Charles Doss is an Associate Professor in the School of Statistics at the University of Minnesota. He earned his PhD from the University of Washington in 2013 under Jon Wellner and holds a B.S. in Mathematics from the University of Chicago. His research focuses on empirical process theory, nonparametric estimation/inference for functions with shape constraints (e.g., concavity, log-concavity), and applications to causal inference, birth-death processes, and unlinked regression. His recent publications address problems such as doubly robust estimation for continuous treatments, heteroscedasticity detection, and convex stochastic optimization. He has received significant funding, including NSF grants DMS-2210312 and DMS-1712664, as well as institutional awards. Warwick Mid-Career Faculty Research Award (2022–2023) NSF DMS-2210312 Grant NSF DMS-1712664 Grant He has served as an Associate Editor for The Electronic Journal of Statistics (2022–present) and The American Statistician (2020–2024). He mentors students such as Guangwei Weng, Daeyoung Ham, and Oliver VandenBerg and contributes to outreach programs like Run the World, a Machine Learning summer camp for high school students.
Professor Lyudmila Mihaylova is a distinguished academic at the University of Sheffield's School of Electrical and Electronic Engineering, where she holds the position of Professor of Signal Processing and Control. She has established herself as a leading researcher in the fields of signal processing, Bayesian methods, and autonomous systems, with significant contributions to particle filtering techniques for intelligent transportation systems. Her work bridges theoretical developments with practical applications across multiple domains including transportation, healthcare, and industrial automation. Prof. Mihaylova's research interests center on nonlinear filtering, sequential Monte Carlo methods, statistical signal processing, and sensor data fusion. Her work spans both theoretical advancements and practical implementations, with particular focus on high-dimensional problems including vehicular traffic flow estimation, image processing, and localization in sensor networks. She has extensive experience with various image modalities such as optical, thermal, LIDAR, SAR, and hyperspectral imaging. Her group actively develops novel methods for autonomous intelligent systems focusing on sensing, tracking, decision making, and machine learning applications. Analysis of Prof. Mihaylova's recent publications reveals a strong trend toward uncertainty quantification in machine learning models, particularly for safety-critical applications. Her work increasingly integrates traditional signal processing techniques with modern deep learning approaches, with applications spanning sewer inspection robotics, medical diagnostics (particularly sleep apnea detection), UAV swarm tracking, industrial manufacturing, and autonomous vehicle systems. A significant portion of her recent research focuses on developing robust methods that can handle incomplete or outlier-corrupted data while providing reliable uncertainty estimates. Among her notable professional achievements: President of the International Society of Information Fusion (ISIF) Senior member of the IEEE Signal Processing Society Associate Editor for IEEE Transactions on Aerospace and Electronic Systems Associate Editor for Elsevier Signal Processing Journal Prof. Mihaylova has successfully mentored numerous PhD students and postdoctoral researchers, many of whom have gone on to prominent academic and industry positions. Her research has been supported by major funding bodies including EPSRC, EU, MOD/DSTL, and industry partners, with recent projects including 'Protecting Environments with UAV Swarms' (InnovateUK, 2022-2024), 'ShiRAS: Towards Safe and Reliable Autonomy in Sensor Driven Systems' (NSF-EPSRC, 2019-2023), and 'Confident safety integration for Cobots' (Lloyd's Register Foundation, 2019-2020). Her research group follows a collaborative approach with the philosophy 'We share knowledge, we grow.' Prof. Mihaylova maintains active research collaborations with institutions worldwide and has held previous academic positions at Lancaster University (2006-2013) and University of Bristol (2004-2006), along with research visiting positions at the University of Ghent, Katholic University of Leuven, and the Bulgarian Academy of Sciences.
Serge Belongie is a Professor at the Department of Computer Science (DIKU) at the University of Copenhagen, where he holds dual affiliations with the Pioneer AI research section and the Image Analysis, Computational Modelling, and Geometry section. His academic position places him at the forefront of interdisciplinary research connecting computer vision with language models, geospatial analysis, and cultural understanding. Professor Belongie's research program encompasses several critical domains in modern artificial intelligence: Advanced computer vision and image analysis techniques Vision-language model integration and multimodal systems 3D point cloud processing and semantic segmentation Geospatial representation learning for environmental applications Fine-grained object recognition and detection Cultural context understanding in AI systems His recent publication record reveals a sophisticated trajectory toward developing precise control mechanisms for vision-language models, with applications spanning forensic analysis, cultural heritage preservation, and social media understanding. The research demonstrates increasing sophistication in handling cultural context and enabling fine-grained manipulation of visual content through natural language interfaces. Professor Belongie maintains an active research group producing significant scholarly output, with over 280 research publications documented in his academic profile. His work is supported by research funding that enables cutting-edge exploration in multimodal AI systems with practical societal impact. He plays a key role in the Pioneer AI center at the University of Copenhagen, which focuses on advancing artificial intelligence through interdisciplinary collaboration and innovative research approaches that bridge theoretical computer science with real-world applications.
Dr. John O. Miller is an Associate Professor of Operations Research in the Department of Operational Sciences at the Air Force Institute of Technology (AFIT), where he has served since 1997 in roles including Military and Civilian Deputy Department Head and Director of the Center for Operational Analysis. A retired U.S. Air Force Lieutenant Colonel, he combines more than three decades of military experience with scholarly expertise in simulation modeling, defense logistics, and operations research. Education: Ph.D. in Industrial Engineering, The Ohio State University, 1997 M.S. in Operations Research, Air Force Institute of Technology, 1987 M.B.A., University of Missouri at Columbia, 1983 B.S. in Biology, United States Air Force Academy, 1980 Dr. Miller’s research focuses on the development and application of simulation methodologies—especially agent-based and discrete-event modeling—to military logistics, weapon system evaluation, and combat readiness. His work often integrates multivariate statistics, experimental design, and optimization techniques to address Air Force and Department of Defense challenges such as sortie generation, munitions supply chains, and directed-energy weapon assessment. Across more than 40 refereed articles, recent publications demonstrate a sustained emphasis on: Metamodeling of large-scale simulations using dynamic Bayesian networks and bootstrapping Agent-based exploration of air-to-air missile concepts and aircraft maintenance manpower Statistical evaluation of pattern-recognition and automatic-target-recognition algorithms Logistics degradation modeling for bomber fleets and brigade combat teams These contributions underscore his leadership in military simulation and defense-focused operations research. Scientific & Teaching Honors: AFIT Instructor of the Quarter, 2005 Tau Beta Pi Engineering Honor Society (Alumnus Member), 2001 AFIT Student Chapter ORSA Outstanding OR Educator, 1999 MORS Barchi Prize Nominee, 1998 Alpha Pi Mu & Omega Rho Honor Societies USAFA Department Instructor of the Year, 1993 Dr. Miller has advised numerous M.S. and Ph.D. students whose dissertations and theses advance simulation optimization, military logistics, and combat modeling. His teaching interests span simulation modeling and analysis, design of experiments, probability and statistics, and operations research methods for defense applications. He maintains active professional memberships in INFORMS, the Military Operations Research Society, and the Air Force Association, and he frequently presents at both invited and organized conferences, fostering collaboration among military, academic, and industry analysts.
James Urquhart Allingham is a Research Scientist at Google DeepMind , working on the Gemini project. He completed his PhD in the Machine Learning Group at the University of Cambridge under the supervision of José Miguel Hernández-Lobato, with funding from EPSRC, the Michael E. Fisher Studentship in Machine Learning, and the Qualcomm Innovation Fellowship. He was also part of the ELLIS PhD program, advised by Eric Nalisnick at AMLab UvA. Current affiliation: Google DeepMind (Research Scientist) PhD: University of Cambridge (Machine Learning Group) Academic networks: ELLIS PhD program, Darwin College His research focuses on the intersection of Bayesian deep learning and probabilistic methods in deep learning. Key areas include deep generative models , zero-shot classification , prompt engineering , Monte Carlo gradient estimation , and applications to sustainability and climate change . His work has explored energy-based models , neural architecture search , and equivariance in convolutional networks . Selected scientific awards and grants include the Michael E. Fisher Studentship , Qualcomm Innovation Fellowship , and MPhil in Advanced Computer Science with Distinction . He has collaborated with institutions such as the Amsterdam Machine Learning Group (AMLAB) and University of the Witwatersrand .
Xiaoyu Che is an Assistant Professor of Biostatistics at Columbia University's Mailman School of Public Health, where he serves as the principal biostatistician in the Center for Infection and Immunity (CII) at Columbia University Irving Medical Center. His work bridges statistical methodology with biomedical research, focusing on complex disease mechanisms through advanced data analysis approaches. Dr. Che received his academic training at prestigious institutions: BS in Mathematics from Zhejiang University (2006) PhD in Mathematics from Claremont Graduate University (2013) Dr. Che's research program centers on the development and application of statistical methods for multi-omics analyses, with particular focus on understanding the pathogenesis of chronic and neurodevelopmental conditions. His work spans multiple domains including Autism Spectrum Disorder (ASD), Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS), and Gulf War Illness (GWI). He employs sophisticated biostatistical approaches to integrate diverse biological data types, revealing novel insights into disease mechanisms. His methodological expertise includes Bayesian statistics, metabolomic analysis, immune signature identification, and microbiome characterization, all aimed at translating complex biological data into meaningful clinical insights. Analysis of Dr. Che's publication record reveals a strong thematic focus on applying advanced statistical methods to understand complex disease mechanisms. His work consistently bridges biostatistical innovation with biomedical discovery, particularly in the areas of neurodevelopmental disorders and chronic fatigue conditions. The publications demonstrate progression from foundational methodological work to increasingly sophisticated multi-omics integration approaches, reflecting both technical growth and expanding research impact. His collaborative approach is evident through numerous high-impact publications with interdisciplinary teams across Columbia University and beyond. Dr. Che teaches BIST P8104: Probability in the Biostatistics MS degree program at Columbia, demonstrating his commitment to training the next generation of biostatisticians. While specific grant information isn't detailed in the provided materials, his extensive publication record across multiple high-impact journals suggests successful grant funding supporting his research program. As principal biostatistician in the Center for Infection and Immunity, Dr. Che plays a critical role in the analytical framework of the center's research initiatives. His work supports the center's mission to understand the relationship between infectious agents and human health through rigorous quantitative analysis. The collaborative nature of his research is evident in the diverse range of co-authors spanning immunology, virology, microbiology, and clinical medicine.
Andrew Zammit Mangion is an Associate Professor at the University of Wollongong , affiliated with the School of Mathematics and Applied Statistics . His research focuses on spatio-temporal statistics, computational methods, and environmental informatics, with applications in climate science and geospatial data analysis. Education : PhD in Statistics (University of Sheffield, 2012), B.Eng. (University of Malta, 2007) Research Themes : Spatio-temporal modeling, Bayesian inversion frameworks (e.g., WOMBAT v2.S), deep learning integration, and statistical software development (e.g., FRK package) Grants & Projects : ARC DECRA Fellow (2018), Chief Investigator on ARC Discovery Project (greenhouse gases), ARC Special Research Initiative (Securing Antarctica's Environmental Future), and ARC Industrial Transformation Hub (TIDE). Collaborations : University of Bristol, University of Edinburgh, ESA CCI, NASA OCO-2 data projects Scientific Awards include the prestigious Australian Research Council Discovery Early Career Researcher Award (DECRA). His work spans Antarctic ice sheet analysis, CO2 flux inversion, and scalable spatial statistical models for environmental monitoring.
Reed Essick is an Assistant Professor at the Canadian Institute for Theoretical Astrophysics (CITA), University of Toronto. His research focuses on experimental gravity, astrophysical signals, and nuclear physics, with particular emphasis on neutron stars, black holes, and gravitational waves. He develops advanced statistical methods like hierarchical Bayesian inference and nonparametric analysis for interpreting observational data from pulsars and gravitational wave detectors. Dr. Essick collaborates extensively with international observatories such as LIGO, Virgo, and KAGRA, contributing to cutting-edge projects like multimessenger astronomy and precision cosmology. His work bridges computational astrophysics with observational techniques, addressing fundamental questions about dense matter and strong-field gravity. Key contributions include studies on gravitational wave equation-of-state constraints, pulsar timing analysis, and the application of machine learning to detector data. His research leverages both ground-based interferometers and space-based observations to explore extreme astrophysical environments.
Professor Efthymios Pavlidis is a faculty member in the Department of Economics at Lancaster University Management School (LUMS). He holds the rank of Professor and specializes in macroeconomics, international finance, and time series econometrics. His research focuses on housing market dynamics through collaborations like the International Housing Observatory (with the Federal Reserve Bank of Dallas) and the UK Housing Observatory. He is a Fellow of the Higher Education Academy, reflecting his commitment to academic excellence in teaching and research. His research interests include speculative bubble detection, real estate price forecasting, and testing parity conditions in financial markets. Pavlidis actively supervises PhD students in applied time series econometrics, emphasizing practical applications in financial markets and housing economics. He is involved in numerous academic activities, including organizing conferences and workshops such as the Dynare Conference and the Lancaster Economics Seminar. Key contributions include developing econometric methods for detecting market exuberance and analyzing real exchange rates. His work bridges theoretical econometrics with practical policy implications, particularly in housing and energy markets. Pavlidis collaborates internationally, evidenced by his participation in global academic networks and institutions like the European Economic Association and the Royal Economic Society. His teaching includes the course ECON222 Intermediate Macroeconomics I, and he maintains an office in the Management School (B015), with weekly office hours on Tuesdays. A comprehensive overview of his research and projects is available at his personal webpage: https://sites.google.com/view/etpavlidis/ .
Sigrid Källblad Nordin is an Associate Professor at KTH Royal Institute of Technology, affiliated with the Department of Mathematics (Division of Probability, Mathematical Physics, and Statistics). Her research focuses on Mathematical Finance, Probability Theory, and Stochastic Analysis, with an emphasis on measure-valued processes, martingale optimal transport, and model uncertainty. She holds a DPhil from the University of Oxford (2014). Her work bridges theoretical advancements in stochastic control, optimization, and financial applications. Recent research includes Bayesian optimal adaptive control, robust option pricing, and dynamically consistent investment strategies under uncertainty. She teaches courses such as Financial Mathematics and Financial Derivatives, and supervises PhD students Linn Engström and Chaorui Wang. Publications span journals like Annals of Applied Probability , Finance and Stochastics , and SIAM Journal on Control and Optimization , reflecting contributions to optimal transport, stochastic processes, and financial modeling. She is currently hiring a new PhD student and welcomes inquiries about master thesis supervision.
Dr. Christos Papavassiliou is an Associate Professor in the Department of Electrical and Electronic Engineering at Imperial College London, part of the Faculty of Engineering. His research focuses on instrumentation electronics, memristor modeling, signal integrity, and novel device technologies such as SiGe devices, RF MEMS, and ReRAM. He leads the Space Lab and collaborates with the National Centre for Scientific Research in Athens. He holds senior membership in IEEE and is a member of the IET. Education: Ph.D. in Applied Physics, Yale University (1983–1989) MPhil in Applied Physics, Yale University (1983–1988) MS in Applied Physics, Yale University (1983–1985) B.S. in Physics, MIT (1979–1983) Research Interests: Memristor-based neuromorphic computing and stochastic systems High-performance instrumentation hardware and data acquisition Multi-state memristive memory and selectorless arrays Integration of memristors with CMOS for hybrid circuits Applications in biomedical wearables and edge AI deployment Key Contributions: Developed novel memristor models for circuit simulation Pioneered work on memristor-based true random number generators Advanced understanding of resistive drift and energy-constrained storage Designed FPGA-based systems for analog circuit emulation Labs & Teams: Active in the Space Lab at Imperial College, focusing on interdisciplinary research in electronics and space applications.
Miaoyan Wang is an Associate Professor in the Department of Statistics at the University of Wisconsin-Madison, part of the School of Computer, Data & Information Sciences. She holds early tenure and is a faculty affiliate in the Mathematical Foundations of Machine Learning, Institute for Foundations of Data Science (IFDS), and Center for Demography of Health and Aging (CDHA). She is currently on sabbatical as a visiting associate professor at Stanford University and Lawrence Livermore National Laboratory. Education: PhD in Statistics from the University of Chicago (2015), BS in Mathematics from Fudan University (2010). Postdoctoral training included positions at UC Berkeley (Computer Science) and the University of Pennsylvania (Math+X). Research focuses on statistical machine learning, with emphasis on matrix/tensor data analysis, high-dimensional statistics, nonparametric learning, and applications in genetics. Her work bridges theory and practice, addressing challenges in computational efficiency and statistical optimality for complex data structures. Awards include the prestigious NSF CAREER Award (2022), multiple best paper awards (ASA, IMS, NEURIPS), and recognition from ASHJ and IGES. Her group has secured grants totaling $3.4 million, including NSF funding for foundational machine learning research and collaborative projects in population genomics. Advising includes PhD students Chanwoo Lee and Jiaxin Hu, with former students Yuchen Zeng and Zhuoyan Xu. She teaches advanced statistical methods and computational courses, emphasizing rigorous theoretical foundations and practical applications. Key collaborations include work on tensor decomposition algorithms, statistical genetics, and interdisciplinary projects with biology and computer science departments. Her lab contributes open-source software tools for data analysis, including packages for tensor block models and multiway clustering.
Jonathan Weinstein is a Professor of Economics and Director of Graduate Studies in the Department of Economics at Washington University in St. Louis. He holds a PhD from the Massachusetts Institute of Technology (MIT). His research focuses on microeconomic theory and game theory, with particular emphasis on strategic behavior, incomplete information, and Bayesian inference. Notable recent contributions include works on 'Direct Complementarity' and 'Reputation without Commitment.' He serves on committees such as the Master Program Management Committee and the PhD Committee. His research explores topics like rationalizability in infinite games, the impact of risk attitudes on strategic interactions, and the integration of Bayesian methods with classical hypothesis testing. His work bridges theoretical rigor and practical applications in economic decision-making under uncertainty. Education: PhD in Economics from MIT Committees: Master Program Management Committee, PhD Committee Recent Papers: 'On a Mistranslation of a Mistake about Minimax' (2022), 'The Effect of Changes in Risk Attitude on Strategic Behavior' (2016) Contact: j.weinstein@wustl.edu | Office: Seigle Hall 382
Professor Min An is a Professor of Construction and Risk Management at the University of Salford, leading the Infrastructure Research Group within the School of Science, Engineering & Environment. He holds an honorary professorship at two overseas universities (China and Portugal) and serves on the editorial boards of 12 international journals. With over 40 years of experience, his career spans academic roles at Heriot-Watt University, Coventry University, and the University of Birmingham, alongside industry roles as a civil engineer and researcher. His research focuses on safety and risk management in construction, transportation systems, and energy sectors, with over 200 publications. Key areas include railway and highway safety, offshore oil & gas risk assessment, and nuclear reliability management. He has secured funding from EPSRC, EU, DfT, and industry partners, leading 20+ projects. Notable achievements include developing methodologies for infrastructure safety and maintaining collaborations with 30+ industrial partners. Professor An has supervised 30 PhD students and over 280 postgraduate projects, contributing to industry workshops and best practices. Awards include multiple science technology prizes and conference best paper/keynote recognitions. His teaching spans risk management, construction safety, and project management across MSc programs.
Karl Friston is a renowned neuroscientist and Professor at the Institute of Neurology, University College London . As Scientific Director of the Wellcome Trust Centre for Neuroimaging, he has pioneered transformative methodologies in brain imaging, including statistical parametric mapping (SPM) , voxel-based morphometry (VBM) , and dynamic causal modelling (DCM) . His theoretical work on the free-energy principle and active inference has reshaped understanding of brain function. Key Positions : Scientific Director (Wellcome Trust Centre), Fellowships at MRC units, Keck Foundation Fellow Research Focus : Functional integration in the human brain, computational models of neuronal interactions, schizophrenia, and Bayesian brain theory. Scientific Awards : Wiley Young Investigator Award (1996) Golden Brain Award (2003) Fellow of the Royal Society (2006) Weldon Memorial Medal (2013) EMBO Membership (2014)
