Reinforcement Learning for Crystal Structure Design
Currently exploring offline reinforcement learning methods for learning a policy for sequentially designing crystal structures. 
Portfolio
Graph generative models for binding site-specific molecule generation
Designing novel deep graph generative model for generating new ligand molecules that can bind to a given target receptor binding site.
Analysis of drug response and gene expression data of AML cells
Computational methods to identify drug-gene correlations and molecules that can induce leukemic cell maturation.
Incorporating Geometry into Score-Based Model for Crystal Structure Design
Two ways to incorporate crystal symmetry information as an inductive bias into a generative model for crystal structure design.
Analyzing the effects of visual representations in visual navigation tasks
Two ways for encoding visual inputs for navigation task using RL – pretraining a contrastive learning-based SimCLR model and VAE-based generative approach.
Deep generative models for single-cell gene expression analysis
Evaluated state-of-the-art unsupervised deep learning techniques including variational autoencoders for single-cell gene expression data analysis. 
Generating drug-like molecules from gene expression signatures using transformer
Designed an attention-based transformer model for de novo generation of drug-like molecules that can induce a desired transcriptomic profile. Accepted as poster at MLCSB COSI, ISMB 2022. 
Parallel analyses of canonic polyadic tensor decomposition algorithm
CPU- and GPU-level parallelization of tensor decomposition algorithm using OpenMP and OpenACC. 
RNA-seq data analysis of human oral squamous cell carcinoma
Identified somatic mutations in RNA-sequencing data of human oral squamous cell carcinoma samples.