Bio-X
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Gianluca Iaccarino
Professor of Mechanical 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.
Multidisciplinary Teaching
I consider the classical boundaries between disciplines outdated and counterproductive in seeking innovative solutions to real-world problems.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. -
Andrei Iagaru
Professor of Radiology (Nuclear Medicine)
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; -
Juliana Idoyaga
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
Evidence-based medicine
Clinical and molecular epidemiology
Human genome epidemiology
Research design
Reporting of research
Empirical evaluation of bias in research
Randomized trials
Statistical methods and modeling
Meta-analysis and large-scale evidence
Prognosis, predictive, personalized, precision medicine and health
Sociology of science -
Haruka Itakura, MD, PhD
Assistant Professor of Medicine (Oncology)
BioDr. Haruka Itakura is an Assistant Professor of Medicine (Oncology) in the Stanford University School of Medicine, a data scientist, and a practicing breast medical oncologist at the Stanford Women’s Cancer Center. She is board-certified in Oncology, Clinical Informatics, Hematology, and Internal Medicine. Her research mission is to drive medical advances at the intersection of cancer and data science, applying state-of-the-art machine learning/artificial intelligence techniques to extract clinically actionable knowledge from heterogeneous multi-scale cancer data to improve patient outcomes. Her ongoing research to develop robust methodologies and apply cutting-edge techniques to analyze complex cancer big data was catapulted by an NIH K01 Career Development Award in Biomedical Big Data Science after obtaining a PhD in Biomedical Informatics at Stanford University. Her cancer research focuses on extracting radiomic (pixel-level quantitative imaging) features of tumors from medical imaging studies and applying machine learning frameworks, including radiogenomic approaches, for the integrative analysis of heterogeneous, multi-omic (e.g., radiomic, genomic, transcriptomic) data to accelerate discoveries in cancer diagnostics and therapeutics. Her current projects include prediction modeling of survival, treatment response, recurrence, and CNS metastasis in different cancer subtypes; detection of occult invasive breast cancer; and identification of novel therapeutic targets. Her ultimate goal is to be able to translate her research findings back to the clinical setting for the benefit of patients with difficult-to-treat cancers.