Deepesh Goel

Machine Learning Engineer II, NYU Langone Health · New York, NY

I build and evaluate machine learning systems. Most of my recent work has been in healthcare and applied research: medical imaging, clinical prediction, and the evaluation of language models.

Deepesh Goel

Background

I'm currently a Machine Learning Engineer II at NYU Langone Health, where I assess the technical and clinical feasibility of AI/ML approaches across the health system and build the models and data pipelines used to validate them before anything scales. I hold an MS in Computer Science from NYU's Courant Institute, with an AI concentration, and a BTech in Computer Science from IIT Mandi.

Recent projects have been in medical imaging, LLM evaluation, and self-supervised representation learning.

Selected work

Fake news detection collapses across the human-AI divide

ICBDA 2026

GNews-Gemini, a dataset of LLM-generated disinformation spanning five manipulation strategies, tested against ISOT by cross-dataset transfer. Detectors scoring 99.3% and 96.9% within their own data fell to 48.3% and 51.5% across the authorship boundary. An authorship classifier reached 99.7%, indicating the models were measuring who wrote the text rather than whether it was true.

RoBERTa Dataset construction Generalisation LLM evaluation

Writeup

Validating diffusion MRI against tumour histology

ISMRM 2026

Can a non-invasive MRI scan stand in for a biopsy? Kidney cancer patients were imaged before partial nephrectomy, and IVIM parameters plus their radiomics were correlated against both pathologist scoring and quantitative multiplex immunofluorescence from the resected tissue. Tissue diffusivity tracked cellular density; pseudo-diffusivity tracked vascularity.

Diffusion MRI Radiomics Digital pathology Spearman

Writeup

Extracting figures and tables from scientific PDFs

Live tool ↗

Most scientific figures are vector graphics, so the usual image-extraction approach silently misses them. This pipeline uses a layout model instead, links each figure and table to the body paragraphs that discuss it, and returns structured JSON per item. 320/320 tables and 496/540 figures across 100 papers from 10 publishers.

docling Vision LLM Gradio Structured output

Writeup

A JEPA world model that learns without reconstructing

Self-supervised

A recurrent Joint-Embedding Predictive Architecture trained on 2.5M frames of two-room navigation, learning structured state representations with no pixel reconstruction at all. VICReg regularisation kept it from collapsing to a constant; final agent position error was 1.89 MSE.

PyTorch Self-supervised VICReg HPC

Writeup

Also

Inventory microservice, deployed on Kubernetes

GitHub ↗

A Flask and PostgreSQL REST service with full CRUD, condition filtering and restock endpoints, built test-first to 95%+ coverage across unit and BDD suites. Shipped through a Tekton pipeline onto OpenShift, and deployed to a local K3s cluster as a three-replica service behind a load-balanced route. Team project for NYU's DevOps and Agile Methodologies course.

Publications

[1]

Fake News Detection Across the Human-AI Divide

Deepesh Goel, et al.

International Conference on Big Data Analytics (ICBDA), 2026

Oral Best Presentation Award Writeup →
[2]

Cross-validation of intravoxel incoherent motion metrics with quantitative and qualitative histology in excised kidney tumors

Nima Gilani, Deepesh Goel, Valeria Mezzano, Gyles Ward, Fang-Ming Deng, et al., Eric Sigmund

International Society for Magnetic Resonance in Medicine (ISMRM), 2026

[3]

A diffusion weighted MR study of individual kidney function decline in kidney cancer patients

Nima Gilani, Xiaochun Li, Deepesh Goel, et al., Hersh Chandarana, Eric Sigmund

International Society for Magnetic Resonance in Medicine (ISMRM), 2026

Pre-surgical DTI and IVIM metrics, acquired across bipolar and flow-compensated sequences at systolic and diastolic gating, were tested against the one-year change in individual kidney measured GFR. Twenty-three parameters correlated significantly with functional decline.

Experience

Jul 2026 – present

Machine Learning Engineer II

NYU Langone Health · New York, NY

  • Assessing the technical and clinical feasibility of AI/ML approaches across the NYU Langone health system.
  • Building and benchmarking ML models and data pipelines to prototype and validate solutions before system-wide scaling.
Sep 2025 – May 2026

AI/ML Researcher

Predictive Analytics and AI Research Lab, NYU Courant · Advisor: Prof. Anasse Bari

  • Built GNews-Gemini, a dataset of LLM-generated disinformation spanning five manipulation strategies, and used it to show that cross-dataset detector accuracy collapses from 97–99% to 48–51% across the human-AI authorship boundary. Oral presentation and Best Presentation Award at ICBDA 2026. Full writeup.
  • Built a layout-model and vision-LLM pipeline for extracting figures and tables from scientific PDFs, reaching 100% table detection and 92% linked-figure detection across 100 papers from 10 publishers. Full writeup.
May 2025 – May 2026

Machine Learning Research Intern

NYU Langone Health, CAI2R · PI: Dr. Eric Sigmund

  • Investigated whether IVIM MRI parameters (Dt, fp, Dp) and their radiomics act as non-invasive markers of renal tumour biology, in patients imaged before partial nephrectomy.
  • Related MRI-derived features to HALO-quantified multiplex immunofluorescence and pathologist scoring of the resected tissue, establishing which imaging parameters track cellularity, vascularity and fibrosis. Full writeup.
Tata Innovation Labs

Machine Learning Research Intern

Tata Innovation Labs, Delhi · Mentor: Pankaj Malhotra

  • Fine-tuned TimeNet (a pretrained GRU) with LASSO on 48-hour physiological windows for 25-phenotype prediction on MIMIC-III, reaching AUROC 0.812 across 60,000+ ICU stays, matching task-specific LSTM baselines without training from scratch.
  • Built a SQL ETL pipeline with optimised joins, one-hot encoding and zero-padding, cutting preprocessing time from 8 hours to 45 minutes.

Education

MS

Computer Science, AI concentration

New York University · Courant Institute of Mathematical Sciences

BTech

Computer Science and Engineering, minor in Management

Indian Institute of Technology (IIT) Mandi

Technical

Languages

Python · SQL · C++ · R · JavaScript · MATLAB

ML & Deep Learning

PyTorch · TensorFlow · Keras · scikit-learn · XGBoost · BERT · NLP

Generative AI & LLMs

Hugging Face Transformers · RAG · FAISS · fine-tuning · LLM APIs · LangChain

Data Science

NumPy · Pandas · SciPy · NLTK · spaCy · Spark · OpenCV · Matplotlib · seaborn · SPSS · hypothesis testing

Infrastructure

PostgreSQL · Docker · Kubernetes · AWS / GCP · REST APIs · Git · Tekton CI/CD · HPC

Also

Tableau · Jupyter · Flask · OpenShift · pytest · Behave / Selenium

Contact

Email is the surest way to reach me.