Dr. Jingyi Jessica Li has been promoted to Full Professor, effective July 1, 2022.

Dr. Li has joint appointments in the Departments of Statistics, Biostatistics, Computational Medicine, and Human Genetics. Her research focuses on developing rigorous, robust, interpretable, and efficient statistical methods to address important questions in biomedical sciences, especially those around high-throughput genomic studies. The significance of her work’s impact is evident from her success in developing widely-used bioinformatics tools to advance transcriptomics studies at tissue and single-cell levels, and publications in influential journals such as Science, Nature, Cell, PNAS, Nature Communications, Science Advances, and Genome Biology. Dr. Li’s talent and achievements have been recognized with multiple awards, including an NSF CAREER Award, Sloan Research Fellowship, Math Scholar Award from the Johnson & Johnson Women in STEM2D Program, MIT Technology Review 35 Innovators under 35 China, and Harvard Radcliffe Fellowship.

Please congratulate Jessica for her promotion!

Aspiring data scientists, you are invited to attend the in-person panel discussion happening during the evening of Thursday, November 17, 2022 at the James West Alumni Center. Learn from real-world practitioners. Details are here.

Statistica Sinica featured their latest special issue on sliced inverse regression (SIR) to honor Ker-Chau Li for his ground-breaking contributions to dimension-reduction techniques in high-dimensional statistics. Over his career Professor Li also made substantial contributions in mathematics / computational statistics, high-dimensional data analysis, bioinformatics, systems biology, and applied research. Li served as a Co-editor of Statistica Sinica, 1999-2002, and as the Director of the Institute of Statistical Science, Academia Sinica, 2006-2012. He was elected as Academician of Academia Sinica in 2012, and subsequently as a member to the World Academy of Sciences in 2014. He was also the founder of the Mathematics in Biology Lab at the Institute of Statistical Science, Academia Sinica. The special issue is available here.

Please congratulate Kerchau when you next see him.

Guido has joint appointments in the Department of Statistics and the Department of Mathematics. His research focuses on the theoretical analysis of machine learning and artificial intelligence. His work provides insight into why these methods work so well, rather than the engineering of more refined methods. He was the recipient of the 2022 Sloan Research Fellowship and a CAREER Award from the National Science Foundation.

There are few things more important to a department than the development and promotion of junior faculty.

Congratulations to Guido for his tenure promotion!

A multi-campus, multi-disciplinary UC team have been awarded $1 million from the National Science Foundation to plan a new center to prevent and rapidly contain disease outbreaks and their negative effects on health, the economy and society. See this link for more details.

Professor Mark S. Handcock is only statistician on the team.

Congratulations to Mark!

Dr. Jingyi Jessica Li received an inaugural funding, the Chan-Zuckerberg Initiative Single-Cell Biology Data Insights Award. Her project is “Enhancing Rigor and Reliability of Single-Cell Data Science.” Her team will address the widespread issue of inflated false discovery rates (FDRs) in single-cell data analysis, in particular, the data reuse “double-dipping” issue. Her team will also develop a versatile simulator to generate realistic single-cell multi-omics data and spatial transcriptomics data with ground truths, thus allowing the single-cell community to perform fair and informative benchmarking of computational tools.

More details are available here:
https://chanzuckerberg.com/science/programs-resources/single-cell-biology/data-insights/enhancing-rigor-and-reliability-of-single-cell-data-science/

Congratulations to Jessica for winning another prestigious award!

Jingyi Jessica Li, Associate Professor in the Department of Statistics at UCLA, was selected to be a Radcliffe Institute Fellow (Helen Putnam Fellow) for the 2022-2023 academic year.

The Harvard Radcliffe Institute Fellowship Program, now in its 22nd year, offers scholars and practitioners in the arts, humanities, journalism, sciences, and social sciences a chance to pursue their latest passions. This year, the program traditionally accepted only 50 fellows for the 2022-2023 class from across Harvard University and around the world. The historical acceptance rate in recent years was below 3%.

Dr. Li is an interdisciplinary expert in statistics and genomics. During her Radcliffe Fellowship year, she will write the first book to clarify common confusions in genomics data analysis by connecting cutting-edge genomics research questions with fundamental statistical and machine-learning methods, focusing on the distinctions and choices among the methods—which are apparently similar but fundamentally different—so that quantitative genomics researchers will have clear guidelines to follow in their development of bioinformatics tools.

Link to Dr. Li’s webpage on the Radcliffe Institute website: https://www.radcliffe.harvard.edu/people/jingyi-jessica-li

Congratulations to Jessica!

We’re deeply saddened to announce the passing of Don Ylvisaker. Don was instrumental in establishing the Department of Statistics and was a great friend, mentor, and colleague to many of us. We are preparing a special In Memoriam page as well as a full obituary to honor Don’s lasting contributions to our community, which we hope to share with you very soon.

Don is preceded in death by his son Peter, and sisters Mary and Sylvia. He is survived by his wife Anna (Ricci), sisters Elisabeth and Shirley, as well as many nieces, nephews and cousins.

The family would like donations in Don’s memory to be made to VNA Health at 509 E. Montecito Street, Suite 200, Santa Barbara, CA 93103. More details are available here.

A daylong celebration of the life of Don Ylvisaker happened on Saturday, March 18, 2023.

The UCLA Introduction to Data Science (IDS) Project of Center X and the Department of Statistics is proud to announce generous funding from Valhalla Foundation to expand the IDS mission to improve data literacy for K-12 students and to increase diversity in STEM education.

For information about the Introduction to Data Science Project, please see http://introdatascience.org.

Montúfar, who leads the Mathematical Machine Learning Group — centered at UCLA and the Max Planck Institute for Mathematics in the Sciences, in Germany — works on deep learning theory and mathematical machine learning. Through investigations of the geometry of data, hypothesis functions and parameters, he and his team are developing the mathematical foundations of deep learning and improving learning with neural networks. Montúfar is the recipient of a starting grant from the European Research Council and a CAREER award from the National Science Foundation, and he serves as research mentor with the Latinx Mathematicians Research Community. He and his team have organized a weekly online math machine learning seminar since 2020.

Please join me to congratulate Guido for receiving the prestigious Sloan Research Fellowship!

Read more details at this link from the UCLA Newsroom.

The story on this research is posted in the UCLA Newsroom here.

Explainable Artificial Intelligence (XAI) models, through explanations, aim at making the underlying inference mechanism of AI systems transparent and interpretable to human users. Humans can easily be overwhelmed with too many or too detailed explanations; interactive communication process help in understanding the user and identify user-specific content for explanation, says Song-Chun Zhu, the project’s principal investigator and a professor of Statistics and Computer Science. So Zhu and his team set out to improve existing XAI models – to pose the explanation generation as an iterative process of communication between the human and the machine.

Arjun Reddy Akula, Ph.D. candidate at UCLA who led this work, said “In our framework, we let the machine and the user solve a collaborative task, but the machine’s mind and the human user’s mind only have a partial knowledge of the environment. Hence, the machine and user need to communicate with each other, using their partial knowledge, otherwise they would not be able to optimally solve the collaborative task”. He also said, “Our work will make it easier for non-expert AI human users to operate and understand the AI based systems”.

This work has been published in the prestigious Science journal and can be accessed here: https://www.cell.com/iscience/pdf/S2589-0042(21)01551-0.pdf