Trisha Van Zandt is a tenured Professor in the Department of Psychology and holds a courtesy appointment in the Department of Statistics at The Ohio State University. Her research integrates cognitive psychology with advanced computational methodologies. Primary affiliation: Department of Psychology, College of Arts and Sciences Courtesy appointment: Department of Statistics Research Focus: Specializing in computational modeling of cognitive processes, her work addresses: Bayesian analysis of chronometric data Signal detection and response time modeling Cognitive mechanisms in ADHD and decision-making Computational neuroscience approaches to attention Mathematical psychology foundations Publication Trends: Over 15 years of research demonstrates expertise in: Bayesian hierarchical modeling Cognitive architecture and decision-making Neurocomputational methods Attention and response inhibition Methodological Contributions: Includes innovations in: Approximate Bayesian computation Hidden Markov modeling Chronometric data analysis Perceptual conflict tasks
John Hughes is an Associate Professor and Chair of the Department of Biostatistics and Health Data Science at Lehigh University's College of Health. He holds a PhD in Statistics from Penn State University and has over 29 years of experience in academia, with previous appointments at institutions like the University of Minnesota and the University of Colorado. His research focuses on methodological advancements in statistical modeling for dependent data, Bayesian methods, and statistical computing. His interdisciplinary work spans environmental health, bioimaging, magnetic resonance safety, and vaccine hesitancy. He has developed numerous software packages for R and Perl, including copCAR and batchmeans. Education: PhD in Statistics, Penn State University MS in Statistics, Penn State University MS in Applied Computer Science, Frostburg State University BS in Mathematics and Computer Science, Frostburg State University Teaching: Courses include Advanced R Programming, Biostatistics, Population Health Data Science, and Computational methods. His research interests emphasize spatial and spatiotemporal data analysis, with applications to public health and medical imaging. He has consulted for organizations such as the Minnesota Center for Chemical and Mental Health and Temple University. His software contributions include packages like krippendorffsalpha for agreement measurement and copCAR for spatial regression modeling. Recent work focuses on improving statistical inference methods, analyzing vaccination refusal patterns, and developing frameworks for copula-based agreement coefficients. His articles highlight innovations in Bayesian computation, spatial epidemiology, and nonparametric statistics. Dr. Hughes leads academic initiatives in biostatistics and health data science, fostering interdisciplinary collaborations and advancing statistical methodologies for real-world applications.
Stefan M. Wild serves as Director of the Applied Mathematics and Computational Research (AMCR) Division and Senior Scientist at Lawrence Berkeley National Laboratory, while holding an adjunct faculty position in the Industrial Engineering and Management Sciences (IEMS) department at Northwestern University's McCormick School of Engineering. He also serves as a Senior Fellow at NAISE (Northwestern Initiative for AI and Society). Dr. Wild earned his Ph.D. and M.S. in Operations Research from Cornell University (2009, 2007) and his M.S. and B.S. in Applied Mathematics from the University of Colorado, Boulder (2003, 2002). His academic journey includes an Argonne Director's Postdoctoral Fellowship (2008-2010) and the DOE Computational Science Graduate Fellowship (2005-2008). His research program focuses on developing numerical optimization and automated learning algorithms for challenging science and engineering problems at interfaces involving computer simulations, complex data, and physical experiments. Dr. Wild leads multiple community software projects including BAND, parMOO, libEnsemble, deepHyper, NUCLEI, POptUS, and surmise, with applications spanning nuclear physics, materials science, and astrophysics. His work bridges derivative-free optimization, uncertainty quantification, high-performance computing, and scientific machine learning. Dr. Wild has advised numerous postdocs who have established successful careers at national laboratories and academic institutions. His editorial responsibilities include Mathematical Programming Computation, INFORMS Journal on Computing, Data Science in Science, and SIAM Review. U.S. Department of Energy Early Career Research Award (2020) STS Forum Future Leader (2018) IDC HPC Innovation Excellence Award (2015) SIAM SIGEST Award (2014) Strategic Laboratory Leadership Program (UChicago Booth School) (2013) DOE Computational Science Graduate Fellowship (2005-2008) As AMCR Division Director, he leads a diverse team of applied mathematicians, computational scientists, and software engineers addressing some of the world's most challenging computational problems across scientific and engineering disciplines. His leadership emphasizes both technical excellence and commitment to inclusion, diversity, equity, and accountability in scientific research.
Professor Pierre Pinson is the Chair of Data-centric Design Engineering at the Dyson School of Design Engineering, Imperial College London (UK). He also serves as a Chief Scientist at Halfspace (Denmark), Editor-in-Chief of the International Journal of Forecasting, and holds affiliated roles at Technical University of Denmark and Aarhus University. His research focuses on generating societal value from data through interdisciplinary approaches combining mathematics/statistics with applications in energy, logistics, and business analytics. Notable contributions include probabilistic energy forecasting, market design innovations, and optimization under uncertainty. Research interests span forecasting methodologies, stochastic optimization, game theory (with energy applications), and data markets. He has held visiting roles at institutions like the University of Oxford and the European Centre for Medium-Range Weather Forecasts. Awards include the prestigious INFORMS Edelman Award (2024) and IEEE Fellow status. Prof. Pinson actively advises PhD candidates with strong applied mathematics/programming backgrounds. His work bridges academia and industry, addressing challenges in renewable energy integration, grid management, and decentralized market mechanisms. Collaborative projects include the Smart4RES initiative for advanced renewable forecasting. Awards: INFORMS Edelman Award (2024), IEEE Fellow, Simons Fellowship (2021) Labs/Teams: Dyson School Research Group, Halfspace R&D Team, CoRE (Aarhus University) Grants: Imperial College President's PhD Scholarship, EU Smart4RES Project
Prof. Dr. Ullrich Köthe is an Associate Professor and group leader in the Visual Learning Lab Heidelberg at the University of Heidelberg . He focuses on Explainable Machine Learning , leveraging Invertible Neural Networks to enhance transparency and utility in image analysis and medical applications . He also maintains the widely used VIGRA image analysis library. Education: PhD in Informatics, University of Hamburg, 2000 Habilitation in Informatics, University of Hamburg, 2008 His research interests center around machine learning , image analysis , and scientific computing , particularly the development of robust algorithms for medical imaging , computer vision , and life sciences . His work on invertible neural networks and parameter-free segmentation has led to significant advancements in the field. Recent publications highlight his contributions to Bayesian inference , neural network interpretability , and stochastic modeling , with applications ranging from disease outbreak dynamics to connectomics . Key trends include generative models , parameter-free segmentation , and likelihood-free inference . Scientific Awards: DAGM 2003 Main Prize DAGM Best Paper Award 2008 He has supervised numerous Master and Bachelor theses in machine learning and image analysis , with teaching roles in Advanced Machine Learning and Explainable AI . His collaborative research grants include funding from HARMAN International (2024). He leads the Explainable Machine Learning subgroup and has contributed to open-source software projects like ilastik and VIGRA , which are critical tools in bioimage analysis .
Solesne Bourguin is an Associate Professor in the Department of Mathematics and Statistics at Boston University. She is a member of the Probability and Statistics research group. Her research focuses on advanced topics in stochastic analysis, including stochastic dynamical systems, fractional Brownian motion, Malliavin calculus, Stein's method, and free probability theory. She teaches graduate and undergraduate courses such as Probability Theory II and Linear Algebra. Her research interests emphasize the theoretical foundations of stochastic processes, with applications to nonlinear statistics and random matrices. Recent work explores quantitative analysis of stochastic iterative algorithms and Gaussian approximations in high-dimensional settings. She has contributed to understanding fluctuation dynamics in multiscale systems driven by fractional Brownian motion and has published in leading journals like the Electronic Journal of Probability and Stochastic Processes and their Applications. Bourguin's recent publications highlight her expertise in topics like functional Gaussian approximations, moderate deviation principles, and spherical Poisson waves. These studies often combine techniques from Malliavin calculus and Stein's method to derive precise probabilistic estimates. She also investigates the limiting behavior of correlated Wishart matrices in high-dimensional regimes, contributing to random matrix theory. Scientific awards and grants are not explicitly listed in the provided information. She actively participates in academic activities, including organizing Boston University's Statistics and Probability Seminar Series. Her teaching reflects her commitment to both foundational and advanced mathematical education, spanning topics from linear algebra to stochastic processes.
Lulu Kang is an Associate Professor in the Department of Mathematics and Statistics at the University of Massachusetts Amherst. She previously held positions at Illinois Institute of Technology (Associate Professor, 2016–2023; Assistant Professor, 2010–2016). Her research focuses on statistical design of experiments, machine learning, uncertainty quantification, Bayesian statistics, and optimization, with applications in engineering, healthcare, and biophysics. Education: Ph.D. and M.S. in Industrial Engineering/Operations Research from Georgia Institute of Technology (2005–2010), and B.S. in Mathematics from Nanjing University, China. Research interests include optimal experimental design, Gaussian process modeling, variational inference, and interdisciplinary applications. She co-leads the 2025 Uncertainty Quantification and AI for Complex Systems program at the Institute for Mathematical and Statistical Innovation. Key awards: SPAIG Award (ASA, 2020), NSF grants totaling over $400k, and recognition for early-career contributions. Active in editorial roles for Technometrics and SIAM/ASA Journal on Uncertainty Quantification . Grants include NSF DMS awards on energetic variational inference and design of experiments, and interdisciplinary projects on antibiotic resistance forecasting and material science. Her GitHub repository hosts code for Bayesian experimental design, reflecting her commitment to open science and practical applications of statistical methods.
Michael Mahoney is a Professor in the Department of Statistics at the University of California, Berkeley. He holds roles as Vice President and Director of the Big Data Group at the International Computer Science Institute (ICSI), Group Lead for the Machine Learning and Analytics Group at Lawrence Berkeley National Laboratory (LBNL), and a core member of the RISELab within the Department of Electrical Engineering and Computer Sciences (EECS). He is also an Amazon Scholar. His research focuses on the applied mathematics of data, including randomized numerical linear algebra (RandNLA), optimization, and their applications in machine learning, climate science, genetics, and other domains. He teaches courses such as Linear Algebra for Data Science and has led initiatives like the FODA (Foundations of Data Analysis) Institute under the NSF TRIPODS program. His work emphasizes scalable algorithms, including contributions to RandBLAS/RandLAPACK frameworks and tools like SuperBench and SqueezeLLM. Mahoney collaborates with industry and academia, advancing methods for scientific machine learning and efficient AI infrastructure. Roles: Professor (UC Berkeley), Vice President (ICSI), Group Lead (LBNL), RISELab Member Education: Ph.D. in Computer Science (Yahoo! Research and Stanford University background) Research interests include algorithmic and statistical foundations of big data, with applications in internet analysis, climate modeling, and genomics. His work bridges theory and practice, developing scalable tools for high-dimensional data analysis. He advises numerous students and postdocs, contributing to projects like RandNLA, neural scaling laws, and physics-informed learning. His labs and teams focus on foundational methods for scientific machine learning and efficient AI systems.
Zhaozhi Fan is a Professor of Statistics at Memorial University, where he has been affiliated since 2005. He holds a PhD from Georg-August University of Göttingen, Germany (2001). Prior to this, he served as a visiting professor at Case Western Reserve University and the University of New Hampshire. His research focuses on both mathematical and applied statistics, with key interests in statistical inference of heavy tailed distributions, measurement error models in survival analysis, longitudinal categorical data analysis, and free probability theory. Current work emphasizes quantile regression with measurement errors. No specific grants, advising records, or affiliated laboratories are detailed in the provided information.
Vinod Nigade is a Visiting Professor in the Department of Computer Science at Vrije Universiteit Amsterdam (VU). His research focuses on edge computing, deep learning systems, and network security. He holds a PhD in Computer Science from VU, defended in 2023 with his thesis Latency-Critical Inference Serving for Deep Learning . Key research areas include: Dynamic edge networks and latency-optimized inference systems IoT communication protocols and battery-free device synchronization Neural intrusion detection in programmable networks Distributed deep learning architectures Collaborations span academia-industry projects involving real-time systems, network programmability, and cybersecurity. His work addresses challenges in timely video analytics, service-level objectives in edge computing, and scalable exploit detection in distributed environments. Publications emphasize practical solutions for latency-critical applications, with contributions to ACM, IEEE, and other top venues. Current research trends include accelerating IoT discovery protocols and enhancing network security through AI-driven systems.
Dr. Yi Wu is an Assistant Professor at the School of Computer Science within the Gallogly College of Engineering at the University of Oklahoma. His research focuses on mobile sensing, wearable computing, cybersecurity, and smart healthcare applications, leveraging machine learning and signal processing. He holds a Ph.D. in Computer Science from the University of Tennessee, Knoxville, and has conducted postdoctoral research at Emory University, alongside industry internships at Snap Inc. and Truveta. Education: Ph.D., Computer Science, University of Tennessee, Knoxville M.S., Computer Engineering, Rutgers University B.S., Automation and Engineering, University of Electronic Science and Technology of China Research Interests: Dr. Wu specializes in mobile sensing technologies for health monitoring, wearable device security, and adversarial attacks on IoT systems. His work bridges hardware innovation with software engineering, emphasizing real-world applications like cycling fitness tracking (SmarCyPad) and AR/VR security (Face-Mic). Key Themes: Human-computer interaction, privacy-preserving biomedical systems, and embedded sensor networks. Publications Trends: His recent work spans cybersecurity vulnerabilities in AR/VR systems, lightweight biosensor technologies, and astrophysical studies of rotating stellar systems. The latter appears to represent a secondary research focus or collaborative area, with publications extending to 2023 despite primary CS affiliation. Awards: None explicitly listed. Grants/Advising: No details provided in available texts. Labs/Teams: No specific lab affiliations mentioned beyond departmental resources.
Dr. Lucas Kook is a Researcher at the Department of Statistics and Mathematics, WU Vienna University of Economics and Business. His research focuses on causal inference, machine learning, and statistical modeling, with applications in sports analytics, medical data analysis, and healthcare outcomes. He actively develops R packages such as deeptrafo for neural network-based statistical inference. His work bridges theoretical advancements in distributional regression and practical implementations in healthcare and sports domains. Key research areas include algorithm development for significance testing in supervised learning, causal feature selection, and nonparametric methods for conditional independence. Recent studies explore the efficacy of thrombolytic therapies in retinal artery occlusion and the predictive power of deep learning models in stroke patient outcomes. Collaborations span interdisciplinary fields, emphasizing reproducibility and methodological rigor in simulation studies.
Dionysis Kalogerias is an Assistant Professor in the Department of Electrical & Computer Engineering at Yale University. He holds a PhD from Rutgers University and previously served as an Assistant Professor at Michigan State University, with postdoctoral positions at the University of Pennsylvania and Princeton University. His research focuses on machine learning, optimization, risk-aware decision-making, and their applications in autonomous systems, wireless communications, and financial risk management. Education: PhD in Electrical & Computer Engineering, Rutgers University MEng/MSc in Electrical & Computer Engineering, University of Patras, Greece Research Interests: Dr. Kalogerias explores mathematical optimization, statistical learning, and decision-making under uncertainty, with emphasis on applications in resource allocation, autonomous systems, and financial risk management. His work bridges theory and practice, addressing challenges in wireless networks, robust control, and algorithmic trading. Awards: ICASSP Best Paper Award (2020) Rutgers SOE Outstanding Graduate Student Award (2017) Rutgers ECE Graduate Program Academic Achievement Award (2017) Advising & Grants: Recipient of NSF grant for reliable wireless autonomous networks Advising PhD student Baturay Saglam at Yale Collaborations: Active involvement in federated learning marketplaces (FEDSTR) and decentralized AI protocols.
Yunhui Guo is an Assistant Professor in the Department of Computer Science at the Erik Jonsson School of Engineering and Computer Science, University of Texas at Dallas. His research focuses on advanced machine learning techniques including multimodal learning, continual learning, audio-visual recognition, and domain adaptation. He explores challenges in model robustness, cross-modal interactions, and efficient training strategies for deep neural networks. Key research areas include: Developing robust multimodal models for video entailment and dynamic 3D human reconstruction Improving audio-visual segmentation and sound separation through novel adaptation frameworks Advancing continual learning methods to handle domain shifts and out-of-distribution data Creating submodular optimization strategies for active learning in 3D object detection His recent work emphasizes real-world applications like medical image analysis (skin cancer sub-typing), robotics (LiDAR segmentation), and secure AI systems (model watermarking). The research also addresses foundational AI topics such as model uncertainty quantification and adaptive predictive systems. Publications focus on cutting-edge areas like multimodal LLM adaptation, hierarchical out-of-distribution detection, and bimodal online adaptation techniques. Current projects explore the intersection of multimodal perception and lifelong learning systems.
Denis Belomestny is a Professor of Applied Stochastics at the Department of Mathematics, University of Duisburg-Essen. His academic journey includes a PhD from Lomonosov Moscow State University (2002), postdoctoral work at the University of Bonn, and research positions at WIAS Berlin and Humboldt University Berlin. He currently leads research at the intersection of stochastic processes and financial mathematics. PhD in Mathematics, Lomonosov Moscow State University (2002) W3 Professorship in Applied Stochastics, University of Duisburg-Essen (2011–present) His research focuses on statistics of stochastic processes , optimal stopping/control , and Monte Carlo methods , with applications in financial mathematics and machine learning. Key collaborations include work with John Schoenmakers on multilevel approximation algorithms and Alexey Naumov on variance reduction techniques. Recent publications (2025–2023) explore deep neural networks for SDEs , generative adversarial networks , and nonparametric estimation in complex stochastic models. His work spans stochastic differential equations , financial derivatives pricing , and machine learning-driven statistical inference . He supervises doctoral students, including Sascha Nolte (research: robust optimal stopping without reference models). Current projects involve McKean-Vlasov SDEs , gamma-driven processes , and reinforced optimal control .
