Aadarsha Gopala Reddy

Aadarsha Gopala Reddy

M.Sc. Computer Science graduate from Washington University in St. Louis

About

Hello, I’m Aadarsha

I recently completed my M.S. in Computer Science at Washington University in St. Louis, where my thesis investigated using domain-adversarial learning to create vehicle-agnostic cognitive signatures from naturalistic driving data for early Alzheimer’s detection. I have hands-on experience in machine learning, data engineering, and full-stack development, and am seeking software, data, or ML engineering roles.

During my graduate studies, I served as a Graduate Teaching Assistant for CSE 5114 (Data Manipulation and Management at Scale), guiding students through data pipelines, distributed systems, and capstone projects. I also worked as a Graduate Assistant with WashU’s Taylor Family Center for Student Success, leading data assessment and storytelling initiatives for first-generation and limited-income student programs. Previously, I interned as an AI Engineer at Crittero, Inc., building a full-stack social media simulation and recommendation system with persona-based data generation.

What I Worked On

Research: Built an end-to-end pipeline processing 26,968 participant-weeks of naturalistic driving data from 304 participants, comparing six model families (GRU-DANN, DANN, logistic regression, random forest, XGBoost, MLP) for cognitive impairment prediction under leave-one-participant-out evaluation.

Teaching: Guided students in implementing data pipelines and real-time processing systems using Python, Snowflake, SQL, Airflow, Spark, Kafka, and Flink. Conducted oral midterm exams simulating technical interviews and mentored capstone project groups on data architecture.

Student Success: Led data analysis and social media analytics for evidence-based decision-making at the Taylor Family Center, administered the center’s website, and developed process manuals for data evaluation best practices.

Leadership: Served as Vice President of the Graduate-Professional Council (GPC) Chamber of GPSC, chaired the Constitution Committee, and served on two university advisory boards (Student Affairs and Career Engagement).

  • Multimodal Alzheimer’s Prediction — Combined CNNs for MRI analysis with clinical data models for early detection, documented in NeurIPS format.

  • Datacenter Cooling Optimization — Applied deep reinforcement learning (DDQN, PPO, SAC) to achieve 35.8% energy efficiency improvement.

  • Interactive Storybook — Full-stack application with Vue.js, Node.js, and MongoDB featuring AI-powered story generation via OpenAI API.

  • MS Thesis: Vehicle-Agnostic Driving Signatures — Master’s thesis comparing six model families for cognitive impairment prediction from naturalistic driving data, with domain-adversarial and sequence-based modeling under leave-one-participant-out evaluation.

Check out more on my Projects page.

Background

I completed my M.S. in Computer Science at WashU (May 2026, GPA 3.67), with coursework in AI for Health, Deep Learning, Computer Vision, Computational Biology, and Data Manipulation at Scale. I earned the Bauer Leaders Academy (BLA) Leadership Development Badge. I hold a B.A. in Computer Science & Data Analytics from Ohio Wesleyan University, where I was recognized with the Golden Bishop Award and Mortar Board membership.

Previously, I interned as an AI Engineer at Crittero, Inc. and as a Data Analyst at Lab714, where I built full-stack applications and delivered data-driven insights.

Let’s Connect

I’m always interested in collaborating on projects at the intersection of technology and impact. Feel free to reach out or explore my CV for more details.