Beata Csatho PhD is a Professor in the Department of Earth Sciences at the University at Buffalo, affiliated with the College of Arts and Sciences. Her research focuses on remote sensing, glaciology, climate change, and geophysics. She holds a PhD in Geophysics from the University of Miskolc, Hungary (1993). Her work integrates geophysical, remote sensing, and climatic data to study ice sheet dynamics and cryospheric changes. She leads the Remote Sensing lab and teaches courses like GLY 465/565 (Environmental Remote Sensing) and GLY 325 (Geophysics). Recent research emphasizes Greenland and Antarctic ice dynamics, ICESat-2 validation, and developing tools like Ghub for collaborative glaciology. She advises PhD and Master's students and collaborates on major projects like ISMIP7 and IceBridge. Her lab focuses on advancing laser altimetry, DEM correction, and cryosphere observation techniques.
Teddy Mekonnen is an Orlando Bravo Assistant Professor of Economics at Brown University's Department of Economics. Previously, he was a Linde Postdoctoral Fellow at Caltech (California Institute of Technology). He holds a PhD in Economics from Northwestern University (2017) and a BA in Economics with a Mathematics minor from Washington University in St. Louis (2011). His research focuses on information economics and mechanism design, particularly studying informational externalities, incentives for information acquisition/sharing, and their applications to industrial organization and political economy. He also explores decision theory and static/dynamic settings. Teaching responsibilities include courses such as ECON 1110 (Intermediate Microeconomics), ECON 2060 (Microeconomics II), and ECON 2970 (Workshop in Economic Theory). His recent research spans topics like search market efficiency, competition dynamics, and Bayesian comparative statics. Though no explicit grants or awards are listed, his work reflects deep engagement with theoretical and applied economic problems.
Carlos Guestrin is the Fortinet Founders Professor of Computer Science at Stanford University and serves as Director of the Stanford AI Lab (SAIL) and Senior Fellow at the Institute for Human-Centered AI (HAI). He holds dual roles as Chief Scientist at Visual Layer and Virtue AI. His research focuses on machine learning methods, explainability, fairness, and ethics of AI, alongside systems for scalable AI deployment. Education details are not explicitly provided, but his work spans foundational contributions to machine learning systems (e.g., XGBoost) and explainable AI frameworks like Anchors and LIME. He emphasizes ethical AI through projects like CheckList for model testing and Model Equality Testing for API transparency. His scientific contributions include advancing optimization techniques (AdaScale SGD, TVM compiler) and ethical benchmarks for generative AI. He has been recognized as a Member of the National Academy of Engineering for his transformative impact on AI systems and their societal applications. Guestrin leads interdisciplinary initiatives at SAIL and HAI, fostering collaboration between technical innovation and human-centered design. His work bridges theory and practice, addressing challenges in healthcare (diabetes management systems) and AI security.
John Wakeley is a Professor of Organismic and Evolutionary Biology at Harvard University's Faculty of Arts and Sciences. He leads the Wakeley Lab, focusing on theoretical population genetics, mathematical models of genetic variation, and evolutionary processes. His research integrates analytical and computational methods to study contemporary and historical factors shaping genetic diversity. As of 2023, he is not accepting new graduate students for the academic year 2023-2024. Wakeley's work emphasizes coalescent theory, population structure, and evolutionary game theory. Notable contributions include developing statistical tools for analyzing ancient DNA and advancing models of ancestry reconstruction. Recent projects explore topics such as recurrent mutation in rare variants and the implications of big family effects on coalescence patterns. His lab members include researchers like Louis Fan, Jack Edwards, and Erin Ciccone, collaborating on diverse projects in theoretical and applied population genetics. Key scientific outputs include studies on iterated survival games and genomic analyses of butterfly radiation.
Yian Ma is an Assistant Professor at the University of California San Diego (UCSD). His research focuses on scalable inference methods, time series analysis, and sequential decision making, with an emphasis on developing Bayesian algorithms for uncertainty quantification and establishing their computational-statistical guarantees. Prior to UCSD, he served as a post-doctoral fellow at UC Berkeley. He holds a Ph.D. from the University of Washington and a bachelor’s degree from Shanghai Jiao Tong University. Education: Ph.D., University of Washington Bachelor's, Shanghai Jiao Tong University Research Interests: Yian Ma’s work bridges theoretical foundations and practical scalability in machine learning. He explores advanced Bayesian methodologies to address challenges in dynamic data analysis and decision processes, ensuring rigorous guarantees for both computational efficiency and statistical accuracy.
Prof. Dr. Bernd Lucke is a full professor at the University of Hamburg , affiliated with the Faculty of Business, Economics and Social Sciences and Department of Economics . He holds the Chair for Economic Growth and Business Cycles and has been active in research, teaching, and public policy discourse. Research Interests : Macroeconomics, Monetary Policy, European Economic Policy, Business Cycles, Econometrics, Development Policy, and Public Finance. Publications : His recent work (2025–2020) focuses on synthetic control methods for EU monetary analysis, ECB policy critiques, debt sustainability, digital currency legislation, and expropriation impacts on FDI. Earlier works span econometric testing of Ricardian equivalence, productivity shocks, and growth modeling. Media Contributions : Regular political commentary in outlets like Cicero and Frankfurter Allgemeine Zeitung , often critiquing ECB decisions, EU fiscal integration, and inflation dynamics. Contact : bernd.lucke@uni-hamburg.de
Petter N. Kolm serves as a Clinical Professor of Mathematics and Program Director at New York University, with his office located in Warren Weaver Hall (520). He can be contacted at petter.kolm@nyu.edu or 212-998-4855, and holds an editorial board position at the Journal of Portfolio Management. His academic qualifications include: Doctorate in Mathematics from Yale University M.Phil. in Applied Mathematics from the Royal Institute of Technology in Stockholm M.S. in Mathematics from ETH Zurich Dr. Kolm's research centers on quantitative finance, with primary focus areas including quantitative trading strategies, delegated portfolio management, financial econometrics, risk management, and optimal portfolio strategies. His work integrates advanced mathematical modeling with practical investment applications, bridging theoretical frameworks and real-world market dynamics through rigorous empirical analysis. Analysis of his 15 most recent publications reveals consistent emphasis on portfolio optimization techniques—particularly Bayesian methods and the Black-Litterman model—alongside significant contributions to algorithmic trading systems, factor-based equity portfolio construction, and machine learning applications for financial sentiment analysis. His scholarly output demonstrates evolution from foundational portfolio theory toward contemporary computational finance challenges. As Program Director, Dr. Kolm oversees academic programming and likely mentors graduate students in quantitative finance, though specific advisee details are not documented. His prior industry role at Goldman Sachs Asset Management provided direct experience in developing hedge fund strategies, informing his applied research approach. Dr. Kolm's professional trajectory includes significant industry engagement through his tenure in Goldman Sachs' Quantitative Strategies Group, where he developed quantitative investment systems. His current academic leadership position leverages this practical experience to shape quantitative finance education and research at NYU.
Carlos Fernandez-Granda is an Associate Professor of Mathematics and Data Science at New York University, holding joint appointments at the Courant Institute of Mathematical Science and the Center for Data Science. He currently serves as the Interim Director of the Center for Data Science. His academic career spans over a decade at NYU, where he has taught probability and statistics to data-science students. Dr. Fernandez-Granda's research focuses on designing and analyzing data-science methodology, with current emphasis on machine learning applications in medicine, climate science, and scientific imaging. His work bridges theoretical foundations with practical applications across multiple domains. His notable contributions include: Development of the COBRA (COnfidence-Based chaRacterization of Anomalies) score for automatic assessment of impairment and disease severity Applications of machine learning to improve climate projections through the M2LInES project Research on magnetic resonance fingerprinting for quantitative tissue parameter estimation Development of AI systems for medical diagnostics including Alzheimer's detection and breast cancer diagnosis Dr. Fernandez-Granda is the author of the book "Probability and Statistics for Data Science," published by Cambridge University Press. The book serves as a comprehensive guide to the two pillars of data science, featuring real-world datasets and addressing fundamental challenges like overfitting, the curse of dimensionality, and causal inference. His research has been supported by grants from the National Science Foundation (Division of Mathematical Sciences, grants 1616340 and 2009752) and the Alzheimer's Association (grant AARG-NTF-21-848627). Dr. Fernandez-Granda is actively involved in several collaborative projects: M2LInES project: An international collaboration focused on improving climate projections using machine learning to capture unaccounted physical processes at the air-sea-ice interface Math and Data group: Exploring the intersection of mathematical theory and data science applications
Daniel Kifer is a Professor in the Computer Science and Engineering department at Pennsylvania State University, with affiliations to the Huck Institutes of the Life Sciences. His work bridges computer science, privacy-preserving machine learning, and geoscience applications. With over 10,000 citations and a high h-index, he focuses on methods to unify theoretical and applied research. Research Interests: Differential Privacy, Privacy-Preserving Machine Learning, Physics-Informed Neural Networks, Landslide Prediction, and Formal Verification of Privacy Systems. Recent projects include grants from the National Science Foundation: SaTC: CORE: Small (2024): privacy-preserving user data embedding in machine learning pipelines. SaTC: CORE: Medium (2017-2023): formal methods for differential privacy and accuracy optimization. His research outputs span domains like geoscience, database systems, and policy analysis, emphasizing precision and scalability of privacy-preserving algorithms.
Kathrine von Graevenitz is Deputy Head of the Environmental and Climate Economics research unit at ZEW - Leibniz Centre for European Economic Research in Mannheim, Germany, and Professor of Empirical Environmental Economics at the University of Mannheim since October 2021. She is sponsored by the Leibniz Association through the Program for Women Professors and serves on the scientific consultative group to the Research Data Centres of the German Statistical Offices. Her research spans environmental and urban economics with a methodological focus on applied microeconometrics. She investigates the impact of environmental and climate regulation on firm performance, the drivers of renewable energy technology adoption, and revealed preferences in housing markets, particularly addressing spatial correlation and endogeneity challenges. Her work often utilizes administrative micro-data to evaluate policy impacts on manufacturing sectors, housing markets, and environmental outcomes. Analysis of her recent publications reveals a strong focus on empirical evaluation of climate policies, particularly their effects on German manufacturing competitiveness, electricity pricing mechanisms, and renewable energy adoption. Her research combines rigorous econometric methods with practical policy relevance, frequently cited by German economic policy institutions including the German Council of Economic Experts. Her work demonstrates a consistent trajectory from urban environmental valuation (her PhD focus) to broader climate policy evaluation in industrial and housing sectors. Cited in the annual report of the German Council of Economic Experts 2022 Cited in corresponding VoxEU column Winner of the Department of Economics Prize for best thesis at University of Essex (2004/2005) Member of Female Economists' Network of German Ministry for Economic Affairs As Deputy Head of ZEW's Environmental and Climate Economics unit, von Graevenitz leads research on environmental regulation impacts and manages collaborations with policy institutions. Her work has been covered by major German media outlets including Zeit Online, Süddeutsche Zeitung, and Tagesspiegel, demonstrating its policy relevance. She has been involved in multiple policy briefs for ZEW and government bodies, translating academic research into practical policy recommendations. Her research is conducted within ZEW's Environmental and Climate Economics unit, which focuses on empirical analysis of environmental policies, climate economics, and sustainability transitions. The unit works closely with German policy institutions and contributes to national climate policy discussions, particularly regarding manufacturing sector impacts and energy transition challenges.
Yize Zhao is an Associate Professor in the Department of Biostatistics at Yale School of Public Health and an Associate Professor in the Department of Biomedical Informatics & Data Science at Yale University. She holds affiliations with multiple Yale research centers including the Yale Center for Analytical Sciences, Yale Alzheimer's Disease Research Center, Yale Wu Tsai Institute, Yale Center for Brain and Mind Health, and Yale Computational Biology and Bioinformatics. Dr. Zhao's research focuses on developing statistical and AI methods to analyze large-scale complex biomedical data including medical imaging, genomics, and electronic health records. Her methodological expertise spans Bayesian statistics, feature selection, predictive modeling, data integration, missing data analysis, and network analysis. Her research interests span multiple biomedical domains with a strong focus on mental health, psychiatry, neurodegenerative diseases, and aging. Her recent work includes brain-to-behavior modeling, multi-layer biomedical networks, imaging genetics and genomics, and the integration of multi-modal biomedical data with real-world data. Dr. Zhao's work has resulted in numerous high-impact publications, with recent research focusing on Alzheimer's disease, brain network analysis, and advanced statistical methods for neuroimaging. Her publications show a strong trend toward integrating multi-modal data sources and developing sophisticated statistical approaches to address complex biomedical questions. Thelma and Marvin Zelen Emerging Women Leaders in Data Science Award from the Institute of Mathematical Statistics (IMS) COPSS Emerging Leader Award from the Committee of Presidents of Statistical Societies (COPSS) YSPH Investigator Research Award Yale Alzheimer's Disease Research Center Research Scholar Award Elected member of the International Statistical Institute Dr. Zhao serves as an Associate Editor for Biometrics and is a standing member of the NIH Biodata Management and Analysis (BDMA) study section. Her research is supported by multiple NIH grants, highlighting the significance and impact of her work in biostatistics and biomedical data science.
Jan Oskar Engene is an Associate Professor in the Department of Comparative Politics at the University of Bergen, Norway. His academic career spans several decades with significant contributions to both terrorism studies and heraldry research. Engene's research interests have evolved significantly over time, beginning with a strong focus on terrorism, political violence, and European security. His early work centered on the TWEED (Terrorism in Western Europe: Explaining the Trends) dataset, which documented terrorist incidents across Western Europe from 1950 onward. Over the years, his research expanded to include political symbolism, heraldry, and flag history, particularly regarding Norwegian political symbols and their evolution. His work demonstrates a unique interdisciplinary approach, bridging political science with historical and symbolic analysis. Analysis of Engene's publication trends reveals a clear evolution from predominantly terrorism-focused research in the 1990s and early 2000s toward increasing engagement with heraldry, political symbolism, and Norwegian historical identity markers in recent years. The earlier works focus on quantitative terrorism analysis, counterterrorism policy, and European security frameworks, while more recent publications examine municipal heraldry, county coats of arms, and historical political symbols. This shift demonstrates Engene's ability to apply political science methodologies to diverse research areas while maintaining a consistent focus on political symbolism and identity. Engene has supervised numerous Master's theses at the University of Bergen, primarily in the areas of terrorism studies, European politics, and political identity. His supervision record includes students researching topics such as state responses to terrorism in Italy, Nordic political systems, and regional identity in Western Europe. While specific grant information isn't detailed in the available materials, his TWEED project represents a significant research undertaking that likely involved external funding. Engene maintains active engagement with public discourse through media appearances, having participated in numerous television and radio programs discussing terrorism, security policy, and political symbolism. His work with the TWEED project established him as a leading expert on European terrorism patterns, while his more recent heraldry research has positioned him as a specialist in Norwegian political symbolism and historical iconography.
Sylvia Richardson is an MRC Investigator at the MRC Biostatistics Unit and holds a Research Professorship at the University of Cambridge, where she served as Director of the Biostatistics Unit from 2012 to 2021. She is affiliated with the Cambridge Mathematics of Information in Healthcare Hub (CMIH) at the Centre for Mathematical Sciences. Her work bridges advanced statistical methodology with critical healthcare applications, particularly in the analysis of complex biomedical data. Richardson's research spans multiple domains of biostatistics with a strong emphasis on Bayesian approaches. Her work has significantly advanced spatial modeling and disease mapping techniques, developed sophisticated methods for handling measurement error in epidemiological studies, and pioneered mixture and clustering models for integrative analysis of heterogeneous data sources. Her research addresses fundamental challenges in analyzing longitudinal health data, multimorbidity patterns, and complex disease trajectories. Her publication record demonstrates consistent methodological innovation applied to pressing healthcare challenges. Recent work focuses on traumatic brain injury outcomes, multimorbidity progression, genomic analysis, and statistical approaches to pandemic data. The articles reveal a strong pattern of methodological development driven by real-world healthcare challenges, with particular attention to longitudinal analysis, Bayesian computation, and integrative modeling approaches that can handle diverse and complex data structures. While specific awards are not detailed in the available information, Richardson's leadership as Director of the MRC Biostatistics Unit for nearly a decade and her continued Research Professorship reflect significant recognition of her contributions to the field. Her work with major international consortia like CENTER-TBI demonstrates her role in large-scale collaborative research efforts addressing critical health challenges. Richardson's research has substantial implications for healthcare policy and practice, particularly in understanding disease progression, developing predictive models for patient outcomes, and creating methodological frameworks that can integrate diverse data sources to generate meaningful clinical insights. Her work continues to influence both statistical methodology and healthcare applications through ongoing research and leadership in the field.
Laura Toni is an Associate Professor in the Department of Electronic and Electrical Engineering at University College London's Faculty of Engineering Sciences. She serves as the leader of a research team focused on advanced signal processing and machine learning applications, documented at https://lasp-ucl.github.io . Additionally, she holds prestigious affiliations as an ELLIS (European Laboratory for Learning and Intelligent Systems) Member and Turing Fellow Alumni. PhD in Electrical Engineering, University of Bologna (2009) MS in Electrical Engineering, University of Bologna (2005) Professor Toni's research spans theoretical and applied aspects of machine learning with particular emphasis on graph-based approaches. Her work integrates signal processing techniques with modern AI methodologies to address complex problems in communication systems, multimedia processing, and scientific discovery. She has made significant contributions to reinforcement learning theory, graph signal processing, and their applications across diverse domains including drug discovery and immersive technologies. Analysis of her recent publications reveals a strong focus on graph-based machine learning approaches, with increasing emphasis on reinforcement learning applications. Her work demonstrates a progression from theoretical foundations to practical implementations, particularly in multimedia processing, network science, and drug discovery applications. Many of her recent papers combine graph neural networks with diffusion models and reinforcement learning for complex prediction and generation tasks. Professor Toni has received notable recognition through her ELLIS membership and Turing Fellow Alumni status, which represent significant achievements in the European AI research community. ELLIS (European Laboratory for Learning and Intelligent Systems) Member Turing Fellow Alumni As an academic leader, Professor Toni supervises postgraduate students and leads a research team at UCL, focusing on cutting-edge projects at the intersection of signal processing and machine learning. Her team has secured research funding through various channels including European initiatives and industry partnerships, enabling them to pursue ambitious projects in graph learning, reinforcement learning, and multimedia processing. The team actively collaborates with institutions worldwide, including previous connections with UCSD and EPFL. Professor Toni leads the LASP research group at UCL (https://lasp-ucl.github.io), which focuses on Large-scale Adaptive Signal Processing for intelligent systems. The team comprises researchers working on graph signal processing, reinforcement learning, and multimedia applications, with strong connections to both theoretical foundations and practical implementations across various domains including healthcare, communications, and immersive technologies.
Dr. Steven A. Miller is a Professor of Psychology in the Department of Psychology at Rosalind Franklin University of Medicine and Science, within the College of Health Professions. He joined RFUMS in 2013 and serves as a statistics consultant for the university. His academic background includes a PhD in Social Psychology from Loyola University Chicago, an M.S. in Psychology from Illinois State University with specialization in Clinical Psychology, and an M.S. in Mathematics from Loyola University Chicago with specialization in Probability and Statistics. PhD in Social Psychology, Loyola University Chicago M.S. in Psychology, Illinois State University (Clinical Psychology specialization) M.S. in Mathematics, Loyola University Chicago (Probability and Statistics specialization) Dr. Miller's research focuses on the intricate relationship between personality characteristics/individual differences and emotional experiences. He investigates anxiety and emotional disorders, social cognitive models of personality, and applies quantitative methodology to psychological questions. His work examines intra-individual variability in emotional responses and how situational factors interact with personality to shape emotional experiences. He employs diverse methodologies including experience sampling studies and laboratory experiments to explore these complex dynamics. His recent publications demonstrate a strong focus on psychopathy, emotion regulation, network analysis of personality, and the tripartite model of anxiety and depression. His work spans clinical, forensic, and general populations, often employing sophisticated statistical techniques. There's a clear trajectory toward more complex modeling approaches including network analysis, longitudinal modeling, and advanced psychometric techniques across his publication history. Accredited Professional Statistician (PStat®) with the American Statistical Association Chartered Statistician (CStat) with the Royal Statistical Society Dr. Miller actively mentors graduate students, with numerous student co-authors appearing in his publications. He teaches advanced statistical courses including multivariate statistics, longitudinal models, and categorical data analysis. He is currently accepting doctoral students for the 2026/2027 academic year. His collaborative research spans multiple institutions including DePaul University and Texas A&M, focusing on emerging adults, romantic relationships, and chronic illness. His research laboratory examines the fundamental relationship between personality and emotion, exploring how situational contingencies and individual expectancies shape emotional responses. Current collaborative projects investigate daily experiences of emerging adults, psychopathy in romantic relationships, and social media use among individuals with chronic illness using diverse methodological approaches.
