Vijay Sadashivaiah | PhD | Machine Learning Scientist

Vijay Sadashivaiah

I am a Senior Scientist in Machine Learning in the Data AI and Genomics Group at Merck Research Laboratories. My research focuses on pretraining large foundation models, optimizing training through efficient kernels, and evaluating model performance. My broader research interests include foundation models, deep learning, machine learning systems, training optimization, and explainable AI.

I received my Ph.D. in Computer Science from Rensselaer Polytechnic Institute (2025), where I was advised by Professor James A. Hendler and Professor Pingkun Yan. I also hold a M.S. in Computer Science from Rensselaer Polytechnic Institute, a M.S. in Biomedical Engineering from Johns Hopkins University, and a B.S. in Electrical Engineering from PES Institute of Technology. I have been advised by wonderful mentors: Professor Sridevi Sarma at JHU, Dr. Qiang Chen and Dr. Kristen Maynard at LIBD, Professor Carl Petersen at EPFL, and Professor Achuta Kadambi at MIT.

Email / Resume / Google Scholar / Twitter / LinkedIn / GitHub

Recent News

03/2025: Started as Senior Scientist in Machine Learning at Merck Research Laboratories
02/2025: Successfully defended my Ph.D. in Computer Science at Rensselaer Polytechnic Institute
05/2024: I returned as a Research Intern at Bosch Center for AI
04/2024: I passed my candidacy exam to become a Ph.D. candidate
04/2024: Our work on studing bias in vision language models was accepted to NAACL TrustNLP workshop
01/2024: Our work on suppressing user determined semantic concepts in representation transfer was accepted to TMLR
05/2023: Started as summer intern at Bosch Center for AI
01/2023: Our paper on spectral unmixing was published at BMC Neuroscience
11/2022: I was a finalist at Quad Fellowship
10/2022: Our work on Improving langugage model predictions using knowledge graphs was accepted to DL4KG Workshop at ISWC-22
07/2022: I was awarded a best poster award at ISWS summer school in Bertinaro, Italy
04/2022: I was accepted to ISWS summer school in Bertinaro, Italy
02/2022: Our work on improving transfer learning using multi-armed bandits was accepted to ICLR-22
01/2022: Started my research co-advised by Professor James A. Hendler and Professor Chris R. Sims
05/2021: Started a summer project with Trustworthy AI and RL groups at IBM Research
01/2021: Started MS/PhD in Computer Science at Rensselaer Polytechnic Institute

Research

TEDDY: A Family of Foundation Models for Understanding Single Cell Biology
Alexis Chevalier, Soumya Ghosh, Urvi Awasthi, James Watkins, Julia Bieniewska, Nichita Mitrea, Olga Kotova, Kirill Shkura, Andrew Noble, Michael Steinbaugh, Vijay Sadashivaiah, George Dasoulas, Julien Delile, Christoph Meier, Leonid Zhukov, Iya Khalil, Srayanta Mukherjee, Judith Mueller
GenBio Workshop at ICML, 2025
paper

We present TEDDY, a family of transformer-based foundation models for single-cell biology, scaled to 116 million cells. The models (70M, 160M, and 400M parameters) can identify disease states and distinguish diseased from healthy cells, with performance improving predictably with data volume and parameter count.

Explaining Chest X-ray Pathology Models using Textual Concepts
Vijay Sadashivaiah, Mannudeep K Kalra, Ronny Luss, Pingkun Yan, James A. Hendler
AIM-FM at NeurIPS, 2024
paper

We propose to explain chest X-ray pathology models using textual concepts. This is achieved by leveraging the joint latent space of image and text in vision-language models.

Beyond Visual Augmentation: Investigating Bias in Multi-Modal Text Generation
Fnu Mohbat, Vijay Sadashivaiah, Keerthiram Murugesan, Amit Dhurandhar, Ronny Luss, Pin-Yu Chen
TrustNLP at NAACL, 2024
paper

We evaluated the influence of bias on multimodal text generation models. In particular, we studied the impact of visual augmentation using state-of-the-art diffusion models when generating text.

To Transfer or Not to Transfer: Suppressing Concepts from Source Representations
Vijay Sadashivaiah*, Keerthiram Murugesan*, Ronny Luss, Pin-Yu Chen, Christopher R. Sims, James A. Hendler, Amit Dhurandhar (* equal contribution)
Transactions on Machine Learning Research (TMLR), 2024
paper

We propose to suppress user-determined semantically meaningful concepts (viz. eyeglasses, smiling) from intermediate representations in computer vision tasks.

Auto-Transfer: Learning to Route Transferrable Representations
Keerthiram Murugesan*, Vijay Sadashivaiah*, Ronny Luss, Karthikeyan Shanmugam, Pin-Yu Chen, Amit Dhurandhar (* equal contribution)
International Conference on Learning Representations (ICLR), 2022
paper / code

We introduce multi-armed bandit based representation routing to improve transfer learning in computer vision tasks.

	Improving Language Model Predictions via Prompts Enriched with Knowledge Graphs Ryan Brate, Minh-Hoang Dang, Fabian Hoppe, Yuan He, Albert Meroño-Peñuelar, Vijay Sadashivaiah Deep Learning for Knowledge Graphs Workshop at ISWC, 2022 paper We propose to imporve language model predictions by enriching the prompts from knowledge graphs.
	SUFI: An automated approach to spectral unmixing of fluorescent multiplex images captured in mouse and postmortem human brain tissues Vijay Sadashivaiah, Madhavi Tippani, Stephanie C Page, Sang Ho Kwon, Svitlana V Bach, Rahul A Bharadwaj, Thomas M Hyde, Joel E Kleinman, Andrew E Jaffe, Kristen R Maynard, BMC Neuroscience, 2021 paper / code An automated approach to spectral unmixing of fluorescent images
	Single-nucleus transcriptome analysis reveals cell-type-specific molecular signatures across reward circuitry in the human brain Matthew N Tran, Kristen R Maynard, Abby Spangler, Louise A Huuki, Kelsey D Montgomery, Vijay Sadashivaiah, Madhavi Tippani, Brianna K Barry, Dana B Hancock, Stephanie C Hicks, Joel E Kleinman, Thomas M Hyde, Leonardo Collado-Torres, Andrew E Jaffe, Keri Martinowich Neuron 2021 paper / code A single-nucleus RNA-sequencing resource of 70,615 high-quality nuclei to generate a molecular taxonomy of cell types across five human brain regions.
	KCNH2-3.1 mediates aberrant complement activation and impaired hippocampal-medial prefrontal circuitry associated with working memory deficits Ming Ren, Zhonghua Hu, Dr. Qiang Chen, Andrew E Jaffe, Yingbo Li, Vijay Sadashivaiah, Shujuan Zhu, Nina Rajpurohit, Joo Heon Shin, Wei Xia, Yankai Jia, Jingxian Wu, Sunny Lang Qin, Xinjian Li, Jian Zhu, Qingjun Tian, Daniel Paredes, Fengyu Zhang, Kuan Hong Wang, Venkata S Mattay, Joseph H Callicott, Karen F Berman, Daniel R Weinberger, Feng Yang Molecular Psychiatry 2019 paper
	Modeling the interactions between stimulation and physiologically induced APs in a mammalian nerve fiber: dependence on frequency and fiber diameter. Vijay Sadashivaiah, Pierre Sacré, Yun Guan, William S Anderson, Sridevi V Sarma Journal of Computational Neuroscience 2019, EMBC 2018, EMBC 2017 paper 1 / paper 2 / paper 3 / paper 4 / code We constructed a mechanistic, stochastic and functional models of nerve fiber to quantify interactions.
	Voltage-sensitive dye imaging of mouse neocortex during a whisker detection task Alexandros Kyriakatos, Vijay Sadashivaiah, Yifei Zhang, Alessandro Motta, Mattieu Auffret, Carl CH Petersen Neurophotonics 2017 paper We studied the sensory motor interactions in mice brain using voltage sensitive dye imaging.