While this event has concluded, please revisit us in the fall of 2023 for details regarding our 2024 event. If you are interested in future DBDS events, please contact firstname.lastname@example.org. Thank you.
Tuesday, January 24, 2023 from 8:30 am to 1:30 pm
Stanford University, Huang Building MacKenzie Room
DBDS faculty, PhD/MS graduate students, postdocs, staff scientists and external collaborators will gather for this inaugural event featuring:
- Welcome and Overview of DBDS Plans from Department Chair, Sylvia Plevritis
- Faculty lightning talks on topics ranging from clinical data science and imaging to multi-omics to translational bioinformatics
- Mini- career fair with DBDS graduate students and postdocs, including online resume book
- Industry panel discussion
- Time for networking and make new connections
8:00-9:00 am: Check-in and light breakfast
9:00-9:30 am: Welcome and Department Overview; Sylvia Plevritis, Chair of DBDS
9:30AM-11:00AM: Faculty lightning talks & Q&A
- Professor James Zou
Title: GENERATIVE AI FOR BIOMEDICINE
Abstract: I will discuss applications of generative AI in biomedicine, including the first experimentally validated new antibiotic molecule that was created by AI.
- Professor Daniel Rubin
Title: MAKING AI MODELS FOR MEDICAL IMAGING ROBUST
- Abstract: There are exciting prospects of AI for applications in medical imaging, but it is costly to develop AI models and performance may be limited. We are developing methods to improve AI model development using a variety of approaches,
- Professor Barbara Engelhardt
Title: MACHINE LEARNING FOR STRUCTURED BIOMEDICAL DATA
Abstract: Research in the Engelhardt Group focuses on building and applying methods for structured biomedical data, including case-control, time-series, spatial, and CRISPR-perturbed data. I'll discuss some recent work on spatial dimension reduction, live-cell imaging, and electronic healthcare records.
- Professor Julia Salzman
Title: TOWARDS STATISTICAL GENOMICS FOR A SUSTAINABLE FUTURE
Abstract: A unifying goal of biomedical data science is to discover the mechanisms and signatures of diverse biological processes from the genetic basis of heritable disease to emerging pathogen threats to antibiotic resistance to pan-species adaptation to climate change and beyond. I will discuss how a new statistical approach in genomics called NOMAD could enable unsupervised discovery, monitoring and prioritize interventions to maximize a sustainable future.
- Professor Teri Klein
Title: PHARMACOGENOMICS CENTER OF EXCELLENCE FOR PERSONALIZED HEALTH
Abstract: We are developing a Center of Excellence for pharmacogenomics research and best practices in the implementation of clinical pharmacogenomics. This talk will outline the latest developments at Stanford and future plans.
- Professor Nigam Shah:
Title: TRANSLATING DATA SCIENCE INNOVATIONS TO IMPROVE CLINICAL CARE
Abstract: We will review the successes as well as collaboration opportunities for data science based interventions to improve care at Stanford Medicine.
11:00AM-12:00PM: Collaboration Forum
12:00 pm - 12:30 pm Lunch
12:30 pm-1:30 pm External Partners panel