Transformer-Based Model (TFT) Adaptation for Anomaly Detection

Re-engineered NVIDIA’s Temporal Fusion Transformer, originally designed for multi-horizon forecasting, into a reconstruction-based anomaly detection framework for discontinuous sensor windows. Designed encoder–decoder reconstruction with MAE-based anomaly scoring, eliminated unseen-category issues, simplified tensor structures, and stabilized training without future inputs.

In parallel, evaluated diffusion-based foundation models for anomaly detection, focusing on diagnostic behavior, reconstruction quality, and suitability for production use.

Skills: Transformers, diffusion models (evaluation), sequence modeling, reconstruction learning, anomaly detection, PyTorch

CNN-Based Well Anomaly Detection

Developed a physics-informed deep learning pipeline for anomaly detection on high-frequency well and sensor time-series data. Designed CNN-based classifiers and signal-processing workflows to detect degradation patterns, transient flow abnormalities, and equipment malfunction signatures.

Skills: CNNs, time-series ML, physics-informed modeling, Python, Azure ML

Machine Learning for Mortgage Risk: An Interpretable Approach (PNC Bank)

Developed interpretable ML models to assess climate-driven mortgage default risk under macroeconomic and weather uncertainty. Applied SHAP-based explainability to support transparent and decision-relevant risk analysis.

Skills: LSTM, Temporal Fusion Transformer, time-series modeling, interpretable ML, risk analytics, Python

Application of Convolutional Neural Network to Predict Airfoil Lift Coefficient

Early deep learning surrogate model integrating physics-informed constraints to predict aerodynamic coefficients with high accuracy.

Skills: CNNs, surrogate modeling, aerodynamics Ref: AIAA 2018

Learning Aerodynamics with Neural Networks

Developed a Custom Element Spatial CNN (ESCNN) using coordinate-based inputs to predict aerodynamic behavior with reduced model complexity.

Skills: Data representation, physics-aware ML, deep learning Ref: AIAA 2021, Scientific Reports 2022

DockingNet: Deep Neural Network for Airfoil Optimization

Hybrid generative–predictive neural network for accelerating airfoil design optimization without CFD-based iteration.

Skills: Generative modeling, optimization, lift/drag estimation Ref: AIAA 2022

Dedalus: AI-Powered Inventory Assistant (In Progress)

NLP-enabled assistant for querying fragmented inventory and test data across Excel, Access, and PDF sources; migrated backend to SQLite for structured access and downstream analytics.

Skills: NLP, LLMs, data integration, Python, SQLite

Electrochemical Model for Dissolvable Alloys

Hybrid physics–data model predicting dissolution rates under realistic operating conditions using calibrated regression.

Skills: Multiphysics modeling, regression, electrochemistry

Hemodynamics Surrogate Model (Medical AI)

UNet-based deep learning surrogate model predicting stenotic blood flow fields in seconds from CFD-trained data.

Skills: Deep learning, UNet, biomedical CFD, Python

Petit Appétit: Weekly Meal & Grocery Planner

Web-based pipeline for meal planning and grocery list generation using Dask, AWS S3, and web scraping; planned NLP-based extensions.

Skills: Data pipelines, scraping, Dask, Python, Django

Deep Learning Chess AI (Exploratory)

Self-learning chess AI trained using reinforcement learning and neural policy optimization through self-play.

Skills: Reinforcement learning, TensorFlow, policy optimization

COVID-19 and Education Completion Analysis

Global data analysis using UNESCO and World Bank datasets to quantify the impact of COVID-19 on education outcomes.

Skills: Data imputation, regression analysis, clustering, policy-oriented analytics