CV

Pradeep Singh

PostDoc / Research Engineer
INRIA Center, Université Côte d’Azur, Nice, France

Summary

Research engineer and AI scientist focused on machine learning systems for biomedical data, with experience spanning clinical AI, predictive modeling, multimodal learning, and deployable research software. Current work at INRIA centers on small foundation models, representation learning, and practical model design for biology data. Background includes building real-world ICU decision-support tools, working with de-identified clinical datasets, and translating methods into usable software artifacts for healthcare settings.

Core Strengths

Machine learning for structured biomedical and clinical data.
Predictive modeling for time-series, multimodal, and high-stakes applications.
Software development for research workflows and decision support tools.
Data harmonization, interoperability, and clinical data pipelines.
Applied foundation models, representation learning, and model fine-tuning.
Cross-disciplinary collaboration with clinicians, researchers, and engineers.

Experience

PostDoc / Engineer
INRIA Center, Université Côte d’Azur, France | Present

Working on small foundation models and representation learning for biology data.
Developing practical ML methods for scientific and biomedical use cases.

Data Scientist
IIIT-Delhi & AIIMS Delhi | 2023 – Present

Built predictive models and applied AI pipelines for healthcare use cases.
Contributed to translational research across ICU prediction, data harmonization, and software tooling.

Research Intern
INRIA Centre, Université Côte d’Azur, France | Aug 2024 – Nov 2024

Worked on methods and systems for machine learning research in biology-oriented data settings.

Senior Research Fellow
AIIMS Delhi, India | Jan 2019 – Aug 2020

Developed machine learning models for early ICU deterioration detection.
Built pipelines for thermal video-based diagnostics and clinical decision support.
Implemented dashboards and tools to support real-time healthcare workflows.

Selected Impact

SAFE-ICU Data Resource — Contributed to a de-identified pediatric ICU data resource that supported multiple machine learning and translational studies in critical care.
ThermoGnose — Developed a pipeline for early prediction of hypothermia in pediatric ICU settings using routinely collected physiological data.
ThermalShockNet — Developed thermal imaging-based deep learning methods for non-contact hemodynamic shock prediction.
ContraIndicator — Built a clinically oriented tool for detecting and visualizing potential drug-drug interactions in pediatric critical care.
SIgnose — Contributed to an ICU-focused machine learning project in the TavLab ecosystem with both paper and code outputs.
FAIR Healthcare Data Harmonization — Harmonized non-curated healthcare data using NLP and LLM-based pipelines to improve interoperability.

Technical Projects

ICU AI application suite: DDI checker, shock prediction apps, and AMR dashboard.
Voice-enabled clinical tools using OpenAI Whisper.
LLM-powered recommendation system using GPT, Sentence Transformers, LangChain, FAISS, and Weaviate.
Foundation model fine-tuning with BERT, Mistral, and LLaMA on ICU data.
Unsupervised signal embeddings for multimodal ICU prognostication.

Education

Ph.D. in AI for Healthcare
IIIT Delhi, India | 2020 – 2025

UGC-NET Qualified

M.Tech in Computer Science
NIT Surathkal, Karnataka | 2016 – 2018

GATE Scholarship Recipient

B.Tech in Information Technology
Rajkiya Engineering College, Banda, U.P. | 2010 – 2014

Technical Skills

Programming: Python, R, SQL, JavaScript, HTML/CSS
ML / AI: PyTorch, TensorFlow, JAX, SpaCy, NLTK
Deployment: Flask, Django, Docker, Kubernetes
Databases: MongoDB, PostgreSQL, SQLite, Weaviate, Pinecone
Visualization: Matplotlib, Seaborn, GGplot
Languages: English, Hindi

Publications and Software

References

Dr. Tavpritesh Sethi, Professor, IIIT Delhi
Dr. Rakesh Lodha, Professor, AIIMS Delhi
Dr. Sajan Saini, Associate Professor, Department of Pediatrics, PGIMER, Chandigarh