Stanford University
Showing 18,561-18,580 of 36,179 Results
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C. Karen Liu
Professor of Computer Science
BioC. Karen Liu is a professor in the Computer Science Department at Stanford University. Prior to joining Stanford, Liu was a faculty member at the School of Interactive Computing at Georgia Tech. She received her Ph.D. degree in Computer Science from the University of Washington. Liu's research interests are in computer graphics and robotics, including physics-based animation, character animation, optimal control, reinforcement learning, and computational biomechanics. She developed computational approaches to modeling realistic and natural human movements, learning complex control policies for humanoids and assistive robots, and advancing fundamental numerical simulation and optimal control algorithms. The algorithms and software developed in her lab have fostered interdisciplinary collaboration with researchers in robotics, computer graphics, mechanical engineering, biomechanics, neuroscience, and biology. Liu received a National Science Foundation CAREER Award, an Alfred P. Sloan Fellowship, and was named Young Innovators Under 35 by Technology Review. In 2012, Liu received the ACM SIGGRAPH Significant New Researcher Award for her contribution in the field of computer graphics.
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Li Liu
Sir Robert Ho Tung Professor
Current Research and Scholarly InterestsResearch interests:
Archaeology of early China (Neolithic and Bronze Age); ritual practice in ancient China; cultural interaction between China and other parts of the Old World; early domestication of plants and animals in China; theory of development of complex societies and state formation; settlement archaeology; urbanism; zooarchaeology; starch analysis; use-wear analysis; mortuary analysis; craft specialization -
Lianli Liu
Clinical Assistant Professor, Radiation Oncology - Radiation Physics
Current Research and Scholarly InterestsAI-driven medical imaging for accelerated imaging speed and improved image quality, including:
Accelerated imaging for in-treatment patient monitoring and post-treatment patient follow up;
Functional imaging for treatment response evaluation and prediction.
Optimizing clinical quality assurance workflow through AI, including:
Radiation beam data modeling for efficient commissioning;
Model-based error detection for accurate dosimetry. -
Lili Liu
Postdoctoral Scholar, Epidemiology
BioLili (Larry) Liu, PhD, is a postdoctoral fellow in the Department of Epidemiology & Population Health at Stanford University. As an integrative epidemiologist, Dr. Liu unifies molecular biomarkers, large-scale population cohorts, and real-world health data into coherent, hypothesis-driven research with a sustained focus on how early-life exposures, genetic variation, lifestyle, and pharmacological factors shape inflammation, biological aging, and chronic disease risk across the life course. Trained in cancer genetic and nutrition epidemiology with complementary expertise in pharmacoepidemiology, his doctoral research at Vanderbilt University included a multi-ancestry GWAS of urinary prostaglandin E2 metabolite (PGE-M), development of PGE-M–derived dietary and lifestyle scores via elastic net with extensive bootstrapping, and Mendelian randomization analyses linking PGE-M to colorectal cancer across ancestries. At Stanford, Dr. Liu extends his research to maternal–fetal and placental epidemiology, building nationwide claims-based pregnancy cohorts (e.g., MarketScan) to examine gestational diabetes and downstream liver disease risk, and creating mother–infant pair cohort to investigate systemic antibiotic exposure in relation to subsequent inflammatory bowel disease and celiac disease. Parallel collaborations focus on extracellular vesicles and angiogenic signaling in placental health. Methodologically, Dr. Liu works at the interface of causal inference, pharmacoepidemiology, and machine learning with reproducible data engineering (R/Python, SQL, HPC), with the overarching goal of translating mechanistic insights into actionable biomarkers and risk tools for chronic disease prevention in diverse populations.
