MySQL, Python, Tableau, R

Naman Dariya

Richardson, TX ● ***********@*****.***

945-***-**** ●www.linkedin.com/in/namandariya

EDUCATION

THE UNIVERSITY OF TEXAS AT DALLAS, Richardson, Texas August 2022 - May 2024 Master of Science in Computer Science

MEDI-CAPS UNIVERSITY, Indore, MP(India) August 2016 - May 2020 Bachelor of Technology in Electronics and Communications Engineering COMPUTER SKILLS

Operating Systems: Windows, Linux

Languages: Python, R, C++

Databases: MySQL, SQL Server

Spark Libraries: Sparknlp, MLlib,Graphx,SQl

Python Libraries: Pandas, NumPy, Matplotlib, Pyspark, Tkinter, pytorch, tensorflow,keras,nltk,scikit-learn, Taichi Software Skills: Matlab, Comsol v4.3, Cuda

Tools : Git, Github, Docker

INTERNSHIPS

Summer Intern: Indianic InfoTech Ltd. Ahmedabad, Gujarat (India) June 2019-July 2019

Created unique designs for various websites.

Participated in customization and integration of existing client webpages with mobile applications.

Engaged in software development cycle, including analysis, design, and testing. Technical Trainee: Bharat Sanchar Nigam Limited Ratlam, MP (India) July 2017-August 2017

Researched to test equipment feasibility and performance.

Configured, managed and evaluated multi-protocol network elements for effective end to end communications.

Troubleshot VOIP and multimedia systems effectively. ACADEMIC PROJECTS

Optimizing Neural Networks for GPU-Accelerated Workloads (Summer 2023)

- Developed a neural network from scratch using PySpark and Hadoop Distributed File System (HDFS), focusing on optimization for GPU-accelerated environments.

- Conducted performance analysis to ensure scalability and efficiency. Generative Networks for Realistic Image Generation (Spring 2023)

- Implemented generative networks in Python to create realistic images, leveraging insights gained from training sets.

- Explored GPU acceleration concepts for image generation. Image Reconstruction using Autoencoders and One-Hot Encoding (Spring 2023)

- Applied autoencoders for reconstructing images from a noisy dataset, showcasing practical experience in image processing and deep learning.

- Investigated techniques for efficient data representation. Analyzing Branch Prediction Parameters with Benchmarks (Fall 2022)

- Utilized two benchmarks to assess the accuracy of branch prediction parameters on a three-branch predictor.

- Contributed to the understanding of performance bottlenecks.

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