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Alexander OstermannView profile

Professor

• Numerical Analysis
• Scientific Computing
• Partial Differential Equations
• +3 more

Prof. Alexander Ostermann is a Full Professor of Numerical Analysis and Scientific Computing at the University of Innsbruck, leading the Department of Mathematics and the Research Area Scientific Computing. He holds roles such as Dean of the Faculty of Mathematics, Computer Science, and Physics, and editorial board memberships in journals like GAMM Mitteilungen and Advances in Applied Mathematics and Mechanics . His research focuses on numerical methods for PDEs, exponential integrators, splitting techniques, and high-performance computing. He has supervised numerous PhD/Master’s students and collaborates internationally on projects like EuroCC Austria and Vienna Scientific Cluster. Education : Dr. rer. nat. (1988) and Mag. rer. nat. (1984) from Universität Innsbruck. Professional milestones include roles at the University of Geneva and extensive administrative leadership in academic governance. Research Interests : Development and analysis of numerical methods for time-dependent PDEs, including exponential integrators, splitting methods, and error estimation under low-regularity conditions. Applications span computational physics, fluid dynamics, and quantum mechanics. Publications & Impact : Over 100 peer-reviewed articles, including work on Fourier integrators for the nonlinear Schrödinger equation and dynamical low-rank approaches. His methods are applied in high-performance computing for complex systems like Vlasov-Maxwell equations. Advising & Teams : Advisor to PhD students in numerical analysis and computational mathematics. Collaborates with teams on projects involving scientific computing frameworks and software development (e.g., avaflow for mass flow simulations).

Dmitry SavostyanovView profile

Lecturer

• Numerical Mathematics
• Tensor Decompositions
• High-Dimensional Problems
• +3 more

Dr. Dmitry Savostyanov is a Lecturer at the University of Essex, School of Mathematics, Statistics and Actuarial Science (SMSAS). His research focuses on developing efficient algorithms for high-dimensional problems using low-rank tensor product approximations, linear algebra, and numerical methods. He holds a PhD in Computational Mathematics from the Russian Academy of Sciences and a PGCert in Higher Education from the University of Brighton. Qualifications: PhD (2006), MSc (2003), BSc (2001), PGCert (2016). Appointments: Senior Lecturer at University of Brighton (2014–2020), Senior Research Fellow at University of Southampton (2012–2014), and Visiting Research Fellow at University of Chester (2011–2012). His research interests span numerical mathematics, tensor decompositions, and computational methods for high-dimensional systems. Recent work includes applications in epidemiological modeling, quantum control, and NMR simulation. He has collaborated on software tools like the TT-toolbox for tensor train formats. Publications highlight contributions to tensor interpolation, quantum algorithms, and efficient numerical solvers. No scientific awards are explicitly listed, but his work reflects sustained innovation in computational mathematics. No student advisees or lab affiliations are detailed in the provided materials. Grants and funding sections exist but lack specific details.

Michael MascagniView profile

Professor

• Computational Science
• Monte Carlo Methods
• Numerical Analysis
• +5 more

Michael Mascagni is a Professor at Florida State University with joint appointments in the Department of Computer Science, Department of Mathematics, Department of Scientific Computing, and the Graduate Program in Molecular Biophysics. He holds courtesy affiliations with the Department of Chemical and Biomedical Engineering, and maintains collaborative roles as a Guest Researcher at NIH's Laboratory for Biological Modeling and Faculty Appointee at NIST's Applied and Computational Mathematics Division. His academic credentials include a Ph.D. in Mathematics from NYU (1987), and dual B.S. degrees in Mathematics and Biomedical Engineering from the University of Iowa. His research integrates computational science, Monte Carlo methods, and parallel computing to solve complex problems in biophysics, materials science, and numerical analysis. Key interests include scalable random number generation, stochastic PDE solvers for electrostatics (Poisson-Boltzmann), computational neuroscience, and high-performance algorithm design. His work emphasizes applications in molecular biophysics, financial modeling, and fault-tolerant computing. Recent publications demonstrate a strong focus on advancing Monte Carlo techniques, including novel algorithms for matrix computations, discrepancy estimation, and biomolecular electrostatics. His articles frequently intersect computational mathematics, parallel architectures, and biological applications, with emerging themes in randomized linear algebra, quasirandom methods, and neural network reproducibility. Scientific Awards & Honors: Fulbright Senior Specialist Roster (2008) FSU Developing Scholar Award (2001) NAS/NRC Postdoctoral Fellowship (1988-1989) ACM Distinguished Scientist (2011-Present) ACM Senior Member (2009-Present) He directs research in scalable algorithms and collaborates with national labs including NIH and NIST. His computational infrastructure contributions include the SPRNG library and ZENO software for biomolecular properties. Current projects explore Grid-based Monte Carlo, randomized linear solvers, and actomyosin ring modeling in cellular division.

Pramod PisharadyView profile

Assistant Professor

• Medical Imaging
• MRI Techniques
• White Matter Microstructure
• +5 more

Pramod Pisharady, PhD, is an Assistant Professor in the Department of Radiology at the University of Minnesota, affiliated with the Center for Magnetic Resonance Research. His research focuses on advanced MRI techniques, white matter microstructure analysis, and neuroimaging applications in neurodegenerative diseases such as ALS and Friedreich ataxia. He specializes in diffusion MRI, biophysical modeling, and multimodal imaging approaches. His work integrates computational methods like sparse Bayesian learning and noise reduction algorithms (e.g., NORDIC PCA) to improve imaging accuracy. Key contributions include studies on T1 relaxation dynamics, spinal cord imaging in ALS, and cerebellar atrophy analysis in genetic disorders. Collaborations with consortia like ENIGMA-Ataxia highlight his commitment to large-scale neuroimaging research. Research interests span MRI technology development, neurodegeneration biomarkers, and clinical translation of imaging findings. While awards are not explicitly listed, his prolific publication record reflects recognition in his field. No student advisees or grants are detailed in the provided text, but his lab focuses on advancing magnetic resonance methodologies for neurological diagnostics.

Zachary G. IvesView profile

Professor

• Data Integration
• Data Provenance
• Machine Learning Systems
• +6 more

Zachary G. Ives is the Adani President's Distinguished Professor and Chair of the Computer & Information Science Department at the University of Pennsylvania. His research focuses on data integration, provenance, machine learning systems, and scalable computing. Notable contributions include the Juneau project for data management in computational notebooks and the Orchestra system for collaborative data sharing. He leads initiatives like the ASSET Center for Safe, Explainable, and Trustworthy AI and the Warren Center for Network and Data Science. Education: While explicit degrees aren’t detailed, his roles and academic contributions imply advanced degrees in Computer Science. Current courses include CIS 2450 (Big Data Analytics) and NETS 212 (Scalable and Cloud Computing). Research spans data lakes, sensor networks (ASPEN project), and biomedical applications like seizure prediction via the IEEG Portal. His work bridges databases, ML, and distributed systems, with emphasis on trustworthiness and explainability. Key awards include the NSF CAREER Award, Lindback Teaching Award, and ACM Fellow status. Grants from NSF, NIH, DARPA, and industry partners (Amazon, Google) support his work. He advises numerous PhD students and collaborates with faculty across disciplines, including Neurology and Biostatistics. Labs/Teams: Penn Database Group, ASSET Center, Warren Center, and collaborations with Meta Research and Mayo Clinic. Active in academic service as SIGMOD PC co-chair and journal editor.

• Artificial Intelligence
• Machine Learning
• Cybersecurity
• +5 more

Tala Talaei Khoei serves as an Assistant Teaching Professor at Northeastern University, conducting interdisciplinary research at the intersection of artificial intelligence, cybersecurity, and healthcare innovation. Her academic profile reflects active engagement in developing practical AI solutions for critical real-world challenges. Her research program spans three primary domains: (1) Healthcare AI applications including pediatric pneumonia/asthma detection through respiratory sound analysis using Google's HeAR model; (2) Cybersecurity systems focused on ransomware detection, GPS spoofing countermeasures for UAVs, and IoT security frameworks; and (3) Generative AI advancements in large language models, emotional AI, and sustainable computing practices. This tripartite focus demonstrates her commitment to translating theoretical AI research into tangible societal benefits. Analysis of her 15 recent 2025 publications reveals accelerating research velocity with particular emphasis on data-efficient AI techniques for small datasets, multimodal security threat detection, and ethical implementation of generative models. Her work consistently bridges theoretical machine learning innovations with domain-specific applications, showing growing influence in both healthcare informatics and cybersecurity communities through high-impact survey papers and novel framework development.

• Computer Science
• Numerical Analysis
• Algebraic Algorithms
• +4 more

Dr. Victor Y. Pan is a Distinguished Professor of Mathematics and Computer Science at Lehman College, The City University of New York (CUNY). He has been affiliated with Lehman College since 1988 and holds one of the highest academic ranks reserved for influential scholars. His research focuses on numerical and algebraic algorithms, with a particular emphasis on polynomial computations, matrix structures, and root-finding methods. Dr. Pan's work bridges numerical and symbolic computing, aiming to optimize computational efficiency while ensuring accuracy. Educational Background: Ph.D. in Mathematics from Moscow University Research experience at the Soviet Academy of Science Research Interests: His key areas include polynomial root-finding, matrix eigenproblems, structured matrices (e.g., Toeplitz, Hankel, and Cauchy), and low-rank approximation. He has pioneered methods combining numerical and algebraic techniques to enhance computational speed and precision. Recent work emphasizes algorithms for sparse polynomials, superfast root-finders, and efficient matrix computations. Publications & Impact: With over 200 peer-reviewed papers and three books, Dr. Pan’s research has influenced global computational mathematics. His articles address topics like fast root-finding, matrix eigenvalue problems, and low-rank approximation at sub-linear cost. His work is widely cited in computer science and applied mathematics. Awards & Recognition: Appointment as Distinguished Professor (CUNY, 2000) Global recognition as a leader in theoretical computer science and numerical analysis Advising & Grants: Recipient of continuous NSF funding for over 20 years. He has mentored 17 Ph.D. students through his seminar program, focusing on algebraic and numerical computing. His seminar fosters collaborative research in topics like polynomial equations, coding theory, and eigen-solving techniques. Labs & Teams: Leads the Algebraic Numerical Computing Seminar at CUNY’s Graduate Center, integrating Computer Science and Mathematics students. The seminar explores cutting-edge topics such as displacement-structured matrices, polynomial root-finding, and eigen-solving algorithms.

Tao LiView profile

Professor

• Low-Dimensional Topology
• Hyperbolic 3-Manifolds
• Heegaard Splittings
• +2 more

Tao Li is Professor of Mathematics at Boston College, specializing in low-dimensional topology and hyperbolic 3-manifolds. His research focuses on Heegaard splittings, laminar branched surfaces, and developing algorithms for topological classification. Li has made significant contributions to understanding the rank-genus relationship in 3-manifolds and methods for detecting essential surfaces. He teaches courses including Algebraic Topology and supervises PhD students in topology research. Li's publications appear in Communications in Analysis and Geometry, Geometry & Topology, and Journal of the American Mathematical Society. His algorithmic work includes procedures for identifying laminar 3-manifolds and calculating Heegaard genus.

• Magnetic Resonance Imaging (MRI)
• Quantitative Susceptibility Mapping
• Diffusion Tensor Imaging
• +3 more

Douglas Kelley, PhD holds the title of Research Associate and Adjunct Professor at the University of California, San Francisco (UCSF). His work focuses on advanced MRI techniques, particularly in ultra-high field (7T) imaging, quantitative susceptibility mapping, and diffusion MRI. He has contributed to innovations in RF coil design, image reconstruction algorithms, and applications in neuroimaging, oncology, and musculoskeletal imaging. Key research areas include: Development of high-resolution MRI methods for brain tumor characterization and neurodegenerative diseases Optimization of 7T MRI protocols for phase imaging, T1-weighted imaging, and diffusion tensor imaging Advancements in RF coil architectures and parallel imaging strategies Applications of quantitative susceptibility mapping in clinical diagnostics His publications span over 30 years, with a focus on advancing imaging hardware, software algorithms, and clinical applications of MRI technology. Notable contributions include studies on hippocampal atrophy in Alzheimer's disease, multiple sclerosis lesion characterization, and the use of ultrashort echo time (UTE) and zero echo time (ZTE) imaging. Dr. Kelley has collaborated extensively with researchers in radiology, biomedical engineering, and neurology, contributing to both technical innovations and translational medical research.

Kyle A CaudleView profile

Professor

• Forecasting
• Time Series Analysis
• Multilinear Algebra
• +3 more

Kyle A. Caudle is a Professor of Mathematics at the South Dakota School of Mines and Technology, holding a Ph.D. from George Mason University, an M.S. from Salve Regina University, and a B.A. from Western State College. His research spans forecasting, tensor analysis, and anomaly detection, with applications in engineering and data science. Caudle develops computational tools for time series forecasting, tensor decomposition, and graph representation learning. He created software packages like Flow Field, rTensor2, and LTAR, published on CRAN. His interdisciplinary projects include collaborations with NIST and Naval Surface Warfare Centers on surface ship maintenance and anomaly detection. Publications emphasize multilinear algebra, temporal forecasting, and machine learning. Recent work advances tensor factorization for high-dimensional data, hierarchical graph networks, and deep generative models. Awards include the 2015 Peter Holmes Prize for innovative statistics teaching and accreditation as a Professional Statistician (ASA, 2013). He mentors graduate students and co-developed the Ph.D. in Data Science.

Donsub RimView profile

Assistant Professor

• Numerical Analysis
• Partial Differential Equations (PDEs)
• Model Reduction
• +6 more

Donsub Rim is an Assistant Professor in the Department of Mathematics at Washington University in St. Louis. He holds a PhD from the University of Washington, advised by Randall J. LeVeque and Gunther Uhlmann. His research focuses on numerical analysis of PDEs, inverse problems, and low-rank neural network representations (LRNRs) for real-time solutions in geophysics, medical imaging, and plasma physics. Key areas include stability analysis of neural networks for tsunami early warning, fast inversion of the approximate discrete Radon transform (ADRT), and computational applications in geophysical hazard assessment. Rim is on leave during 2024-2025 as a Visiting Scholar at the University of Washington. Previously, he was a visiting scholar at Tohoku University's IRIDeS (2023) and affiliated with the Courant Institute and Columbia University. His work bridges mathematical theory with practical tools like the 'adrt' Python library for signal processing. Research Interests: Numerical methods for PDEs, model reduction techniques, neural network applications in high-consequence domains (e.g., tsunami forecasting), and ADRT-based algorithms for dimensional splitting and sparse approximations. Collaborations include projects on probabilistic tsunami hazard assessment (PTHA), earthquake kinematic effects on tsunami propagation, and meta-learning approaches for physics-informed networks. Publications highlight contributions to real-time prediction systems, stability analysis of neural networks, and manifold approximations for transport-dominated problems. His software developments, such as the ADRT Python package, emphasize open-source tools for scientific computing. Rim has advised or collaborated with researchers at institutions including Columbia University, Tohoku University, and the University of Washington. Grants and future work involve advancing LRNR frameworks for nonlinear hyperbolic problems, improving tsunami early warning systems via GNSS data, and exploring applications of sparse physics-informed backpropagation in geophysics and plasma physics.

Ran ChenView profile

Assistant Professor

• Reinforcement Learning
• Data-driven Decision-making
• Optimization
• +3 more

Ran Chen is an Assistant Professor of Statistics and Data Science at Washington University in St. Louis. She holds a Ph.D. from the Wharton School of the University of Pennsylvania and a B.S. in Pure and Applied Mathematics from Tsinghua University. Prior to her current position, she was a postdoctoral researcher at MIT’s Laboratory for Information and Decision Systems. Her research focuses on reinforcement learning , data-driven decision-making , optimization , and statistical machine learning , with applications in healthcare and revenue management. Key areas include high-dimensional and nonparametric statistics, interpretable models for business decisions, and algorithmic frameworks balancing statistical accuracy and computational efficiency. Her work spans theoretical, methodological, and applied contributions, such as doubly high-dimensional contextual bandits for retail optimization, personalized reinforcement learning frameworks, and novel Poisson-MNL models for dynamic customer arrivals. She has presented her research at institutions like Harvard, UC Davis, and the INFORMS Business Analytics Conference. Teaching experience includes courses on Python for data science, forecasting methods, and introductory statistics at the University of Pennsylvania. She has advised students in optimization and machine learning contexts but no named advisees are listed in the provided text. Notable publications include advancements in convex function estimation, low-rank matrix models for bandits, and supervised centrality estimation in networks. Her research emphasizes practical applications while maintaining rigorous statistical foundations.

Daniel GoldreichView profile

Associate Professor

• Tactile Perception
• Bayesian Inference
• Sensory Compensation in Blindness
• +7 more

Daniel Goldreich is an Associate Professor at McMaster University, holding joint appointments in the departments of Psychology and Neuroscience & Behaviour. His research bridges experimental and theoretical approaches to investigate tactile perception and neural processing. University: McMaster University School: College of Social Sciences Rank: Associate Professor Research Interests: Goldreich's laboratory explores how the nervous system decodes sensory input to generate perception, focusing on tactile phenomena such as perceptual learning, sensory compensation in blindness, sex differences in tactile acuity, developmental and aging changes in touch, and sensory illusions. His work integrates physics, neurophysiology, and probability calculus to build mathematically rigorous models of perception. Teaching Activities: He has taught advanced neuroscience courses since 2018, including Bayesian Inference (PSYCH 4KK3), Quantitative Methods (PSYCH 730), and various lab/research practicum courses at undergraduate and graduate levels. His teaching emphasizes hands-on learning and computational approaches. Contact: Phone: 905-525-9140 ext. 28666 Email: goldrd@mcmaster.ca

Brian TrippeView profile

Assistant Professor

• Probabilistic Machine Learning
• Bayesian Computation
• Computational Biology
• +2 more

Brian Trippe is an Assistant Professor of Statistics at Stanford University with a joint affiliation in Stanford Data Science. His research develops probabilistic machine learning methods to solve critical challenges in biotechnology and medicine, particularly focusing on reliable molecular design under physical constraints. Education: PhD in Computational and Systems Biology, MIT (2022) MPhil in Engineering, University of Cambridge (2017) BA in Biochemistry and Computer Science, Columbia University (2016) His work centers on probabilistic machine learning and Bayesian computation applied to computational biology , with breakthroughs in protein engineering yielding hundreds of experimentally validated molecular structures. By incorporating prior knowledge and providing theoretical guarantees, his methods address biotechnology's unique demands for data efficiency and physical constraint satisfaction. Analysis of his recent publications reveals dominant trends in diffusion models for protein design and Bayesian inference frameworks , demonstrating cross-cutting applications from ocean current modeling to genomic analysis. These works consistently bridge statistical theory with experimental validation through major collaborations like the University of Washington's Institute for Protein Design. Brian actively mentors students at Stanford and explicitly encourages applications from underrepresented groups. His collaborative network spans Columbia University, MIT, and the Baker lab, driving interdisciplinary advances in computational biotechnology while pursuing long-term goals in genetic engineering foundations.

Tomaso A PoggioView profile

Professor

• Computational Neuroscience
• Machine Learning
• Artificial Intelligence
• +4 more

Tomaso A. Poggio is the Eugene McDermott Professor in the Department of Brain and Cognitive Sciences at MIT. He also serves as Director of the Center for Brains, Minds, and Machines, and is an Investigator at the McGovern Institute for Brain Research and the Computer Science and Artificial Intelligence Laboratory (CSAIL). His research focuses on computational neuroscience, machine learning, and the theoretical foundations of deep learning. Dr. Poggio holds a Ph.D. in Theoretical Physics from the University of Genoa (1971) and has held roles at the Max Planck Institute for Biological Cybernetics (1972–1981) before joining MIT. He is a member of the American Academy of Arts and Sciences and a Founding Fellow of AAAI, with an h-index exceeding 100 (Google Scholar). His research interests include neural network architectures, adversarial robustness, generalization bounds, and biologically plausible learning mechanisms. He has pioneered work on hierarchical learning, neural collapse, and the interplay between SGD and network rank minimization. Key contributions span computational vision, invariant recognition, and the theoretical underpinnings of deep learning. Poggio’s awards include the Otto-Hahn-Medaille (Max Planck Society), the Okawa Prize (2009), and the Swartz Prize (2014). His labs and initiatives bridge neuroscience, AI, and cognitive science to address fundamental questions in intelligence.