Hello, I’m Yugesh Panta, an aspiring computer engineer based in Brooklyn, New York. Currently pursuing my Master’s in Computer Engineering at NYU Tandon School of Engineering, I have a strong foundation in machine learning, full stack development and software engineering. With a passion for solving complex problems and a dedication to continuous learning, I am eager to contribute my skills in Data Science, Machine Learning, and Software Engineering roles.

Contact Me:

Phone Number: +1 (332) 276-3602

Email-Id: yugesh1620@gmail.com ; yp2651@nyu.edu

LinkedIn: linkedin.com/in/yugesh-panta-904375246

GitHub:https://github.com/Yugesh1620

Education

M.S. in Computer Engineering

Tandon School of Engineering, NYU

  • Expected Graduation: May 2025
  • CGPA: 3.778/4
  • Relevant Courses: Advanced Machine Learning, High-Performance Machine Learning, Real-Time Embedded Systems, Computing Systems Architecture, Introduction to Machine Learning, Probability and Stochastic Processes

B.Tech in Electronics and Communication Engineering

Manipal Institute of Technology

Jul 2019 – May 2023

CGPA: 8.38/10

Relevant Courses: ML, AI, Computer Vision, DSA, OOP using C++, Cyber Security, COA, Microprocessors, VLSI, Signals and Systems, DSP, Communication Networks, Wireless Communication

Skills

Programming Languages:

  • Python
  • C/C++
  • Java
  • PHP
  • R
  • MATLAB
  • Ruby

Tools and Technologies:

Machine Learning and Data Science

  • Pytorch
  • Transformers
  • RAG pipeline
  • QloRA
  • Pandas
  • Seaborn
  • NLTK

Machine Learning and Data Science

  • Ruby on Rails
  • MySQL
  • Spring
  • HTML
  • CSS
  • RestAPI
  • Angular 12
  • Ionic
  • Xampp

Embedded Systems and Hardware

  • Verilog
  • RTL
  • VHDL
  • ARM 7
  • ARM Cortex
  • Intel 8086
  • Arduino
  • Keil
A ramp along a curved wall in the Kiasma Museu, Helsinki, Finland

Projects

Our comprehensive suite of professional services caters to a diverse clientele, ranging from homeowners to commercial developers.

Fine-Tuning and RAG Pipeline for Nutrition Label Classification (Link)

  • Feb 2024 – May 2024
  • Developed a system to read nutrition labels and identify ingredients, providing explanations from trusted sources.

Continual Learning with Dynamic Architecture and Replay Strategies (Link)

  • Jan 2024 – May 2024
  • Mitigated catastrophic forgetting on the Permuted MNIST dataset using various replay strategies.

NeuroGuard: Embedded Parkinson’s Detection System (Link)

  • Feb 2024 – May 2024
  • Developed an embedded system for Parkinson’s detection using gyroscope data.

BlockChain System for Vehicle Data Security                                        

  • Aug 2022 – Dec 2022
  • Engineered a blockchain system for the confidentiality, integrity, and authenticity of vehicle data.
  • This won the 1st runner-Up position in Mercedes-Benz Techflame Competition.