Stanford - Department of Biomedical Data Science

James Zou at the DeSci Foundation: Large language models and future of science.
2024-01-22 08:00 - 22:00 (America/Los_Angeles)
James Zou will be presenting at the DeSci Foundation The Future of Science Seminar Series on the topic of Large language models and future of science.
The event will be held on Wednesday 22th January 2024 at 8am PST and is free to attend. A recording will be made available to those who register in advance.
Abstract
In this talk, we will explore the transformative potential of large language models (LLMs) such as ChatGPT in reshaping scientific inquiry and communication through several vignettes. First, we will discuss how LLM can provide informative feedback to improve scientific manuscripts. Then we will show how to use LLMs to organize biomedical images to make it easier for clinicians and researchers to retrieve relevant information. Finally we will investigate using LLMs for outreach and education.

For more information: https://cassyni.com/events/4fJ1CwqCNeC73oLJQsG7yX

CCSB Seminar Series
2024-01-19 10:00 - 11:00
Seminar Series:

Third Friday of the month with two exceptions: no seminar in August and in December the seminar will take place on the 13th
First seminar
10:00- 11:00 AM
Location: Clark Center, S360 (behind the coffee shop)

Weekly Seminar: 1/18
2024-01-18 08:00 - 17:00
Date: 1/18/24
Adit Radhakrishnan
Title: How do neural networks learn features from data?
1:30-3:00 pm in MSOB x303
More info here.

Alex Derry's Dissertation Defense
2023-12-08 10:00 - 12:00
The Department of Biomedical Data Science invites you to a Dissertation Defense
Alex Derry
Friday, December 8th, 2023
10:00 am PST
Location: Y2E2 111
Deep learning on local sites for protein structure and function analysis
Understanding how the three-dimensional structure of a protein leads to its function is important for determining disease mechanisms, developing targeted therapeutics, and engineering new proteins with desired functional characteristics. The expansion of protein structure databases due to experimental and computational advances provides an unprecedented opportunity to learn structure-function relationships in a data-driven manner. Deep learning methods that operate on protein structures have shown promise for specific tasks, but their utility for functional analysis has been limited due to inconsistencies in model training and evaluation, lack of labeled proteinfunction data, and an inability to reconcile global predictions with local biochemical mechanisms. In this dissertation, I explore these challenges and propose a framework for protein analysis based on learning on local sites rather than the entire protein structure. First, to establish standards for model development and evaluation, I present work on (1) developing a suite of benchmark datasets, processing tools, and baseline models, and (2) quantifying the effect of differing structure compositions in the training data. I then describe a self-supervised learning method that leverages evolutionary relationships to learn general-purpose representations of local structural sites and show how these representations enable improved performance on downstream tasks involving classification, search, and annotation of functional sites. By clustering millions of sites, I propose a framework for protein analysis based on conserved structural motifs which enables the discovery of functional relationships across protein classes. Finally, I present a method for explainable function annotation that predicts the overall function of a protein as well as the individual residues which are responsible.
Zoom: https://stanford.zoom.us/j/95316385692
(PW: [REDACTED]

DBDS Holiday Extravaganza
2023-12-07 17:00 - 20:00
December 7, 2023; Stanford Faculty Club
More info to come!

Faculty Meeting: (November + December) December 6, 2023
2023-12-06 11:30 - 13:00 | MSOB Conference Room
Lunch will be provided.
Zoom link: https://stanford.zoom.us/j/97249583050?pwd=[REDACTED]

Kevin Wu: Medical AI After Deployment: Data-driven analyses and methods for clinically viable AI
2023-11-08 14:00 - 15:00 | CCSR 0235
Wednesday, November 8th, 2023
2:00 pm – 3:00 pm PST
CCSR 0235
Zoom Link: https://stanford.zoom.us/j/95984592133?pwd=[REDACTED]
Title: Medical AI After Deployment: Data-driven analyses and methods for clinically viable AI
Abstract: Medical AI algorithms have undergone significant development and regulatory approval, with over 600 FDA-approved medical AI devices currently. However, their actual clinical safety and impact remain unclear. First, we analyze FDA submission documents and find that the majority of FDA approvals do not report multi-site evaluation, and nearly none have prospective analyses. Second, we track the occurrences of newly released AI billing codes in a nationwide insurance claims database and find that only a handful of products have meaningful clinical adoption. Finally, we systematically track device updating in FDA submissions and find that the majority of devices have not had updates to model weights since initial approval. Given these limitations, we propose several methods to address common issues with algorithmic deployment. First, we present a framework for understanding the marginal contribution of distribution shifts to overall model degradation. Second, we present a method for efficient missing data collection in the context of fixed models. Finally, we present ways to improve the robustness of evaluating medical LLMs.

Faculty Development Committee Meeting
2023-11-08 12:00 - 13:00
Wednesday, November 8, 2023 from 12:00 PM to 1:00 PM
https://stanford.zoom.us/j/98257342544?pwd=[REDACTED]
Attendees: Olivier Gevaert; Theresa McCann-Packer; Iain M Johnstone; Manisha Desai; Teri Klein; James Zou; Julia Adela Palacios; Lu Tian; Ni Deng; Daniel Rubin

Workshop on GenAI in Healthcare: Red Teaming
2023-10-25 15:00
Please join us for an interaction session to test GenAI models for potential issues with biases, inaccuracies, and other matters related to healthcare. Red-teaming is a form of evaluation that elicits model vulnerabilities that might lead to undesirable behaviors. We will be looking for such behaviors on healthcare-related tasks.
All faculty, graduate students and postdocs are welcome. We will break into groups with table leaders from Stanford Healthcare, School of Medicine, CS and external partners providing technical and clinical insights. At the end we’ll have a read-out of findings, and then continue the conversations during happy hour.
REGISTER HERE
Date: October 25th
Location: Chem-H Building Room E241
Agenda:
3:00pm | Check in begins
3:15pm | Table groups begin Red Teaming
4:45pm | Happy hour
6:00pm | Event concludes
If you have questions, please contact Roxana Daneshjou or Karen Matthys. Faculty, if you’re interested in leading a table group please let us know.

DBDS Retreat September 18-20 Asilomar Conference Center
2023-09-15 08:00 - 17:00
BMI 2023 Retreat Agenda
September 18-20, 2023 | Asilomar Conference Center | Pacific Grove, CA
Monday, September 18

Time
Activity
Where

3:30-5:00 PM
Check In and Welcome Reception
The Meadows/BBQ Area

5:00-5:45 PM
Group Photo & Headshots
Back steps of Hearst Social Hall

6:00-7:00 PM
Dinner
Seascape Room

7:00-7:45 PM
State of the Department
Seascape Room

7:45-8:45 PM
Alumni Fireside Chat
With Serge Saxonov and Iwei Yeh
Seascape Room

8:45-11:00 PM
Bonfire
The Meadows/BBQ Area

Tuesday, September 19

Time
Activity
Where

7:30-8:20 AM
Breakfast
Seascape Room

8:20-8:30 AM
Break

8:30-9:30 AM
Faculty Lightning Talks
Merrill Hall

9:30-9:45
Break

9:45-10:45 AM
Ice Breaker
Merrill Hall

10:45-11:00 AM
Break

11:00-12:00 PM
Poster Session I
Merrill Hall

12:00-1:00 PM
Lunch
Seascape Room

1:00-1:10 PM
Break

1:10-2:10 PM
Faculty Lightning Talks
Merrill Hall

2:10-3:00 PM
Casual Faculty Forum
Surf and Sand

2:10-5:00 PM
Sandcastle Competition (optional)
Asilomar Beach Area

5:00-6:00 PM
Poster Session II
Merrill Hall

6:00-7:10 PM
Dinner
Seascape

7:00-7:10 PM
Break

7:10-9:00 PM
Community Game Night
Merrill Hall

Wednesday, September 20

Time
Activity
Where

7:30-8:30 AM
Breakfast
Seascape

Please check out of your room by 11 AM

8:30-9:30 AM
Faculty Lightning Talks
Merrill Hall

9:30-10:30 AM
Roadmap Overview and Breakouts

10:30-11:45 AM
New Student Orientation

11:00-11:45 AM
Returning students Cohort Huddles

11:45 AM
New Student Lunch
Merrill Hall
