Published in Analytical Chemistry (2025), Machine learning augmented vibrational spectroscopy for cancer cell quantification
VIKAS YADAV
Ph.D. graduate in deep learning & generative AI driven spectroscopy and computational modeling
vikas.yadav@chemistry.iitd.ac.in
+91-7876749665
MS-724, Main Building, IIT Delhi, Hauz Khas, India, 110016
Professional Summary
PhD graduate and expert in generative AI, deep learning, and Raman spectroscopy, with recognized contributions to spectral translation (89% accuracy) and biomedical diagnostics, including cancer cell classification and DNA mutation detection (up to 99% accuracy). Experienced in NLP, LLMs, and RAG- based systems for scientific text automation, with a focus on building intelligent pipelines that integrate AI, spectroscopy, and biology for translational research.
Work and Research Experience
Early-Doc Fellow
Indian Institute of Technology Delhi, India
2025-Present
- Intelligent Search Systems: Built NLP and LLM-powered search systems with RAG for advanced query handling, PostgreSQL-driven structured insights, and large-scale automation on 1 lakh+ scientific research articles with 83% precision.
- Generative AI: Engineered a GAN-based spectral translation model that converted diffraction-limited patterns into fully resolved Raman spectra with nearly 89% accuracy, enabling high-fidelity molecular feature recovery, enhanced resolution, and reliable chemical identification from low-cost, grating-free spectrometers.
- Advanced AI Models: Implemented efficient ML models (GANs and Transformers) in Python, TensorFlow, and PyTorch with production-grade Docker deployment on HPC, achieving 87%+ consistency across inference pipelines.
Research during PhD
Indian Institute of Technology Delhi, India
2019-2025
- Generative AI & Deep Learning: Applied generative AI, deep learning, and advanced data analytics to address challenges of weak signals, spectral complexity, and noise in experimental datasets with an accuracy of 92%
- ML Model Development: Developed models for classification and regression tasks, automated complex data interpretation, and improved resolution and feature extraction through GANs and deep neural networks
- Data Framework Design: Built frameworks for handling and representing large, high-dimensional data, enabling predictive insights and robust decision-making
- Computational Strategies: Integrated computational strategies using Python, MATLAB, and developed in-house codes, alongside physics-informed models, to simulate and optimize light–matter interactions and smart substrate design
- Patent Achievement: This research led to a patent on enhancing data resolution using generative AI-driven methods
Education and Training
Ph.D. in AI & ML, Generative AI, Chemistry
Indian Institute of Technology Delhi, India (IIT Delhi)
2025
Specialized in deep learning & generative AI driven spectroscopy and computational modeling with focus on machine learning applications in chemical analysis.
M.S. in Chemistry
Kurukshetra University, India
2018
Advanced studies in theoretical and computational chemistry with emphasis on spectroscopic methods.
B.S. in Physics, Chemistry & Mathematics
Kurukshetra University, India
2016
Comprehensive foundation in physical sciences with strong mathematical background.
Technical & Core Skills
Machine Learning & AI
Programming & Tools
Data Science & Domain
Other Technologies
Selected Publications and Patents
Peer Reviewed Papers (18 research articles, 1 Book Chapter, and 1 Patent)
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ACS Analytical Chemistry, 2025
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ACS Analytical Chemistry, 2025
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RSC Analyst, 2025
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RSC Nanoscale, 2025
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Wiley Small Methods, 2025
Patents
Enhancing the resolution of raman spectra through Generative AI
Yadav, V.; Tiwari, A.; and Siddhanta, S.
Indian Patent (Application No: 202411029912, PD056676IN-SC)
Awards and Honors
Oral Presentation
ICORS 2024, Rome
ACS Spring 2025, USA
Best Poster Prize
HealthTech Horizons
IIT Delhi, 2024
Early Doc Fellowship
Research Excellence
IIT Delhi, 2025
Travel Awards
Research Excellence Travel Award
Research Scholar Travel Award
(IIT Delhi)
CSIR-UGC NET JRF
AIR 83 & 89
National Fellowship
GATE Qualified
2019 & 2020
National Examination
Communication & Linguistic Proficiency
English
Fluent
Hindi
Native
Punjabi
Basic
German
A1
Teaching and Supervision Experience
Courses Taught
Specialized Training: Deep Learning Specialization (MATLAB/MathWorks, Python) | Machine Learning in Spectroscopy | Cheminformatics | Molecular modelling & Thermodynamics | Linux for advanced expertise | Docker & N8N
Student Supervision
Supervised: 3+ Master's students in ML-assisted spectroscopy projects, providing guidance on research methodology, data analysis, and academic writing.
References
Prof. Soumik Siddhanta
Department of Chemistry
Indian Institute of Technology Delhi, India
Email: soumik@iitd.ac.in
Tel: +91 11 2654-8442
Prof. Tarak Karmakar
Department of Chemistry
Indian Institute of Technology Delhi, India
Email: tkarmakar@iitd.ac.in
Tel: +91 11 2659-8549
Prof. Rupesh Chaturvedi
School of Biotechnology
Jawaharlal Nehru University, New Delhi, India
Email: rupesh@mail.jnu.ac.in
Tel: +91 11 2673-8893
Prof. Sameer Sapra
Department of Chemistry
Indian Institute of Technology Delhi, India
Email: sapra@iitd.ac.in
Tel: +91 11 2659-1561
Professional Memberships & Activities
Memberships
American Chemical Society - Active member contributing to the advancement of chemical sciences
Referee & Reviewer
Journals: Analytical Methods; RSC Advances; Digital Discovery - Providing peer review services for leading scientific publications
Conferences: Presentations and Posters
16 conferences, 2 abroad presentations (USA & Rome)
International Presentations
- Poster presentation in "ACS Spring 2025" held in San Diego, USA March 23-27, 2025.
- Oral presentation in "International Conference on Raman spectroscopy 2024 (ICORS-2024)" held in Rome, Italy organized by Sapienza University de Roma.
- Poster presented in "HealthTech Horizons: Integrating Bioimaging, Diagnostics, and Therapy" organized by IIT Delhi, India (Best poster prize)
