as STA 108 (Regression Analysis), STA 137 (Applied Time Series Analysis), STA 135 (Multivariate Data Analysis), STA 221 (Big Data & High Performance Statistical Computing), and STA 250 (Topics in Applied and Computational Statistics) at UC Davis. He previously taught at Duke University and the University of Toronto. He has developed several open-source R packages for statistical methodology: RMT4DS : Random matrix tools for data scientists (CRAN/GitHub) Sie2nts : Sieve methods for non-stationary time series (CRAN/GitHub) SIMle : Estimation and inference for nonlinear and non-stationary regression (CRAN/GitHub) UHDtst : Two-sample tests for high-dimensional covariance matrices (GitHub)
Dietmar Maringer is Professor of Computational Economics and Finance at the University of Basel's Faculty of Business and Economics (WWZ), where he leads research at the intersection of finance, computational methods, and artificial intelligence. His work focuses on risk management, portfolio optimization, algorithmic trading, and financial simulations. His research interests span computational finance, artificial intelligence in finance, data analysis, risk management, portfolio optimization, algorithmic and high-frequency trading, financial networks, complex adaptive systems, and market simulations. He applies advanced computational and heuristic optimization techniques to solve real-world financial problems, contributing significantly to quantitative finance and financial engineering. His recent publications demonstrate a consistent focus on applying evolutionary algorithms, reinforcement learning, and numerical optimization to portfolio management, market impact modeling, and financial forecasting. The research integrates econometrics, machine learning, and financial theory, emphasizing practical implementation and robust risk-aware decision-making. Several best-paper awards Maringer has served as Chair of the Portfolio Optimization Section of the IEEE Computational Economics and Finance Technical Committee from 2008 to 2018 and is frequently involved in organizing and program committees of international conferences. He has advised or collaborated with numerous researchers, though specific student names are not listed. His research has been supported through academic affiliations and likely institutional or conference-based grants, though explicit funding sources are not detailed. He is affiliated with several research groups, including IEEE Computational Economics and Finance TC, COMISEF, ERCIM, Centre for Innovative Finance, and the European Financial Management Association, reflecting a broad collaborative network in computational finance and economics.
Ameya Jagtap is an Assistant Professor (Tenure-Track) in the Department of Aerospace Engineering at Worcester Polytechnic Institute (WPI), USA. Prior to this, he served as an Assistant Professor of Applied Mathematics (Research) at Brown University from 2021 to 2024. He holds a Ph.D. and M.E. in Aerospace Engineering from the Indian Institute of Science (IISc), and completed postdoctoral research at TIFR-CAM (India) and Brown University's Division of Applied Mathematics. His research bridges mechanical/aerospace engineering, applied mathematics, and computation, focusing on scientific machine learning algorithms that integrate data and physics. Key areas include physics-driven deep learning, uncertainty quantification, multi-scale simulations, and novel neural network architectures like quantum and graph networks. He serves on editorial boards for Neural Networks , Neurocomputing , and others. His work emphasizes interpretable neural operators for PDE solutions, domain decomposition methods, and adaptive activation functions to enhance PINN convergence. Notable contributions include XPINNs (extended physics-informed neural networks) and causal sweeping frameworks for PDEs. His research has been widely cited, particularly for PINN applications in supersonic flows and high-dimensional PDEs. Jagtap has delivered invited talks at institutions like Los Alamos National Laboratory, Tsinghua University, and the Alan Turing Institute. He is also recognized as a Top 2% World Scientist by Stanford University.
Michael Trick is the Senior Associate Dean for Faculty and Research and Higgins Professor of Operations Research at the Tepper School of Business, Carnegie Mellon University. His research focuses on combinatorial optimization, sports scheduling, constraint programming, and operations research applications. He has contributed to seminal work on sports timetabling (e.g., scheduling college basketball conferences) and the traveling tournament problem. Trick's work bridges theoretical advancements with practical applications, including integer programming, branch-and-price methods, and stochastic dynamic programming. He has led initiatives in operations research education and served as President of INFORMS, emphasizing the field's societal impact. His research also spans voting systems, optimization for newspaper zoning, and algorithmic design for complex scheduling problems. Trick's expertise spans academic leadership, research methodology, and cross-disciplinary applications. His articles address topics like auction design for spectrum allocation, bike-sharing logistics, and robust scheduling strategies. He collaborates across academia and industry, contributing to both theoretical advancements and real-world operational challenges. His work often highlights the practical utility of mathematical programming techniques in diverse domains.
Meng He is a Professor in the Faculty of Computer Science at Dalhousie University. He obtained his PhD from the Cheriton School of Computer Science at the University of Waterloo in 2008 and held postdoctoral and research positions at Carleton University and the University of Waterloo before joining Dalhousie. He is affiliated with the Algorithms & Bioinformatics research cluster and is actively recruiting graduate students for master's and PhD studies, as well as supervising honors theses and USRA internships. His research focuses on the design and analysis of efficient algorithms and data structures, particularly in the areas of computational geometry, databases, text retrieval, and bioinformatics. His work often involves developing succinct and dynamic data structures for fundamental problems in graph theory, trees, and geometric data. His recent publications show a strong focus on path and distance queries in various graph types (especially interval graphs and trees), range counting, mode queries, and succinct representations. The research trend emphasizes theoretical foundations combined with practical efficiency, often addressing dynamic and space-constrained scenarios. Alberto Apostolico Best Paper Award of CPM 2017 Dr. He has supervised numerous PhD and master's students and collaborators, frequently co-authoring with researchers such as J. Ian Munro, Travis Gagie, Gonzalo Navarro, and Norbert Zeh. His research has been supported by grants from NSERC and other funding agencies, though specific grant details are not listed in the provided text. He has also contributed significantly to the academic community through editorial work for journals like Computational Geometry - Theory and Applications and Algorithmica , and by organizing major conferences such as CCCG and WADS. He leads a research group focused on algorithms and data structures, fostering collaborations both within Dalhousie and internationally. Future work is likely to continue exploring the theoretical and practical aspects of dynamic and succinct data structures, with applications in large-scale data processing and information retrieval systems.
Ming Zheng is a Professor in the Department of Mechanical, Automotive & Materials Engineering at the University of Windsor, Faculty of Engineering. He is the Director of the Clean Combustion Engine Laboratory and holds a Canada Research Chair in Clean Diesel Engine Technologies. He is a Fellow of both SAE and ASME and a Professional Engineer (PEng). Education: Ph.D., Mechanical Engineering, University of Calgary, Canada, 1993 M.Sc., Thermal Energy and Automotive Engineering, Tsinghua University, China, 1988 B.Sc., Mechanical Engineering, Transport Technology Institute, China, 1982 PDF, Mechanical Engineering, Hokkaido University, Japan, 1995 Dr. Zheng's research focuses on clean and high-efficiency combustion technologies for internal combustion engines. His key interests include low-temperature combustion (LTC), homogeneous charge compression ignition (HCCI), active flow control aftertreatment for emission reduction, advanced ignition systems (e.g., multi-coil, corona), alternative and biofuels (e.g., ethanol, n-butanol), combustion modeling, diagnostics, and real-time adaptive control. His work aims to achieve simultaneous reductions in NOx and soot emissions while improving fuel efficiency. The analysis of his recent publications reveals a strong and consistent research trend centered on advanced combustion strategies using alternative fuels like ethanol and n-butanol. His work extensively explores dual-fuel combustion, the impact of fuel injection strategies, and the use of advanced control algorithms (e.g., extremum seeking control) to manage complex combustion processes. A significant portion of his research is dedicated to developing and optimizing active aftertreatment systems, such as Lean NOx Traps, to handle the unique exhaust characteristics of these clean combustion modes. Scientific Awards: SAE Fellow (2016) ASME Fellow University Award for Excellence in Research, Scholarship and Creative Activity (2007 and 2005) Canada Research Chair in Clean Diesel Engine Technologies (awarded 2003) Dr. Zheng has been a prolific advisor, supervising numerous PhD and Master's students on topics ranging from biofuel testing and low-temperature combustion to aftertreatment modeling and control. His research is highly collaborative, supported by significant grants and contracts from government agencies (NSERC, Auto21, CRC) and major industrial partners like Ford, International Truck and Engine Company, and Imperial Oil. He has secured over $2.6 million in cash awards and approximately $2.1 million in-kind contributions since 2003. Dr. Zheng leads the Clean Combustion Engine Laboratory , a state-of-the-art facility equipped with multiple modern diesel engine test cells (including a Ford common-rail engine and a Yanmar single-cylinder engine), advanced emission analyzers, real-time control systems (FPGA, Can-Bus), and sophisticated diagnostic and modeling tools (LabVIEW, GT-Power, Chemkin, MATLAB/Simulink). The lab specializes in experimental research on combustion, emissions, and aftertreatment, with a focus on active flow control technologies.
Dr. Leila Moslemi Naeni is a Senior Lecturer at the University of Technology Sydney (UTS), School of Built Environment, with a dual appointment in the Faculty of Design, Architecture and Building. She previously served as a Lecturer at UTS (2016-2018) and Sessional Lecturer at Curtin University (2015-2016). PhD in Computer Science (University of Newcastle, 2017) MSc in Industrial Engineering (Sharif University of Technology, 2007) BSc in Industrial Engineering (Iran University of Science and Technology, 2004) Her research focuses on project management under uncertainty, integrating fuzzy systems and mathematical modeling with applications in construction, ESG reporting, and disruptive technologies. She developed innovative methods for statistical control charts in project monitoring and leads research on leveraging blockchain and digital tools for sustainable infrastructure. Recent publications highlight her work on resource-constrained scheduling algorithms, ESG integration in megaprojects, and gamification in project management education. She serves as Review Editor for Frontiers in Environmental Science and on the Editorial Board of Smart and Sustainable Built Environment . 2016 PMI NSW Research Award 2022 Walt Lipke Award 2013 FEBE Postgraduate Research Prize As an active research mentor, she supervises PhD students in machine learning applications, disaster management technologies, and social infrastructure investments. Her teaching emphasizes simulation-based learning, collaborating with Oulo University (Finland) to quantify educational value.
Lacra Pavel is a Professor in the Edward S. Rogers Sr. Department of Electrical and Computer Engineering at the University of Toronto, Faculty of Applied Science and Engineering. She joined the department in August 2002 after industry experience at Nortel Networks and Solinet Systems, and remains active in the System Control Group and Photonics Group. Her educational background includes: Diploma of Engineering (with distinction) in Automatic Control, Technical University Gh. Asachi of Iasi, Romania (1989) PhD in Electrical and Computer Engineering, Queen's University at Kingston (1996) Research focuses on integrating game theory, control theory, and optimization within networked systems. She pioneered applications in noncooperative/evolutionary game theory for network control, nonlinear/robust control frameworks, and energy-efficient optical/transportation networks. Current work develops mathematical foundations for learning in games via control-theoretic approaches to enable autonomous multi-agent network optimization. Recent publications (2019-2013) reveal dominant trends: distributed Nash equilibrium seeking using passivity-based and operator-splitting methods, stability analysis for optical network power control with time-delays, and extensions to transportation systems like railway timetabling. Key subfields include graphical games, ADMM algorithms, and Lyapunov-based boundary control for distributed parameter systems. Scientific recognition includes: Fellow of the IEEE (2025) for contributions to game theory, control, and optimization for network systems Connaught New Staff Award, University of Toronto (2003) New Opportunities Infrastructure Award, CFI/OIT (2003) Award for Innovation, Solinet Systems (2001, 2002) Inventor Recognition Award, Nortel Networks (2000) She has advised over 20 graduate students including current PhD candidates and former students now at MIT, Princeton, Amazon, and Ciena. Major grants include the CFI/OIT New Opportunities Infrastructure Award (2003). Her research bridges theoretical game control with practical implementations in optical and transportation networks. As co-director of the System Control Group and member of the Photonics Group, she leads teams developing algorithms for autonomous network optimization. Current projects focus on stochastic approximation methods for multi-agent learning and energy-efficient network design.
Shachar Lovett is a researcher at the University of California, San Diego (UCSD), specializing in computational complexity, combinatorics, and theoretical computer science. His work spans advanced topics in communication complexity, pseudorandomness, and coding theory, often intersecting with problems in additive combinatorics and Boolean function analysis. Education : Not explicitly detailed in the provided text. Research Interests : Lovett's research focuses on computational complexity, particularly in communication and circuit complexity, combinatorial structures like sunflowers and high-dimensional expanders, and the analysis of Boolean functions through Fourier and Gowers norms. His work explores the limits of deterministic vs. randomized computation, the structure of codes over finite fields, and the interplay between additive combinatorics and theoretical computer science. Article Trends : His recent publications address exact vs. approximate representations of Boolean functions, quasipolynomial bounds in combinatorics, hypercontractivity in high-dimensional expanders, and advancements in the log-rank conjecture. These works emphasize connections between computational complexity, discrete mathematics, and pseudorandomness, often yielding improved bounds or novel frameworks for understanding Boolean function behavior. Scientific Awards : No specific awards or honors were mentioned in the provided text. Advising and Collaborations : Lovett collaborates extensively with researchers like Hamed Hatami, Kaave Hosseini, and Jiapeng Zhang, contributing to fields such as non-malleable codes, matrix multiplication algorithms, and communication complexity. No formal student advising details were provided.
