Find Academic Supervisors

Norman R. SwansonView profile

Professor

• Financial Econometrics
• Forecasting
• Machine Learning
• +6 more

Norman R. Swanson is a Distinguished Professor and James Cullen Chair in Economics at Rutgers University. He holds a PhD from the University of California, San Diego, and a degree from the University of Waterloo. Primary Affiliations: Department of Economics, Rutgers University Previous Positions: Pennsylvania State University, Texas A&M University, Purdue University, IBM Canada His research focuses on financial econometrics , forecasting , machine learning and big data , and time series analysis . He has published over 100 peer-reviewed articles and served as editor for journals like the Journal of Econometrics and Journal of Business and Economic Statistics . His work often bridges theoretical econometrics with practical applications in finance and macroeconomics, emphasizing robustness and predictive accuracy. The articles listed reflect his expertise in volatility modeling , jump detection , data reduction , and forecasting methodology . Key trends include the use of shrinkage methods, factor models, and simulation-based testing in high-frequency financial and macroeconomic contexts. Scientific Awards: Fellow of the Journal of Econometrics Fellow of the International Association of Applied Econometrics He has acted as a visiting scholar at institutions like the University of Maryland and the Federal Reserve Bank of Philadelphia. His consulting work spans firms such as Union Bank of Switzerland and DFA Capital Management, with expertise as a legal expert witness in financial services cases.

Dokyun LeeView profile

Associate Professor

• Generative AI
• Economics of Unstructured Data
• Human-AI Collaboration
• +5 more

Dokyun Lee is a Kelli Questrom Chair Associate Professor of Information Systems and Computing & Data Science at Boston University , where he founded the Business Insights through Text (BIT) Lab and co-leads the BU Digital Business Institute Generative AI Lab . His research focuses on the responsible application, development, and impact of AI in business, particularly analyzing unstructured data (e.g., text, images) across digital consumer management, platform design, and human-AI collaboration. Education : PhD in Operations, Information & Decisions (Wharton School), MS in Statistics (Yale University), BA in Computer Science (Columbia University) His work addresses Generative AI (unintended consequences, human-integration frictions), Economics of Unstructured Data (content extraction, marketing), and AI Regulation in contexts like advertising, market competition, and creativity. Recent articles explore LLMs in corporate risk nowcasting, generative AI's effects on online communities, and interpretable deep learning for churn management. His research trends span NLP, computer vision, and causal inference, with applications in digital marketing, e-commerce, and innovation analytics. Scientific Awards : INFORMS ISS Gordon B David Young Scholar, INFORMS ISS Sandy Slaughter Early Career, CDO Magazine Leading Academic Data Leader, AMA Don Lehmann Award 2024, MSBA Teaching Award He has secured grants from Adobe, Google, Microsoft, MassMutual, and Prudential Foundation. His labs (BIT Lab, Generative AI Lab) mentor students and collaborate on AI-driven solutions for business challenges.

Will TownesView profile

Assistant Professor

• Biostatistics
• Epidemiology
• Infectious Disease Forecasting
• +7 more

Will Townes is an Assistant Professor in the Department of Statistics and Data Science at Carnegie Mellon University's Dietrich College of Humanities and Social Sciences. He joined CMU in 2022 after completing a postdoctoral fellowship in computer science at Princeton University with Barbara Engelhardt. His academic journey includes a Ph.D. in biostatistics from Harvard University under Rafael Irizarry's supervision, an M.S. in math and statistics from Georgetown, and earlier work in tropical ecology fieldwork in the Philippines. Dr. Townes specializes in applied statistics with primary focus areas in biomedical and public health domains. His research centers on wastewater-based epidemiology, wearable devices, and auxiliary signals for infectious disease tracking and forecasting as part of the Delphi research group. He has developed normalization, feature selection, and dimension reduction methods for single cell RNA-Seq and spatial transcriptomics data analysis. His broader research interests span biostatistics, epidemiology, genomics, time series forecasting, and theoretical aspects of Tweedie distributions. His recent publications reveal a strong emphasis on wastewater surveillance methodologies, single-cell data analysis techniques, and infectious disease forecasting models. The research demonstrates a consistent focus on computational scalability and efficiency through approximate inference techniques. Dr. Townes approaches statistical problems with a pragmatic perspective, comfortable with probabilistic (Bayesian) models while drawing inspiration from diverse statistical perspectives. Member of DELPHI Lab Group at CMU Active contributor to genomics and biostatistics research Focus on computational efficiency in statistical methods Dr. Townes mentors several students including Gabrielle Thivierge (PhD candidate working on infectious disease forecasting methods), Julia Elrod, and Anna Rosengart. He teaches data science courses at CMU, including a field course in Costa Rica where students work with community partners on real-world data projects involving water quality indicators, spring flow rates, and ecological monitoring.

John AllenView profile

Associate Professor

• Meteorology
• Severe Weather
• Climatology
• +10 more

Dr. John Allen is an Associate Professor of Meteorology in the Department of Earth and Atmospheric Sciences at Central Michigan University, where he also serves as Director of the Earth and Ecosystem Science Ph.D. Program and Associate Director of the NSF Artificial Intelligence Institute for Environmental Sciences (AI2ES). He is actively engaged in interdisciplinary research focusing on severe convective storms, their climatology, and responses to climate change. Education: Ph.D., Earth Sciences (Meteorology), The University of Melbourne, 2013 B.S., Research Honours (Meteorology), The University of Melbourne, 2008 B.S., Meteorology and Applied Mathematics, The University of Melbourne, 2007 Dr. Allen’s research centers on global severe weather, particularly tornadoes and hail, and their interactions with climate variability and change. He employs machine learning, statistical modeling, and environmental datasets to improve forecasting and risk assessment. His work spans climatology, atmospheric dynamics, and operational meteorology, with a strong emphasis on translating climate projections into actionable resilience strategies. His recent publications demonstrate a strong trend in applying artificial intelligence and deep learning to severe weather prediction, including hail nowcasting, frontal boundary detection, and hodograph analysis for hazard discrimination. These works reflect a focus on both theoretical understanding and practical forecasting applications. Scientific Awards: National Science Foundation CAREER Award (2020) Central Michigan University Provost’s Award for Outstanding Research (2019/2020) College of Science & Engineering Award for Outstanding Research (2019) AGU Editors' Citation for Excellence in Refereeing (2015) European Severe Storms Laboratory Heini Tooming Award (2015) AMS STAC Outstanding Early Career Award (2022) AMS Editor’s Award for Reviews (2022) Dr. Allen advises numerous Ph.D. students and postdoctoral researchers, and leads multiple funded projects with NSF and NIST. He has served as an editor for Weather and Forecasting and Artificial Intelligence for the Earth Systems . His research group, the Allen Research Group, conducts fieldwork and collaborates internationally on hail and tornado studies. Key Research Projects: NSF-funded ICECHIP hail field campaign (co-lead, summer 2025) Quantifying Risk of Wind and Hail Storms in a Warming Climate (NIST-funded) Geospatial Predictive Analysis of Hail and Wind (AON Inc.) Left-moving Supercell Storm Processes (NSF) Improving High-Impact Hail Forecasts (NSF PREEVENTS)

• Climate Dynamics
• Numerical Weather Prediction (NWP)
• Atmospheric Modeling
• +3 more

Professor Eigil Kaas is affiliated with the Niels Bohr Institute at the University of Copenhagen . His work spans climate dynamics , numerical weather prediction (NWP) , and atmospheric modeling . As former Section Head of Climate and Computational Geophysics , he leads research on climate-chemistry coupling and sea ice impacts. Education : MSc (1987) and PhD (1993) in Meteorology from University of Copenhagen Research Focus : Climate dynamics and physics Numerical methods in atmospheric models Machine learning for weather prediction Arctic sea ice-climate interactions Thunderstorm electricity and radiation Coupled atmosphere-ocean modeling Article Trends : Recent work combines neural networks with radiative transfer optimization Focus on storm dynamics and gamma-ray flashes Extreme precipitation modeling under climate change Pioneering tidal flow studies in Faroe Island fjords Teaching Legacy : Instructor of Atmospheric Physics and Dynamical Meteorology courses Developed zonally averaged climate model for educational use Mentored 12 PhD/MSc students with DMI/ECMWF collaborations Professional Roles : Chairman of BFI Group 28 (Geosciences & Climate) Scientific Advisory Committee member at ECMWF Project lead in EU ENSEMBLES and PEGASOS initiatives

Wayne WobckeView profile

Associate Professor

• Artificial Intelligence
• Data Science
• Machine Learning
• +7 more

Associate Professor Wayne Wobcke is a faculty member in the School of Computer Science and Engineering at the University of New South Wales (UNSW), where he has been employed since 2002. His academic career includes previous positions at the University of Sydney until 1998, British Telecom Labs in the UK for three years, and the University of Melbourne for one year. He holds a PhD in Computer Science from the University of Essex (1989), an MSc from the University of Queensland (1985), and a BSc (Hons) in Mathematics/Computer Science from the University of Queensland (1984). Dr. Wobcke's research spans both theoretical and practical aspects of artificial intelligence and data science. His work encompasses intelligent agents, data mining, agent-based modeling, dialogue management, personal assistants, recommender systems, and computational social science. He has collaborated extensively with industry through three Cooperative Research Centres (Smart Internet Technology CRC, Smart Services CRC, and Data to Decisions CRC), where he served as a Programme Manager and Project Leader for over 10 years. Notable achievements include developing a voice-controlled mobile application for email and calendar interaction (a precursor to Apple's Siri) and deploying a people-to-people recommender system for online dating on one of Australia's largest dating sites. His recent research focuses on data science in humanitarian contexts and machine learning applications in official statistics, conducted in collaboration with BPS (Statistics Indonesia) and STIS (Politeknik Statistika, Indonesia). His publication record shows a consistent trajectory of impactful research, with recent work concentrating on poverty targeting, domain adaptation, natural language processing for recommender systems, and political opinion mining. Scientific Awards: Best Paper Nomination, 11th Workshop on Argument Mining (2024) UNSW Arc Postgraduate Research Supervisor Award (2017, 2018) AAAI Deployed AI Application Award, Twenty-Sixth Annual Conference on Innovative Applications of Artificial Intelligence (2014) Best application paper runner up, 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining (2013) Dr. Wobcke has successfully supervised numerous research students, with Irwan Rahadi currently working on 'Causal Modelling and Machine Learning for Official Statistics'. His grant portfolio includes significant funding from the Australian Research Council and various Cooperative Research Centres, totaling over $3.7 million since 2003. He teaches COMP9414 Artificial Intelligence and COMP9727 Recommender Systems at UNSW.

• Artificial Intelligence
• Computer Vision
• Multimedia
• +4 more

Alberto Del Bimbo is a Full Professor of Computer Engineering at the Department of Systems and Computer Science, University of Florence, Italy. He serves as Director of the Media Integration and Communication (MICC) Center, a National Center of Excellence focused on Artificial Vision, Artificial Intelligence, and Multimedia Technologies. His career spans academia and leadership roles, including Deputy Rector for Research and Innovation Transfer (2000-2006) and Director of the Department of Systems and Computer Science (1997-2000). Education : Master Degree in Electronic Engineering (1977), University of Florence. Research Interests : Artificial Vision, Multimedia, Multimodal Interaction, Image/Video Analysis, Surveillance, and Industry Automation. Academic Leadership : Editorial roles including Editor-in-Chief of ACM TOMM , and leadership in IEEE, IAPR, and ACM conferences. Projects : MICC Center’s work on neuromorphic computing, deepfake detection, and AI-driven surveillance systems, with industrial partnerships (Leonardo SpA, Thales Italia, IARPA). Article Trends : Recent work focuses on neuromorphic event-based vision, multimodal emotion prediction, compatible AI representations, and deepfake detection using local surface frames. Applications span smart environments, cultural heritage, and real-time surveillance. Scientific Awards : ACM Distinguished Scientist (2016) ACM Award for Outstanding Technical Contributions to Multimedia (2016) IEEE Senior Member IAPR Fellow Labs & Teams : Leads the MICC research team at the University of Florence, collaborating with international institutions and companies on vision and AI innovation.

Lesley De CruzView profile

Research Fellow

• Climate Modeling
• Regional Climate Downscaling
• AI for Environmental Systems
• +3 more

Lesley De Cruz is a Research Fellow at Vrije Universiteit Brussel's Electronics and Informatics department within the School of Electronics and Computer Science, with concurrent research affiliation at the Royal Meteorological Institute of Belgium since 2012. Current projects include climate-resilient urban modeling through BRGEOZ464 and flood prediction systems under FWOAL1127. Research focuses on AI-driven climate modeling with expertise in nonlinear dimension reduction (DIRESA framework), urban climate resilience, and renewable energy meteorology. Key methodologies integrate deep learning with environmental physics for applications including real-time flood prediction offshore wind farm optimization LEGO-based urban climate prototyping regional climate downscaling Recent publications (2023-2025) demonstrate strong trends in geospatial AI for environmental systems, with 64% focusing on climate change impact analysis, 54% on urban applications, and 51% on control measure development. The DIRESA framework represents a significant contribution to nonlinear dimension reduction in climate informatics. Awards include: Matìère Grise Science Communication Trophy (2024) Major media recognition for breakthrough rainfall prediction model (2021) Supervises multiple PhD candidates and leads public engagement initiatives including CurieuCity and Dag Van de Wetenschap. Current grants total 12 active projects (2021-2029) with €2.8M+ funding, primarily focused on climate adaptation and sustainable technology development. Labs and teams include the Climate Informatics Group at VUB and collaborative networks with Ghent University and the Flemish Institute for Carbon-Aware Technologies.

Olivia Romppainen-MartiusView profile

Professor

• Climate Science
• Meteorology
• Natural Hazards
• +3 more

Prof. Olivia Romppainen-Martius is a Professor and Group Leader of the Climate Impacts research group at the Institute of Geography, University of Bern. She co-directs the Mobiliar Lab for Natural Risks and focuses on short-term climate dynamics, high-impact weather systems, and midlatitude weather patterns. Her work integrates advanced modeling techniques with real-world applications in disaster risk reduction. Her research spans climate variability analysis, probabilistic forecasting of extreme events, and the development of early warning systems. She leads collaborative projects combining machine learning with Earth system modeling to improve understanding of weather extremes under climate change. Key interests include flood forecasting, hailstorm dynamics, and Mediterranean cyclone behavior. Prof. Romppainen-Martius has pioneered web-based visualization tools for impact-based flood warnings and has contributed to kilometer-scale Earth system modeling frameworks like nextGEMS. Her methodologies emphasize statistical reconstruction of historical weather events and the application of AI to convective weather prediction. Her research outputs reflect a strong focus on bridging climate science with practical risk management strategies, particularly in alpine and pre-alpine environments. Current efforts include long-term analysis of Swiss hailstorms and development of crowd-sourced data integration for meteorological observations.

Serena NgView profile

Professor

• Econometrics
• Empirical Macroeconomics
• Time Series Analysis
• +7 more

Serena Ng is the Edwin W. Rickert Professor of Economics at Columbia University and an Affiliated Faculty member in the Department of Statistics. Her research spans econometrics, empirical macroeconomics, time series analysis, and big data methods, with a focus on factor models, missing data, and macroeconomic forecasting. She has developed influential datasets such as FRED-MD and FRED-QD, widely used in macroeconomic research. Her research interests include: High-dimensional econometric modeling Factor analysis and principal components Missing data and matrix completion Dynamic modeling of disasters and climate shocks Macroeconomic forecasting and nowcasting Structural vector autoregressions and DSGE identification Her recent publications (2021–2025) reflect a strong trend toward integrating machine learning and computational methods into econometric modeling, particularly in handling large datasets, imputing missing values, and analyzing the macroeconomic impact of climate and disaster shocks. She has also contributed to foundational work in uncertainty measurement and time-varying parameter models. Her scientific contributions are recognized through extensive publication in leading journals. While no specific awards are listed, her editorial and collaborative roles (e.g., with the Journal of Econometrics) indicate high standing in the profession. She advises doctoral students in economics and statistics, though no names are publicly listed. She has received funding from major institutions including the National Science Foundation and NIH for interdisciplinary research. Her work bridges econometrics with environmental and health economics, particularly in projects related to climate adaptation and disaster impacts. She maintains a laboratory-like research group focused on macroeconometric modeling and big data analysis, contributing to the development of tools for real-time economic monitoring and policy analysis.

• Machine Learning
• Natural Language Processing
• Predictive Maintenance
• +4 more

Tony Lindgren is an Associate Professor at the Department of Computer and Systems Science, Stockholm University, affiliated with the Data Science Research Group and Natural Language Processing Research Group. His work bridges data science and NLP , focusing on interpretable models, constraint programming, and predictive maintenance systems. Research interests include: Machine Learning for explainability and fairness Constraint Programming in maintenance optimization Natural Language Processing for risk analytics and troubleshooting Recent publications demonstrate trends in multi-objective optimization (2025 satellite scheduling), conformal prediction (2024 CoPAL), and fault detection (2024 Automotive Nowcasting). His work often integrates domain-specific constraints with scalable algorithms across applications like food safety and autonomous vehicles. Software tools developed by Lindgren include: Example-based Feature Tweaking Rule Indexing Frameworks His research groups focus on AI-driven decision support for high-stakes domains, combining technical innovation with societal impact considerations.

Yonggang LiuView profile

Associate Professor

• Ocean Circulation
• Coastal Ocean Observing
• Numerical Modeling
• +7 more

Dr. Yonggang Liu is an Associate Professor at the University of South Florida College of Marine Science , specializing in Physical Oceanography . He leads the Ocean Circulation Lab and focuses on coastal ocean dynamics, air-sea interactions, and environmental applications. Education: PhD in Physical Oceanography from University of South Florida, Postdoctoral work at University of Washington Email: yliu@usf.edu His research interests include: Coastal ocean circulation and Loop Current interactions Real-time monitoring systems (COMPS, TBON, HF Radar) Numerical modeling (WFCOM, TBCOM, EFCOM) Harmful algal bloom prediction (Karenia brevis) Storm surge and Sargassum tracking Pollutant/oil spill transport modeling Recent publication trends show expertise in: Coastal ocean model validation Eddy-resolving numerical simulations Machine learning applications (Self-Organizing Maps) Marine environmental hazard response Multi-decadal circulation analysis Hydrodynamic-biogeochemical coupling He contributes to scientific leadership as: Editor for Artificial Intelligence for the Earth Systems Guest Editor for Ocean Science Science Committee member at SECOORA

• Infectious Disease Modeling
• Vector-Borne Disease Epidemiology
• Mathematical Forecasting
• +4 more

Michael Johansson serves as a Research Professor at Northeastern University's Roux Institute, maintaining offices in London, UK and Portland, ME. His work bridges public health research and operational response through advanced statistical and mathematical modeling of infectious diseases, with primary focus on vector-borne pathogens including dengue, Zika, chikungunya, and West Nile virus. Previously with the CDC in Puerto Rico for over a decade, he co-founded the Epidemic Prediction Initiative and led modeling efforts during Zika, COVID-19, and dengue emergencies. His research integrates biological, ecological, climatic, socioeconomic, and behavioral factors to understand disease emergence and transmission dynamics. Key methodologies include network-based forecasting models, climate-disease interaction analysis, and development of early warning systems. Current work emphasizes improving surveillance, burden estimation, and control strategy evaluation for arboviral diseases through quantitative tools. Recent publications demonstrate expertise in dengue synchronization across the Americas, West Nile virus forecasting, and addressing biases in mobility data for outbreak modeling. His work spans from molecular-level antibody response analysis to global risk mapping, consistently emphasizing operational implementation of research findings. Johansson actively contributes to public health practice through conference presentations including the 2025 American Mosquito Control Association Annual Meeting. His modeling frameworks have directly informed CDC emergency responses and the development of collaborative forecasting initiatives that pioneer open, transparent disease prediction.

• Econometrics
• Applied Microeconomics
• Quantitative Methods
• +3 more

Daniel Gutknecht, Ph.D., is a Professor in the Department of Economic Policy & Quantitative Methods (EQ) at the Faculty of Economics, Goethe University Frankfurt am Main. His academic work spans econometrics, applied microeconomics, and quantitative methods, with a focus on causal inference and time series analysis. Econometrics Methodology Quantile Regression Panel Data Analysis Nonlinear Models Recent research contributions include advanced econometric techniques such as staggered adoption DiD designs, sparsity tests for high-dimensional regressions, and intercept estimation in nonlinear selection models. His publications address critical challenges in quantile forecast optimality, nowcasting monotonicity, and heaped duration data modeling, reflecting interdisciplinary applications in public health and macroeconomic policy. Current teaching includes Advanced Econometrics 1 and Fundamentals of Econometrics for the Winter semester 2025/26. Contact details: Office RuW 3.211, Theodor-W.-Adorno-Platz 4, Frankfurt am Main; email: gutknecht@wiwi.uni-frankfurt.de .

• Accounting
• Financial Accounting
• Macroeconomics
• +5 more

Ryan T Ball is a Clinical Assistant Professor of Accounting at the University of Michigan's Ross School of Business. His research focuses on the interplay between high-frequency economic activities and low-frequency accounting information, with applications in macroeconomic forecasting, tax decision analysis, and debt market contracting. He holds a PhD and MBA from the University of North Carolina, along with engineering degrees from Ohio University. Education: PhD in Accounting, University of North Carolina (2008) MBA, University of North Carolina (2003) MS in Structural Engineering, Ohio University (1998) BS in Civil Engineering, Ohio University (1996) His research explores two primary areas: (1) mixed-data sampling methodologies for linking accounting metrics to macroeconomic trends, and (2) the contractual and valuation roles of accounting information in debt markets. Notable contributions include pioneering work on real-time earnings forecasting and the valuation implications of equity cross-listings. Teaching Excellence: Five-time recipient of the Ross Neary Teaching Award and first faculty member to win the University of Michigan Golden Apple Award (2016). Known for innovative pedagogical approaches, including viral TikTok educational content. Research Trends: Recent work emphasizes machine learning applications in panel data analysis, high-frequency financial nowcasting, and behavioral aspects of managerial decision-making. His 2024 paper on price-earnings ratio nowcasting exemplifies cutting-edge integration of econometric techniques with real-time data.