Topic Descriptions

Topic 1: Reports of Center and MCSB Activities 

Topic 2: Additional new and ongoing activities 

genPALs

Slack channel: https://genpals.slack.com/join/shared_invite/zt-1fz92x6fr-r4kBYIsXv0vR0e65WbtUYw#/shared-invite/email

Website: https://uci-genpals.github.io/

MCSB DEI Committee

MCSB Community Agreement (DRAFT)

MCSB Community Agreement Feedback Form: https://forms.gle/AsfBRRwgz93Hm2fm9

Topic 3: Breakout Sessions

Session A:  Tough Calls in MCSB Curriculum Design: If you add it, what gets cut?

Training in applied math in 2023 is facing exciting new challenges and opportunities. Data science and machine learning are undoubtedly rooted in mathematics but they may require different or expanded types of training as compared to a “traditional” applied math curriculum. What specific, foundational, mathematical topics in data science, machine learning, and statistics are needed by today’s students? And since time is limiting, what “old-school” mathematical topics can, if necessary, be removed? In a similar vein, what specific topics in biology are needed by today’s students that are underrepresented in current graduate biology curricula? Are there topics that can be removed or deemphasized? Join with members of the MCSB curriculum committee to help address these important questions.

Session B: Stoking the interface: Strengthening ties between mathematics and biology at UCI.

Through UCI’s Center for Multiscale Cell Fate Research (CMCF), the NSF and Simons Foundation have fostered research at the interface between mathematics and biology. With the NSF-Simons grant soon drawing to a close, how should CMCF continue to promote this mission? Should the annual symposium continue in its present form? What center activities have proved most useful? How can alliances forged by CMCF be turned into new funding opportunities? From what sources should funding be sought, and on what topics? Session participants will be challenged to chart a path forward for research at UCI that advances the frontiers of both mathematics and biology. 

Session C: Slouching toward ChatGPT: How will AI impact the practice of science?

As the public has become familiar with the capabilities of AI to retrieve vast amounts of content and emulate both language and art, a major role for AI as an adjunct to creative endeavors in our society seems almost certain. But what about in science, where the goal is not only to be creative and interesting, but also make inferences that are actually true? Can we simultaneously reap the benefits of AI while guarding against its (already notorious) disregard for truth? Or can AI be trained to distinguish fact from fiction and, if so, how would one go about ascertaining that it was doing so, when healthy disagreement about interpretation is a mainstay of scientific discourse? In the meantime, what should be the position of scientific journals, granting agencies, and thesis committees on AIgenerated or AI-assisted content? As we head off into a brave new computer-aided world, can the practice of science, as we know it, survive, and resist the call of “truthiness” over truth?