About Me

I’m Sahithi, an fourth-year undergraduate student at Caltech pursuing a B.S. in Computer Science with a minor in Information and Data Science. My research interests lie in multimodal machine learning (e.g., vision-language), interpretability, and applications in healthcare. Leveraging the complexities of healthcare data, I aim to drive progress in multimodal machine learning while addressing key clinical challenges.

Here's my CV and my contact info.

Experience

Research Fellow – Stanford Medical AI & Computer Vision Lab

June 2024 – Present

Finding natural language differences between sets of radiology chest X-rays with a focus on addressing domain shift, locality understanding, and reasoning over large sets. Implemented a domain-specific two-stage proposer-ranker approach, combining image captioning with large language models to propose differences based on both visual data and corresponding captions.

Research Fellow – Caltech Computational Vision Lab

May 2023 – Sep 2023

Investigated geometry of CLIP’s vision-language embeddings and studied the modality gap. Developed a novel pipeline that combined dimensionality reduction, spectral clustering, decomposition, and latent vector similarity analysis.

Research Intern – NASA JPL

Sep 2022 – April 2023

Built an interpretable machine learning pipeline for Martian frost detection. Applied Grad-CAM and TCAV for local and global explainability.

STEP Intern – Google

June 2022 – Sep 2022

Designed and implemented a full-stack internal dashboard tool for analyzing remote procedure calls. Built a C++ backend and a JavaScript, HTML, and CSS frontend for querying and data visualization.

Research Assistant – University of Chicago

June 2021 – Aug 2021

Explored self-driving car pipelines and built a cloud-based pipeline for real-time testing. Utilized CHI@Edge for edge computing and Docker for containerized deployment as part of the Chameleon Cloud team.

Publications

  1. S. Ankireddy , Y. Zhang, X. Wang, M. Varma, H. Guo, C. Langlotz, and S. Yeung-Levy, RadDiff: Describing Differences in Radiology Image Sets with Natural Language, Machine Learning for Health (ML4H), 2024.
    Paper
  2. S. Ankireddy, S. Lu, G. Doran, U. Rebbapragada, S. Diniega, J. Widmer, and M. Wronkiewicz, Interpretable Machine Learning for Martian Frost Detection, 6th Planetary Data Workshop, 2023.
    Paper
  3. Sahithi Ankireddy, Assistive Diagnostic Tool for Brain Tumor Detection using Computer Vision, 2020 IEEE MIT Undergraduate Research Technology Conference (URTC), Cambridge, MA, USA, 2020, pp. 1-4, doi: 10.1109/URTC51696.2020.9668906.
    Paper
  4. Sahithi Ankireddy, A Novel Approach to the Diagnosis of Heart Disease using Machine Learning and Deep Neural Networks, 2019 IEEE MIT Undergraduate Research Technology Conference (URTC), Cambridge, MA, USA, 2019, pp. 1-4, doi: 10.1109/URTC49097.2019.9660581.
    Paper

Contact Me

Please feel free to reach out to me!

Email: sankired@caltech.edu