Computational Biology • Bioinformatics • AI Research

Building intelligent systems for
biology and medicine.

Computational biology researcher and AI engineer working across explainable graph learning, molecular toxicity prediction, bioinformatics, and healthcare AI. My research spans molecular machine learning, transcriptomics, and reliable AI systems for scientific and clinical applications. Currently working in healthcare AI and data science at nference. Based in India.

Get in touch → LinkedIn GitHub Scholar

Research outputs

~500

NLQs reviewed

First-author works

Active bioinformatics projects

01 —

Experience

Consultant – Data Science

nference

Contributing to healthcare AI and data-science workflows involving clinical data and computational methods.
Working at the intersection of biomedical data, machine learning, and language technologies in a research-aligned environment.

Sep 2026 – Present
India

Software Engineer Intern — GenAI Evaluation & AI Reliability

IQVIA

Designed and maintained evaluation datasets and Ground Truth pipelines for enterprise LLM workflows; reviewed ~500 natural language queries to improve semantic accuracy and downstream model reliability.
Built automated benchmarking and prompt-testing loops in Python to evaluate latency, structured output correctness, response consistency, and semantic reliability across Mistral-based models.
Conducted systematic failure analysis and prompt-level optimization to improve production GenAI workflow stability.
Developed internal investigation tooling and reproducible documentation to accelerate diagnostic speed across evaluation workflows.

Jun 2025 – Dec 2025
India

Independent Technical Research Consultant

Freelance

Authored technical research content across machine learning, artificial intelligence, healthcare AI, and cloud technologies.
Supported academic publications, technical documentation, and research communication projects.
Translated complex engineering and research concepts into accessible material for multidisciplinary audiences.
Produced long-form technical content covering deep learning, NLP, generative AI, and data science applications.

Aug 2024 – Sep 2026
Remote

02 —

Selected Projects

Bioinformatics

Comparative Evaluation of Deep Tabular and Generative Language Models for Breast Cancer Subtyping

Compared TabNet and LLM-based approaches on high-dimensional transcriptomic data; reduced 54,675 genes to 10,000 features and achieved 96.77% accuracy with attention-based deep tabular learning.

ICDAM 2026

GenAI

LLM Evaluation & GenAI Workflow Benchmarking

Built evaluation workflows for enterprise LLM systems, benchmarking semantic reliability, response consistency, structured output correctness, and latency across production-facing GenAI use cases.

IQVIA

GNN / XAI

Evaluating the Role of Attention-Based Message Passing in Explainable Molecular Toxicity Prediction

Published independent research evaluating isotropic GCN and attention-based GAT message passing for molecular toxicity prediction on Tox21, alongside an assessment of bond-level explanations generated with GNNExplainer.

Published · 2026 ↗

Comp Bio

PathXComm: A Pathway-Aware Explainable Graph Neural Network Framework for Cell–Cell Communication Inference in the Tumor Microenvironment

Ongoing research integrating pathway-aware biological priors, explainable graph neural networks, and tumor microenvironment modeling for cell–cell communication inference.

Ongoing

Finance AI

Real-Time Adaptive Multi-Modal Stock Prediction

Developed a temporal graph attention framework combining financial signals, sentiment-aware market modeling, volatility-sensitive reweighting, and event-triggered attention mechanisms.

IEEE CAI ↗

NLP

Hate Speech Detection Using Ensemble Learning

Built an ensemble machine learning pipeline using SVM, Naive Bayes, and Decision Trees for online hate speech detection, achieving 89.53% accuracy on benchmark datasets.

AIP ↗

03 —

Papers & Publications

Sole Author

Evaluating the Role of Attention-Based Message Passing in Explainable Molecular Toxicity Prediction

In Silico Research in Biomedicine · Elsevier · 2026. Read the published article ↗

Published

Sole Author

PathXComm: A Pathway-Aware Explainable Graph Neural Network Framework for Cell–Cell Communication Inference in the Tumor Microenvironment

Independent Research — In Development, 2026

Ongoing

First Author

Comparative Evaluation of Deep Tabular and Generative Language Models for High-Dimensional Transcriptomic Subtyping in Breast Cancer

ICDAM 2026 — Springer LNNS

Accepted

First Author

Review of Deep Reinforcement Learning and Artificial Neural Networks in Healthcare Systems ↗

Elsevier Book Chapter, 2025

Published

First Author

Hate Speech Detection Using Machine Learning: An Ensemble Technique ↗

American Institute of Physics (AIP), 2025

Published

Co-Author

Real-Time Adaptive Multi-Modal Stock Prediction with Temporal Graph Attention and Dynamic Interaction Networks ↗

IEEE Conference on Artificial Intelligence (CAI), 2025

Published

05 —

Research Interests

Computational Biology

Bioinformatics Cancer Genomics Transcriptomics Cell–Cell Communication Precision Oncology

Molecular AI

Drug Discovery Molecular Toxicity Toxicophore Localization RDKit

Modeling

Graph Neural Networks Explainable AI Tabular Deep Learning Attention Models

GenAI

LLM Evaluation RAG Prompt Testing AI Reliability

Building intelligent systems for biology and medicine.

Experience

Selected Projects

Papers & Publications

Technical Skills

Research Interests

Building intelligent systems for
biology and medicine.