Resume

Programming & Data Analysis: Python (pandas, NumPy, scikit-learn, SciPy), R, SQL, PySpark, Bash

Machine Learning & AI: LLMs, Random Forests, Support Vector Machines (SVMs), Logistic Regression, Naive Bayes, Transformers, Clustering, CNNs, U-Net, BERT, TensorFlow, PyTorch, Keras

Natural Language Processing (NLP): Named Entity Recognition (NER), Text Classification, Sentiment Analysis, Summarization, cTAKES, spaCy, Hugging Face Transformers, OCR (AWS Rekognition, Tesseract), Tokenization, Sentiment Analysis (see projects page and publications)

Statistical Modeling & Epidemiology: Cox proportional hazards models, Causal mediation analysis, Linear and logistic regression, Longitudinal mixed models, Splines, Multiple imputation

Data Modalities: Structured & unstructured EHR, Clinical notes, Radiology reports, Biomarker time series, Neuroimaging, Microscopy and 3D-imaging.

Cloud & Infrastructure: AWS (EC2, S3, Lambda, SageMaker, DynamoDB), Google Cloud Platform (multi-core CPUs), Docker, JupyterHub

Tools & Visualization: Git, GitHub, matplotlib, seaborn.

Research Strengths: Longitudinal data analysis, Disease trajectory modeling, Diagnostic prediction, Real-world evidence (RWE), Interpretable ML, Reproducible pipelines

Collaborative Experience: Cross-functional work with clinicians, neuropsychologists, imaging scientists, and epidemiologists across academic and clinical research settings.