Last month on May 23rd, the UCLA Society of Women in Statistics (SWS) and Department of Statistics and Data Science organized and hosted the inaugural “Distinguished Women in Statistics and Data Science Workshop” at the UCLA Luskin Center. The half-day event aimed to help foster an inclusive environment for anyone interested in statistics and data science. A diverse group of more than 50 participants, comprising graduate students, postdocs, and faculty members from various departments, including Statistics, Mathematics, Computer Science, and Linguistics attended. The chair of the Department of Statistics and Data Science, Professor Hongquan Xu, delivered the opening speech.
The workshop featured a distinguished speaker, Professor Jun Xie, who gave a talk on ‘Out-of-Distribution Generalization with Sliced Inverse Regression.’ Twenty-three years ago, Xie was one of the first students to receive a PhD in statistics from UCLA in 2000. She currently serves as the graduate vice chair in the Department of Statistics at Purdue University. “It’s amazing to see how much this department at UCLA, and the field of statistics as a whole, has grown,” said Xie. “I am grateful to SWS for the work they have done to put on this event.”
Xie’s talk delved into the out-of-distribution generalization problem in machine learning theory, and proposed a novel approach to apply sliced inverse regression techniques to learn invariant patterns and relationships across different distributions. Besides achieving high accuracy, the algorithm is easy to implement and comes with theoretical guarantees. “The sliced inverse regression technique was developed by my Ph.D. advisor, Professor Ker-Chau Li at UCLA more than twenty years ago,” said Xie. “Ker-Chau is a true pioneer of his time and the work I just shared can be a good example of how classical statistical models can make an impact on modern machine learning.”
The workshop also featured a panel discussion centered around women in academia. Esteemed panelists included Professor Ariana Anderson from the Department of Psychiatry and Behavioral Sciences at UCLA, Professor Jessica Jaynes from the Department of Mathematics at Cal State University, Fullerton, and Professor Karen McKinnon from the Department of Statistics and Data Science at UCLA. Notably, both Anderson and Jaynes earned their PhDs from the Department of Statistics and Data Science at UCLA, adding a sense of familiarity and connection to the discussion.
“In line with SWS’s mission, this event was meant to foster inclusivity and support for everyone working in the field of statistics and data science,” said PhD student and current SWS president Jiayi Li. “I really appreciate the fact that all speakers, as spouses and/or mothers, shared their experience in balancing research work and personal life, which sounded challenging but actually inspiring. While our organization is certainly focused on supporting women, this workshop was open to all genders and backgrounds,” she added.
The workshop also provided attendees with valuable networking opportunities. Participants had the chance to interact with their peers, faculty members, and professionals in the field, fostering collaborations and strengthening the community at UCLA and beyond.
With the success of this inaugural workshop and continuing support from the Department of Statistics and Data Science, SWS is already planning a similar event in the Fall. “Our next workshop will be more focused on industry life, and we expect to have several experienced female leaders working in the data science industry share their insights with us,” said Li. Li, together with other co-organizers, Stella Huang, and Tanvi Shrinke acknowledge the support from department staff including Chie Ryu, Laurie Leyden, Kyle Chang, Enrique Reyes, and Verghese Nallengara.