Cesar Federico Caiafa holds an Adjunct Professor position in the Engineering Department at the University of Buenos Aires (FIUBA) and serves as an independent researcher at IAR (Instituto Argentino de Radioastronomía) and CONICET (National Council for Scientific and Technical Research). He completed his Electronics Engineering degree in 1996 and earned a PhD in Engineering from the University of Buenos Aires in 2007. His research focuses on tensor factorizations and parsimonious representations applied to astronomy, biomedicine, neuroscience, and computational imaging. As a visiting professor at Laboratoire de Physique (LPENSL) from January to February 2025, he collaborates on developing novel machine learning methods for microwave tomography, including unrolled neural networks and self-supervised learning. His visit strengthens Franco-Argentine ties in computational imaging and aims to establish an international research network (IRN-CNRS). Key collaborations include work with Nelly Pustelnik (unrolled networks), Pierre Borgnat (neuroscience signal processing), and Julien Tachella (SiSyPh team). He will also deliver specialized courses on tensorial methods in machine learning and signal processing at ENS Lyon and Inria. His research spans interdisciplinary fields such as brain connectomics, medical imaging analysis, and optimization algorithms for high-dimensional data. His contributions bridge theoretical advancements with practical applications in healthcare and astronomy.
Prasad Raghavendra is a Professor in the Electrical Engineering and Computer Sciences (EECS) Department at the University of California, Berkeley. His research focuses on theoretical computer science, particularly in optimization, complexity theory, approximation algorithms, hardness of approximation, and statistics. He is affiliated with the Center for the Theoretical Foundations of Learning, Inference, Information, Intelligence, Mathematics and Microeconomics at Berkeley (CLIMB) and the Simons Institute for the Theory of Computing (SITC). PhD in Computer Science and Engineering, University of Washington, Seattle (2009) M.S. in Computer Science and Engineering, University of Washington, Seattle (2007) B.S. in Computer Science, Indian Institute of Technology, Madras, India (2005) Raghavendra's research spans theoretical computer science with a focus on optimization, complexity theory, approximation algorithms, and the hardness of approximation problems. He has made significant contributions to understanding Constraint Satisfaction Problems (CSPs), Sum-of-Squares SDP hierarchies, and their applications in high-dimensional statistics. His work bridges theoretical computer science with statistical inference, exploring computational-statistical gaps and developing efficient algorithms for problems in robust statistics, community detection, and tensor decomposition. Raghavendra's recent publications demonstrate a clear trajectory toward the intersection of theoretical computer science and high-dimensional statistics. His work increasingly focuses on Sum-of-Squares SDP hierarchies for statistical problems, robust algorithms for planted models, community detection in stochastic block models, and heavy-tailed statistics. The publications show a progression from foundational work on CSPs and approximation algorithms toward applications in machine learning and statistical inference, with particular attention to computational barriers and optimal algorithms in high-dimensional settings. Michael and Sheila Held Prize (2018) Okawa Research Grant (2015) NSF Faculty Early Career Development Award (CAREER) (2013) Sloan Research Fellow (2012) Raghavendra has advised numerous PhD students who have gone on to positions at institutions like Stanford Statistics, Google Research, and academic positions. His current and past students include David X. Wu, Sidhanth Mohanty, Tarun Kathuria, and others. His research has been supported by multiple grants including an NSF CAREER award and Okawa Research Grant, focusing on theoretical foundations of learning, inference, and computational complexity. He regularly teaches advanced courses including CS 270 (Combinatorial Algorithms and Data Structures), CS 294 (Constraint Satisfaction Problems), and CS 294 (Efficient Algorithms and Computational Complexity in Statistics). Raghavendra is affiliated with the Center for the Theoretical Foundations of Learning, Inference, Information, Intelligence, Mathematics and Microeconomics at Berkeley (CLIMB) and the Simons Institute for the Theory of Computing. His work often involves collaborations with researchers in theoretical computer science, statistics, and mathematics at Berkeley and beyond. His research group focuses on developing theoretical foundations for high-dimensional statistical problems and exploring computational barriers in inference tasks.
Joshua S Agterberg is an Assistant Professor in the Department of Statistics at the University of Illinois Urbana-Champaign (UIUC), affiliated with the College of Liberal Arts & Sciences. He holds a PhD in Applied Mathematics and Statistics from Johns Hopkins University (2023) and a BBA in Actuarial Science and Mathematics from the University of Wisconsin-Madison (2017). His research focuses on statistical network analysis, high-dimensional statistics, spectral methods, and mathematical data science, with notable contributions to tensor analysis, mixed-membership models, and nonparametric hypothesis testing. Education: PhD in Applied Mathematics and Statistics, Johns Hopkins University, 2023 Bachelor of Business Administration (BBA) in Actuarial Science and Mathematics, University of Wisconsin-Madison, 2017 Research Interests: Statistical Network Analysis (e.g., community detection, graph clustering) High-dimensional statistics (e.g., singular vector estimation, sparse PCA) Spectral methods (e.g., tensor perturbation, matrix decomposition) Applications in data science, neuroscience, and connectomics Awards: Summer 2022 Acheson J. Duncan Travel Award Multiple MINDS Data Science Fellowships Best Presentation Award, JSM Student Competition (Nonparametric Statistics, 2021) Advising & Grants: Supervises PhD students at UIUC in statistics and data science Collaborator on grants related to statistical network analysis and tensor models Member of the Math Alliance to diversify STEM participation Labs & Teams: Active in collaborative research groups at UIUC, Johns Hopkins University, and the University of Pennsylvania, focusing on statistical theory and applications in networks and structured data.
Gregory Ongie is an Assistant Professor in the Department of Mathematical and Statistical Sciences at Marquette University. His research bridges mathematics and machine learning, focusing on computational imaging and data science applications. Research Interests: Mathematics of deep learning architectures Applied algebraic geometry for data modeling Optimization in medical image reconstruction Computational imaging algorithms Publication Trends: Recent work emphasizes inverse problems in imaging, tensor-based matrix completion, and theoretical foundations of neural networks. Key themes include algebraic geometry, deep equilibrium models, and Fourier sampling techniques.
Dr. Keaton Hamm is an Assistant Professor in the Department of Mathematics and Division of Data Science at The University of Texas at Arlington. His research bridges theoretical mathematics and computational data science, with postdoctoral experience at the University of Arizona and Vanderbilt University. Primary research domains include computational mathematics, manifold learning techniques, optimization algorithms, and tensor decompositions. His work develops novel methods for high-dimensional data analysis with applications in machine learning and scientific computing. Recent publications demonstrate strong focus on Wasserstein space methodologies (2023-2025), advancing techniques in optimal transport, dimensionality reduction, and geometric learning. Machine learning research explores adversarial robustness and federated learning systems, while mathematical contributions include innovations in matrix decompositions and approximation theory. No awards or student information was available in the provided profile.
Dr. Wenxing Guo is a Lecturer in the School of Mathematics, Statistics and Actuarial Science (SMSAS) at the University of Essex. Their research focuses on Statistical Machine Learning, including Online Learning and Federated Learning, High-dimensional Inference, and Multivariate Regression and Classification models. Dr. Guo's work emphasizes innovative approaches to data analysis and statistical modeling, with recent contributions in federated learning frameworks and methodologies for handling high-dimensional datasets. Their publications span topics from wavelet-based Bayesian methods to tensor response regression, reflecting a strong foundation in both theoretical and applied statistics. They are actively open to supervising doctoral students in areas such as statistical inference and model development. Contact information includes wg22745@essex.ac.uk and their office at 3A.528 on the Colchester Campus.
Dr. Flavio Vella is an Associate Professor at the Department of Information Engineering and Computer Science (DISI) at the University of Trento. He holds roles on the management board of the national HPC laboratory at CINI and the Steering Committee of ICSC’s spoke4. His research focuses on parallel algorithms for emerging computing systems, machine learning systems, and quantum computing, with an emphasis on irregular computation and large-scale graph analysis. He has industrial experience at NVIDIA and Dividiti, and has contributed to EU projects like ARCHYTAS (AI acceleration) and NET4EXA (exascale networking infrastructure). Dr. Vella earned his Ph.D. from Sapienza University of Rome in 2017. His academic journey includes roles at the Free University of Bozen, CNR Italy, and ETH Zurich. He actively serves HPC communities as Artifact co-chair for PPoPP and Computing Frontiers, and as PC member for IPDPS, SC, and EuroPAR. His work has produced over 40 peer-reviewed publications, including Best Paper Awards at SC22/24 and Best PhD Paper at IPDPS17. His research themes include GPU performance optimization, quantum device reliability, and HPC/AI interconnects. Recent work explores tensor networks, physics-constrained neural networks, and exascale system engineering. Projects like ARCHYTAS (EUDF-2023) and NET4EXA (Horizon) highlight his leadership in European HPC initiatives.
Dr. Anindya Bijoy Das is a tenure-track Assistant Professor in the Electrical and Computer Engineering department at The University of Akron's College of Engineering and Polymer Science, where he teaches courses including Wireless Communications (Spring 2025) and Digital Communication (Fall 2024). Prior to joining Akron in August 2024, he served as a Postdoctoral Researcher at Purdue University (2022-2024) following completion of his Ph.D. at Iowa State University in 2022, where he received the prestigious Karas Award for outstanding dissertation work. His educational background includes: Ph.D. in Electrical Engineering, Iowa State University (2022) M.Eng. in Electrical Engineering, Iowa State University (2018) B.Sc. in Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology (2014) Dr. Das's research focuses on cutting-edge areas at the intersection of machine learning, distributed systems, and communications. His primary interests include federated learning , AI/ML applications , distributed computation , information theory , and biomedical signal processing . Recent work explores the integration of large language models with traditional signal processing techniques, particularly for healthcare applications. His research bridges theoretical foundations with practical implementations, often addressing challenges in edge computing environments where computational resources are limited. The work demonstrates strong connections between theoretical information theory and practical system design. Analysis of his publication portfolio reveals an evolving research trajectory with increasing emphasis on federated learning architectures, privacy-preserving techniques, and the application of reinforcement learning to communication optimization. His work spans wireless communications, information theory, and healthcare applications, with a consistent focus on solving computational bottlenecks in distributed environments. The interdisciplinary nature of his research is evident in publications spanning IEEE Transactions on Information Theory, IEEE Journal on Selected Areas in Communications, and IEEE Signal Processing Magazine. His notable achievements include: Karas Award for Outstanding Dissertation in Mathematical and Physical Sciences and Engineering (2022) Research Excellence Award from Iowa State University (2021) Teaching Excellence Award from Iowa State University (2020) National Champion in Bangladesh Mathematical Olympiad (2008) Multiple Best Paper Awards at international conferences Dr. Das currently leads a research group focused on three main thrusts: improving federated learning algorithms, enhancing distributed computation schemes, and developing novel AI/ML applications. He has secured a $73,000 grant from Autonomous and Connected Systems of Purdue Engineering Initiatives for research on AI tensor computations in edge networks. Actively seeking 1-2 highly motivated PhD students, he emphasizes practical implementation alongside theoretical advances, with applications spanning healthcare, wireless communications, and edge computing environments. His service as a reviewer for top-tier journals including IEEE Transactions on Pattern Analysis and Machine Intelligence and IEEE Transactions on Wireless Communications further demonstrates his standing in the research community.
Thomas D. Howell is a Lecturer in the Department of Computer Science at San José State University, where he has taught since 2002. He holds a Ph.D. in Computer Science from Cornell University (1976) and a BS in Mathematics from the California Institute of Technology (1973). His career spans over 30 years in academia and industry, including roles as a Research Staff Member at IBM Research (1977–1990) and Vice President of Research at Quantum Corporation (1990–2000). He specializes in magnetic recording systems, signal processing for storage media, and data detection algorithms. Educations: Ph.D. in Computer Science, Cornell University, 1976 M.Sc. in Computer Science, Cornell University, 1975 B.Sc. in Mathematics, California Institute of Technology, 1973 His research interests focus on advancing magnetic recording technologies, including error correction, channel design, and high-density storage systems. He has contributed to the development of MR and GMR heads and digital channel technologies. His work often intersects electrical engineering and applied mathematics, addressing challenges in signal integrity and data reliability. Publications span foundational topics like tensor rank analysis, sparse matrix computations, and modern storage system optimization. Recent work emphasizes statistical modeling of recording codes and error rate performance in gigabit-scale systems. Awards: IEEE Fellow (2008) Editor of IEEE Transactions on Magnetics (1997–2000) Chair of Magnetic Recording Conference (2000) He has advised no listed students but has mentored teams in industrial R&D environments. His professional service includes roles on the board of the National Storage Industry Consortium and multiple university advisory councils. Active in industry collaborations, he holds patents on coding techniques and error correction methods critical to modern storage systems. His research is conducted through affiliations with IBM Research, Quantum Corporation, and San José State’s College of Engineering laboratories.
Virginia Vassilevska Williams is Professor of Computer Science and Artificial Intelligence + Decision-making at MIT EECS. Her research focuses on theoretical computer science with emphasis on algorithms, computational complexity, and graph theory. She has made significant contributions to matrix multiplication complexity and fine-grained hardness results. Recent publications explore fundamental problems in graph algorithms including cycle detection, shortest paths, and clique enumeration. Her work demonstrates consistent advancement in understanding computational limits for graph problems and matrix operations. Key research themes include: Breaking barriers in matrix multiplication exponents Establishing hardness thresholds for approximation algorithms Developing efficient graph traversal methods for sparse structures Her 2024 publications continue this trajectory with refinements to the laser method for matrix multiplication and improved clique listing techniques. The research consistently pushes boundaries in algorithm optimality proofs and computational complexity theory.
Dr. Sheehan Olver is an Associate Professor in Applied Mathematics and Mathematical Physics at the Department of Mathematics, Imperial College London. He holds affiliations in Applied Mathematics and Mathematical Physics, Applied and Numerical Analysis, and Mathematics research and teaching staff. His research focuses on numerical analysis, computational methods, and spectral methods for differential equations, singular integral equations, and Riemann–Hilbert problems, with applications in integrable systems and random matrices. Education: PhD in Applied Mathematics from the University of Cambridge (2008). Smith-Knight/Rayleigh-Knight Prize Winner (2006). Research Interests: Spectral methods, orthogonal polynomials, fractional differential equations, representation theory applications, and numerical solutions of integrable systems. His work emphasizes efficient, sparse numerical techniques for solving complex mathematical problems across domains like fluid dynamics and quantum mechanics. Labs/Teams: Active in software development for computational mathematics, including packages like ApproxFun.jl and RHPackage . Collaborates widely with institutions such as the University of Oxford, Cornell University, and the University of Sydney. Grants/Awards: While no specific awards are listed, his extensive publication record and software contributions reflect sustained recognition in computational mathematics.
Srinivas Aluru is a Regents' Professor and Senior Associate Dean at the Georgia Institute of Technology's College of Computing , within the School of Computational Science and Engineering . His research focuses on High Performance Computing , Bioinformatics , Systems Biology , and Applied Algorithms . He has pioneered parallel methods in computational biology, contributing to plant genome assembly and analysis. Current work includes bioinformatics for high-throughput DNA sequencing and systems biology network inference using Bayesian and mutual information approaches. Aluru holds Fellowships from AAAS and IEEE and has received awards such as the NSF Career Award (1997), IBM Faculty Award (2002), and Swarnajayanti Fellowship (2007). He serves on editorial boards for journals like IEEE Transactions on Parallel and Distributed Systems and International Journal of Data Mining and Bioinformatics . His affiliations include the Institute for Data Engineering and Science (IDEaS) and Machine Learning@GT . Research trends in his articles span genomic data processing, parallel algorithms, and network inference, emphasizing scalability and computational efficiency. He leads efforts in error correction, genome assembly, and large-scale gene regulatory network construction.
Daren Wang is an Assistant Professor in the Department of Applied and Computational Mathematics and Statistics at the University of Notre Dame, within the College of Science. He holds a Ph.D. in Statistics from Carnegie Mellon University (2018), an M.S. in Mathematics from the University of Michigan (2011), and a B.S. in Mathematics from the University of Virginia (2010). His research focuses on high-dimensional statistical methods, including scientific computing, nonparametric estimation, and change point detection in functional and temporal-spatial models. He has developed R packages such as 'changepoints' for change point localization and 'functional_regression' for mixed predictor models. His work bridges theoretical statistics, machine learning, and computational methods. Research interests include high-dimensional linear algebra applications to machine learning, nonparametric estimation in complex systems, and the development of efficient algorithms for change point detection in dynamic networks and time series. He advises Ph.D. students from diverse backgrounds, including applied mathematics and computational physics. His contributions include advancements in density estimation via tensor decomposition and variance-reduced sketching techniques.
Bethany Lusch is an Assistant Computer Scientist in the Data Science Group at the Argonne Leadership Computing Facility, Argonne National Laboratory. She holds advanced degrees in applied mathematics and focuses on integrating artificial intelligence with scientific computing, particularly for dynamical systems and PDE-based simulations. Education: PhD in Applied Mathematics, University of Washington, 2016 MS in Applied Mathematics, University of Washington, 2011 BS in Mathematics (Honors), University of Notre Dame, 2010 Her research centers on scientific machine learning, with emphasis on developing machine-learning emulators for expensive simulations such as climate models and computational fluid dynamics. She pioneers methods that embed domain knowledge into deep learning frameworks, particularly through Koopman operator theory to linearize nonlinear dynamics. Her work spans representation learning, reduced-order modeling, and analysis of supercomputing system logs. The 15 most recent publications reflect a strong trend toward using deep learning—especially recurrent and autoencoder architectures—to model complex physical systems. Key themes include surrogate modeling, latent-space dynamics, closure in reduced-order models, and interpretable AI in scientific contexts. Her work bridges theoretical rigor with practical applications in engineering and geophysics. Scientific Awards: NPSC Fellow, National Physical Science Consortium (now GFSD), Aug 2010 – May 2016 Six-Year Graduate Fellowship, NSF-SUMR Scholarship University of Notre Dame Mathematics Department Scholarship, Aug 2007 – Jun 2010 Bethany Lusch has collaborated extensively with researchers at Argonne and the University of Washington. Her projects often involve interdisciplinary teams working on high-performance computing applications. She has contributed to the development of tools like MELA for visual analytics of HPC logs and has secured competitive fellowships and funding. Her prior role as a Research Associate at the University of Washington (2016–2018) preceded her current position at Argonne (2018–present), indicating a continuous research trajectory. She is actively involved in advancing AI-driven scientific discovery, with ongoing work in universal embeddings for PDEs and multifidelity modeling. Her lab affiliations include the Argonne Leadership Computing Facility, where she leverages world-class supercomputing resources to develop and test her models.
Prof. Thomas Huckle is a Professor of Scientific Computing at the Technical University of Munich (TUM), affiliated with the TUM School of Computation, Information and Technology and the Department of Computer Science. His research focuses on numerical linear algebra, parallel computing, and their applications in physics and computer science. Key interests include solving linear problems on parallel architectures, image processing, multigrid methods, preconditioning, and tensor-based high-dimensional problem approximation. Education: Studied mathematics and physics at the University of Würzburg (diploma in mathematics, 1985 PhD, 1991 habilitation). Professional History: DFG-funded research at Stanford University (1993–1994), appointed to TUM in 1995, and member of the Mathematics Department since 1997. Research Interests: Prof. Huckle’s work spans numerical methods for large-scale systems, including structured matrices, regularization techniques, and quantum computing applications. He develops algorithms for parallel computing environments and contributes to software tools like ELPA for eigenvalue problems. Grants and Labs: Engaged in projects such as the ELPA-AEO eigensolver and ESSEX-II initiatives. Active in the SCCS (Scientific Computing and Computational Science) group at TUM, focusing on high-performance computing and numerical methods.
