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CS Student @ Georgia Tech
I'm passionate about building impactful software solutions, combining technology with creativity. Right now, I'm exploring ML applications.
Expected to graduate in May 2027
About Me
I'm Tanush Chintala, a junior studying Computer Science and recipient of the Stamps President’s Scholarship (top 1% of incoming students) at Georgia Tech.
My journey into tech has been driven by curiosity and experimentation, from building full-stack applications in hackathons to developing statistical and ML models to tackle complex problems systematically. I've deeply interested in how sofware and data can be used to design scalable, high-impact solutions across domains.
I'm also interested in startup culture, where learning happens quickly and visions are designed, built, and shipped continuously. I enjoy working end-to-end and taking ownership of problems.
Outside of tech, I'm a basketball fan and avid Dallas Mavericks supporter. I also enjoy traveling, exploring different cuisines, and have been spending more time reading.
Experience
Amazon Web Services
Software Development Engineer Intern
- Designed and deployed Spark observability CLI tool through CI/CD pipelines, adopted by 3 AWS GuardDuty teams (~50 engineers), reducing setup time from ~7 minutes to ~5 seconds
- Introduced Spark History Server support for batch & streaming EMR jobs, unlocking retrospective debugging for terminated clusters and accelerating root-cause analysis across GuardDuty teams
- Implemented versioned Spark workload configs with rollback support, cutting misconfig-related downtime and reducing cluster rerun costs by 15%
Nimbus Health
Data Analyst Intern
- Built SQL dashboards on provider productivity and clinic performance, adopted in weekly reviews across 7 sites to guide staffing and operational improvements
- Automated pipeline (Google Apps Script → AWS S3) to ingest 10K+ Dialpad webhook records weekly, eliminating manual ETL and reducing backend data errors by 30%
- Developed ML models on 2K+ patient call transcripts to auto-route patients, reducing manual call transfers by ~25% and increasing staff call-handling capacity
Cope Lab
Undergraduate Research Assistant
- Trained DeepLabCut ML models on rodent limb data, reducing manual labeling time for kinematic analysis by ~70% in peripheral nerve injury research
- Developed Python control system for lab robotic arm to standardize rodent limb manipulation, improving experiment reproducibility and cutting daily trial setup time by 40%
Cloud Ladder Consulting
Software Engineer Intern
- Built client-facing chatbot for company website with secure PyJWT authentication, ensuring data security during sensitive queries and achieving 70% client interaction rate
- Integrated chatbot with Alexa voice interface, enabling natural language queries and ensuring reliable execution
Projects
AI-powered dental receptionist and patient communications platform that answers calls 24/7, reduces no-shows, and optimizes scheduling. Features include live call answering with triage, smart scheduling engine, waitlist auto-fill for same-day cancellations, new patient intake with OCR for insurance cards, and analytics dashboard. HIPAA compliant with EHR integration.
Fantasy stock trading platform that combines machine learning-powered stock analysis with competitive league-based gameplay. Features include league management, snake draft system, real-time ML-powered analytics, portfolio visualization, and commissioner controls. Uses ML models for growth potential, value, and risk assessment.
Centralized collaborative calendar platform to help Georgia Tech students discover and filter campus events from multiple organizations in one unified calendar view. Features event filtering by category, location, and organization, with calendar and list views for easy navigation of campus activities.
AI-powered sleep analysis platform that simplifies the detection of sleep disorders using Polysomnography (PSG) data, helping doctors quickly and accurately diagnose conditions like insomnia, sleep apnea, and narcolepsy. Built with a deep neural network for sleep pattern analysis, interactive web application with secure authentication, and an AI chatbot for sleep-related queries.
Web-based system that transforms unstructured natural language input into structured calendar events. Uses LLM to parse text and extract event details (title, date, time, location), automatically creates events via Google Calendar API, and visualizes event locations on an interactive Google Maps interface for route planning and schedule management.
Web application that scrapes Cigna's medical coverage policy updates, parses PDF documents, and provides a modern interface for viewing and managing medical policies. Features include search, filter, sort functionality, PDF parsing, content extraction from web pages, and data export capabilities.
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