Fall

Performance Characterization of Open Source Game Engines

The Performance Characterization of Open Source Game Engines project aims to evaluate and analyze the performance of various open-source game engines. With the wide availability of game development frameworks, it is essential to understand the performance implications and capabilities of these engines.

The objectives of this project include:

Multi-Player Game Networking

ECS (Entity Component System) for Game Development

The Entity Component System (ECS) architecture has gained popularity in game development due to its superior performance than OO programming.

Performance Evaluation of Open Source Large Language Model (LLM) Frameworks

Performance of Federated Learning under Realistic Communication Conditions

In recent years, federated learning has emerged as a promising approach for training machine learning models on distributed data sources.

LLM based Chat Agent for Enterprise Application Development

Every company has its own way of doing things, and IT systems are no exception.

CmpE 140 Introduction to Computing for Economics and Management 2022 Fall

Tool to determine microservice boundaries for software-defined vehicles

This project aims to develop a tool that analyzes the functionality and dependencies of different software components in a vehicle and recommends the optimal boundaries for dividing the system into microservices that follow the publisher-subscriber pattern. The goal of this project is to improve the scalability, flexibility, and maintainability of software-defined vehicles by breaking down monolithic systems into smaller, modular, and independently deployable services that can communicate using the publisher-subscriber architecture.

Social Network Analysis on Research SONAR invited you to Social Network Analysis on Research

Analysis of Model Poisoning Attacks during training in Federated Learning

Federated learning is a distributed machine learning technique that aggregates every client model on the server side. There can be various types of attacks to destroy the robustness of this learning system. Model poisoning attack is realized after the training is finished. In this project, we will use a recently developed method* for analyzing the effect of model poisoning attack that might occur during training.

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