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Matt WeinbergView profile

Associate Professor

• Algorithmic Mechanism Design
• Economics and Computation
• Cryptocurrencies
• +5 more

Matt Weinberg is an Associate Professor of Computer Science at Princeton University , specializing in Algorithmic Mechanism Design . His research focuses on domains where user incentives are critical, such as auctions , cryptocurrencies , and social good applications. Prior to joining Princeton's faculty in 2017, he spent two years as a postdoc in Princeton’s CS Theory group and was a Research Fellow at the Simons Institute in 2015 (Economics and Computation) and 2016 (Algorithms and Uncertainty). Weinberg earned his PhD in Computer Science from MIT in 2014 , advised by Costis Daskalakis , and received his BA in Mathematics from Cornell University in 2010 , where he collaborated with Bobby Kleinberg . His research spans Algorithmic Game Theory , Economics and Computation , and Theoretical Computer Science , with recent trends emphasizing blockchain economics and multi-item auction theory . His scientific awards include Simons Institute Research Fellowships. He advises both PhD and MSE students , with a structured philosophy emphasizing independent problem-solving and technical rigor. Weinberg also co-teaches courses like COS 445: Economics and Computing and COS 521: Advanced Algorithms , often collaborating with faculty such as Mark Braverman . His NSF grant CCF-1954927/1955205 supports a crowdsourced list of TCS-focused Master’s programs.

Katarina DomijanView profile

Associate Professor

• Bayesian applications to data analysis
• Machine learning
• Statistical computing
• +3 more

Katarina Domijan is an Associate Professor in Statistics at the Department of Mathematics and Statistics, Maynooth University, Ireland. She holds a PhD in Statistics from Trinity College Dublin (2008) and has been affiliated with Maynooth University since 2008, transitioning from Lecturer/Assistant Professor to her current role in 2024. Her academic career includes editorial roles as Associate Editor for The R Journal (2021–present) and the Journal of Computational and Graphical Statistics (2015–2024). Research Interests focus on Bayesian methods for high-dimensional data, particularly in classification problems. She specializes in feature selection and model visualization, with applications spanning agricultural data analysis (e.g., hyperspectral imaging for lactose prediction), medical diagnostics (e.g., sepsis and cancer detection), and space physics (e.g., Saturn Kilometric Radiation classification). Her work bridges theoretical statistics with real-world challenges, including socio-economic studies and forensic science. Key Research Areas Bayesian statistical inference Machine learning for large feature spaces Statistical computing and model interpretability Data visualization and chemometrics Scientific Contributions include leading projects like VistaMilk Phase II (2024–2030, €152,300) and Measuring Carbon Sequestration (2024–2028, €174,788.90). Her 15 most recent publications highlight advancements in ensemble modeling, spatial statistics, and medical diagnostics. Scientific Awards Associate Editor, The R Journal (2021–present) Associate Editor, Journal of Computational and Graphical Statistics (2015–2024) Student Supervision includes PhD and MSc graduates such as Dr. Bruna Wundervald (2024) and Dr. Mark O’Connell (2017). She also collaborates with researchers across disciplines, including Dr. Nadim Akasheh in food hypersensitivity studies.

Yuanyuan ShiView profile

Assistant Professor

• Energy Systems
• Cyber-Physical Systems
• Machine Learning for Energy Management
• +6 more

Yuanyuan Shi is an Assistant Professor in the Electrical and Computer Engineering Department at the University of California, San Diego (UCSD), with affiliations at the Center for Energy Research and the MICS. Her research integrates machine learning with control theory, focusing on energy systems, cyber-physical systems, and PDE-governed systems, aiming to provide reliable and efficient decision-making in complex environments like power grids and buildings. Assistant Professor, UCSD (2021–present) Postdoctoral Fellow, Caltech (2020–2021) Ph.D., Electrical and Computer Engineering, University of Washington (2020) M.Sc., Electrical Engineering and Statistics, University of Washington B.Eng., Nanjing University, China Her work spans machine learning, optimization, and control theory, with applications in power systems, PDEs, and intelligent systems. She develops algorithms that combine learning with control guarantees, enabling robust solutions for energy management and grid stability. Recent publications highlight her focus on neural operators for PDE and delay systems, stability-constrained reinforcement learning, and multi-agent control in sustainability contexts. These works advance physics-informed models, grid frequency regulation, and commercialized energy storage integration. She has received prestigious awards, including: NSF CAREER Award (2025) Schmidt Sciences AI2050 Early Career Fellowship (2025) Hellman Fellowship (2023) Jacobs School Early-Career Faculty Acceleration Award (2024) MIT Rising Star in EECS (2018) Clean Energy Institute Scientific Achievement Award (2020) At UCSD, her lab collaborates on projects like FedNeMO (federated neural operators) and BEAR-Data (multi-zone building dataset). She co-organized Control Meets Learning seminars and serves as guest co-editor for the Applied Energy special issue on Trustworthy Machine Learning.

Wolfgang LutzView profile

Professor

• Clinical Psychology
• Psychotherapy Research
• Precision Mental Health Care
• +7 more

Prof. Dr. Wolfgang Lutz is a Full Professor and Head of the Department of Clinical Psychology and Psychotherapy at the Faculty of Psychology, University of Trier, Germany. He also serves as Director of the Outpatient Clinic and Postgraduate Clinical Training. Additionally, he holds an Adjunct Professor position at the University of Western Australia and is a Fellow of the Association for Psychological Science (APS). Dr. Lutz's research focuses on advancing clinical psychology and psychotherapy through precision mental health care, treatment personalization, and feedback-informed psychological therapy. His work emphasizes using data-driven approaches to optimize treatment outcomes, with particular attention to depression, anxiety disorders, and PTSD. He has pioneered the development of the Trier Treatment Navigator (TTN), a system for feedback-informed treatment that helps match patients to the most effective therapeutic approaches. His recent publications reveal a strong emphasis on integrating technology and artificial intelligence into psychotherapy research and practice. This includes developing algorithms for personalized therapy, using large language models to analyze therapy sessions, and implementing routine outcome monitoring systems. His research shows how temporal dynamics in therapy processes affect outcomes and how clinical microskills can predict therapeutic alliance and success. Fellow of the Association for Psychological Science (APS) Editor of Psychotherapy Research He leads initiatives to develop a European Psychotherapy Consortium (EPoC) to standardize outcome measurement across countries and promote cooperation in psychotherapy research. His laboratory focuses on precision mental health care, developing tools for treatment personalization, and investigating the mechanisms of change in psychotherapy.

Fengzhu SunView profile

Professor

• Computational Biology
• Bioinformatics
• Statistical Genetics
• +6 more

Dr. Fengzhu Sun is a Professor of Quantitative and Computational Biology and Mathematics at the University of Southern California. His research spans computational biology, bioinformatics, statistical genetics, and mathematical modeling, with a focus on metagenomics, protein interaction networks, and genome sequence analysis. Dr. Sun earned his Bachelors in Mathematics from Shandong University, Masters in Probability and Statistics from Peking University, and PhD in Applied Mathematics from USC. He returned to USC in 2000 as an associate professor after serving at Emory University (1995-2000), becoming a full professor in 2006. His research interests encompass protein interaction networks, gene expression, SNPs, linkage disequilibrium, and their applications in predicting protein functions, gene regulation networks, and disease gene identification. He pioneered alignment-free methods for genome and metagenome sequence comparison, with recent work focusing on virus-host interactions in metagenomic data. His publication record shows consistent innovation, with recent work (2023-2025) emphasizing deep learning approaches (DeepMicroClass, DeepDecon, DeepLINK) and novel statistical methods. His research demonstrates strong interdisciplinary integration of computational methods with biological applications. Fellow of American Association for the Advancement of Sciences (AAAS, 2012) Fellow of American Statistical Association (ASA, 2015) Fellow of Institute of Mathematical Statistics (IMS, 2023) Fellow of International Society for Computational Biology (ISCB, 2024) Fellow of Asia-Pacific Artificial Intelligence Association (AAIA, 2025) Member of International Statistical Institute (ISI, 2012) USC Mellon Mentoring award for faculty mentoring (2012) USC Dornsife College senior Raubenheimer Outstanding Faculty Award (2017) Dr. Sun has mentored numerous successful students and postdocs, many now in academic positions or at leading tech and biotech companies. His research group develops computational methods for complex biological data analysis, with current focus on advanced deep learning for metagenomic classification, cancer cell fraction estimation, and virus-host interaction analysis. He has created influential software tools including DeepMicroClass, ImputeCC, DeepDecon, and ViralCC that have become standard resources in computational biology.

• Computer Science
• Virtual Reality
• Scientific Visualization
• +5 more

David H Laidlaw is a Professor of Computer Science at Brown University, specializing in virtual reality, scientific visualization, and medical imaging. His work spans interdisciplinary applications in neuroscience, biomedical research, and educational tools. Brown University Affiliation Department of Computer Science His research focuses on: Immersive visualization for complex data analysis Diffusion MRI and neuroimaging techniques Human-computer interaction in virtual environments 3D interaction methods for scientific exploration Collaborative visualization tools for multidisciplinary teams Recent trends in his publications highlight advancements in: Graph neural networks for biomedical data Memory-efficient segmentation algorithms Perceptual studies in VR environments Annotation and analysis of placental vasculature Technological innovations in foot dynamics research He teaches courses in virtual reality design and scientific visualization, including: CSCI 1370 - Virtual Reality Design for Science CSCI 1951S - Virtual Reality Software Review CSCI 1951T - Surveying VR Data Visualization Software CSCI 2370 - Interdisciplinary Scientific Visualization

Sylvain CristolView profile

Professor

• Heterogeneous Catalysis
• Quantum Chemistry
• Solid-State Spectroscopy
• +4 more

Sylvain Cristol is a Professor at the University of Lille within the Heterogeneous Catalysis department and the Modeling and Spectroscopy (MODSPEC) group. He teaches quantum chemistry, chemical bonding, statistical physics , and X-ray absorption spectroscopy at the university’s European Master’s program. PhD in Molecular and Organic Chemistry (1997-2000, Université de Provence) Postdoctoral work at Davy-Faraday Research Lab, Royal Institution of Great Britain (2000-2002) His research focuses on modeling hydrodesulfurization and hydrodeoxygenation catalysts for biomass valorization, supported by ANR-PNRB project ECOHDOC (with Caen, Poitiers, and TOTAL). He pioneered operando X-ray absorption spectroscopy for characterizing supported oxides (Mo/Re on alumina/anatase) via the ANR SAXO project (Paris VI, Grenoble, SOLEIL). Collaborative work with Francesco Mauri (Paris VI) advanced NMR parameter modeling in solids. Publications span DFT studies , XANES spectroscopy , and solid-state NMR applied to catalysis. Scientific awards include the UCCS Thesis Prize (highest honors) for his work on dibenzothiophene reactivity on molybdenum sulfide.

Timothy J. MuldoonView profile

Professor

• Biomedical Engineering
• Biophotonics
• Cancer Research
• +5 more

Timothy J. Muldoon is a Professor in the Department of Biomedical Engineering at the University of Arkansas, where he has been since 2012. He holds joint appointments in the (ENGR)-Engineering and (BMEG)-Biomedical Engineering programs. B.S. in Biomedical Engineering from Johns Hopkins University (2002) Ph.D. in Bioengineering from Rice University (2009) M.D. from Baylor College of Medicine (2010) Dr. Muldoon leads the Translational Biophotonics and Imaging Laboratory, focusing on multimodal microendoscopy , multiphoton imaging , and light sheet microscopy for cancer detection and treatment monitoring. His work bridges optical spectroscopy , nanotechnology , and microfluidics to develop novel diagnostic tools. Current research includes optical methods for assessing chemoradiotherapy response in colorectal cancer, metabolic imaging of tumor organoids , and point-of-care blood analysis systems . His publications (30+ peer-reviewed articles) and NIH-funded projects demonstrate clinical translation of optical biopsy technologies. National Institutes of Health Academic Research Enhancement Award (R15) - Cancer Imaging NIH Early Career Reviewer Program (2016) Burroughs Wellcome Collaborative Research Grant (2012) Dr. Muldoon teaches advanced courses in Biomedical Microscopy (BMEG 5504) and Biomedical Instrumentation (BMEG 2904), emphasizing optical techniques and physiological measurements . He has received multiple teaching and service awards at the University of Arkansas.

Tomas KarlssonView profile

Professor

• Solar Wind-Magnetosphere Interaction
• Bow Shock Physics
• Magnetosheath Jets
• +3 more

Tomas Karlsson is a Professor and Deputy Head of Department at the Royal Institute of Technology , specializing in Space and Plasma Physics . He teaches courses such as EF2240 Space Physics , EF2245 Space Physics II , and EI1240 Electromagnetic Theory , while serving as examiner or coordinator for advanced projects and thesis work in space-related fields. His research focuses on the interaction between the solar wind and planetary magnetospheres , with specific interests in bow shock physics , magnetosheath jets , solar wind magnetic holes , auroral physics , and comparative studies of magnetospheres across planets and comets. He employs spacecraft data (e.g., MMS , Cluster , BepiColombo ) and simulations to analyze plasma dynamics and space weather phenomena. The 15 most recent publications highlight trends in solar wind turbulence , magnetospheric boundary processes , and planetary plasma interactions , with recurring themes in SLAMS (Short Large-Amplitude Magnetic Structures) , magnetosheath jet formation , and magnetic hole propagation . These works span statistical surveys, hybrid simulations, and multi-mission data analysis.

Sriraam NatarajanView profile

Professor

• Artificial Intelligence
• Machine Learning
• Healthcare Applications
• +5 more

Sriraam Natarajan is a Professor and Director of the Center for Machine Learning and StaRLing Lab at The University of Texas at Dallas (UTD), part of the Erik Jonsson School of Engineering & Computer Science. He holds additional roles as a hessian.AI Fellow at TU Darmstadt and an RBCDSAI Distinguished Faculty Fellow at IIT Madras. His expertise spans Artificial Intelligence, Machine Learning, and their applications in healthcare, with a focus on Relational Learning, Reinforcement Learning, and Graphical Models. He has been honored as an AAAI Fellow (2025), elected to the AAAI Executive Council, and recognized with the UTD Outstanding Graduate Teaching Award. Education: Completed his PhD in Computer Science at Oregon State University in 2007 under Prof. Prasad Tadepalli. Postdoctoral work at the University of Wisconsin-Madison with Professors Jude Shavlik and David Page. Previously served as faculty at Indiana University and Wake Forest School of Medicine. Research: Active in developing AI systems for healthcare, including predictive models for gestational diabetes and cardiac arrest in children. His work emphasizes integrating human knowledge into machine learning (e.g., Human-in-the-Loop systems) and advancing statistical relational AI frameworks like Markov Logic Networks and Probabilistic Circuits. Publications: Over 100 peer-reviewed articles, including notable works on causal learning, relational reinforcement learning, and knowledge graph construction. Recent focuses include explainable AI and scalable probabilistic models. Awards: AAAI Fellow, RBCDSAI Distinguished Fellowship, UTD Teaching Excellence Award. Advising and Collaboration: Mentored over 30 students, many now in academia and top institutions like IBM Research, Facebook, and Microsoft. Collaborates globally on projects like GLAD (Glocalized Anomaly Detection) and StaRLing Lab initiatives. Labs/Teams: Leads the StaRLing Lab, focusing on statistical relational AI, and directs UTD's Center for Machine Learning. Engaged in interdisciplinary projects with healthcare, robotics, and data science communities.

• Risk Management
• Risk Analysis
• Uncertainty Quantification
• +12 more

Roger Flage is a Professor of Risk Management at the University of Stavanger, affiliated with the Faculty of Science and Technology and the Department of Security, Economics and Planning. His research focuses on foundational and applied aspects of risk analysis, uncertainty quantification, and decision-making under uncertainty, with applications in critical infrastructure, environmental systems, and offshore energy. Roger Flage's research interests lie at the intersection of risk science, safety engineering, and decision theory. He investigates how uncertainty—especially epistemic uncertainty and assumptions—affects risk assessments, and advocates for more transparent and robust frameworks. His work spans theoretical advances, such as the treatment of 'black swan' events and the concept of 'real risk', as well as practical applications in offshore safety, power systems, and geohazards. He emphasizes the integration of data-driven methods, AI, and digital twins while critically assessing their limitations and associated security risks. His recent publications show a strong trend toward integrating dynamic, data-rich, and interdisciplinary approaches to risk analysis. Themes include the role of time in risk, AI applications, infrastructure interdependencies, and environmental risk in the oil and gas sector. He frequently publishes in top-tier journals like Risk Analysis , Reliability Engineering & System Safety , and Safety Science , often in collaboration with leading scholars such as Terje Aven and Seth Guikema. No scientific awards are mentioned in the provided text. Roger Flage has supervised or collaborated with several researchers, though no formal list of advisees is provided. His work is supported through academic collaborations and institutional affiliations rather than explicit grant mentions. He is actively involved in advancing risk science methodology, particularly in the treatment of assumptions and uncertainty, and contributes to both theoretical foundations and real-world applications in safety-critical domains. He is associated with research groups and collaborative networks at the University of Stavanger, particularly within the Department of Security, Economics and Planning. His work often involves interdisciplinary teams focusing on risk in complex engineered systems, including energy, transportation, and environmental systems.

• Statistical methodology
• Social network analysis
• Healthcare quality measurement
• +8 more

James O'Malley is a Professor at The Dartmouth Institute for Health Policy and Clinical Practice and Professor of Biomedical Data Science at the Geisel School of Medicine, Dartmouth College. He holds the prestigious Peggy Y. Thomson Professorship in the Evaluative Clinical Sciences and serves as an Adjunct Professor of Computer Science, demonstrating his interdisciplinary expertise spanning statistics, healthcare policy, and computer science. Dr. O'Malley earned his B.Sc. (Hons) in Statistics from the University of Canterbury, New Zealand (1994), M.S. in Applied Statistics from Purdue University (1999), and Ph.D. in Statistics from the University of Canterbury (1999), followed by a Postdoctoral Fellowship in Biostatistics at Harvard Medical School (2001). His research spans statistical methodology and healthcare applications, with methodological contributions in statistical inference for social networks , multivariate hierarchical models , comparative effectiveness research , and Bayesian analysis . These methods address critical healthcare problems including health-social network relationships , healthcare quality measurement , medical technology diffusion , and comparative effectiveness in vascular surgery, cardiology, and mental health . His work bridges theoretical statistics with practical healthcare challenges through collaborations with physicians, epidemiologists, and health services researchers. Dr. O'Malley's recent publications reveal a strong emphasis on healthcare network analysis, comparative effectiveness research, and methodological innovations. His work examines physician networks and patient outcomes, evaluates surgical interventions, identifies healthcare disparities, and develops novel statistical approaches for complex healthcare data, with significant contributions appearing in high-impact journals across multiple disciplines. Mid-career Excellence award from the Health Policy Section of the ASA Elected fellow of the ASA (2012) ISPOR Award for Excellence in Methodology (2019) Peggy Y. Thomson Professorship (2021) 2025 Research Excellence Award for Senior Faculty in the Foundational Sciences As a dedicated mentor, Dr. O'Malley has supervised numerous post-doctoral fellows and PhD students across multiple programs. He currently leads major research initiatives including NIH/NLM R01LM014233 on Geographic Variations in Health Care, serves as PI for cores in NIH/NIA projects on healthcare inequity in Alzheimer's Disease and Rural Health Care Delivery Science, and contributes to multiple substantial grants totaling millions of dollars. He previously chaired the Health Policy Statistics Section of the ASA and co-chaired the 2011 International Conference on Health Policy Statistics. Dr. O'Malley co-organized the Dartmouth Interdisciplinary Network Research (DINR) seminar series (2014-2020) and serves as an Associate Editor for Statistics in Medicine and Observational Studies, demonstrating his commitment to advancing methodological research and fostering interdisciplinary collaboration in health services research.

Mathias BeiglboeckView profile

Professor

• Optimal Transport
• Martingale Theory
• Mathematical Finance
• +4 more

Mathias Beiglboeck is a Professor at the Faculty of Mathematics, University of Vienna. His research spans Probability, Optimal Transport, and Mathematical Finance, with a focus on martingale constraints and stochastic modeling. Doctorate in Mathematics (2004, TU Vienna) Diploma in Mathematics (2003, TU Vienna) His work bridges geometric and probabilistic methods in finance, addressing problems like the Skorokhod Embedding, Weak Martingale Transport, and applications to financial institutions. Preprints and publications highlight advancements in Wasserstein distances, causal transport, and stability analysis under martingale constraints. Notable projects and awards include the OeNB-Anniversary-Fund Project (2024-), FWF-Project on Mimicking Processes (2022-), a START-Prize (2014-2022), and early recognition for his Master's thesis (2003). Teaching roles at University of Vienna and TU Vienna cover Stochastic Processes, Financial Mathematics, and Mathematical Finance courses. 2024: OeNB-Anniversary-Fund Project 18983 (250,000 EUR) 2023: Mentor in Daniel Bartl's Esprit-Project (280,000 EUR) 2022: FWF-Project on Mimicking Processes (400,000 EUR) 2014-2022: START-Prize (1,000,000 EUR) 2012: Austrian Mathematical Society Prize 2003: Best Master's Thesis Award, Austrian Mathematical Society His research trends integrate adapted Wasserstein distances, disease modeling, and financial applications, with collaborations across institutions like TU Vienna, Bonn University, and MSRI Berkeley. Grants emphasize geometric and entropic transport methods in finance and public health contexts.

Bob KoppView profile

Professor

• Climate Change
• Sea Level Rise
• Paleoclimate
• +3 more

Bob Kopp is a Professor at Rutgers University's Department of Earth & Planetary Sciences and Co-Director of the University Office of Climate Action. He leads the NSF-funded Megalopolitan Coastal Transformation Hub (MACH), focusing on climate risk management in the Northeast U.S. and advancing understanding of coastal climate interactions. He co-directs the Climate Impact Lab, a multidisciplinary collaboration assessing climate economic risks. His research spans climate uncertainty, sea-level dynamics, and climate policy, with leadership roles in IPCC assessments and U.S. National Climate Assessments. Education: Ph.D. in Geobiology from Caltech (2000s) and B.S. in Geophysical Sciences from the University of Chicago. Prior to Rutgers, he served as AAAS Science & Technology Policy Fellow at the U.S. Department of Energy and postdoctoral researcher at Princeton University. Affiliations include Rutgers Climate Institute, Energy Institute, and graduate programs in Atmospheric Sciences, Geological Sciences, Oceanography, Statistics, and Planning & Public Policy. Research interests emphasize climate change impacts, sea-level projections, and policy integration. Key contributions include frameworks for probabilistic sea-level assessments (e.g., PaleoSTeHM) and adaptive strategies for coastal resilience. His work bridges scientific analysis with actionable policy, emphasizing stakeholder engagement in megaregions like the New York–Philadelphia urban corridor. Grants include NSF-funded projects totaling millions, supporting interdisciplinary climate solutions. Awards: Not explicitly listed, but recognized for leadership in IPCC and U.S. climate reports. Grants: Includes NSF support for MACH and Climate Impact Lab. Labs/Teams: Directs MACH, Climate Impact Lab, and collaborates with Rutgers' EOAS and Climate Institute.

Armando Lara-MillanView profile

Associate Professor

• Political Economy
• Historical Sociology
• Ethnography
• +5 more

Armando Lara-Millan is an Associate Professor in the Department of Sociology at the University of California, Berkeley. He earned his PhD from Northwestern University and holds affiliations with the Berkeley Economy and Society Initiative and Stanford's Center for Advanced Study in the Behavioral Sciences. His research bridges political economy, historical sociology, and ethnography to analyze institutional transformations. PhD in Sociology, Northwestern University Current Chair-Elect, Sociology of Law Section, ASA 2022 Distinguished Scholarly Book Award (ASA) His work examines: Political Economy of Institutions : How public hospitals and county jails use caseloads to generate revenue, challenging conventional cost-benefit analyses Global Economic Shifts : Reassessing neoliberal theories through the lens of investment firms, corporate reporting, and the end of low-cost labor Urban Digital Platforms : Investigating Nextdoor and Citizen's impact on neighborhood governance and social control Recent publications span American Sociological Review , Criminology , and Social Problems , with a focus on: Administrative disappearing mechanisms Healthcare spending dynamics Medical debt data collection Urban order reconstruction Awards include the 2022 ASA Distinguished Scholarly Book Award. His email is armando@berkeley.edu and blog posts explore contemporary economic challenges.