جست‌وجوی استاد و پژوهشگر

• Fluid Dynamics
• Turbulence
• Machine Learning
• +۶ مورد دیگر

Adrián Lozano-Durán is an Associate Professor of Aerospace at the California Institute of Technology (Caltech), affiliated with the Guggenheim Laboratory for Aeronautics (GALCIT). He holds a B.S., M.S., and Ph.D. from the Polytechnic University of Madrid (2010–2015) and joined Caltech as a Visiting Associate in 2024 before becoming a faculty member in the same year. His research focuses on fluid dynamics, turbulence, and machine learning applications in computational fluid dynamics (CFD), particularly for aerospace systems. He leads the Aerofluids, Learning & Discovery (ALD) Lab, collaborating with MIT’s AeroAstro department. Key research areas include causal inference in fluid systems, reduced-order modeling, and machine-learning-based closure models for large-eddy simulation (LES). His work addresses challenges in low-speed aerodynamics, supersonic, and hypersonic flows. Notable recent contributions include advancements in LES wall models and information-theoretic approaches to turbulence control. He frequently presents at international conferences and has co-authored high-impact papers in Nature Communications , Journal of Fluid Mechanics , and Physical Review Research . Education: B.S., Polytechnic University of Madrid (2010) M.S., Polytechnic University of Madrid (2012) Ph.D., Polytechnic University of Madrid (2015) Affiliations: GALCIT, Caltech AeroAstro, MIT (collaboration) Advising focuses on students like Álvaro Martínez-Sánchez and Tristan, whose work spans causality in turbulence and flow control. He actively engages in interdisciplinary research, bridging fluid mechanics with machine learning and information theory to advance aerospace engineering solutions.

Byung-Jun Kimمشاهده پروفایل

استادیار

• Nonparametric Regression
• Semiparametric Regression
• High-Dimensional Data Analysis
• +۴ مورد دیگر

Byung-Jun Kim is an Assistant Professor in the Department of Mathematical Sciences at Michigan Technological University, where he joined as a tenure-track faculty in August 2020. His research focuses on statistical methodologies for complex observational data, particularly in nonparametric/semiparametric regression frameworks under high-dimensional and measurement error scenarios. PhD in Statistics from Virginia Tech (2020) BS/MS in Statistics from Chung-Ang University Research Expertise: Multivariate data analysis Covariance matrix estimation and graphical modeling Kernel regression in machine learning Statistical inference with measurement errors

Sewon Minمشاهده پروفایل

استادیار

• Artificial Intelligence
• Natural Language Processing
• Machine Learning
• +۲ مورد دیگر

Sewon Min is an Assistant Professor at UC Berkeley's Electrical Engineering and Computer Sciences (EECS) department and a research scientist at the Allen Institute for AI. Her research focuses on Natural Language Processing (NLP) and Machine Learning, particularly Large Language Models (LLMs), emphasizing data-centric approaches and ethical AI practices. She holds a Ph.D. from the University of Washington (2024) and a B.S. from Seoul National University (2018). Her work includes advancements in retrieval-based models, mixture-of-experts architectures, and data privacy in LLMs. Notable projects include FlexOlmo (flexible data use in LLMs) and OLMoE (open mixture-of-experts models). She has been recognized with the ACM Doctoral Dissertation Award Honorable Mention (2025) and WAGS/ProQuest Innovation in Technology Award. Recent articles highlight her contributions to reasoning models, data tracing (OLMoTrace), and scalable retrieval systems (MassiveDS). She leads the Berkeley NLP Group and collaborates with BAIR, exploring topics like model transparency and ethical data usage.

Changyou Chenمشاهده پروفایل

دانشیار

• Multi-Modal Learning
• Vision and Language
• Foundation Models
• +۷ مورد دیگر

Dr. Changyou Chen is an Associate Professor in the Department of Computer Science and Engineering at the University at Buffalo, State University of New York. His research focuses on Multi-Modal Learning Foundation Models Deep Generative Models Large-scale Bayesian Sampling with applications in document understanding, music-AI integration, and molecular representation learning. Research Trends revealed through his recent publications include Optimizing Multimodal Large Language Models Developing Novel Retrieval-Augmented Generation Frameworks Creating Benchmark Datasets for Visual Text Understanding Advancing Diffusion Models with Domain-Specific Constraints across domains from music sheets to biomedical documents. Scientific Contributions : UB Young Investigator Award (2020) Architect of LoCAL Framework for Long Document Understanding Co-developer of MusiXQA Benchmark Pioneering Work in Probability Contrastive Learning Academic Leadership includes mentoring 10+ graduate students and serving as Area Chair for major AI conferences (ICML, NeurIPS, AAAI, IJCAI). His Labs develop scalable solutions for multimodal reasoning, with recent work demonstrating practical GPU memory optimization through LoRA adapter sharing.

Tunye Qiuمشاهده پروفایل

مربی ارشد

• Applied Economics
• Chinese Economy
• Causal Inference
• +۴ مورد دیگر

Tunye Qiu is a recent PhD graduate in Economics from the Crawford School of Public Policy at the Australian National University (ANU) . He is currently on the job market and holds roles as a Senior Tutor and Associate Lecturer in the Research School of Economics (RSE) at ANU, where he has taught courses in microeconomics, macroeconomics, game theory, and policy impact evaluation since 2021. He also serves as the Program Manager of the China Economy Program and an Assistant Editor for the Journal of Chinese Economic and Foreign Trade Studies. Education : PhD in Economics (2024) at Crawford School, ANU; BEcon (ANU); MEcon (ANU). Research Interests : His work focuses on labor economics, urban economics, demographic economics, and development economics. He explores causal impacts of government policies on China’s housing market, fertility rates, land supply behavior, and housing demand, alongside international trade, labor markets in South Pacific countries, and educational outcomes in Australia. Teaching & Roles : Senior Tutor, Microeconomics 1 (2021-2024), Macroeconomics 1 (2024), Business Economics (2024), Strategic Thinking (2021-2023) at RSE, ANU. Teaching Quantitative Policy Impact Evaluation (2022-2024), Issues in Development Policy (2023-2024), and The Economic Way of Thinking (2023) at the Crawford School. Research Trends : His publications address housing affordability (2024), marriage delay (2024), policy impacts on housing prices (2024), land supply dynamics (2024), trade dynamics with China (2022), and migration patterns in China (2020). These works integrate causal inference methods and policy evaluation frameworks. Email : tunye.qiu@anu.edu.au

Akshaya Jhaمشاهده پروفایل

دانشیار

• Energy Economics
• Environmental Economics
• Industrial Organization
• +۳ مورد دیگر

Akshaya Jha is an Associate Professor of Economics and Public Policy at Carnegie Mellon University’s Heinz College of Information Systems and Public Policy , as well as a Faculty Research Fellow at the National Bureau of Economic Research (NBER) . His work combines economic modeling with causal inference to analyze energy and environmental policy impacts on electricity markets. Education: Ph.D. in Economics, Stanford University B.S. in Economics and Statistics, Carnegie Mellon University Research Interests span energy economics , environmental economics , industrial organization , and public policy , focusing on quantifying economic and environmental trade-offs in electricity supply policies. Recent work includes financial trading in California’s electricity market, Germany’s nuclear phase-out, rooftop solar growth in Western Australia, and electricity blackout determinants in India. Scientific Contributions appear in American Economic Review , Management Science , and PNAS . Article trends reveal expertise in regulatory distortions , market design , environmental externalities , and policy communication . Scientific Awards: Hicks-Tinbergen Award (best paper in Journal of the European Economic Association ) Heinz College Martcia Wade Teaching Award (2023) USAEE Young Professional Research Award (2021)

Anna Weissmanمشاهده پروفایل

پژوهشگر ارشد

• Political Representation
• Local Political Economy
• Political Geography
• +۳ مورد دیگر

Anna Weissman is a Political Scientist completing her PhD at the University of California, Berkeley in 2025. She will join the Center for the Study of Democratic Politics at Princeton University as a Postdoctoral Fellow starting September 2025. Her academic home is within the Department of Political Science in the College of Letters and Science at UC Berkeley. PhD, Political Science, University of California, Berkeley (2025) B.A., Political Science, Philosophy, and German, Tufts University Weissman's research focuses on representation, local political economy, and political geography, with specific attention to who holds power in American local governments and how this drives policymaking. Her work examines municipal officials' embeddedness in their communities and the implications for policy outcomes, particularly relating to land use, with special interest in small-town and rural America. Another significant branch of her research investigates attitudes about descriptive representation at the national level. She employs diverse methodological approaches including causal inference designs for observational data, text analysis, and original surveys and survey experiments. Her publication record shows a strong focus on representation and local politics, with articles appearing in top political science journals including the Journal of Politics and the Journal of Political Institutions and Political Economy. Her work demonstrates consistent attention to how representation functions across different political contexts and scales. Research Associate, Political Science Department, MIT (prior to graduate school) Weissman has served as a Graduate Student Instructor and Tutor at UC Berkeley for multiple courses including Decision Analysis and Quant Methods, Politics of Public Policy, Introduction to American Politics, and research methods courses. Her teaching experience spans both graduate and undergraduate levels, demonstrating her commitment to political science education. Her research portfolio includes significant work on local government dynamics, representation, and political behavior, with several publications in prestigious journals and multiple works in progress examining the relationship between place-based attachment and political attitudes.

Michael Evansمشاهده پروفایل

استاد

• Statistical Computing
• Theoretical Statistics
• Statistical Inference
• +۳ مورد دیگر

Professor Michael Evans is a faculty member at the University of Toronto , affiliated with the Department of Statistical Sciences (St. George campus) and the Department of Computer and Mathematical Sciences at the Scarborough Campus . His research focuses on statistical inference , Bayesian methods , and measuring statistical evidence through his work on the relative belief ratio . He has contributed to ROC analysis , linear models , and stochastic processes , with recent work addressing biases in statistical reasoning and connections to frequentist criteria. Research Themes : Defining and measuring statistical evidence Bayesian inference and prior-data conflict resolution Monte Carlo methods and stochastic process theory Recent Publications explore statistical evidence in scientific practice, medical diagnostics, and Bayesian frameworks. His 2024 Encyclopedia article critiques frequentist criteria, while the 2022 Entropy paper introduces relative belief in ROC analysis. Awards : ASA Fellow (American Statistical Association) Teaching : He teaches advanced courses like STAC62 (Probability and Stochastic Processes) and STAC63 (Probability and Stochastic Processes II), emphasizing theoretical rigor and applications in fields like mathematical finance and machine learning .

• Database Systems
• Graph Databases
• Data Management
• +۷ مورد دیگر

Amol Deshpande is a Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley, College of Engineering. With over 160 publications spanning from 2000 to 2025, his research has significantly impacted the database systems community. His work bridges theoretical foundations with practical systems, evidenced by numerous publications in top-tier venues including SIGMOD, VLDB, and ICDE. Professor Deshpande's research focuses on database systems, with particular expertise in graph databases, data management, probabilistic databases, query optimization, and data provenance. His work addresses fundamental challenges in managing complex data, including efficient graph analytics, dataset versioning, streaming data processing, and privacy-preserving data management. Recent research directions include entity-relationship abstractions beyond traditional relations, standalone catalog engines for large data systems, and graph theoretical approaches to dataset versioning. His publication trends show a consistent focus on evolving database technologies, with early work on probabilistic databases and query optimization, transitioning to graph analytics and data provenance, and more recently addressing modern challenges in data cataloging, privacy-first data management, and serverless stream processing. His research spans both theoretical contributions (e.g., approximation algorithms for stochastic optimization) and practical systems building (e.g., RStore, TreeCat). Professor Deshpande has mentored numerous PhD students who have become active researchers in the database community, including Hui Miao, Souvik Bhattacherjee, and Konstantinos Xirogiannopoulos. His collaborative work spans across institutions, with frequent collaborations with researchers from MIT, University of Maryland, and other leading institutions. His research has been supported by major funding agencies and has influenced both academic research and industry practices in data management. The evolution of his work reflects the changing landscape of data management, from traditional relational systems to modern graph and streaming data challenges.

Panagiotis Zervopoulosمشاهده پروفایل

دانشیار

• operations research
• efficiency measurements
• performance measurements
• +۶ مورد دیگر

Panagiotis Zervopoulos is an Associate Professor at the Department of Business Organization and Administration within the School of Economics, Business and International Studies at the University of Piraeus. Previously, he served as Associate Professor and Director of the PhD Program in Business Administration at the School of Business Administration of the University of Sharjah in the UAE. His research interests focus on operations research, efficiency and performance measurements, optimization methods, and econometrics. Specifically, he specializes in developing new data envelopment analysis (DEA) techniques, Bayesian methods, parametric and non-parametric econometric models, with innovative applications across multiple disciplines. His methodological contributions have been applied in diverse fields including supply chain management, financial systems, environmental efficiency, and corporate governance. Zervopoulos has published extensively in top-tier journals such as European Journal of Operational Research, Journal of the Operational Research Society, Annals of Operations Research, Journal of Financial Stability, and Journal of Cleaner Production. His recent work (2023-2024) demonstrates continued innovation in efficiency measurement techniques, particularly in network DEA models with bias correction, environmental efficiency analysis, and systemic risk measurement. His publications show strong international collaboration patterns, frequently working with researchers from different countries and institutions. He has served as Guest Editor for journals including Socio-Economic Planning Sciences and IMA Journal of Management Mathematics, demonstrating recognition of his expertise by the academic community. Zervopoulos has held research fellowships at prestigious institutions worldwide, including Peking University (China), the London School of Economics (United Kingdom), the Academy of Athens (Greece), and the Foundation for Economic and Industrial Research (Greece). He also participates in the World Economic Survey and the Economic Expert Group of the Ifo Institute (Germany). Professionally, he has served as Senior Consultant Modeling Statistician at IRi Worldwide and as Project Manager and Expert in Quantitative Analysis and Public Sector Reform through technical support projects with the European Public Law Agency.

• Finance
• Asset Pricing
• Liquidity Risk
• +۳ مورد دیگر

Peter Kondor is an Assistant Professor affiliated with the London School of Economics & Political Science (LSE) and Central European University (CEU). His research focuses on finance, asset pricing, liquidity risk, market microstructure, and arbitrage dynamics. Research Interests: Asset pricing with heterogeneous agents Liquidity risk and intermediary capital Over-the-counter market structures Behavioral finance and sentiment analysis Global financial cycles and investment waves Information diffusion and market stability Key Publications Trends: 2011-2025: Explores causal inference in asset pricing, hedge fund impacts on idiosyncratic risk, and liquidity risk dynamics. 2018: Investigates arbitrage capital and liquidity risk in global markets. 2020-2025: Analyzes rational sentiments, narrative momentum, and aggregate earnings.

Hyuck Jin Parkمشاهده پروفایل

استاد

• Landslide Hazard Assessment
• Geotechnical Engineering
• Machine Learning Applications
• +۵ مورد دیگر

Hyuck Jin Park is a Full Professor in the Department of Energy Resources and Geosystems Engineering at Sejong University, South Korea, where he has been teaching and conducting research since 2003. With a Ph.D. in Engineering Geology from Purdue University, his expertise spans geotechnical engineering, landslide analysis, and geospatial technologies. Professor Park has built a distinguished career in landslide hazard assessment, combining traditional geotechnical approaches with modern machine learning techniques to improve prediction accuracy and risk management. His educational background includes: B.S. in Geology from Yonsei University (1990) M.S. in Geophysics from Yonsei University (1993) Ph.D. in Engineering Geology from Purdue University (2011) Professor Park's research focuses on the spatial and temporal probability of landslide occurrence, utilizing fuzzy logic, probabilistic analysis, GIS, Monte Carlo simulation, and machine learning for landslide hazard assessment. His work integrates physically based models with statistical approaches to better understand landslide mechanisms and improve prediction capabilities. He has made significant contributions to the development of methodologies that account for geological uncertainties in hazard assessment, with applications ranging from rock slope stability to rainfall-induced shallow landslides. His recent publications demonstrate a clear trend toward integrating explainable artificial intelligence with traditional geotechnical approaches for natural hazard assessment. Professor Park's work increasingly focuses on making machine learning models transparent and interpretable while maintaining high predictive accuracy. The research spans multiple hazard types including landslides, earthquakes, and floods, with a growing emphasis on climate change impacts and data-scarce environments. With an h-index of 28 and over 3,421 citations, Professor Park has established himself as a leading researcher in his field. His work has been published in high-impact journals including Engineering Geology, Landslides, and Catena, reflecting the significance and quality of his contributions to geotechnical engineering and natural hazard assessment. Professor Park has mentored numerous researchers through collaborative projects and has secured funding for his innovative work in landslide prediction and hazard assessment. His research has involved significant international collaboration, particularly with researchers from Malaysia, Australia, and Yemen, addressing landslide and flood risks in diverse geographical contexts. He leads research activities within the Department of Geoinformation Engineering at Sejong University and has contributed to the development of specialized tools like DEWS (Distance, Elevation, Watershed, and Slope unit) for landslide early warning systems.

• Computational Physiology
• Cardiac Electrophysiology
• Atrial Fibrillation
• +۷ مورد دیگر

Molly Maleckar is a Research Professor at the Computational Physiology Department of Simula Research Laboratory , Oslo, Norway. Her work bridges computational modeling, cardiac electrophysiology, and biomedical applications, with a focus on arrhythmia mechanisms, fibrosis modeling, and machine learning integration in cardiac risk prediction. Research Interests include: Computational Cardiology Ion Channel Dynamics Machine Learning in Medicine Excitable Tissue Modeling Cardiac Fibrosis Analysis Biomedical Simulation Scientific Contributions span 15+ publications (2018-2024) addressing atrial fibrillation, calcium handling, and AI-driven ECG analysis. Key collaborative projects involve patient-specific ventricular modeling and educational initiatives like the Simula Summer School in Computational Physiology .

Harald Tauchmannمشاهده پروفایل

استاد

• Health Economics
• Empirical Health Economics
• Health Insurance
• +۳ مورد دیگر

Prof. Dr. Harald Tauchmann is a Professor of Health Economics at Friedrich-Alexander University Erlangen-Nuremberg (FAU), where he has held a faculty position since 2013. He is affiliated with the School of Business, Economics and Social Sciences, specifically within the Department of Economics. Prof. Tauchmann also participates in multiple research focus areas at FAU, including 'Insurance and Risk' and 'Work in Transition,' demonstrating his interdisciplinary approach to health economics. Prof. Tauchmann received his education at Heidelberg University and the University of Manchester, UK, where he studied economics, political science, and sociology. He graduated in 1998 and completed his doctorate at the Interdisciplinary Institute for Environmental Economics (University of Heidelberg) in 2003. Prior to joining FAU, he worked as a research associate at the Rhineland-Westphalian Institute for Economic Research (RWI) in Essen from 2003 to 2012 and headed a junior research group at the health economics research center CINCH at the University of Duisburg-Essen. His research expertise lies in empirical health economics, with a particular emphasis on health insurance choice and competition, as well as individual health behavior. He has made significant contributions to understanding obesity, health shocks, mental health care payment systems, and thyroid diagnostics through his extensive publication record. His methodological work includes developing specialized Stata modules for econometric analysis, which have been widely adopted by researchers in the field. Prof. Tauchmann's scholarly work demonstrates a consistent focus on applying rigorous econometric methods to pressing health policy questions. His research portfolio shows particular strength in causal analysis of health behavior, especially regarding obesity interventions, health insurance market dynamics, and the economic consequences of health shocks. His recent publications (including several forthcoming in 2025) indicate continued scholarly productivity and relevance to current health policy debates. From March 2021 to April 2022, Prof. Tauchmann served as chairman of the German Society for Health Economics, highlighting his leadership and recognition within the national health economics community. His email contact is harald.tauchmann@fau.de for professional inquiries.

Rishidev Chaudhuriمشاهده پروفایل

دانشیار

• Computational Neuroscience
• Neural Dynamics
• Cognitive Neuroscience
• +۳ مورد دیگر

Rishidev Chaudhuri is an Associate Professor at the University of California, Davis in the Department of Neurobiology, Physiology and Behavior within the College of Biological Sciences. His research focuses on computational neuroscience and neural dynamics, employing mathematical models to investigate how neural circuits generate cognitive processes such as memory, perception, and decision-making. His work explores neural dynamics through models of memory systems, attentional mechanisms, and probabilistic inference. Recent publications highlight advances in understanding hippocampal memory scaffolds, parietal-frontal interactions, and neuromorphic computing inspired by brain architecture. Education: BA in Physics (Amherst College), PhD in Applied Mathematics (Yale University) Centers: Center for Neuroscience; affiliated with Applied Mathematics and Neuroscience Graduate Groups Scientific awards and honors are not explicitly mentioned in the provided materials.