Ayushi Batwara

student, builder, undergraduate instructor

EECS & Business student in the Management, Entrepreneurship, and Technology (M.E.T.) Program at the University of California, Berkeley



Teaching discrete math and probability

As a TA for CS 70, I teach biweekly discussion sections to 40 students, answer conceptual issues in office hours, and grade students' assignments and exams.

Leading a software engineering course

I co-created an intro to software engineering course at Berkeley, which covers practical SWE tools (e.g., Git, Docker, etc.) and has positively impacted 60+ students to date.

Exploring ML and distributed systems

As a member of ML@B, I'm exploring the applications of computer vision to healthcare with peers and independently delving into distributed systems.



I'm a builder 🔨. In the past, I've started a dance company, created a simulation to educate 2.5M+ youth about racial inequities in medicine, and won national hackathons. Now, I'm building software solutions to drive humanity forward.

I'm currently working at the intersection of computer science 💻 and medicine 🧬, using machine learning as a tool to advance public health efforts. I'm leading machine learning development at a Berkeley SkyDeck-backed health-tech startup, working on integrating real-time climate and weather data with individual health data. My technical interests lie in machine learning and full-stack development ⚙️. As a software engineering intern at Dell Technologies, I've gained practical experience creating enterprise-grade SaaS products. My internship project is currently being used in production by the CIRRUS and PowerFlex Organization at Dell.

I'm also passionate about teaching ✏️, and I'm actively working on a course that covers practical use-cases for essential software engineering tools, including Git, Vim, APIs, Docker, and Kubernetes. This course, titled yeSWEcan, will be offered to UC Berkeley students in the fall semester, and course resources will be posted online.

Beyond academics, I love to explore the outdoors and enjoy hiking 🌿. Some of my favorite hikes are in my home state of Washington, including the Snow Lake Trail and Hurricane Hill via Hurricane Ridge. I enjoy reading and watching historical accounts and (auto)biographies in my free time. Some of my favorite books are The Unwinding of the Miracle by Julie Yip-Williams and The Beekeeper of Aleppo by Christi Lefteri.



Spring 2023
  • CS 189: Introduction to Machine Learning
  • CS 170: Efficient Algorithms and Intractable Problems
Fall 2022
  • EECS 126: Probability and Random Processes
  • CS 61C: Great Ideas in Computer Architecture (Machine Structures)
Spring 2022
  • CS 61B: Data Structures
  • CS 70: Discrete Mathematics and Probability Theory
  • EECS 16B: Designing Information Devices and Systems II
Fall 2021
  • EECS 16A: Designing Information Devices and Systems I
  • CS 61A: Structure and Interpretation of Computer Programs


Spring 2023
  • UGBA 105: Leading People
  • UGBA 102A: Introduction To Financial Accounting
  • UGBA 194: Leadership by Persuasion
Fall 2022
  • UGBA 106: Marketing
  • UGBA 107: The Social, Political, and Ethical Environment of Business
  • UGBA 10: Principles of Business
Fall 2021
  • UGBA 196.1: Entrepreneurial Leadership



Software Engineering Intern • Summer 2022
  • Designed and developed APIs to address targeted enterprise customer use-cases for multi-cloud management via Dell’s cloud platform using Swagger, Boto3 API, FastAPI
  • Developed visualizations to manage cloud inventory natively in AWS
  • Participated in Dell’s Internal Hackathon and created an internal “Discover Intern” tool with Python and the Tkinter library


Co-Founder and Machine Learning Lead • 2022
  • Co-founder of a Berkeley SkyDeck-backed startup that won 2nd place at Apple x UCSF’s iHackHealth Hackathon
  • Developed a random forest ML model (99.6% accuracy) to predict an individual’s relative risk in extreme climate conditions across database of 10k+ patients based on health data captured from Apple Health, IBM Weather, EHRs, clinical risks


Machine Learning Researcher • Summer 2022
  • Creating “digital twin pairs” for counties throughout the United States using a machine learning pipeline (CNNs, KNN, Regression) based on satellite imagery data, chronic disease, health system access, demographics, and deprivation to allow public health departments to collaborate with departments across the country most similar to them


COVID-19 Pro-Tips

Demo | Repo

All-in-one interactive website with up-to-date COVID-19 data and analytics, including a medication interaction checker (verifies if a drug combo is safe), visual representation of current cases globally on an interactive map, and an estimator for necessary quantity of household supplies/necessities

Built With:

  • Frontend: Bootstrap
  • Backend: Node.js, ExpressJS, AWS EC2 Ubuntu Instance
  • Languages: JavaScript, HTML, CSS


Demo | Frontend | Backend

An easy-to-use user dashboard that uses a machine learning model to visualize your friends' moods based on analysis of recently played songs on Spotify

Built With:

  • Frontend: React
  • Backend: Flask, Express.js, PostgreSQL
  • Machine Learning: scikit-learn, NumPy, pandas
  • Languages: Python, JavaScript, HTML, CSS
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ML@B Alumni Dinner 2022

Machine Learning at Berkeley
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yeSWEcan: Intro to SWE

Selfie From the First Lecture!
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Dell Technologies Headquarters

Fellow Interns in Austin, TX