Dr. Daniel J. Hsu is a Professor of Computer Science at Columbia University, affiliated with the Foundations of Data Science Center and TRIPODS Institute. His research focuses on algorithmic statistics, machine learning theory, and their applications in public health informatics. He has advised numerous students and postdocs, and his work bridges foundational theory with practical systems like foodborne illness detection via social media analysis. Key roles: Associate Editor (ACM Transactions on Algorithms), Program Chair (ICML 2025, COLT 2019) Research areas: Foundations of Data Science, Machine Learning Theory, Fairness, and High-dimensional Statistics His work on detecting foodborne illnesses using Yelp reviews has been deployed by NYC Health departments. Recent contributions include advancements in transformer architectures, group fairness algorithms, and multi-group learning frameworks. Scientific awards include the Sloan Fellowship and multiple NSF grants. He has pioneered interactive machine teaching methods and developed algorithms for robust parameter estimation in high-dimensional settings.
Gianluca Iaccarino is a Professor of Mechanical Engineering at Stanford University and the Robert Bosch Chairholder. He serves as Director of the PSAAP Center and leads large-scale computational research initiatives in uncertainty quantification, exascale computing, and multiphysics simulations. His academic journey includes a PhD in Mechanical Engineering from Politecnico di Bari (2005), postdoctoral work at Stanford's Center for Turbulence Research, and progression from Research Engineer to full Professor. Education : PhD (Politecnico di Bari), MS/BS in Aeronautical Engineering (University of Naples) Research : Computational engineering, turbulence modeling, uncertainty quantification, biomedical fluid dynamics, and exascale-ready algorithms Publications : 15+ recent articles focus on turbulence modeling, data-driven simulations, and uncertainty quantification across diverse applications in aerospace, biomedical, and energy systems Awards : PECASE (2010), APS Fellow (2019), multiple best paper awards (AIAA, ASME), Terman Fellow (2007) Students : Advises doctoral and master's students in mechanical engineering and computational methods Leadership : Director of PSAAP Center (2014-present), Chair of Mechanical Engineering Department (2024-present)
Martin T. Wells is the Charles A. Alexander Professor of Statistical Sciences at Cornell University, with joint appointments in the Department of Statistical Science, Department of Biological Statistics and Computational Biology, Department of Social Statistics, and as Professor of Clinical Epidemiology and Health Services Research at Weill Medical School. He serves as Editor-in-Chief of the ASA-SIAM Book Series and Co-Editor of the Journal of Empirical Legal Studies. Cornell University, Ithaca, NY Weill Cornell Medical College Research Interests span applied and theoretical statistics, Bayesian methods, biostatistics, clinical epidemiology, and computational biology. His work bridges disciplines like finance, legal studies, and health services research. Article Trends highlight advancements in Bayesian modeling, quantum cognition machine learning, tensor analysis, and misclassification correction, with applications in genomics, finance, and public health. Fellow of the American Statistical Association Fellow of the Royal Statistical Society Contributions include developing statistical software (e.g., rTensor), methodological innovations in clinical trials, and empirical legal studies on civil rights and the death penalty.
Prof. Jean-Philippe Thiran is a Full Professor at École Polytechnique Fédérale de Lausanne (EPFL), where he serves as Director of the Signal Processing Laboratory (LTS5) and Director of the Institute of Electrical and Micro Engineering. He also maintains a part-time Associate Professor position with the Department of Radiology of the University Hospital Center (CHUV) and University of Lausanne (UNIL). Born in Namur, Belgium in 1970, he received his Electrical Engineering degree and PhD from the Université catholique de Louvain (UCL), Belgium, in 1993 and 1997 respectively. He joined EPFL in 1998 and has established himself as a leading researcher in computational imaging. His research focuses on computational imaging , with significant contributions to medical image analysis (particularly diffusion MRI, ultrasound imaging, and digital pathology) and computer vision . His recent work integrates advanced modeling, simulation, and machine learning techniques to extract microscopic tissue information from macroscopic MRI signals. This approach combines hyper-realistic synthetic tissue models, advanced Monte-Carlo simulations, and ML-based estimation techniques for brain microstructure analysis with potential applications to other tissues. Senior Member of IEEE Fellow of the European Association for Signal Processing (EURASIP) Prof. Thiran has authored or co-authored 1 book, 9 book chapters, 250 journal papers and over 270 peer-reviewed conference papers, and holds 12 international patents. He previously served as Co-Editor-in-Chief of the Signal Processing journal (2001-2005) and associate editor of IEEE Transactions on Image Processing. He has chaired major conferences including EUSIPCO 2008 and IEEE ICIP 2015. His laboratory at EPFL brings together interdisciplinary researchers to develop innovative imaging techniques that bridge macroscopic measurements and microscopic tissue properties, with significant potential for medical diagnostics and treatment planning applications.
Afonso S. Bandeira is a Professor in the Department of Mathematics (D-MATH) at ETH Zurich, where he conducts research at the intersection of mathematics, statistics, and computer science. He maintains strong affiliations with several interdisciplinary research centers including the Institute For Operations Research (IFOR), the Max Planck ETH Center for Learning Systems, the ETH Foundations of Data Science, and the ETH AI Center, with a courtesy appointment at D-ITET. Bandeira actively teaches courses including Mathematics of Signals, Networks, and Learning, and Mathematics of Data Science. His research focuses on High Dimensional Probability, Random Matrices, Mathematical Statistics, Theoretical Computer Science, Combinatorics, and Mathematical Optimization. Bandeira's work often explores the theoretical foundations of data science, examining phase transitions in statistical problems, computational barriers, and the geometry of high-dimensional spaces. He maintains a research group blog called Randomstrasse101 that emphasizes open problems in his field. Analysis of his recent publications reveals a strong focus on matrix and tensor concentration inequalities, synchronization problems, nonconvex optimization landscapes, and computational-statistical tradeoffs in high-dimensional inference. His work bridges theoretical mathematics with practical applications in machine learning and data analysis, particularly examining where computational limitations arise in statistical problems. Bandeira actively mentors students and researchers, currently supervising several doctoral candidates including Daniil Dmitriev, Konstantin Donhauser, Anastasia Kireeva, Kevin Lucca, Chiara Meroni, Gil Kur, Petar Nizic-Nikolac, and Almut Roedder. He emphasizes that students working with him should participate in the DACO seminar and group meetings, and encourages prospective students to have completed his Mathematics of Signals, Networks, and Learning or Mathematics of Data Science courses. He leads a research group focused on the mathematics of data science, with regular group meetings and seminars. Bandeira has developed comprehensive lecture notes including 'A Tour Through the Mathematics of Signals, Learning, and Networks' (2025) and 'Mathematics of Machine Learning' (2021), and previously authored 'Ten Lectures and Forty-Two Open Problems in the Mathematics of Data Science' (2015).
Farhad Rachidi-Haeri is a Titular Professor and Head of the Electromagnetic Compatibility (EMC) Group at EPFL. His expertise spans EMC research, lightning electromagnetics, time reversal techniques, and fault location in power systems. He has led the EMC Group since the 1980s, with funding from the Swiss National Science Foundation, European Union, and private sector collaborations. His work involves international partnerships with institutions like the University of Toronto and KTH. Education: PhD in Electrical Engineering from EPFL (1991), M.S. from EPFL (1986). Roles: President of Swiss National Committee of URSI (2012–2020), Editor-in-Chief of IEEE Transactions on EMC (2013–2015), and member of the Academy of Sciences of Bologna Institute (2019). Research Focus: Lightning interaction with infrastructure, electromagnetic field modeling, time reversal applications for fault detection, and high-frequency transient analysis. His work bridges theoretical physics and engineering, addressing challenges in power systems, lightning protection, and aerospace. Awards: IEEE EMC Technical Achievement Award (2005), Berger Award (2016), and Distinguished Honorary Professor at Tsinghua University (2024). Over 400 peer-reviewed papers and 500 conference contributions reflect his prolific research output. Labs/Teams: Leads the EMC Laboratory at EPFL, focusing on experimental and numerical studies of electromagnetic phenomena. Collaborates with global networks on projects like Laser Lightning Control and structural lightning protection for wind turbines.
Di Shi is an Associate Professor at the Klipsch School of Electrical and Computer Engineering , New Mexico State University (NMSU), holding the Paul W. and Valerie Klipsch Distinguished Professorship. He previously founded the AI energy startup AInergy, LLC and held leadership roles at GEIRI North America, NEC Laboratories America, and Arizona State University. Education: PhD in Electrical Engineering, Arizona State University (2012) MS in Electrical Engineering, Arizona State University (2009) BS in Electrical Engineering, Xi'an Jiaotong University (2007) His research focuses on power system data analytics , energy storage , artificial intelligence , and IoT applications for grid stability and renewable integration. His work bridges theoretical innovation with real-world deployment, including software adopted by 15 utility companies. Recent publications highlight his leadership in deep reinforcement learning for grid control, blockchain frameworks for energy management, and tensor decomposition for efficient load modeling. He has secured a $6M NSF grant for AI-driven digital twinning to address climate-aware energy resilience. Awards & Recognition: 2025 Paul W. and Valerie Klipsch Distinguished Professorship 2024 University Research Council Mid-Career Award 2024 IET Fellow Multiple IEEE Best Paper Awards (2019–2022) 2019 L2RPN AI Competition Championship He serves as an editor for IEEE Transactions on Power Systems , IET Generation, Transmission & Distribution , and other journals, and leads the IEEE Task Force on IoT for Power Systems . His team’s patents cover AI-driven load modeling , energy storage scheduling , and state estimation , with 42 granted or pending.
Massimo Mischi is a Full Professor at the Faculty of Electrical Engineering of the Eindhoven University of Technology (TU/e) and chairs the Signal Processing Systems (SPS) Division , the largest division at TU/e with over 250 researchers. He founded the Biomedical Diagnostics (BM/d) Lab in 2012, which now includes 180 researchers and clinical/industrial advisors, focusing on biomedical signal processing for diagnostics and monitoring.
Peter Benner is a Professor and Director at the Max Planck Institute for Dynamics of Complex Technical Systems in Magdeburg, where he leads the Computational Methods in Systems and Control Theory group. He also holds an Honorarprofessor position for Mathematics at Otto-von-Guericke Universität Magdeburg since 2011. Benner has previously served as Managing Director of the Max Planck Institute during multiple periods (2013-2014, 2021-2022, and 2025-2026), demonstrating his leadership in the field. Benner's research focuses on Scientific Machine Learning, Numerical Linear and Multilinear Algebra, Model Order Reduction and Reduced-order Modeling, Numerical Methods in Systems and Control Theory, PDE Constrained Optimization, High-performance and Power-aware Computing, and Mathematical Software development. His work bridges theoretical mathematics with practical engineering applications, particularly addressing challenges in large-scale dynamical systems. Analysis of his recent publications reveals a strong emphasis on developing efficient computational methods for complex systems. Benner has pioneered approaches combining model order reduction with tensor methods to tackle high-dimensional problems in uncertainty quantification and PDE-constrained optimization. His work shows a consistent trend toward integrating data-driven techniques with traditional model-based approaches, particularly for nonlinear and parametric systems. Throughout his career, Benner has actively mentored students and collaborated with researchers worldwide, delivering numerous invited talks at prestigious institutions and conferences across Europe, North America, and Asia. His research has received significant funding, supporting the development of mathematical software and computational methods for industrial applications. Benner leads the Computational Methods in Systems and Control Theory department at the Max Planck Institute, which focuses on developing and implementing advanced numerical methods for large-scale dynamical systems. The group maintains strong connections with both theoretical mathematics and practical engineering applications, particularly in fluid dynamics, energy systems, and control theory.
Song Mei is an Assistant Professor in the Department of Statistics and Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. She received her Ph.D. from Stanford University in 2020 under Andrea Montanari and maintains active research collaborations with institutions including Amazon (as a 2023 Amazon Research Award recipient) and OpenAI (where she is currently on leave). Her research spans the intersection of statistics, machine learning, information theory, and computer science, with particular emphasis on foundational theories for modern AI systems. Key interests include language models, diffusion models, quantum algorithms, and high-dimensional statistics, often leveraging insights from statistical physics literature. Analysis of her recent publications reveals a strong focus on theoretical underpinnings of generative AI, with significant contributions to understanding contrastive pre-training (CLIP), attention mechanisms in LLMs, and mathematical foundations of diffusion models. Her work demonstrates consistent interdisciplinary connections between statistical theory and practical AI development. Sloan Research Fellowship (2025) Noether Early Career Scholar Award (2025) Google Research Scholar Award (2024) Amazon Research Award (2024) She actively advises graduate students through MA programs and has secured significant research grants including Amazon Research Awards. Her work with AGI Labs at Amazon demonstrates applied impact of theoretical research. Current projects focus on mechanistic interpretability of large language models and mathematical frameworks for generative AI. Professor Mei leads research on the statistical principles behind frontier AI models, with particular focus on developing rigorous theoretical frameworks for understanding emergent behaviors in large-scale systems.
Dr. Baijian "Justin" Yang serves as the Associate Dean for Research at Purdue Polytechnic Institute and is a Professor in the Department of Computer and Information Technology at Purdue University. He earned his Ph.D. in Computer Science from Michigan State University, with Master's and Bachelor's degrees in Automation (EECS) from Tsinghua University. Dr. Yang has established himself as a leader in multiple interdisciplinary research domains. Dr. Yang's educational background includes: PhD in Computer Science, Michigan State University (2002) MS in Automation (EECS), Tsinghua University (1998) BS in Automation (EECS), Tsinghua University (1995) His research interests span multiple cutting-edge domains with practical applications: Cybersecurity : Developing novel approaches for threat intelligence, security education, and network defense Big Data : Creating innovative algorithms for dimension reduction, regression with categorical variables, and tensor decomposition Applied Machine Learning : Implementing AI solutions in healthcare, manufacturing, and forestry applications Digital Forestry : Using UAV imagery and remote sensing for forest management and tree species classification Dr. Yang's publication record demonstrates significant impact across multiple disciplines, with recent work focusing on spatial transcriptomics analysis (SiGra), delirium detection using limited-lead EEG, and visual localization technologies. His research bridges theoretical advances with practical applications in healthcare, manufacturing quality control, and environmental monitoring. The interdisciplinary nature of his work is evident in collaborations spanning computer science, healthcare, forestry, and manufacturing domains. His scientific achievements have been recognized with numerous awards: 2023 HRSA Building Bridges to Better Health Competition Winner (Phase 1) and 2nd place ($100,000 prize) in Phase 3 2023 Outstanding Faculty Award in Engagement, Department of Computer and Information Technology, Purdue University 2021 Leadership in Manufacturing Award, Manufacturing Times Digital (MxD) 2021 Good to Great Award, Purdue Polytechnic 2020 Outstanding Faculty Award in Discovery, Department of Computer and Information Technology 2019 University Faculty Scholars, Purdue University As an educator and mentor, Dr. Yang has advised numerous graduate students through their PhD and Master's research. His leadership extends to significant service roles including serving as Faculty Champion for the Holistic Safety and Security research impact area at Purdue Polytechnic from 2018 to 2021, board membership with ATMAE (2014-2016), and participation in the IEEE Cybersecurity Initiative Steering Committee (2015-2017). He holds valuable industry certifications including CISSP, MCSE, and Six Sigma Black Belt, demonstrating his commitment to bridging academic research with industry practice. Dr. Yang leads multiple research projects including "Digital Forestry" for developing tools to quantify forest function, "CHEESE" (Cyber Human Ecosystem of Engaged Security Education), and "CICI" (Supporting Controlled Unclassified Information with a Campus Awareness and Risk Management Framework). His work on "Applied Machine Learning" focuses on solving real-world problems, while his "Dimension Reduction and Memory Amnestic Big Data Regression" project innovates computational algorithms for large-scale data analysis.
Claire Donnat is an Assistant Professor in the Department of Statistics at the University of Chicago, specializing in statistical and machine learning methods for graph-structured and high-dimensional data. Her work bridges theoretical innovation with applications in biomedical research, environmental science, and public health. Education: B.S. and M.S. in Applied Mathematics from Ecole Polytechnique; Ph.D. in Statistics from Stanford University (2020). Her research focuses on three methodological directions: (1) statistical foundations for graph neural networks (GNNs), (2) structured estimation with graph constraints, and (3) multimodal data integration with uncertainty quantification. Key applications include thermotolerance in photosynthetic microbes, family network analysis for child welfare, and spatial transcriptomics. The 15 most recent publications highlight her work in GNNs, CCA, tensor modeling, and epidemic analysis, with keywords spanning statistics, machine learning, and network science. Her methodological contributions address challenges in sparsity, graph topology, and heterogeneous data fusion. Scientific Awards: Facebook Research Award (2021), C3.AI COVID Grand Challenge winner (2020), Lumiata hackathon winner (2020), Stanford Centennial Award (2019), and others. Claire's research group actively recruits postdocs and students for projects involving graph-based modeling, data integration, and biomedical applications. She also provides research consulting in statistical methodology and graph modeling for life sciences.
S. Yaser Samadi is an Associate Professor in the Department of Mathematics at the School of Mathematical and Statistical Sciences, Southern Illinois University Carbondale. He holds a Ph.D. in Statistics from the University of Georgia (2014) and maintains an active research program in advanced statistical methodologies. Education: Ph.D. in Statistics, University of Georgia, 2014 Research Interests: Dr. Samadi specializes in multivariate time series analysis, high-dimensional statistical inference, and tensor data analysis. His work addresses critical challenges in big data, symbolic data, and dimension reduction for time series through Bayesian analysis and sequential methods for dependent and independent data, yielding robust models for complex data structures. Publication Trends: His recent publications (2014-2023) emphasize time series analysis, dimension reduction, and innovative approaches for interval-valued and matrix-valued data. Key contributions include envelope models for vector autoregression, copula-based count data modeling, and sequential analysis techniques, bridging theoretical statistics with econometrics and data science applications. Scientific Awards: Outstanding Teacher of the Year, School of Mathematical and Statistical Sciences (2021) Advising: Dr. Samadi has mentored four Ph.D. students to completion: Rukayya Ibrahim (Assistant Professor, Penn State Harrisburg), Wiranthe Herath (Assistant Professor, Drake University), Tharindu De Alwis (Postdoctoral Fellow, WPI), and Hadi Safari Katesari (Teaching Assistant Professor, Stevens Institute of Technology). His Master's students Samira Zaroudi (CUNY) and Reginald Ziedzor (Amplify) have also achieved notable career placements.
Prof. Bruno Clerckx is a Professor of Wireless Communications and Signal Processing at Imperial College London's Department of Electrical and Electronic Engineering, Faculty of Engineering. He leads the Communications and Signal Processing Group and the Wireless Communications and Signal Processing Lab. Education: M.Sc. and Ph.D. in Electrical Engineering from Université Catholique de Louvain, Belgium Doctor of Science (DSc) from Imperial College London Research Interests: Focuses on wireless communications and signal processing for next-generation networks, including MIMO systems, reconfigurable intelligent surfaces (RIS), rate-splitting multiple access (RSMA), and integrated sensing and communications (ISAC). His work emphasizes 6G technologies, full-duplex systems, and energy-efficient architectures. Key contributions include pioneering research on beyond-diagonal RIS and RSMA prototyping. Awards: 2021 Blondel Medal (France) 2021 Adolphe Wetrems Prize (Royal Academy of Belgium) Fellowships from IEEE and IET IEEE Communications Society Distinguished Lecturer (2021-2023) Labs & Teams: Heads the Wireless Communications and Signal Processing Lab, and is affiliated with the IEEE Special Interest Groups on RSMA and BD-RIS. Collaborates with global institutions including Stanford University, Tsinghua University, and Samsung Electronics. Industry Experience: Former CTO of Silicon Austria Labs and contributor to 4G/5G standards at Samsung. Holds 80+ patents and authored two books on MIMO systems.
Yi Lai is an Assistant Professor in the Department of Mathematics at the University of California, Irvine (UCI). He earned his Ph.D. from UC Berkeley in 2021 under Richard Bamler and served as a Szego Assistant Professor at Stanford University (2021–2024), mentored by Otis Chodosh. His research centers on geometric analysis, with a focus on Ricci flows, steady gradient solitons, and the geometry of 3-manifolds. Dr. Lai's work explores the construction and classification of geometric structures like flying wing solitons, convergence properties of Ricci flow, and curvature behavior in low-dimensional manifolds. His publications frequently address long-time existence of geometric flows and symmetry in solitons, blending PDE theory with differential geometry. At UCI, he teaches courses such as Linear Algebra (Math 121A). No awards, grants, or supervised students are mentioned in available sources.
