Kaushik S

I graduated from IIITDM Kancheepuram with a Dual Degree
(B.Tech + M.Tech) in Computer Engineering. Currently working at Comcast India Engineering Center as a Machine Learning Engineer in the AIOps team.

Previously, I was a Research Intern at Healthcare Technology Innovation Centre(HTIC), IIT Madras, where I was part of the Image Computing group, pursuing research in Deep Learning for Medical Imaging.

I am an international chess player, with a FIDE rating of 2126. I am also an avid quizzer and football fanatic.

Email / CV / Google Scholar / Github / LinkedIn

Research

My current research is in the field of AIOps, where I am exploring the application of Large Language Models (LLMs) and Time Series Foundation Models to enhance anomaly detection capabilities. I am also focused on building agentic workflows for the end-to-end automation of incident management, focusing on triage, root cause analysis, and remediation. Previously, my research centered on Deep Learning for Medical Image Analysis, particularly in the domains of Medical Image Segmentation and Reconstruction.

	THEMIS: Unlocking Pretrained Knowledge with Foundation Model Embeddings for Anomaly Detection in Time Series Mahesh Yadav, Kaushik Sarveswaran, Nagaraj Sundaramahalingam, Aravindakumar Venugopalan AI4TS Workshop, IJCAI 2025 (Oral) Arxiv / Poster
	KD-MRI: A knowledge distillation framework for image reconstruction and image restoration in MRI workflow Balamurali Murugesan, Sricharan Vijayarangan, Kaushik Sarveswaran, Keerthi Ram, Mohanasankar Sivaprakasam MIDL 2020 (Poster) OpenReview / Code
	A context based deep learning approach for unbalanced Medical Image Segmentation Balamurali Murugesan, Kaushik Sarveswaran, Vijaya Raghavan, Keerthi Ram, Mohanasankar Sivaprakasam ISBI 2020 Arxiv / Code
	Conv-MCD: A Plug-and-Play Multi-task Module for Medical Image Segmentation Balamurali Murugesan, Kaushik Sarveswaran, Sharath M. Shankaranarayana, Keerthi Ram, Jayaraj Joseph, Mohanasankar Sivaprakasam MLMI Workshop, MICCAI 2019 (Poster) Arxiv / Code / Poster
	Recon-GLGAN: A Global-Local Context Based Generative Adversarial Network for MRI Reconstruction Balamurali Murugesan, Vijaya Raghavan, Kaushik Sarveswaran, Keerthi Ram, Mohanasankar Sivaprakasam MLMIR Workshop, MICCAI 2019 (Oral) Arxiv / Code / Presentation
	Psi-Net: Shape and boundary aware joint multi-task deep network for Medical Image Segmentation Balamurali Murugesan, Kaushik Sarveswaran, Sharath M. Shankaranarayana, Keerthi Ram, Jayaraj Joseph, Mohanasankar Sivaprakasam EMBC 2019 (Oral) Arxiv / Code / Presentation
	Deep detection and classification of mitotic figures Balamurali Murugesan, Sakthivel Selvaraj, Kaushik Sarveswaran, Keerthi Ram, Jayaraj Joseph, Mohanasankar Sivaprakasam SPIE Medical Imaging 2019 (Poster) Poster

Service

	Reviewer, Machine Learning for Health (ML4H) Symposium, 2021
	Reviewer, Machine Learning for Health (ML4H) Workshop, NeurIPS 2020
	Reviewer, ACM Conference on Health, Inference, and Learning (CHIL) 2020
	Reviewer, Machine Learning for Health (ML4H) Workshop, NeurIPS 2019
	Teaching Assistant, Operating Systems(COM301), IIITDM Kancheepuram

Thanks for the template, Jon Barron!