## BIODS 215: Topics in Biomedical Data Science: Large-scale inference

The recent explosion of data generated in the fields of biology and medicine has led to many analytical challenges and opportunities for understanding human health. This graduate-level course focuses on methodology for large-scale inference from biomedical data. Topics include one-dimensional and multidimensional probability distributions; hypothesis testing and model comparison; statistical modeling; and prediction. This course will place a special emphasis on applications of these approaches to i) human genetic data; ii) hospital in-patient and health questionnaire data, which is increasingly available with the emergence of large precision initiatives like the UK Biobank and Precision Medicine Initiative; and iii) wearable and social network data.

Terms: Win
| Units: 2-3

## BIODS 220: Artificial Intelligence in Healthcare (BIOMEDIN 220, CS 271)

Healthcare is one of the most exciting application domains of artificial intelligence, with transformative potential in areas ranging from medical image analysis to electronic health records-based prediction and precision medicine. This course will involve a deep dive into recent advances in AI in healthcare, focusing in particular on deep learning approaches for healthcare problems. We will start from foundations of neural networks, and then study cutting-edge deep learning models in the context of a variety of healthcare data including image, text, multimodal and time-series data. In the latter part of the course, we will cover advanced topics on open challenges of integrating AI in a societal application such as healthcare, including interpretability, robustness, privacy and fairness. The course aims to provide students from diverse backgrounds with both conceptual understanding and practical grounding of cutting-edge research on AI in healthcare.

Terms: Win
| Units: 3-4

Instructors:
Yeung, S. (PI)

## BIODS 232: Consulting Workshop on Biomedical Data Science

The Data Studio is a collaboration between Spectrum (The Stanford Center for Clinical and Translational research and Education) and the Department of Biomedical Data Science (DBDS). The educational goal of this workshop is to provide data science consultation training for students. Data Studio is open to the Stanford community, and we expect it to have educational value for students and postdocs interested in biomedical data science. Most sessions are workshops that provide an extensive and in-depth consultation for a Medical School researcher based on research questions, data, statistical models, and other material prepared by the researcher with the aid of our facilitator. At the workshop, the researcher explains the project, goals, and needs. Experts in the area across campus will be invited and contribute to the brainstorming. After the workshop, the facilitator will follow up,helping with immediate action items and summary of the discussion. The last session of each month is devot
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The Data Studio is a collaboration between Spectrum (The Stanford Center for Clinical and Translational research and Education) and the Department of Biomedical Data Science (DBDS). The educational goal of this workshop is to provide data science consultation training for students. Data Studio is open to the Stanford community, and we expect it to have educational value for students and postdocs interested in biomedical data science. Most sessions are workshops that provide an extensive and in-depth consultation for a Medical School researcher based on research questions, data, statistical models, and other material prepared by the researcher with the aid of our facilitator. At the workshop, the researcher explains the project, goals, and needs. Experts in the area across campus will be invited and contribute to the brainstorming. After the workshop, the facilitator will follow up,helping with immediate action items and summary of the discussion. The last session of each month is devoted to drop-in consulting. DBDS faculty are available to provide assistance with your research questions. Skills required of practicing biomedical consultants, including exposed to biomedical and health science applications, identification of data science related questions, selection or development of appropriate statistical and analytic approaches to answer research needs. Students are required to attend the regular workshops and participate one to two consulting projects as team members under the supervision of faculty members or senior staff. Depending on the nature of the consulting service, the students may need to conduct numerical simulation, plan sample size, design study, and analyze client data. the formal written report needs to be completed at the end of consulting projects. May be repeated for credit. Prerequisites: course work in applied statistics, data analysis, and consent of instructor.

Terms: Aut, Win, Spr
| Units: 1-2
| Repeatable for credit

Instructors:
Lu, Y. (PI)
;
Sabatti, C. (PI)
;
Tian, L. (PI)
;
Desai, M. (SI)
;
Efron, B. (SI)
;
Lavori, P. (SI)
;
Narasimhan, B. (SI)
;
Tamaresis, J. (SI)

## BIODS 260B: Workshop in Biostatistics (STATS 260B)

Applications of statistical techniques to current problems in medical science. To receive credit for one or two units, a student must attend every workshop. To receive two units, in addition to attending every workshop, the student is required to write an acceptable one page summary of two of the workshops, with choices made by the student.

Terms: Win
| Units: 1-2
| Repeatable for credit

Instructors:
Rivas, M. (PI)
;
Sabatti, C. (PI)

## BIODS 299: Directed Reading and Research

For students wishing to receive credit for directed reading or research time. Prerequisite: consent of instructor.

Terms: Aut, Win, Spr, Sum
| Units: 1-18
| Repeatable for credit

Instructors:
Bustamante, C. (PI)
;
Efron, B. (PI)
;
Hastie, T. (PI)
...
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Instructors:
Bustamante, C. (PI)
;
Efron, B. (PI)
;
Hastie, T. (PI)
;
Lu, Y. (PI)
;
Olshen, R. (PI)
;
Palacios, J. (PI)
;
Rivas, M. (PI)
;
Sabatti, C. (PI)
;
Salzman, J. (PI)
;
Tian, L. (PI)
;
Tibshirani, R. (PI)
;
Yeung, S. (PI)
;
Zou, J. (PI)