Dhruva Abhijit Rajwade

I am working with Prof. Marianna Rapsomaniki's AI/ML For Biomedicine group in CHUV, Lausanne as a RA, working on drug perturbations, causality and and generative modeling for biology. I graduated in May'25 as an Integrated Master's student at IIT Kharagpur in India, where I Majored in Biotechnology and Biochemical Engineering and worked in Computational Biology, Computer Vision and AI4Science.

In my undergrad journey, I have worked with Prof. Koel Chaudhary and Prof. Soumya De at IIT Kharagpur. In the summer of 2023, I got an amazing opportunity to work with Prof. Brian Ingalls at the University Of Waterloo. I worked at Caltech in the summer of '24 with Dr. Shengchao Liu on understanding Protein-DNA interactions using Cross-Attention and language modelling. For my Master's thesis, I extended my work at Caltech by using discrete diffusion for controllable generation of DNA-binding protein sequences, with a focus on discovering novel de novo DBPs. Some of my designs expressed well in the lab, and this is an ongoing project in IIT Kharagpur at Prof. Riddhiman Dhar's group.

I am a recipient of the Caltech SURF fellowship (2024), the MITACS Globalink Research Internship (2023), and have also been selected for the EPFL E3 scholarship (2024) and the ThinkSwiss Research scholarship (2023).

Email  /  Scholar  /  Twitter  /  Github

Research

I'm primarily interested in Deep Learning for Protein Design, Gene regulation and Biological systems, Mathematical modelling of biological networks and dynamics, and AI for science. Most of my work has involved applying robust learning methods to Biological problems in an interpretable manner. Apart from Biology, I am keenly interested in and have also worked in Deep Learning for Vision (specifically SSL, secure ML and Generative modelling), as well as Causal Inference and Graph Learning. More recently, I have been interested in Fourier Neural Operators, Geometric Deep Learning, State-space models and their applications in Biology.

News

Current Projects

Using Discrete Diffusion to learn the distribution of DNA-binding protein sequences. Using our work on Seq2Contact to guide the diffusion process to generate proteins with high affinity for specified DNA targets. Working on optimizing CRISPR-Cas protein design, stitching together different functional domains by inpainting a DNA-binding domain in between, generating DNA sequences for binding to a target protein by inverting the Seq2Contact model, and engineering protein-DNA interactions. This project is in collaboration with Prof. Riddhiman Dhar at IIT Kharagpur. Image shows ESMFold folded sequences sampled by our trained discrete diffusion model, with as less as 25% sequence similarity to the training set and the presence of different DNA-binding domains (through PFam scans). The protein sequences were not cherry-picked, and the structures are coloured by confidence (blue: High, red: Low).

Using MD simulations and trajectory analysis to infer Allosteric networks in the CAP-cAMP system (where the cAMP ligand binds to CAP protein, leading to allosteric orientation changes and finally, transcription). Current progress includes uncovering of three prospective networks using AlloReverse. Future plans include using State-space models to learn the dynamics of protein-ligand interactions, and finding a generalizable method for allosteric discovery. This work is in collaboration with Prof. Soumya De at IIT Kharagpur.

Publications

An extension of our previous work (see below) on using (ATR-FTIR)Spectroscopy and Deep Learning for prediction of Idiopathic Recurrent Spontaneous Miscarriage (IRSM). This work focuses on the classification of IRSM using ATR-FTIR Spectroscopy, which is a non-invasive and cost-effective technique and improves on our previous work on using Raman Spectroscopy in a similar problem setting.

Using Raman Spectroscopy and Machine Learning for prediction of Idiopathic Recurrent Spontaneous Miscarriage (IRSM). Improved this work using ATR-FTIR Spectroscopy in a follow-up study, and currently working on a multi-omics approach to the same problem.

Using different statistical tests and empirical analyses to identify risk factors associated with mortality in Hypersensitivity pneumonitis, a rare lung disease. Checking for Publication bias and heterogeneity in the data, and using meta-analysis to combine results from different studies.

Miscellaneous

Worked on using the Geneva HRCT dataset to segment lungs and classify diseases using Deep Learning. The final model uses a U-Net architecture for segmentation and a CNN for classification. The model was trained on a subset of the dataset and tested on CT scan images obtained externally through collaborations with Hospitals.

Worked on using Calcium ion imaging time-series data to extract functional networks in beta-islets of the pancreas. Used Voronoi Delaunay triangulation (see image) to extract the network, and used graph theory to analyze the network. Code is incomplete and will be updated soon.

This website's template is taken from Jon Barron. Do not scrape the HTML from this page itself, as it includes analytics tags that you do not want on your own website — use the github code instead. Also, consider using Leonid Keselman's Jekyll fork of this page.