State University of New York at BuffaloUnited States
Jinjun Xiong is the SUNY Empire Innovation Professor and Professor of Computer Science and Engineering at the University at Buffalo. He directs the Institute for Artificial Intelligence and Data Science and leads the X-Lab@UB, focusing on accelerating AI systems and solutions. His research spans cognitive computing, big data analytics, and deep learning applications in energy and industrial systems. Education: PhD in Electrical Engineering (University of California, Los Angeles, 2006). Research interests emphasize AI-driven solutions for healthcare, edge computing, and interdisciplinary challenges. His work addresses ethical AI, multimodal systems, and hardware-software co-design for efficient AI deployment. Awards: See full list here (external link provided). Grants and Advising: Advises students in AI, edge computing, and medical applications. Active in securing research funding for interdisciplinary projects. Labs: X-Lab@UB focuses on AI systems, edge computing, and real-world AI solutions. Collaborates with industry partners on deployable AI frameworks.
Steve Peggs serves as an Adjunct Professor in the Department of Physics and Astronomy at Stony Brook University, concurrently holding the position of senior accelerator physicist at Brookhaven National Laboratory (BNL). His primary research focuses on accelerator design and performance optimization, with specialized expertise in linear and nonlinear beam dynamics. Professor Peggs played instrumental roles in designing, building, and commissioning the Relativistic Heavy Ion Collider (RHIC) at BNL. His research extends to multiple international accelerator projects including CESR at Cornell, CERN's SPS collider, Fermilab's Tevatron and Main Injector, and the European Spallation Source. Professor Peggs co-authored the graduate-level textbook Introduction to Accelerator Dynamics published by Cambridge University Press. His scientific contributions were recognized through his election as Fellow of the American Physical Society.
Vladimir Litvinenko is a Professor in the Department of Physics and Astronomy at Stony Brook University. His research focuses on advanced accelerator technologies, plasma-based acceleration, and high-energy physics applications. He leads projects on next-generation linear colliders, coherent electron cooling systems, and laser wakefield acceleration (LWFA). Key initiatives include the Linear Collider Facility (LCF) at CERN and development of superconducting radiofrequency (SRF) photoinjectors for polarized electron beams. His work integrates experimental and theoretical studies of plasma dynamics, beam diagnostics, and novel laser systems. Notable contributions include breakthroughs in CO2-laser-driven LWFA, plasma-cascade instabilities, and electron beam polarization control. He collaborates internationally on projects like the EuPRAXIA compact particle source and Circular Collider using Energy-Recovery Linacs (CERC). Current research emphasizes coherent electron cooling experiments at RHIC, development of high-brightness electron beams, and applications of energy-recovery linacs (ERLs) for future colliders. His group employs solenoid-based beam diagnostics and plasma cascade amplification techniques to address challenges in beam stability and luminosity enhancement. Dr. Litvinenko's innovations span superconducting RF technology, beam dynamics modeling, and multi-color laser systems for advanced acceleration regimes. His work bridges fundamental plasma physics with practical applications in particle colliders and radiation sources, shaping future directions in accelerator science.
University of Massachusetts DartmouthUnited States
Dr. Sigal Gottlieb is Chancellor Professor of Mathematics at UMass Dartmouth and founding director of the Center for Scientific Computing and Data Science Research. A Harvard Business School PLDA graduate and computational mathematics expert, her research develops high-order numerical methods for simulating hyperbolic PDEs with applications in gravitational wave physics. Research Focus: Specializes in strong stability preserving (SSP) time discretizations, WENO/spectral methods for discontinuous problems, and GPU-accelerated algorithms for computational relativity. Current NSF-funded projects include developing efficient black hole spectroscopy techniques. Leadership: Established UMass Dartmouth's computational science research hub and developed new academic programs including the Data Science BS/MS degrees. Recognized as SIAM and AWM Fellow for contributions to computational mathematics.
University of Massachusetts DartmouthUnited States
Yanlai Chen is a Professor in the Department of Mathematics at the University of Massachusetts Dartmouth and serves as Chief Research Officer. He holds a PhD (2007) and MS (2007) from the University of Minnesota Twin Cities, and a BS (2002) from the University of Science and Technology of China. His research integrates numerical analysis, scientific computing, and machine learning. Research interests focus on numerical PDEs, model reduction, machine learning applications in scientific computing, uncertainty quantification, and high-performance computing algorithms. His work bridges theoretical mathematics with practical computational challenges. His publications demonstrate consistent focus on physics-informed neural networks (2021-2024), model reduction techniques (2019-2024), and computational methods for differential equations. Recent work shows increased emphasis on machine learning integration with traditional numerical methods. He has supervised 6 doctoral dissertations and leads the NSF-funded ACCOMPLISH program supporting STEM education through contextualized computing curricula.
Aghalaya S. Vatsala is the Pennzoil Endowed Professor in the Department of Applied Mathematics at the University of Louisiana at Lafayette. His research focuses on differential equations, fractional calculus, numerical analysis, and stability theory. He holds a Ph.D. from the Indian Institute of Technology, Madras (1973), and degrees from Bangalore University (M.S. 1968, B.S. 1966). His work emphasizes impulsive differential equations, reaction-diffusion systems, and numerical methods for fractional equations. Recent research trends include the analysis of sequential Caputo fractional differential equations, stability in large-scale systems, and applications in transport phenomena and predator-prey models. Over 50 publications since 2010 highlight his contributions to fractional calculus and iterative techniques. Key areas of exploration include the convergence of numerical methods, quasilinearization for nonlinear problems, and fractional boundary value problems. His work bridges theoretical developments with applied contexts, such as catalytic converters and ecological modeling.
Georgios Goumas is an Associate Professor in the Division of Computer Science at the School of Electrical and Computer Engineering, National Technical University of Athens. His research focuses on high-performance computing, parallel systems, and efficient resource management for modern computing platforms. Research domains: Serverless computing scalability and memory management Quantum algorithm design for classification Virtual memory optimization Hardware acceleration for sparse computations NUMA-aware concurrent data structures Recent innovations include DaeMon for disaggregated systems, eBPF-based Linux memory management, and multi-GPU matrix multiplication optimizations. Publications demonstrate consistent advancement in low-level system efficiency across quantum, serverless, and heterogeneous computing domains. Dr. Goumas teaches undergraduate and graduate courses on Operating Systems, Parallel Processing, and Code Optimization Techniques. His lab develops open-source tools like Paralia runtime for auto-tuning linear algebra and FPGA-based accelerators for scientific computing.
Professor Dionisios Pnevmatikatos holds the position of Professor at the School of Electrical and Computer Engineering, National Technical University of Athens (NTUA), where he leads research in Computer Architecture and Reconfigurable Computing. He previously served as a Professor at the Technical University of Crete (TUC) from 2000 to 2019, directing the Microprocessor and Hardware Laboratory (MHL) and chairing the department. His academic journey includes a B.Sc. from the University of Crete (1989), M.Sc. and Ph.D. from the University of Wisconsin-Madison (1991 and 1995). Research Interests: Focuses on Computer Architecture, Reconfigurable Computing, Application Acceleration, Custom Architectures, and Hardware Acceleration of Bioinformatics Algorithms. His work spans FPGA-based systems, parallel computing, and energy-efficient designs. Key Projects: Coordinator of FASTER (EU FP7), Principal Investigator in DeSyRe, AXIOM, dRedBox, and EDRAH2020 projects. Active in EU initiatives like H2020 OPTIMA and Vitamin-V for RISC-V ecosystems. Leadership roles in conferences include SAMOS 2018 and FPL 2011 program chairs. Teaching: Courses include Computer Architecture, Digital Systems Design, and Parallel Processing Systems at NTUA. Former roles include teaching at University of Crete and TUC. Labs: Affiliated with Computing Systems Laboratory (CSLab) at NTUA and FORTH-ICS since 1997. Involved in prototyping manycore architectures and network processors.
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.
Marc Moreno Maza is a Professor in the Computer Science and Applied Mathematics Departments at the University of Western Ontario, and a Principal Scientist at the Ontario Research Centre for Computer Algebra (ORCCA). His research focuses on applying computer science to mathematics, particularly polynomial system solving and algorithm design. Key areas include parallel computing, high-performance algebraic algorithms, and GPU acceleration. He leads projects funded by NSERC and industry partnerships, such as 'Hardware Acceleration Technologies for Polynomial Systems' and collaborative work with IBM and CAS Research. His research spans four directions: theoretical foundations of polynomial equations, efficient algorithm development, software implementation (e.g., RegularChains library in Maple), and applications to real-world challenges. He has delivered over 100 talks worldwide on topics like cylindrical algebraic decomposition, GPU-based polynomial arithmetic, and parametric system solving. His work emphasizes optimizing algorithms for modern architectures and leveraging parallelism. Notable contributions include the BPAS and CUMODP libraries for polynomial arithmetic, and the RegularChains library for semi-algebraic set computations. He collaborates internationally, with grants supporting both theoretical and applied research. His team addresses challenges in computational algebra, from theoretical breakthroughs to practical software tools.
South Dakota School of Mines and TechnologyUnited States
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.
Daniel V. Schroeder is a Professor in the Department of Physics at Weber State University, Ogden, Utah. He teaches a wide range of courses from Elementary Astronomy to Quantum Mechanics and has been recognized for his contributions to physics education through textbooks and editorial work at the American Journal of Physics. His research focuses on theoretical and computational physics, particularly in quantum mechanics, thermal physics, and relativity. He has collaborated with undergraduate students on interactive web applications for physics outreach. Ph.D. in Physics from Stanford University B.A. in Physics from Carleton College His recent publications emphasize physics education, computational methods, and book reviews, reflecting his dual role as educator and researcher. Notable contributions include An Introduction to Quantum Field Theory and An Introduction to Thermal Physics . Professional service includes roles at the American Journal of Physics (Consulting Editor, 2017–present; Associate Editor, 2012–2016) and leadership in the AAPT Idaho-Utah Section. He maintains an active personal blog and develops educational software hosted at his website .
Orest Ostapiak is an Associate Professor in the Department of Medical Radiation Sciences within the Faculty of Science at McMaster University. His expertise lies in medical physics and radiation therapy, with a focus on improving treatment accuracy and quality assurance for cancer patients. Dr. Ostapiak teaches courses including Research Methods in Medical Radiation Sciences and Physics and Instrumentation for Radiation Therapy, contributing significantly to the education of future radiation therapists and medical physicists. Dr. Ostapiak's research spans multiple critical areas within radiation oncology: Radiation therapy quality assurance and verification Image-guided radiation therapy techniques Head and neck cancer treatment optimization Prostate cancer radiation therapy Patient immobilization systems Dose calculation methodologies His work addresses practical challenges in clinical radiation oncology, particularly in translating technical innovations into improved patient care. Analysis of Dr. Ostapiak's publication history reveals consistent contributions to radiation therapy quality assurance, with particular emphasis on practical clinical applications. His recent work demonstrates growing interest in optical surface monitoring systems and advanced treatment delivery techniques like volumetric modulated arc therapy. A notable pattern across his research is the focus on solving specific clinical challenges in head and neck and prostate cancer treatments through physics-based solutions. Dr. Ostapiak has been instrumental in national radiation therapy quality assurance initiatives, including Canada's 13-year clinical trial radiation therapy quality assurance program. His scholarly impact is reflected in numerous publications in high-impact journals including Medical Physics, Journal of Thoracic Oncology, and Radiotherapy and Oncology, with his work referenced in multiple patents. As an educator, Dr. Ostapiak has maintained consistent teaching responsibilities across multiple academic years, instructing both foundational and advanced courses in medical radiation sciences. His teaching portfolio demonstrates comprehensive expertise across the radiation therapy physics curriculum, from instrumentation principles to clinical applications and research methodology. Dr. Ostapiak's research laboratory focuses on applied medical physics problems in radiation oncology, with particular attention to treatment delivery systems and quality assurance protocols. His work involves close collaboration with clinical partners to address real-world challenges in radiation therapy departments, with current projects emphasizing optimization of treatment techniques and improvement of patient positioning accuracy.
Kathryn Moler serves as Vice President of SLAC National Accelerator Laboratory and holds the Marvin Chodorow Professorship while being a Professor of Applied Physics, Physics, and Energy Science & Engineering at Stanford University. She has extensive administrative experience including serving as Vice Provost and Dean of Research (2018-2023), Transition Dean of the Doerr School of Sustainability (2022), and Senior Associate Dean for Natural Sciences in the Humanities and Sciences Deans Office (2016-2018). Professor Moler's research focuses on superconductivity, quantum materials, and mesoscopic physics, with particular expertise in magnetic imaging techniques. Her laboratory develops advanced tools for measuring magnetic properties of quantum materials at micron length-scales. She specializes in scanning SQUID (Superconducting Quantum Interference Device) susceptometry, which enables imaging of local magnetic fields and susceptibilities with sub-micron spatial resolution. Her work spans fundamental materials physics, exotic Josephson effects, and superconducting device characterization. Analysis of her recent publications reveals a strong focus on unconventional superconductors including UTe 2 , URu 2 Si 2 , and iron-based superconductors. Her work frequently examines vortex dynamics, superfluid density, and magnetic properties at the nanoscale. A significant portion of her research investigates fractional vortices and non-universal flux quantization in multiband superconductors, which could have implications for fluxonics-based computing. Sapp Family University Fellow in Undergraduate Education, Stanford University (2014-) Richtmyer Award for "Outstanding Leadership in Physics Education", American Association of Physics Teachers (2011) APS Fellow, American Physical Society (2008-) Packard Fellow, Packard Foundation (2001-2006) Presidential Early Career Award for Scientists and Engineers (2000) CAREER Award, National Science Foundation (1999-2003) Alfred P. Sloan Research Fellow (1999-2001) Professor Moler actively mentors graduate students and postdocs, currently serving as Doctoral Dissertation Reader for Praveen Sriram, Postdoctoral Faculty Sponsor for Nabhanila Nandi, and Doctoral Dissertation Co-Advisor for Alexander Kiral. She leads the Moler Group, a mesoscopic magnetic imaging laboratory that develops new measurement techniques for quantum materials. Her lab maintains the Stanford Nano Shared Facilities and was previously the Center for Probing the Nanoscale, an NSF Nanoscale Science and Engineering Center. The group also develops open-source software tools like SuperScreen for simulating magnetic responses in superconducting devices.
Peter W. Glynn is the Thomas W. Ford Professor in the Department of Management Science and Engineering (MS&E) at Stanford University, with a courtesy appointment in Electrical Engineering. He earned his B.Sc. (Honours) in Mathematics from Carleton University (1978) and his Ph.D. in Operations Research from Stanford University (1982). He has held leadership roles, including Deputy Chair of MS&E (1999–2005), Director of the Institute for Computational and Mathematical Engineering (2006–2010), and Chair of MS&E (2011–2015). His research focuses on computational algorithms, statistical methodology, and optimization for systems with uncertainty, with applications in finance, healthcare, logistics, and energy systems. Notable contributions include advancements in Monte Carlo simulation, queueing theory, and stochastic modeling. He is a Fellow of INFORMS and the Institute of Mathematical Statistics, and has received prestigious awards such as the John von Neumann Theory Prize (2010) and election to the National Academy of Engineering (2012). Key projects include the DICE model for predicting hospital bed demand during the COVID-19 pandemic and foundational work in energy systems optimization. Glynn has advised numerous doctoral students, many of whom have contributed to fields like stochastic processes, machine learning, and operations research. His work often bridges theory and application, addressing real-world challenges through rigorous mathematical frameworks.
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