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: 3.741/4
- Relevant Courses: Advanced Machine Learning, High-Performance Machine Learning, Computing Systems Architecture, Deep Learning, Machine Learning for Cyber Security, Big Data, Introduction to Machine Learning, Probability and Stochastic Processes , Real-Time Embedded Systems,
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
- LLM
- Tensorflow
- RAG pipeline
- QLoRA
- CUDA
- Pandas
- Seaborn
- NLTK
- Keras
- Git
- Haddop
- Apache Spark
- Microsoft Azure
- NoSQL
- Hive
- HPC
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

Projects
Our comprehensive suite of professional services caters to a diverse clientele, ranging from homeowners to commercial developers.
VidScribe: Video Captioning with BART and T5 Models (Link)
- Sep 2024 – May 2024
- Developed an automated system for generating semantically rich video captions using BART, T5, and CLIP models. Leveraged the MSR-VTT dataset (10,000+ video clips, 200K clip-sentence pairs) to improve accessibility and usability of video content.
Fine-Tuning LLaMA 3-8B to Predict the Correctness of Math Questions(Link)
- Oct 2024 – Nov 2024
- Fine-tuned the LLaMA 3-8B model using Supervised Fine-Tuning (SFT) on a curated 1M-record dataset containing questions, answers, and solutions. Integrated LoRA, quantization, and prompt optimization to improve reasoning capabilities.
Road Risk and Fatality Analysis (Link)
- Sep 2024 – Dec 2024
- Processed FARS data (2015-2020) using Hadoop and Spark to uncover accident trends and critical patterns related to vehicle types, speeds, and fatalities. Identified that over 60% of fatalities occurred at night or involved impaired driving.
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.