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A highly skilled and accomplished professional with a proven track record of success in the product-based software and technology industry, I am now seeking to further advance my expertise through a Master of Science in Computer Science from The University of Texas at Dallas with a focus on Intelligent Systems.

With a deep understanding of cutting-edge technologies including Machine Learning, Artificial Intelligence, Embedded Software Development, and the Internet of Things, I am eager to leverage my skills and experience to drive impactful research and development initiatives in the software industry. By leveraging my technical knowledge and ability to collaborate effectively with cross-functional teams, I am confident in my ability to make a meaningful contribution to the growth and success of any organization.

Shreyans Patel's Projects

custom_unix_shell icon custom_unix_shell

In this project, you will build a simple Unix shell. The shell is the heart of the command-line interface, and thus is central to the Unix/C programming environment. Mastering use of the shell is necessary to become proficient in this world; knowing how the shell itself is built is the focus of this project.

data_structures_practice_problems icon data_structures_practice_problems

Collection of different practice problems for some basic data structures. The problem statement of each exercise is given on the top of the code.

file_system_consistency_checker icon file_system_consistency_checker

In this project, you will be developing a program to check the file system consistency. The program, called as fcheck, reads a file system image and checks its consistency. When the image is not consistent, fcheck should output appropriate error message.

k_means_clustering icon k_means_clustering

In this assignment, you will learn how to cluster tweets by utilizing Jaccard Distance metric and K-means clustering algorithm.

linear_regression_using_gradient_descent icon linear_regression_using_gradient_descent

There are two parts to this assignment. The first part requires you to write code that uses gradient descent for linear regression. In the second part, you will use a ML library on the same dataset and compare your results.

neural_networks icon neural_networks

In this part, you will write code to optimize the performance of a neural net by trying various combination of hyper-parameters and evaluating their results.

parallel_programming_using_threads_and_locks icon parallel_programming_using_threads_and_locks

In this exercise, you will explore parallel programming with threads and locks using a hash table. You should do this on a computer with multiple cores (verify by running ‘cat /proc/cpuinfo’).

seeking_tutor_problem icon seeking_tutor_problem

Using POSIX threads, mutex locks, and semaphores implement a solution that synchronizes the activities of the coordinator, tutors, and the students. Your program should work for any number of students, tutors, chairs and help sought. Allocate memory for data structures dynamically based on the input parameter(s).

stock_prediction_using_rnn icon stock_prediction_using_rnn

The project has two key components to it. Understanding a recent machine learning technique and associated algorithm(s). Apply it to a standard dataset of sufficient complexity. You have to code the main part of the algorithm without using any built-in library. You can use libraries for pre-processing, loading, analysis of results, etc.

warehouse_management_system icon warehouse_management_system

This is a project I made over-night to compete in a hackathon (This project did win that hackathon though :|) and for a mini project requirement for 3 credits. Do not expect it to be efficient. You can probably use it as a reference for some approaches useful if you are making a similar project.

xv6_increase_max_file_size_limit icon xv6_increase_max_file_size_limit

In this exercise, you'll increase the maximum size of an xv6 file. You'll change the xv6 file system code to support a "doubly-indirect" block in each inode, containing 128 addresses of singly-indirect blocks, each of which can contain up to 128 addresses of data blocks.

xv6_print_page_tables icon xv6_print_page_tables

To help visualize page tables, you need to write a function that prints the contents of the page tables.

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