Software Engineering Intern (Return)
Broadridge Financial Solutions
Developing an end-to-end Python pipeline converting 400+ DOC/DOCX certification documents into standardized
JSON with PostgreSQL for analysis and organization for asset management platform; Built an LLM-based Jira requirements reviewer that identifies
blockers and evaluates readiness for 50+ associates;
Built employee referral platform integrating 100+ open roles, timelines, and bonuses with internal referral database
Software Engineering Intern
Broadridge Financial Solutions
Built RESTful APIs (Node.js, Model Context Protocol (MCP)) integrating Broadridge’s Confluence/Jira ecosystem,
exposing 5K–10K Confluence pages and 300+ Jira projects through a unified backend; Collaborated on a VS Code AI extension integrating Confluence and Jira through a unified backend;
Refactored 8+ MCP flows, reducing duplicate logic by 30% and cutting request errors by 20%
Machine Learning Neuroscience Research Intern
University of Pennsylvania, The Weber Lab
Trained and tuned LSTM & CNN models for REM sleep P-wave detection with 98% accuracy, 0.05 RMSE, and 100K+
noisy waveform samples through hyperparameter sweeps and cross-recording validation;
Built automated preprocessing/visualization for 20+ EEG/LFP channels, reducing manual cleaning time by 95%
Machine Learning Linguistics Research Intern under Prof. Charles Yang
University of Pennsylvania, Linguistics Department
Developed an algorithm using Python to mimic how children exercise pattern recognition using the Abductive Discovery of Productivity (ADP) and the Tolerance Principle (TP);
Built a recursive decision tree-based algorithm that dynamically resizes based on user input.
CIS 1200 Teaching Assistant
University of Pennsylvania
Programming Languages and Techniques TA; each OCaml, Java, & program design concepts, including functional programming, GUI, & interfaces;
Lead weekly recitation review for 20+ students and office hours for 350+ students;
Develop weekly recitation materials for 50+ TAs including interactive slides & worksheets
Machine Learning Space Weather Research Intern under Dr. A Surjalal Sharma
University of Maryland, Department of Astronomy
Developed a Long Short-Term Memory (LSTM) recurrent neural network model to predict Geomagnetic Auroral Electrojet Indices using Python;
Achieved 97% accuracy (Root Mean Squared Error);
Presented at the 2022 American Geophysical Union Fall Meeting to 30+ members
NOAA/CISESS Intern
University of Maryland, Department of Earth & Space Science
Internship under Dr. Yongzhen Fan of NOAA;
Developed a machine learning-based snowfall detection algorithm using Python for NASA’s Global Precipitation Measurement Mission satellite (GPM); Used inputs from 9 microwave sensors;
Achieved 95% classification accuracy using XGBoost with less than 0.1% false prediction rate;
Increased forecast accuracy in Alaska & the Southern Hemisphere from 0% to 94.6%;
Developed XGBoost, Random Forest, & Linear Regression ML models to predict snowfall from 800+ features
ASPIRE Intern
Johns Hopkins Applied Physics Laboratory
Trained a machine learning neural network to identify litter in videos using Tensorflow Lite and Python; Configured the SSD-MobileNet-V2 object detection model on a Raspberry Pi;
Cleaned & labeled 1,500 images of litter; Achieved 90% recall;
Presented at the AIAA Mid-Atlantic Young Professionals, Students, and Educators (YPSE) Conference to 50+ members
