UCLA Department of Statistics and Data Science is proud to announce that Robert Gould, Teaching Professor, has been awarded the prestigious Founders Award by the American Statistical Association (ASA), the world’s largest community of statisticians and data scientists. This esteemed award recognizes members who have demonstrated exceptional dedication to advancing the mission of the association.

The ASA Founders Award is one of the organization’s highest honors. Recipients are recognized for their extended service and leadership within the ASA or through impactful outreach efforts. Honorees typically have held various leadership roles, where their contributions have continued to benefit the association beyond their tenure.

This award is a reflection of his dedication and remarkable service to the statistical community, and we are proud to celebrate his accomplishments.

Founded in 1839, the American Statistical Association serves members in more than 90 countries, promoting sound statistical practices and research that inform public policy and improve human welfare.

For more information about the ASA Founders Award and the American Statistical Association, please visit https://www.amstat.org/your-career/awards/founders-award and https://www.amstat.org.

The UCLA Department of Statistics and Data Science proudly congratulates Professor Jingyi Jessica Li on being named a 2025 Guggenheim Fellow by the John Simon Guggenheim Memorial Foundation. This year marks the 100th class of Guggenheim Fellows—an enduring legacy of honoring individuals who have demonstrated exceptional scholarly productivity or creative ability in the arts and sciences.

Founded in 1925, the Guggenheim Fellowship is one of the most prestigious honors in the United States, awarded through a rigorous peer-review process. The fellowship provides recipients with the freedom to pursue their work with as few constraints as possible, supporting bold and innovative scholarship across disciplines. More details about the Guggenheim Fellowship are available here.

Professor Li is a leading expert at the intersection of statistics, data science, and genomics. Her research focuses on developing principled, efficient, and interpretable statistical methods to advance biomedical science. She addresses critical challenges in transcriptomics, single-cell analysis, and machine learning, with particular emphasis on reducing false positives and improving the reliability of genomic discoveries.

Her contributions have significantly advanced our understanding of gene expression, the quantification of the central dogma of molecular biology, and the development of statistical frameworks for large-scale RNA sequencing and disease classification. At UCLA, Professor Li holds joint appointments in Statistics, Biostatistics, Computational Medicine, and Human Genetics, and serves on the faculty of the interdepartmental doctoral program in bioinformatics. She also leads the Junction of Statistics and Biology Lab, where her team bridges statistical innovation with impactful biomedical applications.

The Guggenheim Fellowship will support Professor Li’s ongoing work to enhance the rigor and interpretability of high-throughput genomic data analysis—research with broad implications for biology, medicine, and public health.

We are proud to celebrate this remarkable recognition and honored to have Professor Li as a core member of our department.

Read the article here.

Congratulations to Jiayi Li who was awarded a 2024 Dimitris N. Chorafas Foundation Prize. This international award was presented to only 31 individuals globally in 2024. It recognizes young researchers for their outstanding contributions to science, technology, engineering, and medicine. Jiayi is the first winner of this award from UCLA’s Division of Physical Sciences since its inception in 1992.

More details are available here.

Machine learning models are widely used in scientific research, enabling estimates and analyses that were once unattainable. However, these models often struggle with slow convergence and unreliable results when dealing with complex interactions. To address this, Dr. Xiaowu Dai, an assistant professor of Statistics and Data Science and of Biostatistics at UCLA, has developed a new statistical technique to improve the accuracy and reliability of machine learning estimates.

Dai’s method, called nonparametric estimation via partial derivatives, uses gradient information—either observed or estimated—to accelerate the convergence of machine learning models in complex, high-dimensional studies. This approach demonstrates that nonparametric estimation can achieve near-parametric rates when gradient data is incorporated, allowing the central limit theorem to be applied for the inference of nonparametric functions. Dai’s work builds on a long-standing conjecture by Karlin (1969) and Wahba (1971), who showed that incorporating gradient information adds no value for exact, one-dimensional functions. However, this result does not hold for noisy data. Dai’s method addresses this gap, improving accuracy in noisy, complex datasets.

This new technique has been applied to various questions in econometrics, queuing systems, and biological modeling, consistently outperforming traditional methods. “By incorporating gradient data, we can accelerate convergence and improve estimate accuracy, addressing a major limitation of conventional models that often require large datasets for reliable results,” says Professor Dai. “We believe this method will be a useful tool for practitioners in data-driven fields, ranging from science and social science to engineering.”

This work has been published in the prestigious Journal of the Royal Statistical Society Series B and can be accessed here. Here is another link to the UCLA Division of Physical Sciences article that discusses this work.

The Conference on Information Processing and Management of Uncertainty (IPMU 2024) has awarded Professor Judea Pearl, a joint faculty member in our department, the Kampe de Feriet Award for “his seminal work on probabilistic reasoning in artificial intelligence, including Bayesian networks and causality”. This prestigious award recognizes significant contributions to the field of information processing and the management of uncertainty. For more details, please visit this link.

Please join us in congratulating Professor Pearl!

The management of uncertainty was a hot AI issue in the 1970’s. Please see the following article by Professor Pearl on this topic: “A Personal Journey Into Bayesian Networks“.