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Hans-Georg MuellerView profile

Professor

• Functional Data Analysis
• Metric Statistics
• Fréchet Regression
• +8 more

Hans-Georg Mueller is a Professor in the Department of Statistics at the University of California, Davis. His research spans multiple domains of modern statistical methodology, with groundbreaking contributions to functional data analysis, metric statistics, and nonparametric inference for random objects. Key research areas include Fréchet regression, distributional data analysis, network regression, and optimal transport Applications span longitudinal growth studies, brain development, aging and longevity, plant genomics Research Interests : He has pioneered methods for analyzing complex data structures such as functional data, manifold-valued data, and random objects. His work on the PACE approach for longitudinal data has become foundational in the field. Recent Publications demonstrate strong trends in Fréchet analysis, metric statistics, and distributional data modeling, with applications in both biomedical and environmental domains. Books and Edited Works : Author of the foundational monograph Nonparametric Regression Analysis for Longitudinal Data (1988), and co-editor of influential volumes including Change-point Problems (1994) and Mathematical Modeling in Experimental Nutrition (1998).

Nyteisha BookertView profile

Assistant Professor

• Cybersecurity
• Internet of Things
• Privacy
• +3 more

Dr. Nyteisha Bookert serves as an Assistant Professor of Cybersecurity in the Department of Computer Science & Mathematics at South Carolina State University, where she advances research in emerging technology security. Her academic foundation includes a Ph.D. from North Carolina A&T State University and undergraduate training at South Carolina State University. Education B.S. in Computer Science, South Carolina State University M.S. in Computer Science, North Carolina A&T State University Ph.D. in Computer Science, North Carolina A&T State University Research Focus Her work targets critical vulnerabilities in Internet of Things ecosystems, particularly medical devices (IoMT), employing machine learning to dissect privacy policies and mitigate consumer risks. This research bridges theoretical security frameworks with real-world applications in connected healthcare technologies. Professional Engagement Dr. Bookert contributes to national cybersecurity through prior government agency collaborations while leading community initiatives that introduce underrepresented K-12 students to cybersecurity fundamentals, building pipeline diversity in STEM fields.

James C. GeeView profile

Professor

• Biomedical Image Analysis
• Medical Imaging Computing
• Image Segmentation
• +5 more

James C. Gee is a Professor of Radiologic Science in Radiology at the University of Pennsylvania's Perelman School of Medicine. He serves as Director of the Penn Image Computing and Science Laboratory and Co-Director of the Translational Biomedical Imaging Center , with affiliations in Bioengineering and Applied Mathematics graduate groups. His research focuses on biomedical image analysis, specialization in segmentation, registration, and morphometry applied to neurodegenerative diseases and multi-organ systems. Education : B.S. in Computer Science/Electrical Engineering (University of Washington, 1987), Ph.D. in Computer and Information Science (University of Pennsylvania, 1996) Research : Quantitative medical imaging methods, brain connectomics, neurodegeneration mapping, and translational imaging technologies Publications : 15+ recent works on AI-driven image analysis for Alzheimer's disease, cardiac amyloidosis, and radiomics applications Leadership : Directs MSE-DS Online Degree Program, co-chairs Radiology DCOAP Committee, and founded RISE (Radiology Initiative to Support Inclusive Excellence) His laboratory develops advanced computational tools like ITK-SNAP for biomedical imaging, with applications in both in vivo clinical imaging and ex vivo histology . The work spans cross-disciplinary collaborations in computer science, neuroscience, and clinical medicine.

• Geotechnical Engineering
• Disaster Response and Mitigation
• Digital Data Collection Systems
• +4 more

Dr. J. David Frost is the Elizabeth and Bill Higginbotham Professor of Civil Engineering at Georgia Institute of Technology and a Regents' Entrepreneur. He has held academic positions at Purdue University and Georgia Tech, with a focus on geotechnical engineering and disaster response. As founding director of Georgia Tech's Savannah campus and head of the Geosystems Engineering Group, Frost has shaped academic programs and research initiatives. Education: B.A.I and B.A. in Civil Engineering and Mathematics, Trinity College, Dublin (1980) M.S. and Ph.D. in Civil Engineering, Purdue University (1986, 1989) Research Interests span geotechnical engineering, bio-inspired design, and disaster resilience. His work emphasizes digital data collection systems for subsurface hazard assessment, soil-polymeric material interactions, and geotechnical responses to earthquakes, hurricanes, and anthropogenic disasters. Recent projects integrate ant nest geometry, plant root mechanics, and geosynthetic innovations into infrastructure solutions. Scientific Contributions include two U.S. patents for subsurface data systems, leadership in NSF-funded post-disaster reconnaissance missions (e.g., 9/11, Türkiye earthquakes), and co-founding the Geotechnical Extreme Events Reconnaissance (GEER) Association. His articles reflect expertise in bio-inspired geotechnics, machine learning for disaster modeling, and advanced computational simulations. Awards & Recognition: ASCE Huber Civil Engineering Research Prize NSF National Young Investigator Award Georgia Society of Professional Engineers Engineer of the Year in Education Coastal Business & Education Technology Alliance Leadership Innovation Award Professional Engagement includes chairing the Savannah Area GIS board, advising on ASCE Geo-Legislative Committees, and founding a software company serving 350+ global clients. His work bridges academia, policy, and industry innovation.

Kiwon UmView profile

Assistant Professor

• Physics-based Simulations
• Deep Learning
• Perceptual Evaluation of Simulations
• +3 more

Kiwon Um is a tenured Assistant Professor in the Computer Graphics group at Télécom Paris, France, since October 2019. He focuses on physics-based simulations and data-driven approaches using deep learning, with an emphasis on human visual perception in computer graphics and engineering applications. Education: Ph.D. in Computer Science and Engineering from Korea University His research explores effective simulation of natural phenomena through refined data utilization and develops reliable data acquisition methods for machine learning. He also investigates perceptual evaluation of simulations to advance numerical method understanding. Recent work trends include: (1) turbulence modeling with machine learning integration, (2) elastic material simulation stability, (3) fluid dynamics optimization, and (4) differentiable physics frameworks. His publications range from 2008 to 2025, covering topics like SPH solvers, porous shell simulations, and numerical method validation.

• Magnetic Resonance Imaging (MRI)
• Functional MRI (fMRI)
• Biomedical Imaging Physics
• +6 more

Dr. Gary Glover is a Professor of Radiology (Radiological Sciences Lab) at Stanford University , with courtesy appointments in Psychology and Electrical Engineering. His work focuses on the physics and mathematics of MRI, particularly rapid scanning methods using spiral k-space trajectories for functional brain imaging and multimodal neuroimaging (fMRI/EEG/fPET/fNIRS) combined with neuromodulation techniques like TMS and transcranial ultrasound. Academic Appointments: Radiology, Psychology, Electrical Engineering Professional Affiliations: Bio-X, Stanford Cancer Institute, Wu Tsai Neurosciences Institute Research Interests include: Development of blood oxygen level-dependent (BOLD) and viscoelastic contrast in MRI Functional MR Elastography for brain activation mapping Optimization of MR-ARFI for transcranial ultrasound guidance Automated spinal cord segmentation (EPISeg) using machine learning Scientific Awards : National Academy of Engineering (2013) Gold Medal, ISMRM (2000) Steinmetz Award, General Electric (1985) Lauterbur Lecture, ISMRM (2018) Recent Publications analyze: Fast fMRI sampling and spurious signal correction Dissociated patterns in default mode network anti-correlations Neural correlates of collaborative behavior in triadic fMRI Salience network contributions to depression pathophysiology

Xiaoning QianView profile

Professor

• machine learning
• Bayesian methods
• optimal experimental design
• +5 more

Xiaoning Qian is a Professor in the Department of Electrical and Computer Engineering at Texas A&M University, where he also serves on the Faculty Advisory Committee for the Texas A&M Institute of Data Science (TAMIDS) and the Executive Committee for the Texas A&M TRIPODS Research Institute for Foundations of Interdisciplinary Data Science (FIDS). He holds a joint appointment in the Applied Math group within the Computational Science Initiative at Brookhaven National Laboratory (BNL). Previously, he was an Associate Professor (2018-2022) and Assistant Professor (2013-2018) at Texas A&M, and an Assistant Professor in the Department of Computer Science and Engineering at the University of South Florida (2009-2013). Dr. Qian received his B.S.E. and M.S.E. degrees from Shanghai Jiaotong University, China, and his M.Ph. and Ph.D. degrees in Electrical Engineering from Yale University. Dr. Qian's research focuses on developing mathematical models and computational algorithms in signal processing, machine learning, and Bayesian methods, particularly in learning, uncertainty quantification, and experimental design. His work spans multiple disciplines, with applications in life sciences and materials science. His research group, the Biomedical Imaging, Sensing, and Genomic Signal Processing Group, actively applies probabilistic models and optimization algorithms to solve complex problems in interdisciplinary domains. His research has evolved from foundational work in bioinformatics and biomedical image processing to more recent applications in materials science and broader AI for science initiatives. Dr. Qian has received numerous scientific awards and recognitions including: National Science Foundation (NSF) CAREER Award Segers Family Dean's Excellence Professorship II in the College of Engineering TEES (Texas A&M Engineering Experiment Station) Senior Faculty Fellow Montague-Center for Teaching Excellence Scholar J. T. Oden Faculty Fellow at the University of Texas, Austin Finalist of the 2023 INFORMS QSR Best Paper Faculty Impact Fellow from the Department of Electrical & Computer Engineering As an advisor , Dr. Qian has mentored numerous graduate students through their PhD and MS programs, with many of his alumni securing positions at prestigious institutions and companies including NIH/NCBI, Microsoft, Baidu Research Lab, and Qualcomm. His research has been supported by multiple grants, including an NSF CAREER award and collaborative research funding from the Information Integration and Informatics program. He is actively recruiting postdoc and graduate student research assistants for projects in machine learning and optimization methods with applications in bioinformatics and materials science. Dr. Qian is involved with several research initiatives including the Objective-Based Uncertainty Quantification (ObjectiveUQ) project, which provides a mathematical framework for integrating prior knowledge and data while enabling effective operational and experimental design under uncertainty. He also co-organizes the Bio-Seminar series for the Biomedical Imaging, Sensing & Genomic Signal Processing group at Texas A&M.

Matthew J. GrahamView profile

Research Professor

• Astronomy
• Astrophysics
• Time-domain Astronomy
• +5 more

Matthew J. Graham is a Research Professor of Astronomy at the California Institute of Technology (Caltech), serving as the Project Scientist for the Zwicky Transient Facility (ZTF). His work bridges astronomy, machine learning, and data science, focusing on time-domain sky surveys that produce hundreds of thousands of public transient alerts per night. Previously, he has worked on the Catalina Real-time Transient Survey (CRTS), NOAO DataLab, Virtual Observatory, and Palomar-Quest Digital Sky Survey. Dr. Graham's primary research interests involve applying machine learning and advanced statistical methodologies to astrophysical problems, particularly the variability of quasars and other stochastic time series. His work addresses the unprecedented data volumes generated by 21st-century astronomy while expanding our ability to work with complex information systems beyond simple correlations. His current projects include real-time low latency inferencing via the NSF-funded A3D3 Institute, reinforcement learning for optimizing astrophysical follow-up campaigns, neural differential models for supermassive black hole variability, and functional analysis of multivariate time series. Analysis of Graham's recent publications reveals a strong focus on time-domain astronomy, particularly leveraging the capabilities of the Zwicky Transient Facility. His work spans multiple areas including gravitational wave counterpart identification, active galactic nuclei variability, supernova characterization, and machine learning applications for transient detection. A notable trend is the integration of artificial intelligence techniques to handle the massive data streams from modern sky surveys, enabling real-time analysis and decision-making that would be impossible with traditional methods. Dr. Graham has been instrumental in developing infrastructure for time-domain astronomy, including the alert distribution system for ZTF and data processing pipelines for handling massive transient datasets. His work on the Catalina Real-time Transient Survey established important methodologies for identifying variable and transient sources that continue to influence the field. As Project Scientist for ZTF, Graham leads a major international collaboration involving Caltech, IPAC, and numerous partner institutions worldwide. The facility represents a significant advancement in time-domain astronomy, providing unprecedented coverage of the dynamic sky and enabling discoveries across multiple areas of astrophysics.

Ali YaparView profile

Professor

• Electromagnetics
• Microwave Engineering
• Antenna Technologies
• +3 more

Professor Ali Yapar is a faculty member at Istanbul Technical University in the Electronics and Communication Engineering department. His research focuses on Electromagnetics , Microwave Engineering , and Antenna Technologies , with a particular emphasis on inverse scattering problems and microwave imaging for biomedical applications. He has supervised numerous graduate students and led projects related to breast cancer treatment and rough surface imaging. PhD in Electronics and Communication Engineering from Istanbul Technical University (1997) MSc in Electronics and Communication Engineering (1995) His recent publications analyze advanced techniques for microwave hyperthermia systems, reverse time migration methods, and Newton-based solutions for electromagnetic inverse scattering. Key projects include TUBITAK-funded initiatives on microwave tomography and brain stroke imaging. He serves as a project investigator and executive for electromagnetic research programs. Research areas span Electromagnetic Wave Propagation , Green's Function Applications , and Dielectric Material Analysis . Collaborations include IEEE members and international researchers in computational electromagnetics.

Hamid NazaripouyaView profile

Assistant Professor

• Cyber-physical power systems
• Power electronics
• Automation, sustainability, and resilience of power systems
• +9 more

Hamid Nazaripouya is an Assistant Professor at the School of Electrical and Computer Engineering, Oklahoma State University. His research focuses on modernizing power systems through interdisciplinary approaches combining control theory, power electronics, and AI for smart grid applications. Ph.D., Smart Grids, University of California, Los Angeles (2017) M.S., Manufacturing and Design, University of California, Los Angeles (2015) M.S., Power Systems, Louisiana State University (2013) M.S., Power Electronics, Sharif University of Technology (2010) B.S., Electrical and Computer Engineering, University of Tehran (2007) His research expertise spans cyber-physical power systems , power electronics , and smart grid optimization . Specific interests include mathematical modeling, control systems, and AI-driven solutions for grid resilience, renewable energy integration, and EV infrastructure. Hamid leads the Power Grid Modernization Lab (PGML) , focusing on projects like power system resilience, situational awareness, and power electronics control. He currently seeks PhD candidates with strong backgrounds in power systems, data science, and control theory. Scientific Awards: Distinguished Fellow of Electrical and Computer Engineering His lab offers funded PhD positions starting Spring 2025, emphasizing hands-on research and multidisciplinary collaboration.

Alejandro VillaverdeView profile

Research Professor

• Systems Biology
• Control Theory
• Mathematical Modeling
• +5 more

Alejandro F. Villaverde is a Ramón y Cajal research fellow in the Department of Systems & Control Engineering at the School of Industrial Engineering, University of Vigo, Spain. He also serves as a Research fellow at CITMAga since 2022. Previously, he worked as a postdoctoral researcher at IIM-CSIC from 2016-2020. His research focuses on the modeling of dynamical systems with particular emphasis on biological applications. Villaverde earned his PhD in Systems and Control Engineering from University of Vigo between 2005 and 2009. His academic career has centered at Spanish institutions with a strong interdisciplinary approach bridging engineering, mathematics, and biology. His primary research interests include systems biology, control theory, and mathematical modeling, with specialized expertise in structural identifiability, observability analysis, and computational tools for dynamic modeling of biological systems. Villaverde's work addresses fundamental challenges in building reliable mathematical models of complex biological processes, with applications spanning immunology to microbial communities. His theoretical contributions have practical implications for improving model reliability and predictive power in biological research. Villaverde has published extensively in top journals including PLOS Computational Biology, Bioinformatics, and IEEE/ACM Transactions on Computational Biology. His recent publications (2023-2025) reveal a consistent research trajectory focused on developing theoretical frameworks for biological model analysis, creating practical software tools, and applying these methods to cutting-edge problems. His work shows particular strength in identifying and addressing fundamental limitations in modeling approaches, especially regarding parameter identifiability and model observability constraints. Among the top 2% Scientists Worldwide 2024 (Stanford University list) Recognition as one of the EEI's top valued instructors at University of Vigo's School of Industrial Engineering Villaverde leads multiple significant research projects including DYNAMO-bio (funded by Ministry of Science, Innovation and Universities), SICOMORO (focusing on symmetries in biological communities), and PREDYCTBIO. His group actively develops open-source software tools such as STRIKE-GOLDD for structural identifiability and observability analysis. The laboratory, part of the BICO research group, includes several researchers and students working on various aspects of dynamic modeling in biology, with recent additions including Mahmoud Shams Falavarjani, Adriana González Vázquez, and multiple interns working on specialized projects.

• Applied Mathematics
• Financial Mathematics
• Numerical and Computational Mathematics
• +3 more

Xiaoping Lu is an Associate Professor at the School of Mathematics and Applied Statistics, University of Wollongong, Australia. She has served as Academic Program Director for the Bachelor of Mathematics (Advanced) program since 2008 and holds an ORCID identifier (0000-0003-1090-8437). Her research focuses on applied mathematics and financial mathematics, particularly in option pricing, stochastic volatility models, and computational finance. Research Themes: Transaction cost modeling, regime-switching financial markets, numerical methods for PDEs, utility-indifference valuation, and stochastic optimization algorithms. Awards: 2024 AustMS-WIMSIG Anne Penfold Street Award 2024 Cheryl E. Praeger Travel Award Leadership: President of the Asia Pacific Consortium of Mathematics for Industry (APCMfI) since 2024; leadership roles in ANZIAM and WIMSIG committees. Teaching: Coordinated courses like MATH142, MATH141, and MATH283; currently available for PhD supervision in topics including financial derivatives and stochastic liquidity risk. Funding: Contributed to grants like 'The AI Tutor' (2024) and industry partnerships for advanced mathematics education.

Qin LiView profile

Associate Professor

• Numerical Analysis
• Scientific Computing
• Inverse Problems
• +8 more

Qin Li is an Associate Professor in the Mathematics Department at the University of Wisconsin-Madison. She holds affiliations with the Wisconsin Institutes for Discovery and serves as a senior PI at the Institute for Foundations of Data Science. Her research focuses on numerical analysis, scientific computing, and inverse problems, with a strong emphasis on kinetic theory and multiscale PDEs. Her work spans computational methods for inverse transport and radiative transfer equations, Bayesian approaches in optical tomography, and optimization techniques for solving stochastic and deterministic PDEs. Recent publications highlight applications of diffusion models, Wasserstein gradient flow, and random sampling in inverse problems, as well as control theory for Vlasov-Poisson systems and reconstruction of chemotaxis kernels. She leads a research group within the Mathematics Department and has received funding from the National Science Foundation (NSF), the Office of Naval Research (ONR), and the Wisconsin Alumni Research Foundation (WARF). Her lab, Kinetic At Madison, explores nonlinear hyperbolic PDEs and their applications. She also contributes to teaching as a TA Supervisor.

Chris FullerView profile

Professor

• Acoustics
• Materials Design
• Medical Devices
• +5 more

Chris Fuller, Ph.D., is the Samuel Langley Distinguished Professor of Engineering at the College of Engineering , Virginia Tech. He leads the Vibrations and Acoustics Laboratory (VAL) , focusing on active/passive noise control systems, metamaterials, and their application to aerospace, medical devices, and industrial machinery. Education: Ph.D. (1979) and B.E. (1974) from the University of Adelaide, Australia. Research Interests: Structural acoustics, adaptive materials, machine learning in noise prediction, and biomedical acoustics (e.g., neonatal incubators). Awards: ASME Rayleigh Award (2017), NASA Team Achievement Award (1996), and Fellow of the Acoustical Society of America. Recent Publications: Highlight advancements in drone noise reduction using neural networks, metamaterials for HVAC systems, and poro-elastic materials for low-frequency noise control.

• Automated Transportation Systems
• Connected Vehicles
• Electric Vehicles
• +3 more

Dr. Xiaopeng Li is the Harvey D. Spangler Professor in the Department of Civil and Environmental Engineering at the University of Wisconsin-Madison, with an affiliation in the Department of Electrical and Computer Engineering. He leads the USDOT Rural Autonomous Vehicle Program and previously directed the National Institute for Congestion Reduction. He earned his B.S. in Civil Engineering from Tsinghua University (2006), M.S. in Civil Engineering (2007), M.S. in Applied Mathematics (2010), and Ph.D. in Civil Engineering (2011) from the University of Illinois at Urbana-Champaign. His research focuses on modeling and field experiments for connected, electric, and automated vehicles (CAVs), infrastructure systems analysis, and interdependent network modeling. He has pioneered physics-enhanced machine learning frameworks for vehicle control and developed simulation tools for CAV deployment. His 2025-2024 publications highlight advancements in Connected vehicle trajectory modeling Energy consumption optimization Edge computing for autonomous operations Residual learning control systems Equity analysis in AV deployment Communication technologies for V2X Awards include: TRB Best Paper Award (2025) NSF CAREER (2015) ASCE Fellow (2024) IEEE Senior Member (2022) Multiple institution-specific fellowships He has advised 15+ graduate students, secured $35M+ in grants from NSF, USDOT, and industry partners, and chairs the IEEE ITSS Emerging Transportation Technology Testing committee. His work addresses real-world AV implementation, safety validation, and sustainable transportation systems.