Vikranth Srivatsa

Vikranth Srivatsa

4th Year Student at UC Berkeley

University of California, Berkeley


I’m a 4th year Electrical Engineering and Computer Science at UC Berkeley. I currently do Systems and Machine Learning research at UC Berkeley RISE Labs under Joseph Gonzalez. With Moustafa Adelbaky, I’m researching serverless execution across mutli-cloud, 5G, and the edge. With Yaoqing Yang and Yaodong Yu, I’m working on machine learning distribution shift explainability. I am currently planning to go to 5th years at UC Berkeley.

  • Parallel & Distributed Systems
  • Machine Learning
  • Operating Systems
  • Optimization
  • Undergraduate, 2022

    University of California, Berkeley

  • High School, 2018

    Lynbrook High School

Work Experience

University of California, Berkeley
UC Berkeley Researcher
Feb 2021 – Present Berkeley, CA

Investigating how overparameterization affects generalizations on spurious correlations

Design and run research experiments to test model size, subgroup robustness and counts, and pretraining

Automatically constructed spurious correlation large scale image datasets with Imagenet

Research Intern
Jun 2021 – Dec 2021 Berkeley, CA

Created an efficient serverless scheduling framework across client, 5G networks, and the cloud.

Wrote heuristic and linear programming based algorithms for optimal code placement on compute nodes

Optimized memory and cost with serverless Kubernetes, AWS, GCP, and Azure

Amazon Alexa
Software Development Intern
Jun 2020 – Aug 2020 Santa Clara, CA

Built paramater tracking to process models used in ranking content in the Alexa Mobile App

Designed and built dev tool for experiment tracking, managing A/B model testing, and dashboards

Amazon AWS
Software Development Intern
Jun 2019 – Aug 2019 Seattle, WA

Supported popular open source features on AWS SAM-CLI(5.7K stars)

Added support for cloudformation and cloudformation intrinsic templating resolution in AWS SAM-CLI

Built a Serverless Testing Framework to benchmark and test AWS Serverless infrastructure


Cloudless: Serverless Execution across multicloud, 5g, edge
Cloudless An efficient serverless scheduling framework across client, 5G networks, and the cloud. It uses heuristic and linear programming based algorithms for optimal code placement on compute nodes. It optimizes memory and cost with serverless Kubernetes, AWS, GCP, and Azure. Current planning to publish to NSDI 2022. We are currently researching multi-cloud datastores and dag execution(Step functions across the cloud).
Effect of Model Size on Worst-Group Generalization
Waterbirds Logo Prior work has suggested that overparameterization can hurt test accuracy on rare subgroups. Motivated by the fact that subgroup information is often unknown, we investigate the effect of model size on worst-group generalization under empirical risk minimization (ERM). Our systematic evaluation reveals that increasing model size does not hurt, and may help, worst-group test error under ERM. Published at NeurIPS DistShift Workshop 2021. Full Paper
3D Animation: By a Thread
By a thread 3D Modeling and animation using Maya about a mouse trying to reach the moon. The video was edited using After Effects and Premiere Pro.
Passive Listening Smart Speakers

Passive Listening Device Logo Created a Smart Speaker system to study passive listening devices. Our work is open source and available at (,,

  • Built machine learning NLP models to classify different intents for passive listening and speech
  • Setup architecture and workflow to run smart speaker with dynamic skills for experiment
  • Wrote noise filtering/detection algorithms in order to improve and detect intents
3D Water Simulation
Fluid Simulation A 3D Water Simulation using C++ and Navier Stokes Equations. It uses optimized water physics using KD Trees and parallel processing
Statistical Car Simulation

Car Simulation

  • Modeled car speed, accident rate, lane size, switching lanes, and many more to simulate the traffic
  • Handled multithreaded events in golang synchronizing using mutex/locks
  • Created realtime demo with websockets, cli, and react.js

Courses & Skills


Languages: Python, Golang, Java, C, Scala, Rust, Verilog, Risc-V/x86, C++, CUDA
Web: React, Flask, Django, Node.js, GraphQL, MySQL, Mongodb, Redis, Cassandra
Machine Learning: Pytorch, Tensorflow, Natural Language Processing, Keras, OpenCV
DevOps: Kubernetes, Docker, Jenkins, AWS, GCP, Azure, Serverless
Animation: Maya, Blender, After Effects, Premiere Pro
Game Development: Unity

EE/CS Upper-Division Coursework:


  • CS 162: Operating Systems and System Programming
  • CS W186: Introduction to Database Systems
  • CS 161: Computer Security
  • CS 164: Programming Languages and Compilers

Machine Learning

  • CS W182: Designing, Visualizing and Understanding Deep Neural Networks
  • CS 189: Introduction to Machine Learning
  • EE 127: Optimization Models in Engineering
  • EE 126: Probability and Random Processes
  • CS 188: Introduction to Artificial Intelligence
  • EECS 290: Hardware for Machine Learning
  • CS 194-15: Special Topics - Parallel Programming

Signal Processing

  • EE 123: Digital Signal Processing
  • EE 120: Signals and Systems
  • ME C134 / EE C128: Feedback Control Systems


  • CS 170: Efficient Algorithms and Intractable Problems
  • EECS 151: Digital Design and Integrated Circuits + FPGAs
  • CS 184: Foundations of Computer Graphics


During my spare time, I enjoy reading comics and playing games. I also enjoy creating 3D animation and working on game development.