Harald Oberhauser is a Professor of Mathematics at the University of Oxford , serving as a Tutorial Fellow at St. Hugh's College . He is affiliated with the Mathematical Institute and the Oxford-Man Institute of Quantitative Finance , with research supported by DataSig , The Turing Institute , and CIMDA . Education: Master’s in Mathematics, University of Vienna PhD in Stochastic Analysis, University of Cambridge (Statslab) His research bridges probability theory , stochastic processes , and analysis with applications in machine learning , statistics , algebra , and topology . Recent work emphasizes kernel methods , signature-based models , and topological data analysis for sequential data and stochastic systems. Collaborative projects span Bayesian inference , probabilistic safety , and nonlinear filtering . His scientific awards include the NeurIPS 2020 Spotlight Paper for "A Randomized Algorithm to Reduce the Support of Discrete Measures." He supervises DPhil students such as James Foster , Francesco Cosentino , and Patric Bonnier , alongside postdoctoral researchers like James Foster and Darrick Lee . Key collaborations and software tools include KSig (for signature kernels) and Python implementations for kernel quadrature and measure reduction .
Hamsa Sridhar Bastani is an Associate Professor of Operations, Information, and Decisions (OID) as well as Statistics and Data Science at the Wharton School of the University of Pennsylvania, where she co-directs the Wharton Healthcare Analytics Lab. Her academic journey includes graduating summa cum laude from Harvard in 2012 with an A.M. in physics and an A.B. in physics and mathematics, completing her PhD in Stanford's Electrical Engineering department under Mohsen Bayati, and spending a year as a Herman Goldstine postdoctoral fellow at IBM Research. Dr. Bastani's research focuses on developing novel machine learning algorithms for learning and optimization, including methods for sequential decision-making (bandits, reinforcement learning, active learning), learning from auxiliary data sources (transfer learning, meta-learning, surrogates), and designing effective human-AI interfaces (interpretability, fairness). She is passionate about applying machine learning and AI to tackle high-impact societal problems across domains like healthcare, public policy, and education. Her recent work explores how AI systems affect and augment human behavior, with the goal of designing AI tools that help humans thrive. She has collaborated with national governments to deploy algorithms at country scale for improving public health outcomes, including working with the Government of Greece to nearly double the efficacy of their national border COVID-19 screening via reinforcement learning, and with the Government of Sierra Leone to improve patient access to essential medicines by nearly 20% via decision-aware learning. She also co-led the first large field study deploying generative AI tutors in high school math classes, demonstrating critical risks for human overreliance and deskilling. Dr. Bastani's publications reveal trends in applying advanced machine learning techniques to real-world problems, particularly in healthcare and social impact domains. Her work often combines theoretical rigor with practical implementation through randomized controlled trials and field evidence. Recent publications show increasing focus on the human-AI interface, especially examining risks of generative AI in educational contexts and developing frameworks for responsible human-AI collaboration. Her research has been published in leading outlets including Nature, Management Science, Operations Research, and PNAS. Scientific Awards Wagner Prize for Excellence in Operations Research Practice (2021) INFORMS Pierskalla Award for Best Paper in Healthcare (2021, 2019, 2016) Public Sector in OR Best Paper Award (2024) INFORMS Data Mining Best Paper Award (2022) Wharton Teaching Excellence Awards (2021, 2020, 2019) George Nicholson Best Student Paper Competition (2016) MSOM Best Student Paper Competition (2016, 2020) National Science Foundation Fellow (2012-2017) As an advisor, Dr. Bastani has mentored numerous PhD students who have gone on to prestigious positions including Assistant Professors at Cornell Johnson, ASU Carey, and UC Berkeley Haas, as well as Director of Responsible AI at PwC. She has secured significant research funding through collaborations with government entities and has served as an Associate Editor for Operations Research, M&SOM, and OR Letters. Her work has been supported by partnerships with national governments and organizations like the Penn Center for Health Incentives and Behavioral Economics. She primarily teaches OIDD 321: Introduction to Management Science, for which she received multiple Wharton Teaching Excellence Awards. Dr. Bastani co-directs the Wharton Healthcare Analytics Lab, which focuses on applying data science and machine learning to healthcare challenges. She also serves on the Steering Committee for the Penn Center for Health Incentives and Behavioral Economics and on the statistics advisory committee for the AHA Food is Medicine Initiative. Outside academia, she serves on the Workday AI Advisory Board, connecting her research with industry applications.
Prof. Dr. Natalie Neumeyer is a Professor of Mathematical Statistics and its Applications at the Department of Mathematics, Faculty of Mathematics, Computer Science and Natural Sciences, University of Hamburg. Her research focuses on nonparametric and semiparametric statistics, model testing, curve estimation, bootstrap methods, and time series analysis. 2007–present: Professor (W3) at University of Hamburg 2006–2007: Junior Professor (W1) at University of Hamburg 1999–2006: Research Assistant at Ruhr-University Bochum Her academic activities include chairing examination committees for Master's and Diplom programs in Business Mathematics, editorial roles in journals like Annals of the Institute of Statistical Mathematics and Scandinavian Journal of Statistics , and leadership in the DMV Stochastics Section (2018–2023). Recent research involves generalized Hadamard differentiability in copula models, volatility change detection in time series, and specification testing in transformation models. Publications span top journals including Biometrika , Scandinavian Journal of Statistics , and Annals of the Institute of Statistical Mathematics . Her work combines theoretical advancements in empirical processes with practical applications in functional data analysis and financial modeling, demonstrated through a robust portfolio of 54 publications and collaborative projects.
Xi Yu is a Researcher at the Computational Science Initiative of Brookhaven National Laboratory, with expertise in Machine Learning, Artificial Intelligence, and Computational Science. Previously, they served as a Postdoctoral Research Associate (2022–2025) and Research Intern at Mitsubishi Electric Research Laboratories (2021). Ph.D. in Electrical Engineering, University of Florida (2022) M.S. in Electrical Engineering, University of Florida (2019) Their research spans domain generalization, adversarial robustness, information theory, and coral image segmentation, with a focus on applying neural networks and entropy functional methods to diverse challenges in AI and environmental science. Recent publications highlight work on AI training energy efficiency, CLIP-based image-text alignment, and information bottleneck theory. Notable awards include the NYU Tandon Faculty First Look Fellowship (2024) and recognition at ICDM (2021).
Rui Pires da Silva Castro is an Associate Professor in the Department of Mathematics and Computer Science at Eindhoven University of Technology (TU/e), with research spanning signal processing, learning theory, and statistics. He is affiliated with EAISI (Eindhoven Artificial Intelligence Systems Institute) and focuses on non-parametric and high-dimensional statistics, statistical signal/image processing, network inference, and pattern recognition.
Peter Oswald is a Professor and holds a Bonn Research Chair at the Hausdorff Center for Mathematics, affiliated with the Institute for Numerical Simulation at the University of Bonn. His research lies at the intersection of approximation theory, function spaces, and numerical methods for partial differential equations, with a focus on wavelets, splines, and multiscale computational techniques. University: University of Bonn School: Hausdorff Center for Mathematics Department: Institute for Numerical Simulation Email: oswald@ins.uni-bonn.de His research interests include Approximation Theory, Function Spaces and Applied Harmonic Analysis, Multiscale Methods in Scientific Computing, Numerical Methods for PDEs, Finite Elements, Splines, Wavelets, and Mathematical Modelling. These areas reflect a deep commitment to both theoretical foundations and computational applications in modern applied mathematics. The analysis of his recent publications reveals a consistent focus on iterative and subspace correction methods, preconditioning in sparse grid contexts, function space theory (especially Besov and Hilbert spaces), and the mathematical underpinnings of finite element and wavelet-based discretizations. His work often bridges pure and computational mathematics, with increasing exploration of stochastic and randomized algorithms in numerical linear algebra and high-dimensional problems. Peter Oswald has not been mentioned as having formal advisees in the provided texts, and no scientific awards are listed. However, his extensive collaboration with Michael Griebel and publication in prestigious journals and book series (e.g., Springer Series in Computational Mathematics) underscores his active and influential role in the mathematical community. He is involved in advanced research on space splittings, iterative solvers, and high-dimensional function approximation, contributing to both theoretical developments and practical computational frameworks. His work supports broader efforts in scientific computing, uncertainty quantification, and the numerical solution of complex physical models.
Steffen Ventz is an Associate Professor and Medtronic Faculty Fellow in the Division of Biostatistics and Health Data Science at the School of Public Health, University of Minnesota. He is also affiliated with the Masonic Cancer Center. Prior to joining the University of Minnesota, he was a Research Scientist at Harvard University (2018-2022) and an Assistant Professor at the University of Rhode Island (2015-2018). He completed his PostDoctoral training at Harvard T.H. Chan School of Public Health (2013-2015). His educational background includes: PhD in Mathematical Statistics, Bocconi University, Italy, 2013 MPhil in Mathematical Statistics, Bocconi University, Italy, 2010 MA in Mathematical Demography, University of Rostock, Germany, 2007 BS in Mathematical Demography, University of Rostock, Germany, 2005 Dr. Ventz's research focuses on methodological and applied statistics. His methodological interests include Bayesian statistics, statistical decision theory, adaptive sequential statistical methods, and data integration. His applied research spans oncology and infectious diseases, where he develops and applies novel statistical methods to address complex healthcare challenges. His work has significant implications for clinical trial design, particularly in developing more efficient and informative trial methodologies that can accelerate the development of new treatments. His recent publications demonstrate a strong focus on Bayesian methods for clinical trial design, particularly in oncology. He has pioneered approaches for data integration across studies, external control arms, and adaptive trial designs that optimize resource allocation and decision-making. His work often bridges theoretical statistical innovation with practical clinical applications, making significant contributions to both statistical methodology and medical research. Dr. Ventz has received recognition as a Medtronic Faculty Fellow, highlighting his contributions to biomedical research. His work has been published in leading journals across statistics, oncology, and medical research. As an educator, Dr. Ventz has taught courses in survival analysis, linear models, Bayesian statistics, and other biostatistical methods at the University of Minnesota, University of Rhode Island, and Bocconi University. He has also conducted workshops internationally on Bayesian adaptive methods for clinical trials. Dr. Ventz leads a research group at the University of Minnesota that includes several graduate students working on topics such as imaging genetics, transfer learning, causal inference, and clinical trial design. His group is actively recruiting post-doctoral fellows and graduate students interested in biostatistical methodology and applications.
Edgar Dobriban is an Associate Professor of Statistics and Data Science at the University of Pennsylvania's Wharton School, with a secondary appointment in Computer and Information Science. He leads a research group focused on problems at the interface of statistics, machine learning, and AI. Education PhD in Statistics, Stanford University (2017) BA in Mathematics, Princeton University (2012, Summa cum Laude/with Highest Honors) Research Focus His work spans uncertainty quantification, AI safety, robustness, high-dimensional asymptotic statistics, distributed learning, fairness, and COVID-19 testing methodologies. Current projects include developing conformal prediction methods, jailbreaking robustness benchmarks (JailbreakBench), and safety alignment techniques for large language models. Publication Trends Recent papers predominantly address AI safety and reliability, featuring novel methods for uncertainty quantification in language models (calibration, conformal prediction), adversarial robustness (jailbreaking defenses), and distribution shift adaptation. Theoretical foundations blend with practical applications in high-dimensional statistics. Awards and Honors Peter Gavin Hall IMS Early Career Prize (2024) Sloan Research Fellowship (2023) ICSA Outstanding Young Researcher Award (2023) NSF CAREER Award (2021) AFOSR/Army Research Office YIP Awards (2024, 2023) COPSS Emerging Leader Award (2023) Research Leadership He leads the Wharton Statistics and Data Science research group, recruiting PhD students through Statistics & Data Science, CIS, and AMCS programs. Current projects involve collaborations with Penn Medicine and the NSF-Simons Mathematical and Scientific Foundations of Deep Learning initiative. He co-founded the ASA StatsUpAI Special Interest Group and co-organized the Shenzhen Conference on Random Matrix Theory (2023).
Stefan Lüdtke is an Assistant Professor (Juniorprofessor) for Marine Data Science at the University of Rostock (since July 2023) and concurrently serves as a junior research group leader at ScaDS.AI Leipzig . Previously, he was a postdoctoral researcher at the Institute for Enterprise Systems, University of Mannheim (2021-2023) and completed his PhD at the University of Rostock (2016-2021). Education & Career Timeline: 2023 – present: Juniorprofessor (Assistant Professor) for Marine Data Science, University of Rostock 2023 – present: Junior research group leader, ScaDS.AI Leipzig 2021 – 2023: Postdoc, Institute for Enterprise Systems, University of Mannheim 2016 – 2021: PhD studies, University of Rostock Research Interests: Dr. Lüdtke’s research integrates neuro-symbolic machine learning with practical applications spanning heterogeneous tabular data , marine ecology , and underwater technology . His work bridges symbolic reasoning and modern gradient-based learning to tackle complex real-world problems such as hyperspectral imaging for environmental monitoring, knowledge-graph completion, and robust human-activity recognition. Key focus areas include: Design of memory-augmented decision-tree ensembles and gradient-based tree learning. Data-centric evaluation and quality assessment of machine-learning models on tabular and sensor data. Domain adaptation and self-training techniques for activity recognition in changing environments. Application of AI to marine robotics and glacier-dynamics mapping. Publication Trends: Across 40+ peer-reviewed works (2017-2025), Lüdtke demonstrates a steady shift from foundational probabilistic-filtering and lifted-inference methods toward cutting-edge neural-symbolic hybrids and tabular-data-centric learning. Recent high-impact venues show contributions in machine-learning theory , computer vision , and environmental informatics , with a notable uptick in interdisciplinary projects combining AI and marine science. Scientific Awards & Honors: No specific awards are listed in the provided material. Advising & Funding: No named students or explicit grant details are disclosed in the text. Laboratories & Teams: He leads the Marine Data Science junior research group at the University of Rostock and the ScaDS.AI Leipzig junior research group, fostering cross-institutional collaboration in AI and data science.
Dr Tiejun Ma is an Associate Professor at the University of Southampton , specializing in risk analysis and decision-making. His research integrates quantitative modelling with real-time big data analytics to address financial market forecasting challenges. He has secured over £4 million in research grants across 30 projects funded by EPSRC, ESRC, Innovate UK, and industry partners like Huawei. Member of the Centre for Risk Research Member of CORMSIS (Centre for Operational Research, Management Science and Information Systems) Member of the Centre for Digital Finance Dr Ma's research spans cryptocurrencies, behavioral economics, data anonymization, and financial risk management. His work combines applied data science, mathematical modelling, and behavioral analysis to tackle complex uncertain environments. Key projects include: Huawei-funded Failure Detection Algorithm for SDN/NFV EPSRC-funded Modelling the Wisdom of the Crowd Modelling Complex Uncertain Environments with E.V. Analytics Ltd His publications appear in leading journals like the European Journal of Operational Research , Quantitative Finance , and Journal of Computer Science and Technology . He supervises PhD students in Business Studies & Management.
Dr. Guy Hawkins is an Associate Professor in the School of Psychological Sciences at the University of Newcastle, Australia. His research focuses on developing and testing computational and mathematical models of cognitive processes, with a primary interest in decision-making. His work spans from low-level speeded perceptual decisions through to high-level cognition, including statistical reasoning and consumer preferences. Dr. Hawkins earned his PhD in 2013 and his Bachelor of Psychology (Honours 1) in 2008, both at the University of Newcastle. Prior to his current position, he held postdoctoral research positions at the University of Amsterdam's Brain and Cognition Center (2014-2016) and UNSW Sydney (2013-2014). In 2017, he was awarded an Australian Research Council Discovery Early Career Researcher Award (DECRA). His research examines the decision mechanisms and strategies that people use to select consumer products and service options. A key finding is that people make fewer reasoning errors when information is presented as counts ('8 out of 10') rather than probabilities ('80%'). This has practical applications in healthcare, where physicians could describe prognoses using counts to help patients make better-informed treatment choices. His work also investigates how people update their decision caution relative to time constraints and option quality. Dr. Hawkins' recent publications reveal a strong focus on evidence accumulation models, decision-making under time pressure, and the relationship between cognitive processes and neural activity. His research often employs computational modeling approaches to understand the psychological processes underlying decision behavior. 2024 John Keats Early Career Award, Society for Mathematical Psychology 2020 William K. Estes Early Career Award, Society for Mathematical Psychology 2018 Fellow of the Psychonomic Society 2017 Australian Research Council Discovery Early Career Researcher Award 2017 Vice-Chancellor's Award for Early Career Research and Innovation Excellence, University of Newcastle 2013 Clifford T. Morgan Best Article Award, Psychonomic Society Dr. Hawkins collaborates extensively with researchers across the globe, including at universities in Australia, USA, Canada, UK, The Netherlands, and Norway. He is part of the Newcastle Cognition Laboratory and the Functional Neuroimaging Laboratory. His current research projects include investigating cognitive neuroscience frameworks for attentional control, evaluating human-machine interfaces in vehicles, and studying perceptual inference anomalies in schizophrenia.
Aarya Patil is an LSST Discovery Alliance Catalyst Fellow at the Max Planck Institute for Astronomy in Heidelberg, Germany. She specializes in large-scale data-driven studies of the Milky Way's formation and evolution, leveraging computational methods and open-source software development. PhD in Astronomy & Astrophysics (University of Toronto) Key contributor to Astropy project (finance committee member) Active in science education through Astropy Training School and Pan-African School for Emerging Astronomers Research Focus Aarya's work combines galactic astrophysics with statistical computing , particularly through: Functional Principal Component Analysis (FPCA) of stellar spectra Chemical tagging validation via spectral structure analysis Development of open-source tools like fpca.py and delfiSpec Application of Sequential Neural Likelihood (SNL) for stellar parameter inference Scientific Contributions specdims repository implements methods to extract intrinsic spectral features while accounting for systematics, enabling studies of chemical homogeneity in galactic structures like the M67 open cluster. Awards & Recognition Data Sciences Institute Doctoral Student Fellowship (University of Toronto) Google Summer of Code participant (2017) and mentor (2021) Community Engagement Active in open science initiatives, Aarya serves on the Astropy project's finance committee and organizes educational programs bridging data science and astronomy.
Florian Scholze serves as a Researcher at the Faculty of Social and Economic Sciences, Otto-Friedrich University of Bamberg since October 2023, following a similar role at RWTH Aachen University from December 2021. His academic foundation includes a Master of Science in Survey Statistics from Bamberg (2021) and a Bachelor of Arts in Educational, Learning and Training Psychology from the University of Erfurt (2018). Educational background: Master of Science in Survey Statistics (2021), Otto-Friedrich University Bamberg Bachelor of Arts in Educational, Learning and Training Psychology with minor in Educational Science (2018), University of Erfurt His research centers on theoretical challenges in nonstationary time series analysis, specifically investigating weak convergence properties of function-indexed sequential empirical processes and developing bootstrap methodologies for dependent data structures. This work addresses fundamental gaps in statistical theory where traditional stationary assumptions fail, with significant implications for econometric modeling and financial time series analysis. Recent publication trends reveal a concentrated focus on foundational statistical theory, exemplified by his 2024 preprint establishing convergence results under nonstationarity. This research trajectory demonstrates progression toward more complex frameworks like bootstrap uniform functional central limit theorems, indicating deepening specialization in asymptotic methods for nonstandard data processes. Scientific recognition: No awards or fellowships documented Regarding supervision and funding, the available text provides no details about current students, grant acquisitions, or teaching responsibilities. His position within the Chair of Mathematics in Economic Sciences suggests involvement in departmental research activities, though specific resource allocations remain unspecified. Prospective collaborators would engage primarily through theoretical statistics projects within the faculty's research ecosystem. Lab and team context: Scholze is embedded in the Chair of Mathematics in Economic Sciences under Prof. Dr. Anne Leucht, but no dedicated research laboratory or named research group is associated with his work. His collaborative network appears centered around conference participation and co-authorship with established statisticians like A. Steland.
Lisa Torrey is an Associate Professor in the Department of Mathematics, Computer Science, and Statistics at St. Lawrence University. She teaches courses in programming, algorithms, artificial intelligence, machine learning, and computational theory. She earned her Ph.D. in Computer Science from the University of Wisconsin-Madison in 2009, where she researched relational transfer in reinforcement learning under Professor Jude Shavlik. Her research focuses on: Developing intelligent agents using reinforcement learning Human-inspired techniques for task transfer and multi-agent collaboration Educational applications of AI in computer science classrooms Machine learning approaches to crowd simulation and game AI She has recently shifted toward studying human learning dynamics in CS education. Her publications (2010-2020) predominantly explore reinforcement learning adaptations, educational AI frameworks, and knowledge-transfer mechanisms. A clear evolution is visible from theoretical machine learning toward applied educational research. She actively mentors students in AI/machine learning projects, with recent advisees including: Rodrick Mpofu (Explainable Image Classification) Lily Kendall (Sensitivity Analysis for Image Generation) Cooper Anderson (Audio Source Separation) Ellie Nichols (Avalanche Forecast Analysis) 15+ additional students across 2020-2025 No research labs or major grants are explicitly mentioned in available materials.
Zhenzhang Ye is a researcher affiliated with the Computer Vision Group at the Technical University of Munich (TUM) , part of the TUM School of Computation, Information and Technology. His work focuses on Photometry-Based Reconstruction , Optimization , and Geometry Processing , with additional interests in Visual SLAM , Deep Learning , and Biomedicine . Research Interests : Optimization techniques, Photometry-Based Reconstruction, and geometric processing for computer vision tasks. Publications : Active contributor to conferences like CVPR, AISTATS, AAAI, and ICCV, with recent works on 3D human motion prediction, hypergradient estimation, and photometric stereo. Contact: yez@in.tum.de
