Patrick Jaillet is the Dugald C. Jackson Professor in the Department of Electrical Engineering and Computer Science at MIT's School of Engineering. He holds joint appointments with the Laboratory for Information and Decision Systems (LIDS), the Operations Research Center (ORC), the Operations Research and Statistics Group at MIT Sloan, and the Department of Civil and Environmental Engineering. Previously, he served as Head of Civil and Environmental Engineering at MIT (2002-2009) and Chair of the Department of Management Science and Information Systems at UT Austin (1997-2002). Dr. Jaillet's research focuses on online optimization and learning, sequential decision-making under uncertainty, and security and resilience in complex networks. His work spans theoretical foundations in optimization and machine learning with applications in transportation, online market analytics, and network security. He has developed mathematical frameworks for problems involving uncertainty, dynamic resource allocation, and strategic behavior in complex systems. His recent publications reveal strong trends in bridging theoretical optimization with practical machine learning applications. Key themes include Bayesian optimization for black-box functions, online learning with limited information, mechanism design for resource allocation, and network security applications. His work increasingly integrates large language models with traditional optimization techniques, reflecting the evolving landscape of AI-driven decision-making systems. Fulbright Scholar (1990) Fellow of the Institute for Operations Research and Management Science (INFORMS) Best Applications Paper Award at ICAPS 2019 Long-standing Associate Editor for top journals including Operations Research and Transportation Science Dr. Jaillet has advised over 40 doctoral students who now hold prominent positions in academia and industry, including faculty positions at MIT, Georgia Tech, and ETH Zurich, and research scientist roles at Amazon, Microsoft Research, and Google. His research has been consistently funded by major agencies including NSF, ONR, AFOSR, and international partners like Singapore NRF, with current projects focusing on learning algorithms for autonomous security and fundamental tradeoffs in optimization. He leads a vibrant research group spanning MIT's EECS department and ORC, with current funding supporting work on neural bandits, federated optimization, and network security applications. His research group operates at the intersection of theory and practice, with strong connections to industry through collaborations with IBM, Microsoft, Google, and various transportation and technology companies. The group maintains active partnerships with international institutions, particularly through SMART in Singapore, reflecting Dr. Jaillet's global research impact.
Jonathan L. Auerbach is an Assistant Professor in the Department of Statistics at George Mason University . His work bridges statistics and public policy , focusing on causal inference , longitudinal data analysis , and official statistics . He has contributed to understanding urban myths (e.g., New York City rat populations, building height trends), election fraud , and policy evaluation for initiatives like Vision Zero. Education : PhD in Statistics (Columbia University, 2020), BA in Economics (Cornell University, 2010) Research Interests include data science , causal inference , survey methodology , and urban analytics . His recent work addresses climate change impacts on cherry blossom seasons, federal data security , and street vendor demographics in New York City. Scientific Awards include the 2024-2025 President of Washington Statistical Society , 2020-2021 American Statistical Association Science Policy Fellow , and 2019 Howard Levene Outstanding Teaching Award . He has served as an instructor for courses like Capstone in Statistics and Causal Inference , emphasizing technical communication and data ethics .
Dr. Robert D. Moser is a Professor at the University of Texas at Austin and holds the W.A. "Tex" Moncrief, Jr. Chair in Computational Engineering and Sciences I. He is affiliated with the Thermal and Fluid Systems program, the Institute for Computational Engineering and Sciences (ICES), and serves as Director of the DOE-funded Center for Predictive Engineering and Computational Sciences (PECOS). Ph.D. in Mechanical Engineering from Stanford University (1984) His research focuses on computational methods for turbulence modeling, cardiovascular fluid mechanics, and uncertainty quantification in complex physical simulations. He develops large-eddy simulation techniques for aerospace applications and biological flow analysis, while pioneering methods to characterize uncertainties in reentry vehicle simulations and turbulence modeling. Dr. Moser leads interdisciplinary research at PECOS and ICES, combining computational engineering with biomedical applications. His work spans theoretical turbulence physics, numerical methods for Navier-Stokes equations, and practical implementations for aerodynamic and medical device design.
Andreas Niekler is a research associate and lecturer in Computational Humanities at the Institute of Computer Science, Leipzig University, Faculty of Mathematics and Computer Science. He develops computational methods for semantic language analysis and applies them to computational social science and humanities research, with focus on machine learning and data management methodologies. His research interests span Natural Language Processing , Computational Social Science , Digital Humanities , Text Mining , Machine Learning , and Conversational AI . Niekler specializes in developing algorithms including Bayesian Models, Topic Models, Deep Learning, and Support Vector Machines for text analysis applications. He is actively involved in the development of the interactive Leipzig Corpus Miner platform and researches how semantic representations can be applied to literary studies and scientometrics. Niekler's recent publications demonstrate expertise across computational linguistics, social science methodology, and practical applications of text mining. His work shows strong interdisciplinary connections between computer science, linguistics, and social sciences, with particular emphasis on developing robust methodologies for automated content analysis. As an educator, Niekler has extensive teaching experience in computational methods for humanities and social sciences, offering courses on text mining, computational linguistics, and programming in R and Python. He serves as a scientific staff member in the Computational Humanities research group led by Prof. Dr. Manuel Burghardt and represents scientific staff in the Faculty Council of Mathematics and Computer Science at Leipzig University.
Eythan Levy serves as Senior Assistant in Digital Archaeology at the University of Zurich's Institute of Classical Archaeology within the Faculty of Arts and Social Sciences. Previously, he led an SNSF SPARK project at the University of Bern (2024) and conducted postdoctoral research on stamp seals from the Southern Levant (2022-2023). His research interests focus on computational approaches to archaeological problems, particularly: Computer applications and quantitative methods in archaeology Ancient chronology of the Iron Age Levant Northwest Semitic epigraphy and paleography Archaeology of the Southern Levant Ancient Egyptian archaeology and epigraphy His work bridges computer science and archaeology through innovative methodological frameworks. Levy's publication trends demonstrate consistent interdisciplinary output combining computational methods with archaeological analysis. Recent work focuses on chronological modeling tools, epigraphic analysis of Hebrew seals, multispectral imaging of ostraca, and computational approaches to ceramic typology. His research shows strong emphasis on developing formalized schemes for synchronizing archaeological data and creating specialized software solutions. Levy has developed several significant archaeological software tools : ChronoLog : For computer-assisted chronological modeling Scrypt : Web application for computer-assisted decipherment of ancient inscriptions TPQ Composer : For displaying stratigraphic termini post quem Artifacts Analyzer : For analyzing archaeological artifact datasets These tools represent his commitment to creating practical computational solutions for archaeological challenges. His academic background uniquely combines computer science and archaeology: PhD in Archaeology (Tel Aviv University, 2017-2021) PhD in Computer Science (Université Libre de Bruxelles, 2003-2009) Multiple MA degrees in Archaeology and Ancient Oriental Languages Teaching certificate for higher education This dual expertise enables his innovative approach to digital archaeology.
Wolfgang Windl is a Professor in the Department of Materials Science and Engineering at The Ohio State University with a joint appointment in Physics. He co-founded Goniotech LLC and previously worked at Motorola as a Principal Staff Scientist. He holds a doctoral degree in physics from the University of Regensburg and completed postdoctoral research at Los Alamos National Laboratory and Arizona State University. His research specializes in computational materials science, focusing on: Atomistic simulations and density-functional theory Machine learning applications in materials design Semiconductor transport and layered materials (e.g., Dirac semimetals) Atom probe tomography and characterization techniques Analysis of his 15 most recent publications (2023-2025) reveals dominant themes: advanced simulations of field evaporation, topological quantum materials (PtTe 2 , PdTe 2 ), and computational frameworks for materials characterization. His work frequently integrates spectroscopy, tomography, and Bayesian methods to study alloys, 2D materials, and additive manufacturing defects. Awards and Honors Fraunhofer-Bessel Research Award (2006) Four Lumley Research Awards Boyer Award for Teaching Excellence (2015) Faculty Diversity Excellence Award (2020) Two Mars Fontana Best Teacher Awards (2006, 2015) ASEE Best Paper & Diversity Awards (2019) He advises 11+ graduate students (7 alumni, 5 current) and leads the Windl Group research team focused on computational materials modeling. His group develops simulation tools for atomic-scale characterization and collaborates with national laboratories.
Georgios Arvanitidis is an Associate Professor at the Technical University of Denmark (DTU) in the Department of Applied Mathematics and Computer Science, specifically within the Section for Cognitive Systems (CogSys). He has established himself as a leading researcher in geometric machine learning, focusing on the application of differential geometry principles to enhance machine learning models. His work bridges theoretical mathematics with practical applications in artificial intelligence, with particular emphasis on understanding the geometric structure of data manifolds and latent spaces. Dr. Arvanitidis completed his educational journey with a Bachelor's degree from the Department of Informatics at the Aristotle University of Thessaloniki, followed by a Master's degree in Computer Science from Saarland University supported by the Max Planck Institute for Informatics. He earned his PhD at DTU's Cognitive Systems section under the supervision of Søren Hauberg, with additional research experience at Philipp Hennig's Probabilistic Numerics group. Prior to his current position as associate professor, he was a PostDoc at the Max Planck Institute for Intelligent Systems working with Bernhard Schölkopf. Dr. Arvanitidis's research primarily focuses on differential geometry in machine learning , where he explores how geometric structures can enhance representation learning and statistical modeling. His work in generative models investigates how learning the geometry of data manifolds can improve deep learning architectures. In the domain of deep learning theory , he examines why deep learning models generalize effectively on unseen data, with particular attention to the curvature properties of loss landscapes. His research in approximate Bayesian inference applies geometric principles to improve uncertainty quantification in neural networks. Through his innovative approaches, Dr. Arvanitidis has established himself as a leading researcher in geometric machine learning, contributing to both theoretical foundations and practical applications across various domains including robotics and life sciences. The publication trends of Dr. Arvanitidis reveal a consistent and evolving focus on geometric approaches to machine learning problems. His recent work (2023-2025) demonstrates increasing sophistication in applying Riemannian geometry to deep learning architectures, with particular emphasis on latent space geometry, optimization on manifolds, and geometric interpretations of neural network behavior. A notable pattern is the progression from foundational work on geometric representations to more applied research in areas like robotics and causal inference. His publications span top-tier conferences including NeurIPS, ICML, ICLR, and AISTATS, reflecting the high impact of his research. The interdisciplinary nature of his work is evident in collaborations across mathematics, computer science, and robotics domains, with recent papers addressing challenges in multimodal sampling, safety guarantees for dynamical systems, and counterfactual explanations. Dr. Arvanitidis has received several notable scientific awards and recognitions: Sapere Aude starting grant from the Independent Research Fund Denmark (DFF) GADL funding i-Rase, Pathfinder, and EIC (European Innovation Council) funding Best reviewer award for NeurIPS 2019 Best reviewer award for NeurIPS 2018 Best student paper award at Robotics: Science and Systems (R:SS) 2021 Dr. Arvanitidis actively mentors PhD students and researchers, currently supervising Alejandro Valverde, Johanna Gegenfurtner, and Albert Kjøller Jacobsen. He has previously co-supervised Alison Pouplin's PhD and worked with research assistant Georgios Pantis. His group receives substantial funding through multiple prestigious grants including the Sapere Aude starting grant from the Independent Research Fund Denmark, as well as European Innovation Council funding. He has been instrumental in creating opportunities for students interested in geometric machine learning, offering BSc and MSc thesis projects focused on generative models, deep learning theory, and optimization techniques. Dr. Arvanitidis also contributes significantly to the academic community as a reviewer for top conferences including ICLR and TMLR, and as an area chair for NeurIPS, ICML, AISTATS, and UAI. He co-organized the Machine Learning Summer School 2020 in Tübingen, further demonstrating his commitment to education and community building. Dr. Arvanitidis leads a vibrant research group focused on geometric machine learning within the Cognitive Systems section at DTU. His team includes multiple PhD students working on cutting-edge research at the intersection of differential geometry and artificial intelligence. The group has developed notable software tools, including the "geometric_ml" GitHub repository with over 70 stars, which contains implementations for applying Riemannian geometry in machine learning. His research has practical applications in robotics, where geometric approaches enable more robust motion planning, as evidenced by his work on "Reactive Motion Generation on Learned Riemannian Manifolds" which received a best student paper award. Additionally, his methodologies have found applications in life sciences, as mentioned in his 2022 AISTATS paper. The collaborative nature of his work is evident through extensive partnerships with researchers at institutions including the Max Planck Institute for Intelligent Systems, University of Cambridge, and various European universities. His recent news items indicate active engagement with the academic community through talks, conference presentations, and ongoing supervision of new PhD students joining his group.
Associate Professor Feng Chen is a faculty member at the School of Mathematics & Statistics, University of New South Wales, specializing in statistical methodology development and applications. His research bridges theoretical statistics and practical implementations across financial modeling, spatiotemporal processes, and public health analysis. PhD in Statistics from University of Hong Kong (2008) MSc in Applied Probability & Statistics from Lanzhou University (2004) BSc in Mathematics from Lanzhou University (2001) Research focuses include: Nonparametric and semiparametric statistical methods Point process modeling with emphasis on Hawkes processes Statistical computing and algorithm development Applications to financial data, earthquake analysis, and public health Recent publications demonstrate methodological advances in: Hawkes process estimation with complex data structures Renewal process applications in seismology GARCH modeling with missing data Spatiotemporal clustering analysis Scientific recognition includes: UNSW Science Staff Impact Award (2023) Professional roles: Director of Research Postgraduate Studies (2023--) Associate Editor for multiple journals Statistics Honours Coordinator (2013-2018) Active participant in statistical societies
Xin Li is a Professor in the Department of Electrical and Computer Engineering at Duke University and serves as the Associate Vice Chancellor at Duke Kunshan University. He holds a Ph.D. from Carnegie Mellon University (2005) and has held leadership roles in research consortia like the FCRP Focus Research Center and the Center for Silicon System Implementation (CSSI). His research bridges integrated circuits , machine learning , and cyber-physical systems , with applications in autonomous driving, battery lifetime prediction, and smart buildings. Education : Ph.D., Carnegie Mellon University (2005); M.S., Fudan University (2001); B.S., Fudan University (1998) His work emphasizes robust design methodologies for analog/RF circuits, data-driven predictive modeling , and Bayesian inference for high-dimensional variation spaces. Recent publications focus on generative adversarial networks for circuit design, multi-view imputation for incomplete data, and knowledge-driven autonomous systems . He has received numerous accolades, including the NSF CAREER Award (2012) , IEEE Donald O. Pederson Best Paper Awards (2013, 2016) , and IEEE Fellow (2017) . He has served as Editor for journals like IEEE Transactions on Biomedical Engineering and as Chair for conferences including ISVLSI and CAD/Graphics.
Xinyu Jia is currently a Humboldt Research Fellow at the Engineering Risk Analysis Group, Technical University of Munich since June 2024, and concurrently serves as Associate Professor in the Department of Mechanical Engineering at Hebei University of Technology, China since October 2022. Her research focuses on advancing uncertainty quantification, structural reliability, and risk assessment methodologies for engineering systems. Her academic background includes: PhD in Mechanical Engineering, University of Thessaly, Greece (2018-2021) Bachelor of Engineering and Master of Science in Mechanical Engineering, Hunan University, China (2011-2018) Dr. Jia specializes in Bayesian learning frameworks for physics-based models, with particular expertise in uncertainty propagation in structural dynamics and industrial robotics applications. Her work develops hierarchical Bayesian approaches that integrate multi-level data to enhance predictive accuracy for complex engineering systems, addressing critical challenges in structural health monitoring and risk-informed decision making. Analysis of her 2022-2023 publications reveals a concentrated research trajectory in applying Bayesian inference to structural dynamics, with emphasis on hierarchical modeling techniques, variational inference schemes, and nonlinear model updating. These contributions predominantly appear in top-tier mechanical engineering journals, demonstrating methodological innovations that bridge theoretical statistics with practical engineering reliability problems. Her scientific recognition includes: Humboldt Research Fellowship (2023) Marie Curie Early Stage Researcher Fellowship (2018) No specific student advisement records are documented, though her Associate Professor role implies teaching responsibilities. Her fellowship awards represent significant research funding supporting her work in uncertainty quantification. As an active member of TUM's Engineering Risk Analysis Group, she contributes to high-impact projects including digital twins for ships, S3UQDyn, Navigating Risk, and infrastructure resilience initiatives like BIG-ROHU and INFRA.RELEARN, focusing on probabilistic risk modeling across civil and mechanical engineering domains.
Harald Uhlig holds the Bruce Allen and Barbara Ritzenthaler Professorship at the University of Chicago Department of Economics. He is a leading expert in macroeconomics, monetary economics, and financial economics with a focus on dynamic stochastic general equilibrium models, Bayesian econometrics, and economic policy analysis. Ph.D. in Economics, University of Minnesota (1990) Diplom in Mathematics, TU Berlin (1985) His research spans business cycles, growth theory, financial crises, and economic policy, particularly analyzing monetary-fiscal interactions, sovereign debt dynamics, and the impact of financial markets on macroeconomic stability. He has pioneered methods in vector autoregressions and numerical solution techniques for economic models. The 15 most recent articles show a focus on cryptocurrency economics, sovereign debt crises in monetary unions, fiscal stimulus effects, and financial health economics. Subfields include DSGE modeling, asset pricing, and policy analysis under uncertainty. Frank P. Ramsey Prize (2005) Fellow of the Econometric Society (2003) Alfred P. Sloan Fellowship (1989-1990) Fulbright Scholarship (1985-1986) He has advised 21 PhD students across Tilburg University, Humboldt University Berlin, and University of Chicago. Grants include NSF awards for macroeconomic risk analysis and INET funding for research on economic fragility.
Aaron A. King, Ph.D. , is the Nelson G. Hairston Collegiate Professor of Ecology and Evolutionary Biology, Complex Systems, and Mathematics at the University of Michigan and an External Professor at the Santa Fe Institute . He is a Fellow of the American Association for the Advancement of Science and a Biological Sciences Scholar at the University of Michigan. His research integrates mathematical modeling, statistical inference, and empirical data to study ecological and epidemiological systems. Education: Ph.D. in Applied Mathematics, University of Arizona, 1999 M.A. in Mathematics, University of Hawai'i, 1992 B.A. summa cum laude in Mathematics, Rice University, 1989 Research Interests: Dr. King's lab focuses on the dynamics of ecological, epidemiological, and evolutionary systems . His work includes: Modeling host-pathogen systems (COVID-19, influenza, dengue, pertussis, cholera, malaria) Antibiotic resistance in hospital settings Statistical inference for ecological and epidemiological data Integration of genomic and epidemiological data Mathematical frameworks for understanding parasite infections and immune responses Scientific Awards: Fellow of the American Association for the Advancement of Science Biological Sciences Scholar, University of Michigan Teaching and Mentorship: Dr. King teaches courses in Mathematical Ecology , Adaptive Systems , and Statistical Inference . He mentors graduate students and postdoctoral fellows, including Avinash Subramanian and Madeline Peters , and is affiliated with multiple interdisciplinary centers at the University of Michigan. Lab and Collaborations: He leads the King Laboratory of Theoretical Ecology & Evolution , which emphasizes reproducible research and rigorous theoretical approaches. The lab collaborates with institutions like the Santa Fe Institute and accepts students from programs such as Ecology & Evolutionary Biology , Applied & Interdisciplinary Mathematics , and Data Science .
Professor Alex Archibald is the Professor of Atmospheric Chemistry in the Yusuf Hamied Department of Chemistry at the University of Cambridge. His research group investigates atmospheric chemistry-climate interactions through fundamental laboratory studies atmospheric observations numerical model simulations . Research interests focus on chemistry-climate feedbacks , including hydrogen economy impacts biogenic hydrocarbon oxidation air pollution mitigation marine sulfur cycling machine learning applications . Recent publications emphasize hydrogen-soil deposition dynamics ozone-temperature relationships DMS chemistry in Earth systems hydrogen economy climate implications AI-driven climate modeling transboundary pollution studies . Teaching includes Part I Kinetics of Chemical Reactions Part II Chemistry in the Atmosphere Part III IDP1 projects . The research team has 15+ current and former students working on topics from Martian atmospheric modeling to urban temperature extremes.
Leanna L. House is an Associate Professor in the Department of Statistics at Virginia Tech's College of Science. She specializes in Bayesian statistical modeling, with a focus on model averaging, kernel regression, and uncertainty analysis of computer models. Her research spans applications in proteomics, bioinformatics, cosmology, climatology, and hydrology. She teaches courses such as Applied Bayesian Statistics and Hierarchical Models, emphasizing data visualization and interactive analytics. Education: Ph.D. in Statistics, Duke University (2006) M.S. in Statistics, Duke University (2003) M.A.T. in Curriculum Development, Cornell University (1999) B.S. in Biometry and Statistics, Cornell University (1998) Her work integrates data mining and visualization to enhance human-data interaction, with notable contributions to environmental modeling and proteomic analysis. She collaborates on interdisciplinary projects, including climate change studies and route choice models in transportation systems.
Rohan Alexander is an Assistant Professor jointly appointed in the Faculty of Information and the Department of Statistical Sciences at the University of Toronto. His research focuses on improving the trustworthiness of data science through rigorous workflows, including reproducibility and bias mitigation. He co-founded The Data Workshop, a platform for data science best practices, and authored Telling Stories With Data , a book emphasizing reproducible methods. Rohan holds a PhD in Economics from the Australian National University, specializing in economic history. Rohan’s academic roles include Assistant Director of CANSSI Ontario and Senior Fellow at Massey College. His teaching includes courses like Experimental Design for Data Science and Worlds Become Data . He actively contributes to interdisciplinary initiatives like the Schwartz Reisman Institute for Technology and Society. His research interests span quantitative social science, Bayesian methods, text analysis, and computational reproducibility. He emphasizes code transparency and testing in data science projects. Rohan’s work addresses challenges in data measurement, such as systematic missingness, and explores AI’s societal impacts through collaborative projects with academia and industry. Rohan is affiliated with the Data Sciences Institute and leads initiatives on reproducibility. His current supervision includes PhD student Ciara Zogheib. His institutional roles extend to strategic projects like the Climate Positive Energy initiative and School of Cities.
