education

2020 - 2025

Completed a multidisciplinary graduate program spanning artificial intelligence, technical leadership, software engineering, algorithms, distributed systems, cloud computing, and full-stack development while working full-time in industry.

• Artificial Intelligence & Machine Learning: Advanced study in machine learning, deep learning, neural networks, forecasting, and generative modeling, with applications ranging from predictive analytics to molecular property prediction.
• Software Engineering & Systems Design: Developed scalable software systems using Python, Java, C/C++, JavaScript, and TypeScript, with emphasis on software architecture, testing, code quality, and the end-to-end development lifecycle.
• Algorithms & Computational Methods: Applied advanced algorithms, data structures, and optimization techniques to computationally intensive problems, strengthening the theoretical foundation behind scalable AI and software systems.
• Distributed Systems & Cloud Computing: Designed distributed and cloud-native architectures focused on scalability, reliability, fault tolerance, APIs, databases, and deployment across AWS and GCP.
• Full-Stack Application Development: Built modern web applications and backend services using technologies including React, Angular, Node.js, REST APIs, relational databases, and NoSQL systems.
• Technical Leadership: Strengthened capabilities in software engineering leadership, Agile development, technical decision-making, and translating complex requirements into production-ready solutions.

August 2015 - May 2017

Graduate research at the intersection of computational chemistry, machine learning, deep learning, and large-scale molecular simulation, applying data-driven methods to molecular property prediction and drug discovery.

• Machine Learning & Deep Learning: Developed predictive models using Python, Keras, and RDKit for molecular property prediction, ligand classification, clustering, and structure-activity analysis.
• Molecular Dynamics & Simulation: Designed and analyzed large-scale molecular dynamics simulations to study molecular interactions, stability, and chemical properties using computational force fields and simulation methods.
• AI-Driven Molecular Property Prediction: Combined machine learning, deep learning, and molecular simulation data to predict properties including potency, solubility, and molecular behavior.
• Data Science & Advanced Analytics: Analyzed complex molecular datasets using statistical modeling, dimensionality reduction, clustering, feature engineering, and data visualization to identify patterns and structure-property relationships.
• QSAR & Cheminformatics: Applied quantitative structure-activity relationship modeling, graph-based molecular representations, and cheminformatics techniques to connect molecular structure with biological activity.
• Scientific Computing & Data Engineering: Built computational workflows and data systems using Python, databases, and scientific computing tools to process, organize, and analyze large molecular datasets.
• Interdisciplinary Research: Integrated chemistry, biology, simulation, machine learning, and data science to develop computational approaches for complex problems in molecular design and drug discovery.

September 2011 - May 2015

Built an early foundation at the intersection of chemistry, artificial intelligence, machine learning, and computational science, applying data-driven and simulation-based methods alongside experimental analytical chemistry.

• AI & Machine Learning in Chemistry: Applied machine learning and data-driven methods to molecular classification, ligand clustering, and structure-activity analysis, building an early foundation in AI-enabled scientific research.
• Molecular Modeling & Simulation: Used molecular dynamics, CHARMM and OPLS force fields to model molecular and protein systems, analyze interactions, and investigate structural behavior.
• Computational Chemistry & Cheminformatics: Applied computational tools including Schrödinger and MOE to analyze molecular structures, proteins, ligands, and chemical properties.
• Scientific Computing & Data Analysis: Developed Python workflows to automate molecular dynamics analysis, process scientific datasets, and improve the efficiency and reproducibility of computational research.
• Analytical & Experimental Chemistry: Conducted laboratory analysis of chemical materials including inks, pigments, and resins using techniques such as LC-MS and microwave-assisted reaction systems.
• Integrated Computational & Experimental Science: Combined chemistry, AI/ML, molecular simulation, programming, and experimental analysis to investigate complex chemical and biological systems.