Sachish Singla

Quantitative Research • Computer Vision • Large Language Models • Representation Learning

I'm a Computer Science and Engineering student at IIT Kharagpur, currently in Singapore as a visiting research student with the CVML group at NUS. I work mostly on computer vision and representation learning, with a recent detour into quantitative research on high-frequency market data.

Profile photo of Sachish Singla

IIT Kharagpur

Computer Science & Engineering

About

I'm in the fifth year of the integrated B.Tech + M.Tech program in Computer Science and Engineering at IIT Kharagpur. Right now I'm in Singapore as a visiting research student in the CVML group at NUS, working with Prof. Angela Yao on video understanding, specifically representation learning for long-horizon procedural videos, where a model has to carry state across minutes and reason about which step is plausible next rather than classify frames in isolation.

At IIT Kharagpur I work with Prof. Pawan Goyal. Before that, I was at IBM Research and at KAIST in South Korea.

I got into quantitative research along the way and liked it a lot. I interned at Quadeye in the summer of 2026.

Education

Indian Institute of Technology, Kharagpur

Integrated B.Tech + M.Tech (5Y) in Computer Science and Engineering

2022 - 2027

Pursuing integrated dual degree program in Computer Science and Engineering. Actively involved in research, competitive programming, and various technical competitions.

Experience

Visiting Student Researcher

CVML Group, National University of Singapore | Singapore

Jul 2026 - Present

Quantitative Strategist Intern Full-Time Return Offer

Quadeye | Gurgaon, India

May 2026 - Jul 2026

Summer Intern

IBM Research | Bengaluru, India

May 2025 - Jul 2025

Computer Vision Intern

Preimage | Remote

Jan 2024 - Apr 2025

Visiting Student Researcher

KAIST | Seoul, South Korea

May 2024 - Jul 2024

Publications

2025

Mixup-VFL: Leveraging Unaligned Data for Enhanced Regression in Vertical Federated Learning

Sachish Singla, Prudhvi G., Ayush K., Devodita C., Varun T., Avi A., Debashish C.

IEEE ICDCSW 2025

Formulated a novel label-mixing framework for Vertical Federated Learning, reducing RMSE by up to 72% for regression tasks with minimal data overlap. This work addresses the challenge of data heterogeneity in federated learning scenarios where participants have different feature spaces.

Get In Touch

Let's Connect

I'm always interested in research collaborations, internships, and academic discussions. Feel free to reach out.

Email (Institutional) singla.sachish@kgpian.iitkgp.ac.in
Email (Personal) sachishs.15@gmail.com
IIT Kharagpur, India

This opens your email client with the message ready to send to sachishs.15@gmail.com.