Stanford University
Showing 1-10 of 22 Results
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Jun Uchida
Professor of History
Current Research and Scholarly InterestsMy current book project examines the diasporic history of Ōmi shōnin (merchant). Often compared to overseas Chinese and Jewish merchants, merchants of Ōmi (present-day Shiga prefecture) are famous for peddling textiles and other goods across the early modern Japanese archipelago. My aim is to trace their activities into the global age of capital and empire, from cotton trade and manufacturing in China to retail commerce in Korea and Manchuria, and immigration to North America.
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Madeleine Udell
Assistant Professor of Management Science and Engineering and, by courtesy, of Electrical Engineering
Current Research and Scholarly InterestsProfessor Udell develops new techniques to accelerate and automate data science,
with a focus on large-scale optimization and on data preprocessing,
and with applications in medical informatics, engineering system design, and automated machine learning. -
Johan Ugander
Associate Professor of Management Science and Engineering
BioProfessor Ugander's research develops algorithmic and statistical frameworks for analyzing social networks, social systems, and other large-scale data-rich contexts. He is particularly interested in the challenges of causal inference and experimentation in these complex domains. His work commonly falls at the intersections of graph theory, machine learning, statistics, optimization, and algorithm design.
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Scott Uhlrich
Research Engineer
Current Research and Scholarly InterestsExperimental biomechanical analysis of healthy and pathological human movement. Real-time biofeedback to modify motor control and kinematics.
Musculoskeletal modeling and simulation for estimating unmeasurable quantities during movement, like joint forces in individuals with osteoarthritis. Predictive musculoskeletal simulations to design rehabilitation interventions.
Computer vision, wearable sensing, and machine learning to develop tools that democratize biomechanical analysis and translate biomechanical interventions into clinical practice.
Quantitative MRI for analyzing the effect of non-surgical treatments for osteoarthritis on cartilage health. PET-MRI for analyzing relationships between the mechanical loading of tissue metabolic activity. -
Mirko Uljarevic
Clinical Associate Professor, Psychiatry and Behavioral Sciences
BioI am a medically trained researcher focused academic with a background in developmental psychopathology, psychometrics and big data science. My research takes a life-span perspective and is driven by the urgent need to improve outcomes for people with autism and other neuropsychiatric (NPD) disorders and neurodevelopmental conditions (NDD). My primary research interest has focused on combining cutting-edge psychometric procedures and a big data approach to better understand structure of clinical phenotypes across autism and other NPD and NDD and on using this knowledge to improve existing and develop new clinical assessments that are more effective for screening and diagnosis, tracking the natural and treatment-related symptom progression and for use in genetic and neurobiological studies. In addition to my focus on the development of outcome measures, I have collaborated with leading psychopathology researchers and groups in the United States, Europe and Australia on numerous projects spanning a range of topics including genetics, treatment and employment, with a particular focus on understanding risk and resilience factors underpinning poor mental health outcomes in adolescents and adults. Most recently, through several competitively funded projects, I have led the statistical analyses to uncover the latent structure of social and communication and restricted and repetitive behaviors (RRB) clinical phenotypes across NPD and NDD. These findings have enabled us to (i) start capturing and characterizing a highly variable social functioning phenotype across a range of disorders and understanding mechanisms underpinning this variability, (ii) combine phenotypic and genetic units of analyses to advance our understanding of the genetic architecture of RRB, and (iii) focus on identification and characterization of subgroups of individuals that share distinct symptom profiles and demonstrate clinical utility and neurobiological validity. Importantly, this work has provided key information for developing a programmatic line of research aimed at developing novel, comprehensive assessment protocols that combine parent and clinician reports, objective functioning indicators and incorporate state-of-the-art psychometric, mobile and connected technologies and procedures.
I am a co-director of the recently established Program for Psychometrics and Measurement-Based Care (https://med.stanford.edu/sppmc.html) that aims to bring together world-leading expertise in clinical science, psychometrics, and big data analytics to bridge the gap between the science of measurement development and clinical practice and bring improvements to both clinical care and research. -
Jeffrey Ullman
Stanford Warren Ascherman Professor of Engineering , Emeritus
BioJeff Ullman is the Stanford W. Ascherman Professor of Engineering
(Emeritus) in the Department of Computer Science at Stanford and CEO
of Gradiance Corp. He received the B.S. degree from Columbia
University in 1963 and the PhD from Princeton in 1966. Prior to his
appointment at Stanford in 1979, he was a member of the technical
staff of Bell Laboratories from
1966-1969, and on the faculty of Princeton University between
1969 and 1979. From 1990-1994, he was chair of the Stanford Computer
Science Department. Ullman was elected to the National Academy of
Engineering in 1989, the American Academy of Arts and Sciences in
2012, and has held Guggenheim and Einstein Fellowships. He has
received the Sigmod Contributions Award (1996), the ACM Karl V. Karlstrom
Outstanding Educator Award (1998), the Knuth Prize (2000),
the Sigmod E. F. Codd Innovations award (2006), the IEEE von
Neumann medal (2010), and the NEC C&C Foundation Prize (2017).
He is the author of 16 books, including books
on database systems, compilers, automata theory, and algorithms. -
Maxine Umeh Garcia
Instructor, Neurosurgery
BioMaxine was born and raised in Sacramento, CA and transferred to UC Merced in 2007 after attending a community college for 2 years. She received her B.S. in Developmental Biology with a minor in Psychology in 2010. During the last year of her undergrad, Maxine was invited to do research in the lab of Dr. Michael Cleary, studying nervous system development. Because of this research experience, Maxine decided to stay at UC Merced to pursue her Master’s in Quantitative and Systems Biology, graduating in 2013. Immediately after graduating, she started her Ph.D. at UC Davis, where her research centered on triple negative breast cancer – a type of breast cancer that has a high incidence in Black and African women.
After completing her PhD in Biochemistry, Molecular, Cell and Developmental Biology with an emphasis in Translational Research in 2019, Maxine became a postdoctoral fellow at Stanford University in the department of Neurosurgery. Dr. Umeh Garcia’s research focuses on breast cancers that metastasize (or travel) to the brain. Maxine was recently promoted to an instructor position in her department after receiving a major career development award from the National Cancer Institute (K99/R00), which will fund the remainder of her postdoctoral research and provide 3 years of funding for Maxine to establish her own independent research lab. Using her background in bench research, informatics, and translational research, Dr. Umeh Garcia hopes to bring together biologists, data scientists, and clinicians to make important advances in breast cancer diagnosis and treatment. Additionally, as a women and underrepresented minority, Dr. Umeh Garcia is keenly interested in mentoring women and underrepresented students, and in developing novel strategic approaches to increasing diversity in biomedical sciences and academic research.
