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Professor of Mechanical Engineering and Director, Institute for Computational and Mathematical Engineering
Current Research and Scholarly InterestsComputing and data for energy, health and engineering
Challenges in energy sciences, green technology, transportation, and in general, engineering design and prototyping are routinely tackled using numerical simulations and physical testing. Computations barely feasible two decades ago on the largest available supercomputers, have now become routine using turnkey commercial software running on a laptop. Demands on the analysis of new engineering systems are becoming more complex and multidisciplinary in nature, but exascale-ready computers are on the horizon. What will be the next frontier? Can we channel this enormous power into an increased ability to simulate and, ultimately, to predict, design and control? In my opinion two roadblocks loom ahead: the development of credible models for increasingly complex multi-disciplinary engineering applications and the design of algorithms and computational strategies to cope with real-world uncertainty.
My research objective is to pursue concerted innovations in physical modeling, numerical analysis, data fusion, probabilistic methods, optimization and scientific computing to fundamentally change our present approach to engineering simulations relevant to broad areas of fluid mechanics, transport phenomena and energy systems. The key realization is that computational engineering has largely ignored natural variability, lack of knowledge and randomness, targeting an idealized deterministic world. Embracing stochastic scientific computing and data/algorithms fusion will enable us to minimize the impact of uncertainties by designing control and optimization strategies that are robust and adaptive. This goal can only be accomplished by developing innovative computational algorithms and new, physics-based models that explicitly represent the effect of limited knowledge on the quantity of interest.
I consider the classical boundaries between disciplines outdated and counterproductive in seeking innovative solutions to real-world problems. The design of wind turbines, biomedical devices, jet engines, electronic units, and almost every other engineering system requires the analysis of their flow, thermal, and structural characteristics to ensure optimal performance and safety. The continuing growth of computer power and the emergence of general-purpose engineering software has fostered the use of computational analysis as a complement to experimental testing in multiphysics settings. Virtual prototyping is a staple of modern engineering practice! I have designed a new undergraduate course as an introduction to Computational Engineering, covering theory and practice across multidisciplanary applications. The emphasis is on geometry modeling, mesh generation, solution strategy and post-processing for diverse applications. Using classical flow/thermal/structural problems, the course develops the essential concepts of Verification and Validation for engineering simulations, providing the basis for assessing the accuracy of the results.
Professor of Radiology (Nuclear Medicine) at the Stanford University Medical CenterOn Partial Leave from 04/01/2021 To 05/31/2021
Current Research and Scholarly InterestsCurrent research projects include:
1) PET/MRI and PET/CT for Early Cancer Detection
2) Targeted Radionuclide Therapy
3) Clinical Translation of Novel PET Radiopharmaceuticals;
Assistant Professor of Microbiology and Immunology
Current Research and Scholarly InterestsThe Idoyaga Lab is focused on the function and biology of dendritic cells, which are specialized antigen-presenting cells that initiate and modulate our body’s immune responses. Considering their importance in orchestrating the quality and quantity of immune responses, dendritic cells are an indisputable target for vaccines and therapies.
Dendritic cells are not one cell type, but a network of cells comprised of many subsets or subpopulations with distinct developmental pathways and tissue localization. It is becoming apparent that each dendritic cell subset is different in its capacity to induce and modulate specific types of immune responses; however, there is still a lack of resolution and deep understanding of dendritic cell subset functional specialization. This gap in knowledge is an impediment for the rational design of immune interventions. Our research program focuses on advancing our understanding of mouse and human dendritic cell subsets, revealing their endowed capacity to induce distinct types of immune responses, and designing novel strategies to exploit them for vaccines and therapies.
John P.A. Ioannidis
Professor of Medicine (Stanford Prevention Research), of Epidemiology and Population Health and by courtesy, of Statistics and of Biomedical Data Science
Current Research and Scholarly InterestsMeta-research
Clinical and molecular epidemiology
Human genome epidemiology
Reporting of research
Empirical evaluation of bias in research
Statistical methods and modeling
Meta-analysis and large-scale evidence
Prognosis, predictive, personalized, precision medicine and health
Sociology of science
Assistant Professor of Medicine (Oncology) at the Stanford University Medical Center
BioDr. Itakura is an Assistant Professor of Medicine (Oncology) in the Stanford University School of Medicine and practicing oncologist at the Stanford Cancer Center with background in biomedical informatics. She is a physician-scientist whose research mission is to drive medical advances at the intersection of cancer and data science research. Specifically, she aims to innovate state-of-the-art technologies to extract clinically useful knowledge from heterogeneous multi-scale biomedical data to improve diagnostics and therapeutics in cancer. She is a board-certified hematologist-oncologist and informaticist with specialized training in basic science, health services, and translational research. Her clinical background in oncology and PhD training in Biomedical Informatics position her to develop and apply data science methodologies on heterogeneous, multi-scale cancer data to extract actionable knowledge that can improve patient outcomes. Her ongoing research to develop and apply cutting-edge knowledge and skills to pioneer new robust methodologies for analyzing cancer big data is being supported by an NIH K01 Career Development Award in Biomedical Big Data Science. Her research focuses on developing and applying machine learning frameworks and radiogenomic approaches for the integrative analysis of heterogeneous, multi-scale data to accelerate discoveries in cancer diagnostics and therapeutics. Projects include prediction modeling of survival and treatment response, biomarker discovery, cancer subtype discovery, and identification of new therapeutic targets.