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Ying Qing ChenView profile

Professor

• HIV/AIDS Research
• Clinical Trials
• Epidemiological Methods
• +7 more

Ying Qing Chen is a Professor of Medicine at the Stanford Prevention Research Center, part of the Department of Medicine at Stanford University. Additionally, she holds an Affiliate Professor position in Biostatistics. Her roles include serving as Director of the Palo Alto Veteran Affairs Cooperative Studies Coordinating Center since 2021. Dr. Chen earned her BS in Mathematics from Peking University (1992) and her PhD in Biostatistics from Johns Hopkins University (1999). Her research focuses on statistical methodologies for HIV/AIDS prevention , including biomarkers, clinical trial design, epidemiological methods, and survival analysis. She also contributes to studies on vaccine effectiveness, adherence to antiretroviral treatments, and the intersection of social stigma with healthcare access in high-risk populations. Her work spans multinational collaborations, such as the HPTN 052, 069/ACTG 5305, and 075 trials, and she leads projects funded by NIH and industry partners (e.g., Sinovac Biotech). Key areas of expertise include evaluating combination prevention strategies, developing statistical surrogacy measures, and analyzing longitudinal clinical data to inform public health policies. Projects: Co-PI for the Cytomegalovirus (CMV) Vaccine in Orthotopic Liver Transplant candidates (COLT) study (NIH/NIAID U01 AI163090-01, 2021-Present) PI for Research on Global Vaccine Application and Trends (Sinovac Biotech, 2021-Present) PI/MPI for Ganciclovir to Prevent Cytomegalovirus Reactivation (NIH/NHLBI U24 HL147012, 2020-Present) Dr. Chen’s scientific contributions have been recognized with the Fellowship of the American Statistical Association (2018) . She teaches courses such as Introduction to Clinical Trials: Design, Conduct, and Analysis (CHPR 211) and supervises thesis writing through Community Health and Prevention Research Master's Thesis Writing (CHPR 399). Her research emphasizes improving HIV prevention outcomes, addressing challenges in adherence and combination therapies, and leveraging statistical innovations to enhance vaccine efficacy assessments. She has also demonstrated expertise in handling complex longitudinal datasets and conducting feasibility studies for at-risk populations in sub-Saharan Africa.

Chris RasmussenView profile

Adjunct Professor

• Anti-Money Laundering
• Match-Fixing Detection
• Sports Betting Risk Management
• +2 more

Chris Rasmussen serves as an Adjunct Professor in the Investigations Department within the Henry C. Lee College of Criminal Justice and Forensic Sciences at the University of New Haven. He brings extensive frontline experience from international financial crime prevention to his academic role. Professor Rasmussen's professional expertise spans critical areas in financial integrity: Anti-Money Laundering systems implementation Match-Fixing Detection in global sports markets Sports Betting Risk Management protocols Financial Crime Investigation techniques Odds Compilation and manipulation analysis His practical experience directly informs his academic work, with frequent media consultations on high-profile sports integrity cases across soccer, tennis, and handball competitions. Professor Rasmussen regularly provides expert commentary to major international publications including The New York Times, The Athletic, and European media outlets regarding suspicious betting patterns and match-fixing investigations.

Sullivan BlairView profile

Professor

• Graph Algorithms
• Computer Science
• Algorithm Design
• +3 more

Blair Sullivan is a Professor at the Kahlert School of Computing, University of Utah. Her research focuses on graph algorithms, parameterized complexity, and network analysis. She has contributed to theoretical advancements in clustering, graph decomposition, and algorithmic efficiency with applications in robotics, bioinformatics, and social networks. Education details are not explicitly stated in the provided text, but her affiliation indicates a terminal degree in Computer Science or a related field. Her work frequently intersects with interdisciplinary domains such as computational biology and quantum computing. Research interests emphasize algorithm design for large-scale networks, with a focus on graph-based problems such as clustering, coloring, and structural optimization. Recent publications explore hypergraph clustering, robotic motion planning, and fairness in network information access. Her work often bridges theoretical computer science with practical applications, including biomedical data analysis and quantum program compilation. Publications since 2023 reflect a sustained focus on graph-theoretic challenges such as parameterized complexity, edge augmentation for fairness, and decomposition techniques. Notable themes include algorithmic approaches to gerrymandering, genetic association analysis, and hyperbolicity in networks. No scientific awards or grants are listed in the provided text. She is affiliated with the University of Utah’s Kahlert School of Computing, where she contributes to research and education in computational theory and applications.