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Michael DanielsView profile

Professor

• Biostatistics
• Bayesian methodology
• Methodology for longitudinal data
• +1 more

Michael Daniels is a Professor and Chair of the Department of Statistics at the University of Florida, holding the Andrew Banks Family Endowed Chair. He previously held faculty positions at the University of Texas at Austin, Iowa State University, and Carnegie Mellon University. He earned his Sc.D. in Biostatistics from Harvard University (1995) and A.B. in Applied Mathematics from Brown University (1991). University: University of Florida College: College of Liberal Arts and Sciences (CLAS) Department: Department of Statistics His research focuses on Biostatistics , Bayesian methodology , and methodologies for longitudinal and causal inference , with applications in cardiovascular health, muscular dystrophy, and healthcare analytics. He has authored influential books like Bayesian Nonparametrics for Causal Inference and Missing Data (2023) and Missing Data in Longitudinal Studies (2008). Key awards include the Lagakos Distinguished Alumni Award (Harvard) and L. Adrienne Cupples Award (Boston University). He has been funded by NIH grants since 2001 and leads collaborative research in chronic disease management, including studies on opioid use in elderly populations and Duchenne muscular dystrophy progression modeling. Grants & Collaborations: NIH-funded studies on muscle degeneration biomarkers Telemedicine and chronic pain management trials Development of clinical trial simulation tools for neuromuscular diseases His work emphasizes Bayesian approaches to handle missing data and causal inference, with applied focus on improving healthcare outcomes through rigorous statistical methods.

Aleksandra SlavkovicView profile

Professor

• Statistical Data Privacy
• Differential Privacy
• Algebraic Statistics
• +5 more

Aleksandra Slavković is a Professor of Statistics and Associate Dean for Graduate Education at Pennsylvania State University's Eberly College of Science. She holds a PhD in Statistics from Carnegie Mellon University (2004) and has held academic roles since 2004, including appointments at the Institute for Computational and Data Sciences and Penn State College of Medicine. Her research focuses on statistical data privacy, differential privacy, algebraic statistics, and applications in social and health sciences. She has authored over 50 peer-reviewed publications and serves on editorial boards of top journals like Journal of Privacy and Confidentiality and Annals of Applied Statistics . Slavković has received major honors including Fellowships from the Institute of Mathematical Statistics (2021) and American Statistical Association (2018). She leads initiatives to enhance graduate education, including the Science Achievement Graduate Fellows Program, and actively promotes diversity in STEM through her leadership roles. Her recent work emphasizes privacy-preserving techniques for genomic, healthcare, and network data, with contributions to synthetic data generation and secure multiparty computation protocols. Her academic service includes chairing ASA committees and advising at the National Academy of Sciences. She maintains collaborative ties with institutions like Cornell University and UC Berkeley through visiting scholar programs, and her research bridges statistics, computer science, and applied mathematics.

Anandamayee MajumdarView profile

Assistant Professor

• Spatial Processes
• Bayesian Computation
• Environmental Sustainability
• +7 more

Anandamayee Majumdar is an Assistant Professor in the Department of Mathematics at San Francisco State University (SFSU), part of the College of Science & Engineering. She holds a Ph.D. and M.S. in Statistics from the University of Connecticut and Michigan State University, respectively, and earlier degrees from the Indian Statistical Institute (I.S.I.). Her research focuses on spatial and spatio-temporal processes, Bayesian computation, and their applications in biology, health, environmental sustainability, ecology, economics, finance, and industry. She has developed robust statistical models for handling missing data, multivariate processes, and expert-informed modeling, particularly in contexts like tuna catch estimation during the pandemic and financial risk analysis. She is currently exploring expert-integrated spatio-temporal models and public health trends. Professional experience includes roles as Senior Statistician at the Inter-American Tropical Tuna Commission (2021–2023), Professorial positions at universities in Bangladesh and China, and visiting research at UC Davis. She serves as Associate Editor for Applied Stochastic Modeling in Business and Industry and reviews for journals like Computational Statistics and Data Analysis and Biostatistics . Majumdar has contributed to interdisciplinary collaborations, including soil property modeling in urban ecosystems, and has held leadership roles in academic committees, such as the Interdisciplinary M.S./Ph.D. programs at Arizona State University. She actively mentors students and seeks to involve undergraduates and graduates in research projects.

Gaetano MontelioneView profile

Professor

• Protein Structure and Dynamics
• NMR Spectroscopy
• Structural Bioinformatics
• +6 more

Gaetano Montelione is a Professor and Constellation Endowed Chair in the Department of Chemistry and Chemical Biology at Rensselaer Polytechnic Institute (RPI), directing the Center for Biotechnology and Interdisciplinary Studies (CBIS). His laboratory pioneers NMR methodology development for protein structure and dynamics analysis, with extensive expertise in high-throughput structural genomics from leading the NIGMS-funded Northeast Structural Genomics Consortium (NESG) for 16 years. His research spans Protein Structure and Dynamics , NMR Spectroscopy , and Structural Bioinformatics , with critical applications in Membrane Proteins , Virology , and Drug Design . The lab integrates X-ray crystallography, SAXS, and computational modeling to tackle challenging targets like integral membrane proteins and viral systems, emphasizing hybrid approaches where sparse experimental data guides AI-driven structure prediction. Analysis of 2024-2025 publications reveals dominant trends in AI-structural biology integration, particularly AlphaFold2 applications for membrane proteins and protein complexes. Virology research focuses intensely on SARS-CoV-2 protease inhibitors and host-pathogen interactions, while de novo protein design and structural validation methods drive innovation in therapeutic development and fundamental biophysics. The Montelione Laboratory maintains a global collaborative network with experts in evolutionary coupling (Sander, Marks), Rosetta modeling (Baker), and biophysical methods (Luchinat, Tainer). As a central node in structural biology consortia like CASP and wwPDB, the lab develops rigorous quality assessment frameworks while advancing biomedical projects on influenza, DNA repair, and cancer biology through partnerships with Rutgers, Mt. Sinai, and international institutions.

• Artificial Intelligence
• Machine Learning
• Data Mining
• +4 more

Dr. Milos Hauskrecht is a Professor of Computer Science at the University of Pittsburgh's School of Computing and Information. He holds a PhD from MIT (1997) and an M.Sc. from Slovak Technical University (1988). His research focuses on AI, machine learning, and data mining, with applications in medicine and finance. He leads projects in real-time clinical monitoring, anomaly detection, and time-series analysis of EHR data. He has advised numerous PhD and MS students, including notable alumni now at Amazon, DeepMind, and Microsoft. Research interests include reasoning under uncertainty, optimization, and AI-driven medical decision support. Current grants include NIH funding for AI in renal therapy and clinical monitoring. He has published widely in top venues like ICML, NeurIPS, and journals such as Artificial Intelligence in Medicine. His work on conditional outlier detection earned the Homer Warner Award (AMIA 2010). He teaches machine learning and advises on interdisciplinary AI projects.

Gaofeng JiaView profile

Associate Professor

• Structural Engineering
• Risk Assessment
• Infrastructure Resilience
• +4 more

Gaofeng Jia is Associate Professor in Civil and Environmental Engineering at Colorado State University's Walter Scott, Jr. College of Engineering. He specializes in natural hazard risk assessment, infrastructure resilience, and uncertainty quantification. Research integrates simulation-based approaches with high-performance computing for assessing complex engineering systems. Core areas include infrastructure deterioration modeling, wave energy converter optimization, tsunami evacuation planning, and surrogate modeling techniques. Recent work emphasizes physics-informed machine learning for engineering applications. Publications demonstrate consistent methodological innovation in uncertainty quantification across domains including seismic engineering, coastal hazards, renewable energy systems, and structural reliability. Articles employ advanced computational statistics, Bayesian methods, and multi-fidelity modeling. Young Researcher Best Paper Award Best Student Paper Award

Gloria CrispView profile

Professor

• Equity in Higher Education
• Community College Transfer
• Mentoring Frameworks
• +2 more

Dr. Gloria Crisp is a Professor of Adult and Higher Education at Oregon State University and currently serves as Associate Dean in the College of Education. With 25 years of experience in community colleges and bachelor’s granting institutions, her work focuses on equity in higher education , particularly for Latinx students and other minoritized populations. Research Focus : Transfer pathways, mentoring frameworks, and policies reducing educational inequities Key Contributions : Created the College Student Mentoring Scale (CSMS), used globally to assess mentoring effectiveness Awards : ASHE 2020 Mentoring Award Leadership : Former Editor-in-Chief of New Directions for Institutional Research , President of Council for the Study of Community Colleges (2022-23) Publications include over 60 articles in journals like American Educational Research Journal and Research in Higher Education , with citations exceeding 10,000. Her co-edited book Unlocking Opportunity through Broadly Accessible Institutions (2022) challenges deficit narratives about broad-access universities.

Charles LingView profile

Professor

• Machine Learning
• Deep Learning
• Artificial Intelligence
• +4 more

Charles Ling is a Professor of Computer Science at Western University, holding the title of Science Distinguished Research Professor. He also serves as Director of the Data Mining and Business Intelligence Lab and Associate Scientist at the Lawson Health Research Institute. His academic background includes a B.Eng. (CS and EE) from Shanghai Jiao Tong University and MSc/PhD from the University of Pennsylvania (UPenn). Research interests span machine learning, deep learning, AI, and healthcare informatics, with notable contributions to the GlucoGuide diabetes management system. He has authored over 220 peer-reviewed papers and a book titled Crafting Your Research Future , focusing on academic career development. Awarded Fellow of the Canadian Academy of Engineering (CAE) and recipient of the First Prize for Best Clinical Research Presentation (2011). Active in grants (NSERC, FedDev, Mitacs) and organizational roles in top conferences (KDD, ICDM). Supervises 5 PhD and 4 MSc students, with notable advisees including Harry Zhang and Victor Sheng. Leverages AI in education to enhance children's cognitive abilities through video-based programs like Power Thinking , approved by Curriculum Services Canada. His work integrates machine learning with healthcare, finance, and software engineering.

Chiara SabattiView profile

Professor

• Statistical Genetics
• Computational Biology
• Bayesian Statistics
• +2 more

Chiara Sabatti is a Professor of Biomedical Data Science and Statistics at Stanford University, with affiliations to the Stanford Center for Computational, Evolutionary and Human Genomics (CEHG), Bio-X, and the Stanford Cancer Institute. She serves as Associate Director for Stanford Data Science and has led the development of the Data Science Major curriculum since 2012. Research Focus: Statistical models for high-throughput genomics data, causal inference in genetic studies, false discovery rate control, and knockoff methods for variable selection. Key Leadership: Associate Chair for Education and Training (2020-present), Vice Chair of Biomedical Data Science (2018-2019). Her work bridges statistical genetics with data science education, emphasizing robustness and interpretability in scientific findings. Recent publications highlight innovations in genome-wide association studies (GWAS), causal variant localization, and cost-effective sequencing techniques for underrepresented populations. Current projects include developing knockoff-based methods to address population structure and multi-resolution hypothesis testing. Scientific Awards: Institute of Mathematical Statistics (IMS) Fellow (2022) NSF CAREER Award (2003-2008) She mentors doctoral and graduate students in Biomedical Data Science, collaborates with the Data Studio on interdisciplinary projects, and actively recruits curious researchers to her lab. Her outreach efforts focus on expanding data science education and increasing research participation from underrepresented groups.

Haiyan HuangView profile

Professor

• Computational Biology
• Applied Statistics
• High Dimensional Data Analysis
• +3 more

Haiyan Huang is a Professor and Chair of the Department of Statistics at the University of California, Berkeley. She is affiliated with the Center for Computational Biology and the Graduate Group in Biostatistics. Her research focuses on computational biology, applied statistics, and high-dimensional genomic data analysis, with an emphasis on network modeling and translational bioinformatics. Education details are not explicitly listed, but her academic background includes a PhD in Statistics. She has advised numerous graduate students and postdocs, contributing to impactful research in statistical methods for genomics and bioinformatics. Her work spans statistical methodology development, including sparse canonical correlation analysis, biclustering, and single-cell RNA sequencing analysis. Notable projects include predicting tumor heterogeneity, designing random heteropolymers, and linking protein expression to cell motility. Publications highlight her contributions to gene network inference, reproducibility in high-throughput experiments, and integrating multi-platform genomic data. Awards include recognition by Faculty of 1000 Biology for her 2010 PNAS work. Huang teaches advanced courses in computational biology, statistical theory, and consulting. Her lab collaborates with experts in bioengineering, chemistry, and pediatrics, fostering interdisciplinary research. Current projects include precision medicine, deep learning applications, and probabilistic modeling of biological systems. Her team uses variational inference, hidden Markov models, and machine learning to address challenges in single-cell analysis and pharmacogenomics. The lab’s work has been published in top journals like Nature, PNAS, and Bioinformatics, reflecting her leadership in computational genomics.

Robert (Rob) J. Franzese, Jr.View profile

Professor

• Comparative Political Economy
• International Political Economy
• Quantitative Research Methods
• +5 more

Robert (Rob) J. Franzese, Jr. is a Professor and Associate Chair in the Department of Political Science at the University of Michigan. He also serves as the Edie N. Goldenberg Director of the Michigan in Washington Program and Research Professor at the Center for Political Studies (CPS), Institute for Social Research. His research focuses on comparative and international political economy (C&IPE) of developed democracies, emphasizing the interplay between political and economic institutions, structures, and events on macroeconomic policymaking. Franzese is renowned for his work on spatial-econometric models of interdependence and empirical methodologies in political science. Education: PhD in Government from Harvard University (1996), A.M. in Economics and Government from Harvard (1995 and 1992), and B.A. in Economics and Government from Cornell University (1990, Phi Beta Kappa). He teaches courses on quantitative methods, comparative politics, and political economy. Key affiliations include the ICPSR Summer Program in Quantitative Methods and the Center for Political Studies. His research spans topics such as central bank independence, wage bargaining institutions, and spatial econometrics. He has authored/co-authored numerous books and articles on empirical methodology, policy analysis, and interdependence modeling. His methodological contributions include spatial econometric models, multilevel analysis, and model-based estimation for social science theory (EITM). He has advised on policy issues related to globalization, fiscal policy, and labor markets, with a focus on developed democracies. Fransese has directed the ICPSR Summer Program, which trains researchers in advanced quantitative methods. His work bridges political science and economics, emphasizing interdisciplinary approaches to understanding policymaking dynamics and spatial interdependencies.

Shan YuView profile

Assistant Professor

• Statistical Methodology
• Machine Learning
• Neuroimaging Analysis
• +7 more

Shan Yu is an Assistant Professor in the Department of Statistics at the University of Virginia. His research focuses on developing statistical and machine learning methods for large-scale, complex data, with applications in neuroimaging, genomics, spatial epidemiology, and health disparities. He employs advanced techniques including non/semi-parametric regression, functional data analysis, and distributed learning while emphasizing data privacy. Yu received his Ph.D. in Statistics from Iowa State University (2020), advised by Professors Lily Wang and Dan Nettleton, following a B.S. from the University of Science and Technology of China. His work bridges statistical methodology and real-world problems, addressing challenges in environmental science (e.g., nitrogen dioxide inequalities), public health (e.g., pandemic forecasting), and computational biology (e.g., genotype-environment interactions). He collaborates on tools like the GgAM R package for generalized geoadditive models and contributes to open-source projects such as fFLM for functional linear regression. Key research trends include spatially varying coefficient models, fusion learning for heterogeneous data, and integration of satellite data with environmental health studies. His publications span journals in statistics, epidemiology, and environmental science, reflecting interdisciplinary impact.

Douglas H FisherView profile

Associate Professor

• Artificial Intelligence
• Machine Learning
• Computational Sustainability
• +3 more

Douglas H Fisher is an Associate Professor of Computer Science and Computer Engineering at Vanderbilt University's School of Engineering. His research focuses on artificial intelligence, particularly machine learning, and computational sustainability. He holds a Ph.D., M.S., and B.S. in Computer Science from the University of California - Irvine. His work bridges AI with societal challenges, emphasizing sustainability, education technology, and cognitive modeling. Notable areas include integrating sustainability into computing curricula, leveraging AI for peer review systems (pReview), and exploring bias mitigation in neural networks. He has contributed to foundational machine learning techniques, such as rule induction for medical data analysis and decision tree optimization. Fisher's research spans interdisciplinary applications: from geospatial water resource modeling to MOOCs' social incentives. His educational contributions include blended learning frameworks and open educational resources advocacy. He has authored over 100 publications across AI, sustainability, and education, reflecting a commitment to both technical innovation and societal impact.

Svetlana YanushkevichView profile

Professor

• Biometric Technologies
• Machine Learning
• Signal Processing
• +3 more

Dr. Svetlana Yanushkevich is a Professor in the Department of Electrical and Software Engineering at the Schulich School of Engineering, University of Calgary. She is also a Full Member of the Hotchkiss Brain Institute and the Mathison Centre for Mental Health Research and Education. Her research focuses on biometric technologies, decision support systems, biomedical applications, and computational intelligence. She leads the Biometric Technologies Laboratory, developing strategies for risk assessment in biometric systems and healthcare monitoring through machine reasoning and signal processing. Education : BSc/MSc in Electrical Engineering (1989), State University of Informatics and Radioelectronics, Minsk PhD in Electrical Engineering (1992), same institution Dr. Habilitated in Technical Sciences (1999), Warsaw University of Technology Research Interests : Dr. Yanushkevich’s work spans biometric system design (e.g., gait analysis, facial attributes), decision support via probabilistic models (Bayesian networks, causal inference), biomedical applications (stroke rehabilitation, wearable sensors), and computational intelligence for data science. She emphasizes fairness, bias mitigation, and trustworthiness in AI systems, particularly in healthcare and accessibility contexts. Recent Research Trends : Her recent publications address causal modeling for accessibility barriers, UAV operator cognitive workload, and medical device optimization in radiation therapy. She explores AI ethics, stress contagion in human-robot teams, and cross-spectral biometric systems. Awards & Recognition : 2024 FEIC Fellow (Engineering Institute of Canada) 2019 Research Excellence Award (Schulich School of Engineering) 2001 Senior IEEE Membership Advising & Grants : She coordinates courses like ENCM 509 (Biometric Systems Design) and ENEL 610 (Biometric Technologies). Her research is supported by grants focusing on healthcare AI, accessibility technologies, and computational epidemiology. Labs & Collaborations : Her Biometric Technologies Lab collaborates with institutions like Hokkaido University and the IEEE Computational Intelligence Society. Projects include wearable health monitoring, decision support platforms, and AI-driven epidemiological modeling.

Chao TianView profile

Associate Professor

• Information Theory
• Distributed Data Storage
• Coding Theory
• +6 more

Dr. Chao Tian is an Associate Professor in the Department of Electrical and Computer Engineering at Texas A&M University, part of the College of Engineering. He holds a Ph.D. from Cornell University (2005) and a B.E. from Tsinghua University (2000). His research focuses on information theory, distributed storage systems, coding theory, and machine learning applications, including large language models (LLMs) and privacy-preserving techniques like steganography and private information retrieval (PIR). He has received notable awards, including the 2017 IEEE Jack Wolf ISIT Best Student Paper Award and the 2014 IEEE ComSoc DSTC Best Paper Award. Dr. Tian’s work bridges theoretical foundations and practical applications, such as optimizing storage codes for distributed systems, developing secure PIR protocols, and exploring LLMs in energy and steganographic contexts. His group has pioneered methods for variable-order Markov chain modeling with transformers and designed quantization techniques for perceptual quality. Recent activities include a faculty development leave at MIT/Harvard and delivering distinguished lectures globally on AI and information theory. He advises students on cutting-edge topics like LLM-based steganography and diffusion models. His publications span journals like IEEE Transactions on Information Theory and conferences such as NeurIPS and ISIT, with a focus on coding theory, privacy, and machine learning. He has also contributed to open-source tools like the CAI toolbox for investigating information-theoretic limits. Current projects include optimizing cryptocurrency mining in energy markets and advancing federated learning with adversarial robustness.