BIOMEDIN 201:
Biomedical Informatics Student Seminar
Participants report on recent articles from the Biomedical Informatics literature or their research projects. Goals are to teach critical reading of scientific papers and presentation skills. May be repeated three times for credit.
Terms: Aut, Win, Spr, Sum

Units: 1

Repeatable for credit

Grading: Medical Satisfactory/No Credit
BIOMEDIN 205:
Precision Practice with Big Data
Primarily for M.D. students; open to other graduate students. Provides an overview of how to leverage large amounts of clinical, molecular, and imaging data within hospitals and in cyberspacebig datato practice medicine more effectively. Lectures by physicians, researchers, and industry leaders survey how the major methods of informatics can help physicians leverage big data to profile disease, to personalize treatment to patients, to predict treatment response, to discover new knowledge, and to challenge established medical dogma and the current paradigm of clinical decisionmaking based solely on published knowledge and individual physician experience. May be repeated for credit. Prerequisite: background in biomedicine. Background in computer science can be helpful but not required.
Terms: Aut

Units: 1

Grading: Medical Satisfactory/No Credit
BIOMEDIN 214:
Representations and Algorithms for Computational Molecular Biology (BIOE 214, CS 274, GENE 214)
Topics: introduction to bioinformatics and computational biology, algorithms for alignment of biological sequences and structures, computing with strings, phylogenetic tree construction, hidden Markov models, basic structural computations on proteins, protein structure prediction, protein threading techniques, homology modeling, molecular dynamics and energy minimization, statistical analysis of 3D biological data, integration of data sources, knowledge representation and controlled terminologies for molecular biology, microarray analysis, machine learning (clustering and classification), and natural language text processing. Prerequisite: CS 106B; recommended: CS161; consent of instructor for 3 units.
Terms: Aut

Units: 34

Grading: Medical Option (MedLtrCR/NC)
BIOMEDIN 215:
Data Driven Medicine
The widespread adoption of electronic health records (EHRs) has created a new source of ¿big data¿¿namely, the record of routine clinical practice¿as a byproduct of care. This graduate class will teach you how to use EHRs and other patient data to discover new clinical knowledge and improve healthcare. Upon completing this course, you should be able to: differentiate between and give examples of categories of research questions and the study designs used to address them, describe common healthcare data sources and their relative advantages and limitations, extract and transform various kinds of clinical data to create analysisready datasets, design and execute an analysis of a clinical dataset based on your familiarity with the workings, applicability, and limitations of common statistical methods, evaluate and criticize published research using your knowledge of 14 to generate new research ideas and separate hype from reality. Prerequisites: CS 106A or equivalent, STATS 60 or equivalent. Recommended: STATS 216, CS 145, STATS 305
Terms: Aut

Units: 3

Grading: Medical Option (MedLtrCR/NC)
BIOMEDIN 216:
Representations and Algorithms for Molecular Biology: Lectures
Lecture component of BIOMEDIN 214. One unit for medical and graduate students who attend lectures only; may be taken for 2 units with participation in limited assignments and final project. Lectures also available via internet. Prerequisite: familiarity with biology recommended.
Terms: Aut

Units: 12

Grading: Medical Satisfactory/No Credit
BIOMEDIN 217:
Translational Bioinformatics (BIOE 217, CS 275, GENE 217)
Computational methods for the translation of biomedical data into diagnostic, prognostic, and therapeutic applications in medicine. Topics: multiscale omics data generation and analysis, utility and limitations of public biomedical resources, machine learning and data mining, issues and opportunities in drug discovery, and mobile/digital health solutions. Case studies and course project. Prerequisites: programming ability at the level of CS 106A and familiarity with biology and statistics.
Terms: Aut

Units: 4

Grading: Medical Option (MedLtrCR/NC)
BIOMEDIN 221:
Machine Learning Approaches for Data Fusion in Biomedicine
Vast amounts of biomedical data are now routinely available for patients, raging from genomic data, to radiographic images and electronic health records. AI and machine learning are increasingly used to enable pattern discover to link such data for improvements in patient diagnosis, prognosis and tailoring treatment response. Yet, few studies focus on how to link different types of biomedical data in synergistic ways, and to develop data fusion approaches for improved biomedical decision support. This course will describe approaches for multiomics, multimodal and multiscale data fusion of biomedical data in the context of biomedical decision support. Prerequisites: CS106A or equivalent, Stats 60 or equivalent.
Terms: Aut

Units: 2

Grading: Medical Option (MedLtrCR/NC)
BIOMEDIN 224:
Principles of Pharmacogenomics (GENE 224)
This course is an introduction to pharmacogenomics, including the relevant pharmacology, genomics, experimental methods (sequencing, expression, genotyping), data analysis methods and bioinformatics. The course reviews key gene classes (e.g., cytochromes, transporters) and key drugs (e.g., warfarin, clopidogrel, statins, cancer drugs) in the field. Resources for pharmacogenomics (e.g., PharmGKB, Drugbank, NCBI resources) are reviewed, as well as issues implementing pharmacogenomics testing in the clinical setting. Reading of key papers, including student presentations of this work; problem sets; final project selected with approval of instructor. Prerequisites: two of BIO 41, 42, 43, 44X, 44Y or consent of instructor.
Terms: Aut, Win, Spr, Sum

Units: 3

Grading: Medical Option (MedLtrCR/NC)
BIOMEDIN 225:
Data Driven Medicine: Lectures
Lectures for BIOMEDIN 215.With the spread of electronic health records and increasingly low cost assays for patient molecular data, powerful data repositories with tremendous potential for biomedical research, clinical care and personalized medicine are being built. But these databases are large and difficult for any one specialist to analyze. To find the hidden associations within the full set of data, we introduce methods for datamining at the internet scale, the handling of largescale electronic medical records data for machine learning, methods in natural language processing and textmining applied to medical records, methods for using ontologies for the annotation and indexing of unstructured content as well as semantic web technologies. Prerequisites: familiarity with statistics (STATS 216) and biology.
Terms: Aut

Units: 2

Grading: Medical Option (MedLtrCR/NC)
BIOMEDIN 226:
Digital Health Practicum in a Health Care Delivery System
Practical experience implementing clinical informatics solutions with a focus on digital health in one of the largest healthcare delivery systems in the United States. Individual meetings with senior clinical informatics leaders to discuss elements of successful projects. Implementation opportunities include supporting the use of electronic health records, engagement of patients and providers via a personal health record, use of informatics to support patient service centers, and improvement of patient access to clinical data. Consent of course instructors required at least one quarter prior to student enrollment in course.
Terms: Aut, Win, Spr, Sum

Units: 23

Grading: Medical Option (MedLtrCR/NC)
BIOMEDIN 273B:
Deep Learning in Genomics and Biomedicine (BIODS 237, CS 273B, GENE 236)
Recent breakthroughs in highthroughput genomic and biomedical data are transforming biological sciences into "big data" disciplines. In parallel, progress in deep neural networks are revolutionizing fields such as image recognition, natural language processing and, more broadly, AI. This course explores the exciting intersection between these two advances. The course will start with an introduction to deep learning and overview the relevant background in genomics and highthroughput biotechnology, focusing on the available data and their relevance. It will then cover the ongoing developments in deep learning (supervised, unsupervised and generative models) with the focus on the applications of these methods to biomedical data, which are beginning to produced dramatic results. In addition to predictive modeling, the course emphasizes how to visualize and extract interpretable, biological insights from such models. Recent papers from the literature will be presented and discussed. Students will be introduced to and work with popular deep learning software frameworks. Students will work in groups on a final class project using real world datasets. Prerequisites: College calculus, linear algebra, basic probability and statistics such as CS 109, and basic machine learning such as CS 229. No prior knowledge of genomics is necessary.
Terms: Aut

Units: 3

Grading: Medical Option (MedLtrCR/NC)
BIOMEDIN 279:
Computational Biology: Structure and Organization of Biomolecules and Cells (BIOE 279, BIOPHYS 279, CME 279, CS 279)
Computational techniques for investigating and designing the threedimensional structure and dynamics of biomolecules and cells. These computational methods play an increasingly important role in drug discovery, medicine, bioengineering, and molecular biology. Course topics include protein structure prediction, protein design, drug screening, molecular simulation, cellularlevel simulation, image analysis for microscopy, and methods for solving structures from crystallography and electron microscopy data. Prerequisites: elementary programming background (CS 106A or equivalent) and an introductory course in biology or biochemistry.
Terms: Aut

Units: 3

Grading: Letter or Credit/No Credit
BIOMEDIN 290:
Biomedical Informatics Teaching Methods
Handson training in biomedical informatics pedagogy. Practical experience in pedagogical approaches, variously including didactic, inquiry, project, team, case, field, and/or problembased approaches. Students create course content, including lectures, exercises, and assessments, and evaluate learning activities and outcomes. Prerequisite: instructor consent.
Terms: Aut, Win, Spr, Sum

Units: 16

Repeatable for credit

Grading: Medical Option (MedLtrCR/NC)
Instructors: ;
Altman, R. (PI);
Ashley, E. (PI);
Bassik, M. (PI);
Batzoglou, S. (PI);
Bayati, M. (PI);
Bejerano, G. (PI);
Bhattacharya, J. (PI);
Blish, C. (PI);
Boahen, K. (PI);
Brandeau, M. (PI);
Bustamante, C. (PI);
Butte, A. (PI);
Chang, H. (PI);
Cherry, J. (PI);
Cohen, S. (PI);
Covert, M. (PI);
Curtis, C. (PI);
Das, A. (PI);
Das, R. (PI);
Davis, R. (PI);
Delp, S. (PI);
Desai, M. (PI);
Dill, D. (PI);
Dumontier, M. (PI);
Elias, J. (PI);
Fagan, L. (PI);
Feldman, M. (PI);
Ferrell, J. (PI);
Fraser, H. (PI);
Gambhir, S. (PI);
Gerritsen, M. (PI);
Gevaert, O. (PI);
Goldstein, M. (PI);
Greenleaf, W. (PI);
Guibas, L. (PI);
Hastie, T. (PI);
Hlatky, M. (PI);
Holmes, S. (PI);
Ji, H. (PI);
Karp, P. (PI);
Khatri, P. (PI);
Kim, S. (PI);
Kirkegaard, K. (PI);
Klein, T. (PI);
Koller, D. (PI);
Krummel, T. (PI);
Kundaje, A. (PI);
Levitt, M. (PI);
Levitt, R. (PI);
Li, J. (PI);
Longhurst, C. (PI);
Lowe, H. (PI);
Mallick, P. (PI);
Manning, C. (PI);
McAdams, H. (PI);
Menon, V. (PI);
Montgomery, S. (PI);
Musen, M. (PI);
Napel, S. (PI);
Nolan, G. (PI);
Olshen, R. (PI);
Owen, A. (PI);
Owens, D. (PI);
Paik, D. (PI);
Palacios, J. (PI);
Pande, V. (PI);
Petrov, D. (PI);
Plevritis, S. (PI);
Poldrack, R. (PI);
Pritchard, J. (PI);
Relman, D. (PI);
RiedelKruse, I. (PI);
Rivas, M. (PI);
Rubin, D. (PI);
Sabatti, C. (PI);
Salzman, J. (PI);
Shachter, R. (PI);
Shafer, R. (PI);
Shah, N. (PI);
Sherlock, G. (PI);
Sidow, A. (PI);
Snyder, M. (PI);
Tang, H. (PI);
Taylor, C. (PI);
Theriot, J. (PI);
Tibshirani, R. (PI);
Utz, P. (PI);
Walker, M. (PI);
Wall, D. (PI);
Winograd, T. (PI);
Wong, W. (PI);
Xing, L. (PI);
Zou, J. (PI)
BIOMEDIN 299:
Directed Reading and Research
For students wishing to receive credit for directed reading or research time. Prerequisite: consent of instructor. (Staff)
Terms: Aut, Win, Spr, Sum

Units: 118

Repeatable for credit

Grading: Medical Option (MedLtrCR/NC)
Instructors: ;
Altman, R. (PI);
Ashley, E. (PI);
Baiocchi, M. (PI);
Bassik, M. (PI);
Batzoglou, S. (PI);
Bayati, M. (PI);
Bejerano, G. (PI);
Bhattacharya, J. (PI);
Blish, C. (PI);
Boahen, K. (PI);
Brandeau, M. (PI);
Brutlag, D. (PI);
Bustamante, C. (PI);
Butte, A. (PI);
Chang, H. (PI);
Chen, J. (PI);
Cherry, J. (PI);
Cohen, S. (PI);
Covert, M. (PI);
Curtis, C. (PI);
Das, A. (PI);
Das, R. (PI);
Davis, R. (PI);
Delp, S. (PI);
Desai, M. (PI);
Dill, D. (PI);
Dror, R. (PI);
Dumontier, M. (PI);
Elias, J. (PI);
Fagan, L. (PI);
Feldman, M. (PI);
Ferrell, J. (PI);
Fraser, H. (PI);
Gambhir, S. (PI);
Gentles, A. (PI);
Gerritsen, M. (PI);
Gevaert, O. (PI);
Goldstein, M. (PI);
Greenleaf, W. (PI);
Guibas, L. (PI);
Hastie, T. (PI);
HernandezBoussard, T. (PI);
Hlatky, M. (PI);
Holmes, S. (PI);
Ji, H. (PI);
Karp, P. (PI);
Khatri, P. (PI);
Kim, S. (PI);
Kirkegaard, K. (PI);
Klein, T. (PI);
Koller, D. (PI);
Krummel, T. (PI);
Kundaje, A. (PI);
Langlotz, C. (PI);
Levitt, M. (PI);
Li, J. (PI);
Longhurst, C. (PI);
Lowe, H. (PI);
Mallick, P. (PI);
Manning, C. (PI);
McAdams, H. (PI);
Menon, V. (PI);
Montgomery, S. (PI);
Musen, M. (PI);
Napel, S. (PI);
Nolan, G. (PI);
Olshen, R. (PI);
Owen, A. (PI);
Owens, D. (PI);
Paik, D. (PI);
Palacios, J. (PI);
Pande, V. (PI);
Petrov, D. (PI);
Plevritis, S. (PI);
Poldrack, R. (PI);
Pritchard, J. (PI);
Relman, D. (PI);
RiedelKruse, I. (PI);
Rivas, M. (PI);
Rubin, D. (PI);
Sabatti, C. (PI);
Salzman, J. (PI);
Shachter, R. (PI);
Shafer, R. (PI);
Shah, N. (PI);
Sherlock, G. (PI);
Sidow, A. (PI);
Snyder, M. (PI);
Tang, H. (PI);
Taylor, C. (PI);
Theriot, J. (PI);
Tibshirani, R. (PI);
Tu, S. (PI);
Utz, P. (PI);
Walker, M. (PI);
Wall, D. (PI);
Winograd, T. (PI);
Wong, W. (PI);
Xing, L. (PI);
Zou, J. (PI)
BIOMEDIN 370:
Medical Scholars Research
Provides an opportunity for student and faculty interaction, as well as academic credit and financial support, to medical students who undertake original research. Enrollment is limited to students with approved projects.
Terms: Aut, Win, Spr, Sum

Units: 418

Repeatable for credit

Grading: Medical School MD Grades
Instructors: ;
Altman, R. (PI);
Ashley, E. (PI);
Bassik, M. (PI);
Batzoglou, S. (PI);
Bayati, M. (PI);
Bejerano, G. (PI);
Bhattacharya, J. (PI);
Blish, C. (PI);
Boahen, K. (PI);
Brandeau, M. (PI);
Brutlag, D. (PI);
Bustamante, C. (PI);
Butte, A. (PI);
Chang, H. (PI);
Chen, J. (PI);
Cherry, J. (PI);
Cohen, S. (PI);
Covert, M. (PI);
Curtis, C. (PI);
Das, A. (PI);
Das, R. (PI);
Davis, R. (PI);
Delp, S. (PI);
Desai, M. (PI);
Dill, D. (PI);
Dumontier, M. (PI);
Elias, J. (PI);
Fagan, L. (PI);
Feldman, M. (PI);
Ferrell, J. (PI);
Fraser, H. (PI);
Gambhir, S. (PI);
Gerritsen, M. (PI);
Gevaert, O. (PI);
Goldstein, M. (PI);
Greenleaf, W. (PI);
Guibas, L. (PI);
Hastie, T. (PI);
Hlatky, M. (PI);
Holmes, S. (PI);
Ji, H. (PI);
Karp, P. (PI);
Khatri, P. (PI);
Kim, S. (PI);
Kirkegaard, K. (PI);
Klein, T. (PI);
Koller, D. (PI);
Krummel, T. (PI);
Kundaje, A. (PI);
Levitt, M. (PI);
Li, J. (PI);
Longhurst, C. (PI);
Lowe, H. (PI);
Mallick, P. (PI);
Manning, C. (PI);
McAdams, H. (PI);
Menon, V. (PI);
Montgomery, S. (PI);
Musen, M. (PI);
Napel, S. (PI);
Nolan, G. (PI);
Olshen, R. (PI);
Owen, A. (PI);
Owens, D. (PI);
Paik, D. (PI);
Palacios, J. (PI);
Pande, V. (PI);
Petrov, D. (PI);
Plevritis, S. (PI);
Poldrack, R. (PI);
Pritchard, J. (PI);
Relman, D. (PI);
RiedelKruse, I. (PI);
Rivas, M. (PI);
Rubin, D. (PI);
Sabatti, C. (PI);
Salzman, J. (PI);
Shachter, R. (PI);
Shafer, R. (PI);
Shah, N. (PI);
Sherlock, G. (PI);
Sidow, A. (PI);
Snyder, M. (PI);
Tang, H. (PI);
Taylor, C. (PI);
Theriot, J. (PI);
Tibshirani, R. (PI);
Tu, S. (PI);
Utz, P. (PI);
Walker, M. (PI);
Wall, D. (PI);
Winograd, T. (PI);
Wong, W. (PI);
Xing, L. (PI);
Zou, J. (PI)
BIOMEDIN 390A:
Curricular Practical Training
Provides educational opportunities in biomedical informatics research. Qualified biomedical informatics students engage in internship work and integrate that work into their academic program. Students register during the quarter they are employed and must complete a research report outlining their work activity, problems investigated, key results, and any followup on projects they expect to perform. BIOMEDIN 390A, B, and C may each be taken only once.
Terms: Aut, Win, Spr, Sum

Units: 1

Grading: Medical Satisfactory/No Credit
BIOMEDIN 390B:
Curricular Practical Training
Provides educational opportunities in biomedical informatics research. Qualified biomedical informatics students engage in internship work and integrate that work into their academic program. Students register during the quarter they are employed and must complete a research report outlining their work activity, problems investigated, key results, and any followup on projects they expect to perform. BIOMEDIN 390A, B, and C may each be taken only once.
Terms: Aut, Win, Spr, Sum

Units: 1

Grading: Medical Satisfactory/No Credit
BIOMEDIN 390C:
Curricular Practical Training
Provides educational opportunities in biomedical informatics research. Qualified biomedical informatics students engage in internship work and integrate that work into their academic program. Students register during the quarter they are employed and must complete a research report outlining their work activity, problems investigated, key results, and any followup on projects they expect to perform. BIOMEDIN 390A, B, and C may each be taken only once.
Terms: Aut, Win, Spr, Sum

Units: 1

Grading: Medical Satisfactory/No Credit
BIOMEDIN 432:
Analysis of Costs, Risks, and Benefits of Health Care (HRP 392)
For graduate students. How to do cost/benefit analysis when the output is difficult or impossible to measure. Literature on the principles of cost/benefit analysis applied to health care. Critical review of actual studies. Emphasis is on the art of practical application.
Terms: Aut

Units: 4

Grading: Medical Option (MedLtrCR/NC)
BIOMEDIN 801:
TGR Master's Project
Project credit for masters students who have completed all course requirements and minimum of 45 Stanford units.
Terms: Aut, Win, Spr, Sum

Units: 0

Repeatable for credit

Grading: TGR
Instructors: ;
Altman, R. (PI);
Ashley, E. (PI);
Bassik, M. (PI);
Batzoglou, S. (PI);
Bayati, M. (PI);
Bejerano, G. (PI);
Bhattacharya, J. (PI);
Blish, C. (PI);
Boahen, K. (PI);
Brandeau, M. (PI);
Brutlag, D. (PI);
Bustamante, C. (PI);
Butte, A. (PI);
Chang, H. (PI);
Cherry, J. (PI);
Cohen, S. (PI);
Covert, M. (PI);
Curtis, C. (PI);
Das, A. (PI);
Das, R. (PI);
Davis, R. (PI);
Delp, S. (PI);
Desai, M. (PI);
Dill, D. (PI);
Dumontier, M. (PI);
Elias, J. (PI);
Fagan, L. (PI);
Feldman, M. (PI);
Ferrell, J. (PI);
Fraser, H. (PI);
Gambhir, S. (PI);
Gentles, A. (PI);
Gerritsen, M. (PI);
Gevaert, O. (PI);
Goldstein, M. (PI);
Greenleaf, W. (PI);
Guibas, L. (PI);
Hastie, T. (PI);
Hlatky, M. (PI);
Holmes, S. (PI);
Ji, H. (PI);
Karp, P. (PI);
Khatri, P. (PI);
Kim, S. (PI);
Kirkegaard, K. (PI);
Klein, T. (PI);
Koller, D. (PI);
Krummel, T. (PI);
Kundaje, A. (PI);
Levitt, M. (PI);
Li, J. (PI);
Longhurst, C. (PI);
Lowe, H. (PI);
Mallick, P. (PI);
Manning, C. (PI);
McAdams, H. (PI);
Menon, V. (PI);
Montgomery, S. (PI);
Musen, M. (PI);
Napel, S. (PI);
Nolan, G. (PI);
Olshen, R. (PI);
Owen, A. (PI);
Owens, D. (PI);
Paik, D. (PI);
Palacios, J. (PI);
Pande, V. (PI);
Petrov, D. (PI);
Plevritis, S. (PI);
Poldrack, R. (PI);
Pritchard, J. (PI);
Relman, D. (PI);
RiedelKruse, I. (PI);
Rivas, M. (PI);
Rubin, D. (PI);
Sabatti, C. (PI);
Salzman, J. (PI);
Shachter, R. (PI);
Shafer, R. (PI);
Shah, N. (PI);
Sherlock, G. (PI);
Sidow, A. (PI);
Snyder, M. (PI);
Tang, H. (PI);
Taylor, C. (PI);
Theriot, J. (PI);
Tibshirani, R. (PI);
Tu, S. (PI);
Utz, P. (PI);
Walker, M. (PI);
Wall, D. (PI);
Winograd, T. (PI);
Wong, W. (PI);
Xing, L. (PI);
Zou, J. (PI)
BIOMEDIN 802:
TGR PhD Dissertation
Terms: Aut, Win, Spr, Sum

Units: 0

Repeatable for credit

Grading: TGR
Instructors: ;
Altman, R. (PI);
Ashley, E. (PI);
Bassik, M. (PI);
Batzoglou, S. (PI);
Bayati, M. (PI);
Bejerano, G. (PI);
Bhattacharya, J. (PI);
Blish, C. (PI);
Boahen, K. (PI);
Brandeau, M. (PI);
Brutlag, D. (PI);
Bustamante, C. (PI);
Butte, A. (PI);
Chang, H. (PI);
Cherry, J. (PI);
Cohen, S. (PI);
Covert, M. (PI);
Curtis, C. (PI);
Das, A. (PI);
Das, R. (PI);
Davis, R. (PI);
Delp, S. (PI);
Desai, M. (PI);
Dill, D. (PI);
Dumontier, M. (PI);
Elias, J. (PI);
Fagan, L. (PI);
Feldman, M. (PI);
Ferrell, J. (PI);
Fraser, H. (PI);
Gambhir, S. (PI);
Gerritsen, M. (PI);
Gevaert, O. (PI);
Goldstein, M. (PI);
Greenleaf, W. (PI);
Guibas, L. (PI);
Hastie, T. (PI);
Hlatky, M. (PI);
Holmes, S. (PI);
Ji, H. (PI);
Karp, P. (PI);
Khatri, P. (PI);
Kim, S. (PI);
Kirkegaard, K. (PI);
Klein, T. (PI);
Koller, D. (PI);
Krummel, T. (PI);
Kundaje, A. (PI);
Levitt, M. (PI);
Li, J. (PI);
Longhurst, C. (PI);
Lowe, H. (PI);
Mallick, P. (PI);
Manning, C. (PI);
McAdams, H. (PI);
Menon, V. (PI);
Montgomery, S. (PI);
Musen, M. (PI);
Napel, S. (PI);
Nolan, G. (PI);
Olshen, R. (PI);
Owen, A. (PI);
Owens, D. (PI);
Paik, D. (PI);
Palacios, J. (PI);
Pande, V. (PI);
Petrov, D. (PI);
Plevritis, S. (PI);
Poldrack, R. (PI);
Pritchard, J. (PI);
Relman, D. (PI);
RiedelKruse, I. (PI);
Rivas, M. (PI);
Rubin, D. (PI);
Sabatti, C. (PI);
Salzman, J. (PI);
Shachter, R. (PI);
Shafer, R. (PI);
Shah, N. (PI);
Sherlock, G. (PI);
Sidow, A. (PI);
Snyder, M. (PI);
Tang, H. (PI);
Taylor, C. (PI);
Theriot, J. (PI);
Tibshirani, R. (PI);
Tu, S. (PI);
Utz, P. (PI);
Walker, M. (PI);
Wall, D. (PI);
Winograd, T. (PI);
Wong, W. (PI);
Xing, L. (PI);
Zou, J. (PI)