Dilraj kaur

 Experienced Bioinformatician and Data Scientist with a PhD in Computational Biology, specializing in applying Artificial Intelligence (AI) and Machine Learning (ML) techniques to biological sequence data (DNA, RNA, proteins) to advance drug discovery and clinical research. Proven track record of contributing to open-source bioinformatics tools and databases, with over 15 peer-reviewed publications, and direct contributions to more than 8 open-source projects. Expertise in building ML pipelines, decoding biological sequences, and deploying AI-powered bioinformatics tools in production environments. Proficient in Python, TensorFlow, PyTorch, R, and Bash, with hands-on experience in genomic, transcriptomic, and proteomic data analysis. Proven track record in preclinical and clinical data analysis, outcome prediction, and developing scalable bioinformatics pipelines. Passionate about applying AI and statistical models to advance research in drug design, vaccines, cancer therapy, and various disease treatments. 

Bioinformatic Scientist, (Institute of Cancer Research, London, UK), 05/2023 – Present 

Designed and implemented scalable analysis pipelines for BulkRNA-Seq, scRNA-Seq,WGS and methylation data, reducing analysis time by 40%. 

Developed automated variant calling and CNA workflows, processing data from over 50 patients samples and optimizing insights for clinical trials 

Enhanced therapeutic target predictions by 30% using AI/ML models (TensorFlow, PyTorch) on genomic datasets. 

Authored comprehensive documentation tailored for clinical and research end-users, streamlining adoption of bioinformatics tools. 

Technical Data Scientist, (Nencki Institute of Experimental Biology, Warsaw, Poland), 03/2022 – 08/2022 

• Applied ML techniques to genomic and transcriptomic datasets, achieving a 25% increase in biomarker prediction accuracy. 
• Processed and curated datasets exceeding 5TB, ensuring high-quality integration for cross-disciplinary research. 
• Adapted bioinformatics workflows to long-read sequencing data, supporting advanced clinical research on neurodegenerative diseases 
• Enhanced pipeline efficiency by 20% through continuous integration systems like GitLab. 

Teaching assistant, Computational Researcher, (Indraprastha Institute of Information Technology, Delhi, India), 07/2015 – 12/2021 

Developed structural bioinformatic resources and tools used in 50+ academic and clinical studies for protein-ligand interaction prediction. 

Curated and analyzed large public datasets (e.g., TCGA, GEO) to identify disease biomarkers, supporting vaccine development during the COVID-19 pandemic. 

Delivered over 10 training sessions on AI/ML applications in bioinformatics, reaching an audience of 500+ students and professionals. 

Contributed to 8+ open-source projects, including databases for pattern recognition receptors (PRRs), enhancing accessibility for researchers globally. 

• PhD in Computational Biology, (Indraprastha Institute of Information Technology, Delhi, India), 2017 – 2022 

The dissertation focused on the application of machine learning in bioinformatics for Insilco annotation for innate immune receptors and their role in cancer research achieving a 20% improvement in prediction accuracy. 

• MTech in Computational Biology, (Indraprastha Institute of Information Technology, Delhi, India), 2015 – 2017 

Thesis: “Integrative Analysis and Network Modelling of Intensive Care Data for Critical Outcomes” 

Conducted extensive analysis of the MIMIC-III dataset, including health-related data from over 40,000 ICU patients at Beth Israel Deaconess Medical Center. 

Applied Bayesian models and ML techniques to analyse ICU data, identifying critical outcome patterns. 

• B.Tech in Biotechnology, (Amity University, Lucknow, India), 2011 – 2015 

Project: “Expression Analysis of Salt Tolerance Gene of an Endophyte Piriformospora Indica”. 

Developed molecular techniques to study plant growth under abiotic stress conditions. 

Studied the effect of salt stress on rice seedlings with and without the presence of P. indica, using cloning and sequencing of the TEF gene. 

Demonstrated that P. indica aids rice growth under abiotic stress conditions. 

Project: “Isolation of Microorganisms (Bacteria and Fungi) from Soil Samples” 

Implemented and optimized molecular biology and microbiology techniques to isolate and characterize microbes from soil samples. 

Identified Bacillus subtilis (bacteria) and Aspergillus niger (fungi) as the predominant microorganisms.