Three students in the Statistics and Machine Learning minor have received special recognition for their research at the Center for Statistics and Machine Learning’s end-of-the-year celebration. Annie Xu ‘26, Department of Economics, Kiran Biddinger ‘25, Operations Research and Financial Engineering, and Kohei Sanno ‘25, Department of Computer Science each were selected by a committee of faculty to be awarded prizes for their independent work projects.

Kiran Biddinger won for his project, “Population Genetics for Heart Failure: A Multi-Trait Analysis of Rare, Protein-Coding Variants.” The broad question motivating Biddinger’s work was: what are the genetics of heart failure? 

Scientists had already previously developed algorithms that for individuals can detect if there are certain mutations preventing functional protein formation. Biddinger wanted to figure out how to take it a step further and see if he could test a detected genetic mutation for an association with a disease – in this case, heart failure. “As an ORFE student and an SML student, I wondered, ‘what are some statistical ways to do this and what are some frameworks we can employ?’” Biddinger said. 

For his project, Biddinger employed a multi trait algorithm – an approach used to analyze multiple traits simultaneously. Applied to data from a biobank containing the genetic information of 500,000 people, this approach identified 99 genes associated with heart failure. In the fall, Biddinger will begin pursuing a PhD in biomedical informatics at Harvard University.

Kohei Sanno won for his project, “Interpreting learned latent spaces of cryo-EM generative models.” Cryo-EM is an imaging technique which produces 2D projections of proteins. Sanno’s advisor, Professor of Computer Science Ellen Zhong, had previously developed a generative AI model which takes these 2D projections and reconstructs them into 3D protein structures.

For his project, Sanno presented methods for extracting and interpreting insights from cryo-EM AI models. “Given a protein of interest we're interested in determining the different shapes that this protein can take on” said Sanno. “While the model is good at learning these different shapes, interpreting the model is a whole separate challenge.”

The results of the project found Sanno’s presented methods worked well at extracting insights and Sanno plans to continue to work on the research this summer on the Princeton campus. In the fall he will be heading to the University of California, Berkeley, to pursue a PhD in computer science. 

Annie Xu won for her project, “Political Uncertainty and Options-Implied Risks: Evidence from U.S. Federal Elections.” Xu is a rising senior. 

Independent work projects are completed by all students in the Statistics and Machine Learning minor program. In their projects, students demonstrate their understanding of machine learning and statistics and their ability to apply the methods to interesting research questions.

The recipients of this year’s prizes were selected via a committee of more than two dozen professors and instructors affiliated with CSML. “The faculty who reviewed the array of this year’s independent work research are a great service to the students and university,” said Ramadge.

CSML extends its thanks to the faculty listed below all of whom helped in the evaluation of over 120 independent work projects:  

Peter Melchoir, Andrew Rosen, Sigrid Adriaenssens, Olga Russakovsky, Elad Hazan, Lydia Liu, Adji Bousso Dieng, Aleksandra Korolova, Derek Sollberger, Peter Ramadge, Day Yi, Guillermo Sapiro, Sanjeev Kulkarni, Mikkel Plagborg-Moller, Michal Kolesar, Michael Skinnider, Christine Allen-Blanchette, Jeroene Tromp, Elizaveta Rebrova, Amir Ahmadi, Jonathan Pillow, Samuel S. Wang, Filiz Garip, Brandon Stewart, Bo Honoré.