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Kai LiuView profile

Assistant Professor

• Machine Learning
• Optimization
• Data Mining
• +4 more

Kai Liu is an Assistant Professor in the Computer Science Division at Clemson University . He holds a Ph.D. in Computer Science from the Colorado School of Mines (adviser: Dr. Hua Wang). His research focuses on the intersection of Machine Learning and Optimization , particularly in designing robust, efficient, and scalable algorithms for data mining applications. Key areas include non-negative matrix factorization, tensor completion, and healthcare analytics such as Alzheimer's disease analysis. Teaching: Liu has taught advanced courses including Optimization for Machine Learning , Advanced Machine Learning , and Applied Data Science at Clemson University. He also contributed to undergraduate courses like Discrete Structures . Research Contributions: His work emphasizes theoretical guarantees for algorithms, with applications in bioinformatics (e.g., HIV-1 drug resistance prediction), computer vision, and healthcare. Notable methods include spherical principal component analysis and accelerated tensor completion techniques. Professional Service: Liu serves on program committees for top conferences including ICML, NeurIPS, AAAI, and CVPR. He is a recognized expert in first-order optimization methods and high-dimensional statistics. Personal Interests: Beyond academia, Liu engages in traditional Chinese culture (calligraphy, painting, poetry), enjoys hiking, and is an avid reader of biographies. He is also a marathon runner and long-time supporter of Real Madrid.

Haiyan WangView profile

Professor

• Machine Learning
• Nonparametric Inference
• High-Dimensional Data Analysis
• +3 more

Dr. Haiyan Wang is Professor of Statistics at Kansas State University's College of Arts and Sciences. She earned her Ph.D. from Pennsylvania State University (2004) and leads research in machine learning, nonparametric statistics, and high-dimensional data analysis with applications in genomics, lipidomics, and image processing. She directs the Statistical Consulting program and has developed R packages for functional data analysis and high-dimensional testing. Her methodological research spans: 1) Inference for heteroscedastic functional data 2) Rank-based high-dimensional testing 3) Test-based clustering 4) Image quality assessment 5) Variable selection for high-dimensional data. She maintains active collaborations in plant science, bioinformatics, and materials engineering. As Graduate Council representative, she oversees statistics curriculum development. She has advised 19+ graduate students and teaches courses spanning statistical theory, machine learning, and computational statistics.

• Bayesian Computation
• Monte Carlo Methodology
• Causal Inference
• +3 more

Yu-Bo Wang is an Associate Professor in the Department of Mathematical and Statistical Sciences at Clemson University. His research focuses on Bayesian computation, causal inference, and statistical methodologies with applications in health sciences and ecology. He holds a PhD in Statistics from the University of Connecticut (2016), and MS and BS degrees in Statistics from National Chengchi University, Taiwan (2009 and 2006). Education: PhD, Statistics, University of Connecticut, 2016 MS, Statistics, National Chengchi University, Taiwan, 2009 BS, Statistics, National Chengchi University, Taiwan, 2006 Research Interests: His work spans Bayesian computation, Monte Carlo methods, causal inference, and applications in mortality projection. Notable contributions include advancements in marginal likelihood estimation, mediation analysis, and high-dimensional regression techniques. Publications: Recent work includes studies on Bayesian regularized mediation analysis in health outcomes, statistical methods for causal inference in clustered data, and computational innovations in phylogenetics and ecology. His research bridges theoretical statistics with practical applications in dentistry, environmental science, and public health. Awards: No specific awards listed, but his prolific publication record reflects sustained academic excellence. Advising & Grants: Advises graduate students in statistical methodology and collaborates on grants related to Bayesian computation and health data analysis. No specific grant details provided in the text. Labs/Teams: Engages in interdisciplinary teams within Clemson’s College of Science, though specific lab affiliations are not detailed here.

• Nonlinear optimization
• Stochastic approximation
• Algorithm design for big data analytics
• +3 more

Yuyuan Ouyang is an Associate Professor in the Department of Mathematical and Statistical Sciences at Clemson University. He holds a Ph.D. in Mathematics from the University of Florida (2013). His research focuses on nonlinear optimization, stochastic approximation, and algorithm design for big data analytics. His work bridges theoretical foundations with practical applications in machine learning, network flow programming, and convex optimization. Dr. Ouyang teaches advanced courses including Machine Learning I/II, Network Flow Programming, and Nonlinear Programming. His recent publications emphasize gradient sliding methods, decentralized optimization, and saddle-point problem analysis. He has contributed to SIAM Journal on Optimization, Mathematical Programming, and Operations Research Letters. His research trends show a strong emphasis on algorithmic innovation for large-scale systems, with notable work on complexity bounds, convex reformulations, and statistical estimation techniques. He has collaborated widely on topics ranging from variational inequalities to MRI image reconstruction.

Amanda S. HeringView profile

Professor

• Statistical Science
• Environmental Statistics
• Engineering Applications
• +7 more

Dr. Amanda S. Hering is a Professor of Statistical Science at Baylor University and Co-Director of the Co-Graduate Program. She holds a Ph.D. in Statistics from Texas A&M University (2009), an M.S. from Montana State University (2002), and a B.S. from Baylor University (1999). Her research focuses on applying statistical methods to environmental and engineering challenges, particularly in water/wastewater systems, multivariate spatial-temporal analysis, and statistical process monitoring. She has co-authored influential books like Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches (Elsevier, 2020) and serves as Associate Editor for Technometrics , Environmetrics , and others. Dr. Hering leads interdisciplinary projects such as the NSF-funded MoWaTER initiative ($1.157M), advancing data science education and water infrastructure optimization. Her work includes developing autonomous wastewater treatment systems, optimizing desalination processes, and creating fault detection algorithms for industrial systems. Notable recognitions include the Abel El-Shaawari Early Investigator Award (TIES, 2019) and a 2020 Intelligent Water Systems Challenge win for ammonia control models reducing energy consumption. Her research spans statistical methodologies for high-dimensional data, environmental monitoring, and sustainable infrastructure. Current projects involve Bayesian hierarchical models for groundwater analysis and AI-driven optimization of water reuse systems. She collaborates with national labs (e.g., NREL, Oak Ridge) and industry partners (e.g., Aqua-Aerobic Systems) to translate statistical innovation into real-world water solutions.

Nicholas RiosView profile

Assistant Professor

• Experimental Design
• Computational Statistics
• Functional Data Analysis
• +5 more

Nicholas Rios is an Assistant Professor of Statistics at George Mason University (GMU), where he has been affiliated since August 2022. He holds a PhD in Statistics from The Pennsylvania State University (2022), an MS from Montclair State University, and a BS from the University of Delaware. His research focuses on experimental design under real-world constraints, computational statistics, functional data analysis, Gaussian processes, and compositional data analysis, with applications in chemical engineering and pharmaceuticals. Dr. Rios teaches applied statistics (STAT 554) and emphasizes critical thinking in statistical methodology. His doctoral work developed novel algorithms for efficient mixture experiments, particularly addressing the impact of component addition order. His current research bridges theoretical statistical methods with practical industrial challenges, leveraging his expertise in high-dimensional data and constrained experimental designs. His recent publications (2022–2025) explore advanced experimental design techniques, bootstrap aggregation for generalized linear models, and compositional data analysis in chemical recycling. While no scientific awards are explicitly mentioned, his work demonstrates significant contributions to optimizing experimental setups in constrained environments. Dr. Rios’ advising and grants are not detailed in the provided texts, though his CV (linked) may provide further insights into collaborative projects or funding. He actively contributes to statistical education and maintains a personal website for professional engagement.

Jiayang SunView profile

Professor

• Simultaneous Inference
• Multiple Testing
• Data Mining
• +7 more

Dr. Jiayang Sun is a Professor, Bernard Dunn Eminent Scholar, and Chair of the Department of Statistics at George Mason University’s College of Engineering and Computing. She holds a PhD in Statistics from Stanford University and previously served as Professor and Director of the Center for Statistical Research, Computing, and Collaboration (SR2c) at Case Western Reserve University. Her leadership extended to a role as the inaugural ASA/ACM/AMS/IMS/MAA/SIAM Science & Technology Policy Fellow at the USDA ARS from 2019-2020. Dr. Sun’s research focuses on statistical methodologies including simultaneous inference, multiple testing, biased sampling, and machine learning applications in healthcare, biomedicine, and interdisciplinary domains. Her work bridges theoretical statistics with practical challenges in fields like neuroimaging, wound care, and precision medicine. Key contributions include developing predictive tools for pressure injury management and advancing methods for analyzing large-scale biomedical datasets. Education: PhD in Statistics, Stanford University Professional Recognition: Elected Fellow, American Statistical Association (ASA) Elected Fellow, Institute of Mathematical Statistics (IMS) President, Caucus for Women in Statistics (CWS) Key Projects: Development of SCIPUD+, a clinical decision support system for pressure injury care Leadership in PV module degradation analytics through statistical pathway modeling Advancement of subsampling algorithms for feature selection in high-dimensional data Her interdisciplinary collaborations span oncology, environmental science, neuroscience, and energy systems, supported by grants from NSF, NIH, DOE, and others. Dr. Sun advocates for statistical rigor in translating big data into actionable insights for clinical and policy applications.

Daniel HuangView profile

Assistant Professor

• Quantum Computing
• Machine Learning
• Probabilistic Programming
• +3 more

Dr. Daniel Huang is an Assistant Professor in the Department of Computer Science at San Francisco State University. His research focuses on quantum computing, probabilistic programming, machine learning, and theoretical computer science. He explores interdisciplinary areas such as hybrid classical-quantum systems, Gaussian process optimization, and computational chemistry modeling. His work bridges algorithmic design with practical applications, including quantum circuit simulation and molecular geometry optimization. Dr. Huang’s recent publications highlight advancements in GPU-based quantum computing, gradient-constrained neural networks, and probabilistic programming languages like Push. He emphasizes the integration of physical priors into machine learning models and explores disruptive technologies like quantum visualization tools. His research often involves collaborative projects, as seen in works on meta-Gaussian processes and data-parallel inference algorithms. His academic contributions span over a decade, with notable papers in probabilistic program semantics, logic in linear spaces, and compiler optimizations for probabilistic models. Though no awards or grants are explicitly listed, his active publication record reflects sustained scholarly engagement. Contact: danehuang@sfsu.edu , Thornton Hall 906.

Pedro DelicadoView profile

Professor

• Statistics
• Data Science
• Big Data
• +7 more

Pedro Delicado is a Full Professor at the Department of Statistics and Operations Research, Universitat Politècnica de Catalunya (UPC), based in Barcelona. He holds a degree in Mathematics and a doctorate in Economics. His research focuses on advanced statistical methods, data science, functional data analysis, and machine learning applications. He leads the ADBD research group (Analysis of Complex Data for Business Decisions) and collaborates with the Institut de Matemàtiques de la UPC. His current project 'Advanced Statistics and Data Science 2: New data, new models, new challenges' (PID2023-148158OB-I00) addresses modern data challenges, including Big Data, wearable devices, EEG analysis, graph data, and nonlinear dimensionality reduction. He supervises PhD students and has expertise in predictive modeling, explainable AI, and interdisciplinary applications in health, environmental science, and education. Delicado has published extensively in top journals like Machine Learning, Test, and Environmental and Ecological Statistics. He teaches advanced statistics and data science courses, leveraging virtual platforms. His work bridges statistical theory with practical applications, emphasizing interpretability and real-world impact.

Rui FanView profile

Assistant Professor

• Econometrics
• Financial Market Forecasting
• Systemic Risk Assessment
• +3 more

Rui Fan is an Assistant Professor in the Department of Economics at Rensselaer Polytechnic Institute, with additional affiliations to the Lally School of Management. His research centers on nonstationary time series analysis applied to economics and finance, including systemic risk assessment, financial forecasting, and causal inference methodologies. He holds a Ph.D. in Economics from the University of Illinois at Urbana-Champaign (2018), an M.S. in Statistics (2015), an M.A. in Economics from Xiamen University (2011), and a B.A. in Economics from Sichuan University (2008). Research Focus: Fan's work spans four key areas: (1) developing systemic risk indicators for financial markets; (2) analyzing the impact of economic shocks (e.g., Fed policy changes, COVID-19) on market stability; (3) advancing instrumental variable estimation techniques; and (4) creating statistical methods for nonlinear nonstationary data common in finance.

Yanwen XuView profile

Assistant Professor

• Data analytics
• physics-informed machine learning
• reliability analysis
• +4 more

Yanwen Xu is an Assistant Professor in the Department of Mechanical Engineering at the Erik Jonsson School of Engineering and Computer Science, The University of Texas at Dallas. Her research bridges data analytics with engineering design to solve complex reliability challenges under uncertainty. Education: Ph.D. in Industrial Engineering, University of Illinois at Urbana-Champaign (2023) M.S. in Applied Statistics, University of Illinois at Urbana-Champaign (2021) M.S. in Industrial Engineering, University of Illinois at Urbana-Champaign (2019) B.S. in Mathematics and Applied Mathematics, Huazhong University of Science & Technology (2016) Research Interests: Dr. Xu pioneers physics-informed machine learning for engineering reliability, integrating stochastic optimization with uncertainty quantification to develop robust predictive models. Her work spans rare event simulation, sensor design optimization, and battery health prognostics, emphasizing domain-specific physics integration for enhanced accuracy in safety-critical systems. Publication Trends: Her 2022-2023 publications reveal a cohesive focus on physics-informed ML applications in reliability engineering, featuring Gaussian process modeling and sequential sampling techniques. Key themes include sensor design optimization, battery degradation prediction, and rare event probability estimation across mechanical and electrical systems. Scientific Awards: Recognized with prestigious honors including the First Place Thomas L. Fagan, Jr., Student Paper Award at RAMS (2022) and UIUC's Sharp Outstanding Graduate Student Award (2022), her contributions are further validated by: Graduate College Conference Presentation Award, UIUC (2021) 2020 QCRE Best Paper Award from IISE Annual Conference (2020) Dr. Xu leads the RaDA (Reliability and Data Analytics) Lab in ECSW 3.460, mentoring graduate researchers and actively recruiting Graduate Research Assistants for Fall 2024. Her lab drives innovation at the intersection of data science and engineering reliability, with current projects focused on physics-informed predictive modeling for safety-critical applications.

Antonio PellicaniView profile

Research Fellow

• Data Mining
• Bioinformatics
• Social Network Analysis
• +4 more

Antonio Pellicani is a Research Fellow at the Department of Computer Science, University of Bari Aldo Moro (Italy). He holds a PhD in Computer Science and Mathematics from the same institution, completed in 2024 under the supervision of Prof. Michelangelo Ceci. Research Interests: Bioinformatics Time Series Analysis Social Network Analysis Anomaly Detection Process Mining Data Mining His recent work focuses on hybrid computational approaches for analyzing complex data types, including microRNA expression data for disease biomarker discovery and cryptocurrency time series for anomaly prediction. Publications span high-impact journals like Knowledge-Based Systems and Information Fusion , with applications in cybersecurity and biomedical informatics. Laboratory Affiliation: KDDE lab (Knowledge Discovery and Data Engineering) at the University of Bari. International Collaboration: Visiting student at Jožef Stefan Institute’s Knowledge Technologies Department (2023).

Hadi Safari KatesariView profile

Assistant Professor

• Bayesian Statistics
• Multivariate Time Series Analysis
• High-Dimensional Data Analysis
• +7 more

Hadi Safari Katesari is a Teaching Assistant Professor in the Department of Mathematical Sciences at Stevens Institute of Technology within the Charles V. Schaefer, Jr. School of Engineering and Science. He joined Stevens in August 2021 after completing his PhD in Statistics at Southern Illinois University and teaches core statistics courses for Data Science, Mathematics, and Business graduate programs. His educational background includes: PhD in Mathematics with concentration in Statistics, Southern Illinois University (2021) Dr. Safari Katesari develops statistical tools for analyzing complex dynamic and dependent data, with emphasis on dimension reduction for multivariate time series. His research spans Bayesian statistics, copula models for dependency structures, factor analysis, and high-dimensional techniques. Key research domains: Bayesian inference and multivariate time series modeling Factor analysis and cluster analysis for latent structures Copula-based approaches in actuarial science and business analytics His publication trends (2015-2022) reveal consistent innovation in copula methodology applied across actuarial science, insurance, and agricultural analytics, with recent work focusing on count data modeling, survival analysis, and IBNR loss reserving through advanced dependency frameworks. His honors include: Doctoral Fellowship Award (2019-2021) from Southern Illinois University Dr. Safari Katesari actively contributes as a reviewer for the Journal of Mahani Mathematical Research, Risk and Decision Analysis Journal, and Mathematical Reviews. His institutional service encompasses assessment of internship projects, graduate open house coordination, and domestic student recruitment events for data science master's programs. No specific research laboratories or dedicated teams were mentioned in the source materials.

Michael Scott BrownView profile

Professor

• Genetic Algorithms
• Software Engineering
• Educational Technology
• +3 more

Dr. Michael Scott Brown is a Professor in the Department of Information Systems at the University of Maryland Baltimore County (UMBC). Previously, he served as the Graduate Program Director for Online Masters in Information Systems and related certificates at UMBC. Before that, he was Program Director of Computer Science at the University of Maryland Global Campus (UMGC), overseeing software engineering, database systems, and computer science programs. He has extensive industry experience, including roles at Sun Microsystems, Valhalla Data Systems, and CACI, where he contributed to software engineering and project management. Education: Dr. Brown holds a Doctor of Philosophy in Computer Science (Nova Southeastern University, 2010), a Master of Science in Computer (Shippensburg University, 1997), and a Bachelor of Arts in Mathematics/Computer Science (Shippensburg University, 1992). His thesis focused on a species-conserving genetic algorithm for multimodal optimization. Research Interests: His work spans genetic algorithms, software engineering, educational technology, and data analysis. Notable projects include collaborations with NASA on flight system testing, development of ranking systems for high school sports, and contributions to Cytoscape modeling tools. He has been awarded a Fulbright Grant (2019) and two Effective Practice Awards from the Online Learning Consortium (2016, 2018). Recent Articles Trends: His publications emphasize predictive modeling in education (student retention, test scores), machine learning applications, and optimization techniques. He frequently explores intersections between algorithms (genetic, SVM) and real-world challenges like cryptocurrency forecasting and UAV path planning. Awards: Fulbright Grant (2019), OLC Effective Practice Awards (2016 & 2018), NSF grants, and others. Advising & Grants: Supervised projects at NASA and Cytoscape, managed large educational programs, and secured grants like NSF AIM-PQC. His teaching spans 12 institutions, emphasizing adult and online education. Labs/Teams: Collaborated on NASA Core Flight System testing, Washington Post sports rankings, and the EVA Training System for astronauts. Active in interdisciplinary research through projects like the Open Research Laboratory.

Fatemeh EmdadView profile

Teaching Professor

• Applied Mathematics
• Bio-Statistical Analysis
• Data Mining
• +3 more

Fatemeh Emdad is a Teaching Professor in Data Science at Worcester Polytechnic Institute (WPI), joining in 2015 from Babson College. Previously, she held roles as an Assistant Professor in Residence at the University of Connecticut and conducted postdoctoral research at the University of Texas Medical Branch and Shriners Hospital for Children Burn Unit under an NIH fellowship. She earned her PhD in Applied Mathematics from Colorado State University and holds additional degrees from Shiraz University and Tehran Tarbiat Moallem University. Her research focuses on applied mathematics, bio-statistics, healthcare data science, and business analytics. Notable contributions include authoring the book High Dimensional Data Analysis and over 20 peer-reviewed articles. Her work bridges mathematical modeling with clinical applications, particularly in burn injury outcomes and obesity's protective effects post-severe burns. Prof. Emdad is committed to education, having coordinated large-scale courses for 800+ students at UConn and mentored undergraduate research projects at Babson. Her teaching emphasizes real-world applications of mathematics in medicine and STEM outreach, highlighted by presentations at events like the Better Citizen’s Series, addressing women’s academic progression. Her scholarly awards include the top-ranked student award from Shiraz University’s president and NIH funding. She has contributed to curriculum development in quantitative business modeling and remains active in interdisciplinary research collaborations.