About Me
Hi, I'm Shaan. I am a 1st year Computer Science Ph.D. student at the University of California, Riverside, advised by Professor Vagelis Papalexakis. Generally, I am interested in using machine learning as a surrogate model for expensive scientific experiments (such as designing an optimal material, or determining a good neural network architecture). The majority of my work involves learning from multidimensional data through the use of tensor decomposition. This also includes interning and collaborating with scientists at Lawrence Livermore National Laboratory.
Education
University of California, RiversideSept. 2025 – Present
Ph.D. in Computer Science
University of California, RiversideSept. 2021 – June 2025
B.S. in Data Science
Research Experience
University of California, RiversideJune 2024 – Present
Research Assistant
Lawrence Livermore National LaboratoryJune 2025 – Present
Research Intern
Papers
Conference
- Automating Data Science Pipelines with Tensor Completion
Shaan Pakala, Bryce Graw, Dawon Ahn, Tam Dinh, Mehnaz Tabassum Mahin, Vassilis Tsotras, Jia Chen, and Evangelos Papalexakis
IEEE International Conference on Big Data 2024 [PDF] [Code]
Workshop
- Surrogate Modeling for the Design of Optimal Lattice Structures using Tensor Completion
Shaan Pakala, Aldair Gongora, Brian Giera, and Evangelos Papalexakis
NeurIPS AI4Mat Workshop 2025 [PDF] [Code]
- Efficiently Generating Multidimensional Calorimeter Data with Tensor Decomposition Parameterization
Paimon Goulart*, Shaan Pakala*, and Evangelos Papalexakis
ICCV LIMIT Workshop 2025 [PDF] [Code]
- Tensor Completion for Surrogate Modeling of Material Property Prediction
Shaan Pakala, Dawon Ahn, and Evangelos Papalexakis
AAAI KGML Bridge 2025 [PDF]