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PAST ANNOUNCEMENTS & FEATURED EVENTS
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The Universe Rewards Boldness: What Bold Looks Like in the Era of AI
- When: Friday May 01, 2026 | 2:30-4PM
- Where: Room 304., Medical Education and Telemedicine Building (MET), UCSD
- RSVP: here
Description:
Dr. Caiyan Li was born into a poor, rural farming village in China — a place where the size of your fists mattered more than any education. Her father's schooling stopped at fifth grade. Her mother didn't finish second. For a girl in that village, survival was the ceiling. College education, success— these weren't even dreams anyone dared to have.
Caiyan broke through every wall with fierce courage and generous receipts of Boldness from the Universe.
When her middle school teacher announced to the class that girls lose interest and fall behind at this age, she stood up in tears and declared she would finish in the top 3 among 250 students — from 80th place. She did it. When her parents defaulted her future to trade school — the highest a village girl could aim for — she waited an entire day outside the principal's office to change her own registration to high school, without her parents' consent, risking a future as a farmer in an arranged marriage. She then scored the highest in her county among thousands of students and forced her way through the door. When it came time to choose a college, she wrote down Peking University with a score that was a gamble — and won admission. And when a visiting professor from the University of Pennsylvania came to campus, she stayed up all night writing her personal statement, printed her unfinished research paper, and was waiting at his office door at 8am. By 9, he had offered her a spot. A year later, she boarded a flight to Philadelphia alone — $800 in her wallet, one suitcase, and no safety net.
Today, Caiyan is Vice President of Biometrics and Data Science at a biotech company, leading teams at the intersection of data science and AI in drug development. And the boldness hasn't stopped. When AI began reshaping the industry, Caiyan didn't wait for permission — she volunteered to join her company's AI task force, where she helped shape and push the AI strategy to transform the organization. She then built a data infrastructure, powered by AI — changing how the entire organization operates and how teams make informed decisions at every step of drug development.
In this keynote, she traces the thread of boldness through her own life and into the work she is doing right now, to arrive at the question that matters most: What does boldness look like for biostatisticians and data scientists in the era of AI? This is not a technology talk. It is a talk about the human instinct to act before the path is clear — and why that instinct may be the single most valuable asset we carry into a future being reshaped by AI.
2026 Biostatistics Symposium of Southern California (BSSC)
When: January 22–23, 2026
Where: 4500 MacArthur Blvd, Newport Beach, CA 92660
Description:
On behalf of the chapter officers, we would like to invite you to attend the 2026 Biostatistics Symposium of Southern California (BSSC), co-organized by the ASA San Diego chapter and two other ASA chapters in Southern California. This second BSSC will ride on the tremendous success of our inaugural symposium in February of 2025. Please see the attached flyer for more information.
BSSC has received strong support from the local ASA chapters. The presidents are sitting on the board of BSSC. BSSC intends to build a strong collaborative relationship between BSSC and the local chapters. All of you are welcomed to participate in and assist the planning of the event. The registration link is already open. The deadline for early registration fee is December 24th. There will also be a discounted hotel rate for anyone who will stay at the hotel.
If you are planning to participate in the BSSC conference (https://biostatsymposium.org/), please note that there will be promotion and outreach activities for our ASA chapter and the other 2 southern California chapters at the conference reception on the evening of Thursday, January 22, 2026.
SDASA 2022 Traveling Course: An Introduction to Second-generation p-Values and their Use in Statistical Practice
When: Friday, November 4th, 2022, 1:00 pm - 4:00 pm PST
Where: Virtual via Zoom
Instructors: Jeffrey D. Blume, School of Data Science, University of Virginia, and Megan H. Murray, Biostatistics, Vanderbilt University
Description:
Second-generation p-values were recently proposed to address the well-known imperfections of classical p-values. Their implementation can largely be thought of as codifying ‘good standard practice’ for interpreting and reporting classical p-values. Second-generation pvalues maintain the favorable properties of classical p-values while emphasizing scientific relevance to expand their utility, functionality, and applicability. In particular, they can report evidence in favor of the alternative, in favor of the null hypothesis, or neither (inconclusive); they automatically incorporate an adjustment for multiple comparisons and multiple looks; they have lower false discovery rates than classical p-values; and they are easier to interpret. Second-generation p-values have been shown to work well in regularized models. They also lead to significantly improved model selection procedures in linear and generalized linear models. Also, second-generation p-values are non-denominational in the sense that they are readily applied in frequentist, likelihood and Bayesian settings.
Outline and Objectives:
This course will briefly revisit the history of p-values as originally envisioned in significance and hypothesis testing, and the resulting controversy over their scientific interpretation. The importance of distinguishing between three key inferential quantities (the measure of the strength of evidence, design error rates, and false discovery rates for observed data) will be illustrated. The second-generation p-value will be introduced and contrasted with standard methods. The workshop will explain how to design studies in which the second-generation p-value is used as the primary mode of inference. We will cover computation of second-generation p-values (in R), guidelines for presenting results, and when appropriate, how to present accompanying false discovery rates. Multiple examples will be presented using data from clinical trials, observations studies and high-dimensional analysis of large-scale data. Advanced applications in model selection, adaptive monitoring of clinical trials, and regularized models will be shown if time allows. Mathematical details will be kept to a minimum, e.g., statistical properties will be presented but without mathematical proof.
About the Instructors:
Jeffrey D. Blume, PhD is the Quantitative Foundation Associate Dean for Academic and Faculty Affairs and Professor of Data Science at the School of Data Science at the University of Virginia. He recently moved from Vanderbilt University where he served as Vice-Chair for Education in the Department of Biostatistics and Director of Graduate Education in the Data Science Institute. Professor Blume is a fellow of the American Statistical Association and has extensive experience in the operation, analysis, and methodological aspects of Clinical Trials and in the analysis of a wide array of biomedical research. He has won numerous awards for his teaching and mentoring, and for his work on racial disparities in lung cancer screening. His statistical research focuses on the foundations of statistical inference, mediation modeling, diagnostic clinical trials and prediction modeling. Professor Blume has published extensively on the foundations of statistical inference, particularly on likelihood methods for measuring statistical evidence, and he authored the origin publications on second-generation p-values.
Megan H. Murray is a PhD candidate in Biostatistics at Vanderbilt University working with Professor Blume. Her methods research focuses on evaluating and extending the statistical properties of second-generation p-values and on characterizing false discovery rate methodology. She recently co-authored a paper describing her development of a R package (“FDRestimation") for computing, estimating, and visualizing false discovery rates (currently on CRAN). Her biomedical research has focused in the areas of lung cancer, particularly in assessing racial disparities in lung cancer diagnosis, and of graduate student survey data, specifically evaluating if a positive mentor experience leads to more published papers or shorter time to graduation. Megan has won several teaching awards in both Biostatistics and Data Science at Vanderbilt.