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Education

University of Massachusetts Amherst

Manning College of Information and Computer Sciences

Master of Science in Computer Science (MSCS), 2026

  • Certificate: Statistical & Computational Data Science
  • GPA: 4.0
  • Full Graduate Assistantship

Isenberg School of Management

Master of Business Administration (MBA), 2024

  • Business Innovation Fellowship, Institute for Applied Life Sciences
  • GPA: 4.0
  • Full Graduate Assistantship

College of Social and Behavioral Sciences

Bachelor of Arts in Political Science, 2014

Professional Experience

National Grid, New England Field Safety and Health

Intern, Data Analyst, May-Aug 2025

  • Designed and shipped an end-to-end Power BI reporting system with automated daily refresh, replacing manual Excel workflows and enabling consistent operational monitoring for leadership.
  • Built a data ingestion and transformation pipeline using OData sources and Power Query, modeling longitudinal employee health events to support cohort-based trend analysis and drill-down views.
  • Developed validated measures and privacy-aware reporting logic, then delivered a live executive demo and handoff documentation for long-term maintenance by full-time analysts.

University of Massachusetts Amherst, Institute for Applied Life Sciences

Business Innovation Fellow, 2022-2024

  • Built repeatable analytics workflows for startup teams, translating ambiguous market questions into structured datasets, comparative scoring criteria, and decision-ready outputs.
  • Led iterative research sprints for a machine-learning startup in water resource planning, producing a prioritized target list and CRM-ready segmentation.
  • Led a team initiative to define and operationalize generative AI usage guidelines, presenting training to approximately 40 fellows and delivering an adopted handbook for future cohorts.

Commonwealth of Massachusetts, MassHealth

Long-Term Care Eligibility Specialist, 2015-2022

  • Managed a rolling caseload of 30 to 40 long-term care applications under strict deadlines, reviewing complex financial records to determine Medicaid eligibility and compliance.
  • Coordinated with families, attorneys, hospitals, and nursing facilities to resolve documentation gaps quickly, supporting continuity of care in high-stakes, time-sensitive situations.
  • Built Excel-based templates and calculators adopted by the team to improve case processing speed and accuracy for recurring compliance and valuation workflows.

Technical Skills

  • Languages: Python, R, C, C++, SQL, MATLAB; familiarity with Rust and Java
  • ML & Statistical Modeling: PyTorch, TensorFlow, scikit-learn, RStan; CNNs, RNNs, Transformers
  • Data Engineering: Spark/PySpark, ETL pipelines, SQLAlchemy, PostgreSQL
  • Backend & Distributed Systems: REST APIs, Flask, gRPC, Protocol Buffers, Redis; Paxos, Raft
  • Cloud & Infrastructure: Docker, Amazon EC2, Kubernetes, virtual machines (VMware, Multipass)
  • BI & Analytics: Power BI, Power Query, DAX, Tableau, Excel; data modeling and analytics
  • Professional: Team leadership, stakeholder communication, cross-functional teams, mentoring

Campus Involvement and Leadership

UMass Amherst Graduate Student Government (GSG)

Vice President, 2025-2026

  • Led execution of seven graduate community events with 50 to 400 attendees, coordinating logistics, vendors, staffing, and risk-aware operations.

Treasurer, 2024-2025

  • Oversaw a $1M+ graduate student trust fund and annual budget process, ensuring accurate tracking and policy-compliant disbursement.
  • Designed and operated an airport transportation program supporting 300+ arriving international students with real-time delay monitoring and contingency planning.

Awards

  • Lifetime Achievement Award, UMass GSG, 2026
  • President’s Outstanding Impact Award, UMass GSG, 2025
  • Student Leader of the Month, UMass Amherst, November 2024
Selected Coursework
  • Computer Science: Distributed & Operating Systems; Systems for Data Science; Database Design & Implementation; Introduction to Algorithms; Algorithms for Data Science; Computer Systems Principles; Formal Language Theory; Theory & Practice of Cryptography; Randomized Algorithms and Applications to Data Science
  • Machine Learning and Statistics: Machine Learning; Neural Networks: Modern Introduction; Mathematical Statistics I-II; Statistical Methods for Data Science; Accelerated Bayesian Statistics; Applied Semiparametric Regression
  • Mathematics and Scientific Computing: Multivariate Calculus; Intro Linear Algebra; Linear Algebra for Applied Mathematics; Scientific Computing
  • Business and Analytics: Business Intelligence & Analytics; Data Management for Business Leaders; Strategic Information Management; Operations Management; Corporate Finance; Statistics for Business