Dr. Giang Tran is an Associate Professor in the Department of Applied Mathematics at the University of Waterloo, where she leads research in sparse modeling and computational mathematics. She holds a PhD from UCLA and previously served as a Bing Instructor at the University of Texas at Austin. Her research explores sparse optimization techniques with applications in medical imaging, dynamical systems, and data science. Recent publications focus on developing novel algorithms for sparse random feature expansions and dynamical system identification. She mentors numerous graduate and undergraduate researchers through projects on neural networks, transformers, and epidemic forecasting. Awards include the NSERC Discovery Grant and SIAM Student Paper Prize. Dr. Tran teaches advanced courses in numerical methods and functional analysis, contributing to curriculum development in computational mathematics.
Rui Teixeira is an Assistant Professor in the School of Civil Engineering at University College Dublin (UCD). He leads UCD's Centre for Critical Infrastructure Research (CCIR) and focuses on Uncertainty Quantification, Safety, and Risk in civil engineering systems, with applications to infrastructure resilience. His research emphasizes reliability analysis, multi-fidelity modeling, and AI-driven risk assessment. Education: MSc in Civil Engineering, University of Porto, Portugal PhD in Civil Engineering, Trinity College Dublin Professional Certificate in University Teaching and Learning, UCD Research Interests: Development of novel reliability analysis techniques Resilience of infrastructure systems Artificial intelligence applications for risk assessment Probabilistic system evaluation and safety standards Grants & Projects: Smart Enforcement of Transport Operations (SETO), Horizon Europe (2023–2026) Optimality-Tracking Civil Engineering Systems, Enterprise Ireland (2023–2025) Floating Offshore Wind Dynamic Cables (FlOWDyn), Sustainable Energy Authority of Ireland (2024–2027) Teaching: Coordinates courses such as 'Civil Engineering Systems' and 'Design of Structures 1'. Labs/Teams: Director of the Centre for Critical Infrastructure Research (CCIR), focusing on interdisciplinary approaches to infrastructure resilience.
Huan Lei is an Assistant Professor at Michigan State University, holding a joint appointment in the Department of Computational Mathematics, Science and Engineering and the Department of Statistics and Probability. He earned his Ph.D. in Applied Mathematics from Brown University in 2012 under George Karniadakis and a B.S. in Special Class for the Gifted Young from the University of Science & Technology of China in 2005. His research integrates scientific machine learning with numerical analysis to develop structure-preserving algorithms for partial and stochastic differential equations arising in multi-scale systems. His work spans multi-scale modeling , non-Markovian dynamics , coarse-grained molecular simulations , and data-driven parameterization . Recent publications focus on learning generalized Langevin equations with state-dependent memory, consensus-based free energy surfaces, and non-equilibrium coarse-grained models. His team applies these methods to fluid dynamics, biomolecular solvation, and climate systems. NSF CAREER Award (2021) Brown University Dissertation Fellowship (2012) He advises graduate and undergraduate researchers and seeks Ph.D. candidates with expertise in numerical analysis or scientific computing. His group receives funding from NSF, DOE, Ford, and MSU Foundation.
Professor Guoyin Li is a faculty member at the School of Mathematics & Statistics , University of New South Wales (UNSW Sydney). He holds a Ph.D. from The Chinese University of Hong Kong (2007) and has been at UNSW since 2011, currently serving as Professor and Research Director. Research Interests His work spans optimization , variational analysis , and multilinear algebra , with applications in robust optimization , structural engineering , and machine learning . He specializes in nonconvex nonsmooth optimization , tensor eigenvalue problems , and exact semi-definite programming relaxations . Recent Publications His articles focus on robust optimization for structural design, nonlinear approximation techniques, and conic programming for uncertain data. Key journals include Foundations of Computational Mathematics , Mathematical Programming , and Computer Methods in Applied Mechanics and Engineering . Awards and Grants Fellow of the Australian Mathematical Society (2023) 2022 AustMS Medal 2024 Marguerite Frank Award ARC Discovery Grants (2021-2023, 2025-2027) ARC Research Hub Project (2017-2021) Professional Roles He serves on editorial boards of SIAM Journal on Optimization , Optimization Letters , and Journal of Optimization Theory and Applications , and has delivered plenary lectures at international conferences in Austria, Spain, and Canada.
Xujia ZHU is an Associate Professor at CentraleSupélec, Paris-Saclay University, affiliated with the Laboratory of Signals and Systems (L2S). His research focuses on uncertainty quantification, surrogate modeling, stochastic simulators, and reliability analysis. He holds an engineer’s degree in Mechanics from École Polytechnique (2015), a Master’s in Computational Mechanics from TU Munich (2017), and a Ph.D. from ETH Zurich (2022). He was a postdoctoral researcher at ETH Zurich until 2023. Education: Ph.D., Chair of Risk, Safety, and Uncertainty Quantification, ETH Zurich, 2022 Master’s (high distinction), Computational Mechanics, Technical University of Munich, 2017 Engineer’s Degree, Mechanics, École Polytechnique, 2015 Research Interests: Xujia’s work bridges numerical simulations and statistics, addressing topics like uncertainty propagation, sensitivity analysis, and surrogate modeling for stochastic systems. Key areas include polynomial chaos expansions, Bayesian active learning, and applications in seismic fragility analysis. Publications: His recent work emphasizes emulation techniques for stochastic simulators, multi-fidelity methodologies, and Bayesian active learning strategies in reliability analysis. Key themes include sparse polynomial chaos expansions and latent variable modeling. Labs/Teams: Affiliated with L2S, he collaborates on transversal projects in energy, industry, and health, leveraging interdisciplinary approaches in uncertainty quantification and computational modeling.
Jiafeng (Harvest) Xie is an Assistant Professor in the Department of Electrical and Computer Engineering at Villanova University, where he directs the Security and Cryptography (SAC) Lab. He holds a Ph.D. in Electrical Engineering from the University of Pittsburgh and has prior faculty experience at Wright State University. His research focuses on cryptographic engineering, post-quantum cryptography, hardware security, and digital design for telemetry systems. Education includes a Ph.D. from University of Pittsburgh (2013-2014), M.E. from Central South University (2007-2010), and B.E. from Yanshan University (2002-2006). He has received prestigious awards like the 2024 IEEE Philadelphia Engineer of the Year Award and the 2023 Art Ryan Award. His work spans over 66 peer-reviewed publications, with a focus on hardware acceleration for post-quantum cryptographic systems. Research interests include post-quantum cryptographic engineering, fully homomorphic encryption, fault detection methodologies, and digitalization of aeronautical telemetry systems. His grants include NSF SaTC and NIST-funded projects. Teaching includes courses like Embedded Systems and Post-Quantum Computing . Current advisees include Ph.D. students Pengzhou He, Tianyou Bao, and Yazheng Tu, along with several M.S. and undergraduate researchers. The SAC Lab collaborates with AFRL and explores novel cryptographic hardware designs, with recent breakthroughs in compact accelerators for lattice-based cryptography and approximate homomorphic encryption. His work emphasizes algorithm-architecture co-design for security and efficiency in emerging computing systems.
Lieven Vandenberghe is a Professor in the Electrical and Computer Engineering Department and Department of Mathematics at the University of California, Los Angeles (UCLA). His research focuses on convex optimization, semidefinite programming, and applications in signal processing, system identification, and control theory. Books: Co-author of Convex Optimization (2004) and Introduction to Applied Linear Algebra (2018) Courses: Teaches graduate-level courses in linear programming, convex optimization, and numerical computing (ECE236A/B/C, ECE133A/B) Software: Developer of CVXOPT, CHOMPACK, and SMCP for optimization algorithms His research group has produced significant work in sparse matrix computations, operator splitting methods, and applications to machine learning and control systems. Publications span topics like Bregman splitting, proximal gradient methods, and semidefinite programming for signal processing. His advisees include PhD students in optimization and postdoctoral researchers in applied mathematics.
Tobias Neckel is an Associate Professor at the Institute for Informatics at the Technical University of Munich (TUM), where he leads research projects and coordinates academic programs. He has been the project team leader of the IGGSE Project ExaNIML since 2018, main coordinator of the Ferienakademie since 2014, and Program Coordinator of the Bavarian Graduate School of Computational Engineering (BGCE) since 2009. Diploma in Technomathematik from TU München (2005) Dr. rer. nat. in Informatics from TU München (2009) Neckel's research focuses on Uncertainty Quantification, Random Differential Equations, and High Performance Computing. His work develops efficient numerical algorithms using hierarchic and adaptive methods such as octrees/spacetrees and sparse grids, with applications in fluid-structure interactions and incompressible fluid flow simulation. His research bridges theoretical mathematics with practical computational science, emphasizing robust and efficient implementations. His recent publications demonstrate a strong trajectory in multi-fidelity modeling, uncertainty quantification, and high-performance computing. Neckel has made significant contributions to scalable hierarchical approximation methods, dynamic resource management in HPC, and the application of machine learning techniques to computational science problems. His work spans diverse application domains including plasma physics, hydrology, and computational engineering. Lehrfonds prize of the TUM (2014) Ernst Otto Fischer prize of the TUM (2011) Promotionspreis des Bunds der Freunde der TU München (2009) Neckel has supervised numerous graduate students and has been actively involved in curriculum development and teaching innovation. His book "Bits and Bugs: A Scientific and Historical Review of Software Failures in Computational Science" (2019) represents a significant contribution to understanding software reliability in scientific computing. He has organized minisymposia at major conferences including SIAM CSE and SIAM UQ, and serves on program committees for various computational science conferences. As coordinator of the Ferienakademie and the BGCE, Neckel plays a central role in advanced computational engineering education in Bavaria. His research group develops software for exascale computing and contributes to the Transregional Collaborative Research Centre 89 on Invasive Computing. Neckel also maintains international collaborations, with research stays at institutions including the Australian National University and Tokyo Institute of Technology.
Shiwei Zeng is an Assistant Professor in the Department of Computer & Cyber Sciences at Augusta University, School of Computer and Cyber Sciences. His research focuses on theoretical aspects of machine learning, emphasizing robustness, data efficiency, and noise tolerance in models. He holds a Ph.D. in Computer Science from Stevens Institute of Technology (2004), an MS in Computer Science from Stevens Institute of Technology (2019), and dual BS degrees in Electrical and Electronics Engineering (Hong Kong Polytechnic, 2015) and Integrated Circuit Design Technology (Sun Yat-sen University, 2015). Education: Ph.D. (2004), MS (2019) – Stevens Institute of Technology BS (2015) – Hong Kong Polytechnic University (Electrical Eng) BS (2015) – Sun Yat-sen University (Integrated Circuit Design) His research interests include PAC learning, robust statistical methods, and innovative approaches to handling malicious noise and data inefficiency. Recent work explores semi-verified learning from crowdsourced data and list-decodable estimation techniques. Advising and grants: No advisees or grants explicitly listed in the provided information. Labs/Teams: No specific lab or team affiliations mentioned.
Henry Corrigan-Gibbs is an Assistant Professor at the Massachusetts Institute of Technology (MIT) in the Department of Electrical Engineering and Computer Science. He is affiliated with the Computer Science and Artificial Intelligence Laboratory (CSAIL) and collaborates with the PDOS and CSS research groups. Henry co-hosts the MIT Security Seminar series. Education : PhD in Computer Science from Stanford University (advisor: Dan Boneh), Postdoc at École polytechnique fédérale de Lausanne (EPFL) (host: Bryan Ford), and B.S. in Computer Science from Yale University . Research Interests Henry focuses on computer security , cryptography , and systems research , building technologies that: Empower end users through privacy-preserving design Implement strong cryptographic security in real-world deployments Scale to millions of users while maintaining security Key projects include: Tiptoe for private web search Prio for privacy-preserving aggregate statistics used by Apple and Google Express for metadata-hiding communication SafetyPin and True2F for secure authentication Scientific Contributions Standards Influence : IETF and NIST standards recommend his private-aggregation systems Industry Impact : Deployed in Apple iOS , Google Android , and Mozilla Firefox Non-profit Deployment : Co-founder of Divvi Up for real-world Prio implementations Academic Recognition 2023 MIT EECS Jerome Saltzer Award for teaching excellence 2020 ACM Doctoral Dissertation Honorable Mention 2016 Caspar Bowden Award for PET research 2015 IEEE Security & Privacy Distinguished Paper Award Multiple IACR Best Young Researcher Paper Awards (2015–2018) Advising and Collaborations Current advisees include: PhD students: Alexandra Henzinger , Ryan Lehmkuhl Postdoc: Emma Dauterman Collaborates with industry (Apple, Google, Mozilla) and standards bodies (IETF, NIST) to translate research into practice.
Brandon A. Jones is an Associate Professor in the Department of Aerospace Engineering and Engineering Mechanics at the University of Texas at Austin. He holds the Charles Elmer Rowe Fellowship in Engineering and leads the Texas Spacecraft Laboratory (TSL) and the Controls, Autonomy, Estimation, and Learning for Uncertain Systems (CAELUS) Laboratory. His research focuses on space situational awareness, spacecraft navigation, and uncertainty quantification, with applications to orbital mechanics, multi-target tracking, and autonomous systems. Dr. Jones received his Ph.D. in Aerospace Engineering from the University of Colorado Boulder and has held roles at NASA Johnson Space Center and as a Research Assistant Professor. He is an Associate Fellow of the AIAA and former chair of the American Astronautical Society's Space Surveillance Technical Committee. His work includes NASA-funded projects like the SCOPE-1 CubeSat mission for terrain-relative navigation and the Crater-based Navigation and Timing (CNT) system for lunar missions. Key research areas include: Multi-source information fusion for space object tracking Machine learning for crater detection and autonomous navigation Uncertainty propagation in cislunar and highly perturbed orbits Event-based sensor systems for harsh-lighting environments Recent achievements include the 2023 W. A. 'Tex' Moncrief Grand Challenge Award and leadership in collaborative projects with NASA, JPL, and academia. His labs emphasize student-driven CubeSat missions and cutting-edge algorithms for space domain awareness.
Dr. Yun Zhang is a Professor and Canada Research Chair in the Department of Geodesy and Geomatics Engineering at the University of New Brunswick. He holds a PhD from the Free University of Berlin and has pioneered research in remote sensing, image processing, and computer vision since 2000. His patented technologies are licensed to global companies including PCI Geomatics and DigitalGlobe. Research Focus: Optical/radar image processing, digital photogrammetry, AI applications in geomatics, and sensor fusion for UAV systems. His work enables advanced geospatial analysis across environmental, urban, and defense sectors. Distinctions: First Giuseppe Inghilleri Award (ISPRS 2012) NSERC Synergy Innovation Award from Governor General of Canada (2011) ASPRS Talbert Abrams Grand Award (2005) Featured in CFI 20th Anniversary Book for breakthrough innovations Technology Impact: Solutions deployed by NASA, USGS, Google Earth, and DND Canada across five continents. Recognized among top 9 Canadian research achievements in AUTM's global case studies alongside MIT and Stanford innovations.
Jean F. Honorio Carrillo is an Adjunct Professor at Purdue University's Department of Computer Science and a Senior Lecturer at the University of Melbourne's School of Computing and Information Systems. He specializes in machine learning theory, optimization, and their applications to combinatorial and non-convex problems. His research focuses on developing algorithms with theoretical guarantees for structured prediction, robustness, fairness, and federated learning. He has advised numerous students across multiple institutions and holds adjunct roles at Purdue's Statistics Department and MIT CSAIL. Roles: Senior Lecturer (Melbourne), Adjunct Professor (Purdue), Adjunct at MIT CSAIL Research Areas: ML Theory, Non-Convex Optimization, Fairness, Federated Learning Key contributions include breakthroughs in exact inference for structured prediction, optimization frameworks for NP-hard problems, and theoretical foundations for modern ML challenges. His work has been published in top venues like NeurIPS, ICML, and JMLR. He has secured grants from NSF and industry partners, including a 2021 NSF DMS grant for deep learning research. His students have gone on to postdoctoral roles at NUS and UChicago/CMU.
Prof. Karl Kunisch is the Scientific Director at RICAM (Johann Radon Institute for Computational and Applied Mathematics) and a Full Professor of Mathematics at the University of Graz, Austria. He has held academic positions worldwide, including visiting roles at Brown University, INRIA, and Technical University Berlin. His research focuses on Optimization and Optimal Control, Partial Differential Equations (PDEs), Inverse Problems, and their applications in mathematical imaging, medicine, and computational science. Education: 1975: Diploma Degree, Technical University of Graz, Austria 1975: Master Degree, Northwestern University, Evanston, Illinois, USA 1978: Ph.D. Degree, Technical University of Graz 1980: Habilitation, Technical University of Graz Research Interests: Prof. Kunisch’s work spans theoretical and applied aspects of optimal control, including stabilization of PDEs, infinite horizon control problems, and feedback design. He explores numerical methods for PDE-constrained optimization and their applications in medical imaging, cardiac electrophysiology, and machine learning. His projects also address shape optimization and mathematical models for fluid dynamics and quantum systems. Publications Trends: His recent articles emphasize feedback stabilization for nonlinear systems, sparse control approaches, and the intersection of optimal control with machine learning. Key themes include robust algorithms for uncertainty handling, efficient numerical methods for high-dimensional problems, and applications in biomedical engineering. Awards: Pro Scientia-Scholarship (1974–1977) Research Award of Theodor-Körner-Fonds (1979) Fulbright Travel Scholarship (1979/80, 1985) Max Kade Scholarship (1982–83) Japanese Society for the Promotion of Science Fellowship (1990) Christian Doppler Laboratory Fellowship (1992) Advising & Grants: Prof. Kunisch leads the Optimization and Optimal Control research group at RICAM and has directed projects on mathematical data science and inverse problems. His work involves collaborations with institutions globally and has been supported by grants from NASA, the European Union, and national funding bodies. He has advised numerous researchers, though specific student names are not listed here. Labs/Teams: Group Leader of the Group "Optimization and Optimal Control" at RICAM since 2004, contributing to interdisciplinary research in computational mathematics and its applications.
Karl Kunisch is a Professor at the Department of Mathematics and Scientific Computing at the University of Graz and serves as Scientific Director of the Radon Institute of the Austrian Academy of Sciences in Linz. With a distinguished career spanning several decades, he has established himself as a leading researcher in optimization and control theory. Prof. Kunisch completed his PhD and Habilitation at the Technical University of Graz in 1978 and 1980, respectively. His academic journey includes significant positions at Brown University's Lefschetz Center for Dynamical Systems, INRIA Rocquencourt, Universite Paris Dauphine, and he previously served as a professor of numerical mathematics at the Technical University of Berlin. Research Interests: Prof. Kunisch's research focuses on optimization and optimal control, inverse problems and mathematical imaging, numerical analysis and applications, with current emphasis on life sciences applications. His specific areas include Optimal Control of Partial Differential Equations, Nonsmooth Optimization in Function Spaces, and Applications of Optimization and Control in the Life Sciences. His work bridges theoretical mathematics with practical applications across various scientific domains. His recent publications demonstrate a continued focus on advancing the theoretical foundations of optimal control while developing practical numerical methods. Key trends include work on infinite horizon control problems, feedback stabilization techniques, applications to PDE-constrained optimization, and the integration of machine learning approaches with traditional control theory. His research group actively explores connections between theoretical developments and applications in the life sciences. Scientific Recognition: W.T. and Idalia Reid Prize 2021 SIAM Fellow (2017) European Research Council Advanced Grant (2015) Alwin Walther Medaille (2008) SIAM Outstanding Paper Prize (2006) Prof. Kunisch has made substantial contributions to the mathematical community through his editorial work, serving as editor for prestigious journals including SIAM Journal on Control and Optimization, SIAM Journal on Numerical Analysis, and the Journal of the European Mathematical Society. He leads the Research Group on Optimization and Optimal Control at the Johann Radon Institute for Computational and Applied Mathematics (RICAM) and is involved in the ERC-Project OCLOC "From Open to Closed Loop Control".
