Shreyash Kadam
Developer by trade, creative by nature.
Overview
Graduate Research Assistant @ University of Illinois, Chicago
MS CS Student @ University of Illinois, Chicago
Chicago, Illinois, United States
Social Links
About
Hello! I'm Shreyash, a software developer with a passion for building robust and scalable applications.
With over five years of coding experience, I've specialized in creating optimized full-stack solutions using Next.js and TypeScript. However, my curiosity has led me deep into the world of distributed systems, where I leverage the Go language to engineer complex and interesting projects that tackle challenges of concurrency and resilience.
Beyond software development, I have a strong interest in data science and machine learning, where I primarily use Python to build applications and continuously explore the latest advancements in generative AI.
When I'm not coding, I trade my keyboard for a different kind of rhythm, playing percussion instruments like the Tabla and Cajon. I also have a passion for capturing moments through videography and photography.
Let's connect and collaborate!
Stack
Education
- GPA : 3.8 / 4.0
- Courses:
- Computer Algorithms
- Introduction to Data Science
- Object Oriented Languages and Environments
- Responsible Data Science and Algorithmic Fairness
- User Experience Research Methods
- Achievements:
- Received Full Tuition Waiver as part of Research Assistantship position.
- Accepted to the KDD 2025 Undergraduate and Masters Consortium (KDD-UMC 2025) in Canada.
Experience
University of Illinois Chicago
Current Employer- Architected and deployed a cloud-native data analytics platform on AWS, replacing legacy Microsoft Access workflows. The new system automates 20+ manual reports, reducing data processing time from 48 hours to under 15 minutes.
- Implemented all infrastructure using Terraform (IaC) and built a full CI/CD pipeline with GitHub Actions for automated container builds, testing (PyTest), and deployment to AWS ECS on Fargate, achieving 99.9% service uptime for over 500 caseworkers.
- Initiated and developed a proof-of-concept predictive model using XGBoost to identify individuals at high risk of service disruption; backtesting on historical data projects a potential 30% reduction in missed critical appointments.
- TypeScript
- Next.js
- Python
- SQLAlchemy
- Pandas
- OpenPyXL
- FastAPI
- Jira
- Docker
- ETL
- Web Sockets
8kSec LLC
- Developed a multi-tenant threat intelligence dashboard using Next.js, Prisma, and a Python (FastAPI) backend, serving real-time security alerts from multiple data sources to corporate clients.
- Optimized PostgreSQL query performance by introducing indexing strategies and connection pooling, reducing average query latency by 40% and contributing to a 25% reduction in database downtime.
- Engineered a WebSocket-based notification service for real-time threat updates, decreasing information delivery delay from over 20 seconds to under 500ms.
- TypeScript
- Next.js
- Python
- SQL
- AWS
- Material UI
- Agile
- Prisma
- Jira
- Docker
- Cybersecurity
- Machine Learning
- SCM
Sortwind Pvt. Ltd.
- Led the migration of a legacy React SPA to a server-side rendered Next.js application, improving the Lighthouse performance score by 35 points and achieving a 30% faster initial page load speed.
- Designed and implemented RESTful APIs in Node.js/Express, deployed as containerized services behind an NGINX reverse proxy for load balancing, increasing peak traffic capacity by 1.5x.
- TypeScript
- Next.js
- React
- MongoDB
- Stripe
- Tailwind CSS
- Firebase
- GCP
- NGINX
- Node
- Express
- Docker
- Load Balancing
- Distributed Systems
Projects(10)
A production-grade, fault-tolerant distributed key-value store built in Go. This project provides a horizontally scalable storage solution that ensures data consistency and high availability using the Raft consensus algorithm. It features a modern Svelte UI for real-time cluster management and data exploration.
- Distributed CRUD Operations: Simple PUT, GET, and DELETE operations distributed across a multi-node cluster.
- Strong Consistency: Guarantees data consistency across all nodes using the Raft consensus protocol. All writes are committed by a leader and replicated to a majority of nodes.
- High Availability & Fault Tolerance: The system can tolerate node failures. If a leader node fails, the cluster automatically elects a new leader with no data loss.
- Horizontal Scalability: Easily scale the cluster by adding new nodes. The system is designed to handle new nodes joining a live cluster.
- Persistent Storage: Utilizes BoltDB for durable, on-disk storage with ACID guarantees, ensuring data survives node restarts.
- Live Management Dashboard: A modern, real-time web UI built with Svelte allows you to:
- View the status of all nodes (leader, follower, online/offline).
- Add new nodes to the cluster dynamically.
- Stop, restart, and decommission nodes.
- Explore and manage key-value data directly.
- Go
- HashiCorp Raft
- Gin
- BoltDB
- HashiCorp Memberlist
- SvelteKit
- Tailwind CSS
- Vite
- Investigated the impact of popularity bias on recommendation fairness through a comparative analysis of the Music (Last.fm) and Movie (MovieLens 1M) domains.
- Evaluated the critical role of different evaluation strategies (UserTest vs. TrainItems), confirming that the choice of strategy profoundly influences the measurement of bias and accuracy.
- Designed and validated a novel user grouping method, NicheConsumptionRate, to effectively identify users with niche tastes based on their consumption of the least popular items.
- Implemented and assessed a post-processing mitigation technique (multiplicative damping, α=0.5), successfully reducing the magnitude and disparity of popularity bias across user groups.
- Quantified the trade-off between fairness and accuracy, demonstrating that while the mitigation strategy improved fairness, it generally decreased recommendation accuracy (NDCG@10)
- Python
- Cornac
- Pandas
- NumPy
- SciPy
- Matplotlib
- Seaborn
An innovative laser-based sharpshooting training system designed to enhance precision and accuracy for military, law enforcement, and civilian shooters. The system provides a safe, cost-effective alternative to traditional firearm training by eliminating the need for live ammunition and range time.
- Utilizes a laser-equipped toy gun and a circular target embedded with LDR sensors to measure shooting accuracy across varying radius levels, scoring from 1-5 (highest at the center). Data on distance, angle, and points is processed to deliver real-time performance feedback.
- Built a comprehensive ecosystem including:
- Hardware: Raspberry Pi 4, LDR sensors, laser modules, and I2C LCD display for real-time data capture.
- Software: A Node.js/Express/MongoDB server with TensorFlow for machine learning-driven performance analysis, a Next.js/React web app with Firebase authentication for user interaction, and a Flutter mobile app for on-the-go score tracking.
- Leverages machine learning to provide personalized training recommendations, improving user skills efficiently. The system enhances safety by reducing risks associated with live ammunition, making sharpshooting training accessible and scalable for diverse users.
- Supports public safety by improving shooting accuracy for defense and law enforcement personnel while democratizing access to training for civilians. The prototype demonstrates a practical, scalable solution with potential to reduce training costs and risks.
- Repositories:
- Raspberry Pi 4
- Node JS
- MongoDB
- Tensorflow
- Express
- Axios
- numjs
- React JS
- Next JS file routing
- Firebase Google Authentication
- Material UI
- Formik
- Simplebar
- MUI Icons
- date-fns
- dayjs
- Flutter
- Firebase
- Flutter Charts
- JWT
An innovative web application that allows users to rent anything they need with ease. The platform is built with the latest technologies, including Next.js 13 App Router, React, Tailwind, Prisma, MongoDB, NextAuth, and cloudinary for image uploading, ensuring a seamless and user-friendly experience.
- Tailwind design
- Tailwind animations and effects
- Full responsiveness
- Credential authentication
- Google authentication
- Github authentication
- Image upload using Cloudinary CDN
- Client form validation and handling using react-hook-form
- Server error handling using react-toast
- Calendars with react-date-range
- Page loading state
- Page empty state
- Booking system
- Customer booking cancellation
- Owner booking cancellation
- Creation and deletion of items
- Pricing calculation
- Favorites system
- Shareable URL filters
- React
- Next.js 13
- Tailwind CSS
- MongoDB
- Motion
- Node
- Prisma
- Vercel
- NextAuth.js
