Darshan Thaker

Bachelor of Science, Computer Science

Turing Scholars, Class of 2018

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Hi, I'm Darshan Thaker

A Computer Science enthusiast

Ever since I was a kid, I have been interested in Computer Science. I first started programming when I was in 3rd grade in Terrapin Logo. Since then, I have enjoyed expanding my interests in other programming languages including Java, C, Python, and a little bit of C++. In high school, I spent my summers interning at awesome companies, where I helped to create educational apps as well as write open source code. I got a chance to work on amazing technologies such as Flowvisor , a virtual network emulator, as well as Mininet, a virtual network emulator. These were in the SDN (Software-Defined Networking) space.

After getting to college at the University of Texas at Austin, I began to explore my interests in Machine Learning and Artificial Intelligence. I spent my first summer in college interning at Symantec's Center for Advanced Machine Learning team. I worked on a cool project to create a robust machine learning classifier that can identify targeted malicious e-mail attacks. The classifier was trained on Symantec's large dataset of incoming e-mails flagged as targeted or non-targeted attacks.

Outside of these interests, I also competed in high school debate for 4 years, where I greatly improved my researching, argumentative, as well as oratorical skills. I improved from having from one of the worst records on my debate team my freshman year to qualifying to the Tournament of Champions as one of the top 80 teams in the nation. Also, at home, I enjoy playing the piano in my spare time and learning popular songs on the piano through sheet music.

That’s pretty much me and my life right now, but as my life progresses, I wish to do research, go to graduate school, and make influential changes through my work, as this interests me greatly and is one of my current life goals. My research interests include Artificial Intelligence, Machine Learning and particularly, Neural Networks and Deep Learning.

Relevant Coursework

Semester 1

  • CS 314 - Data Structures (Dr. Gordon Novak)

  • CS 311H - Discrete Math for Computer Science: Honors (Dr. Isil Dillig)

  • CC 307D - Intro to Roman Archaelogy (Dr. Denton Walthall)

  • UGS 302 - Economic IQ of the Media (Dr. Wayne Hickenbottom)

Semester 2

  • CS 429 - Computer Organization and Architecture (Dr. Robert Dickerson)

  • CS 178H - Intro to CS Research: Honors (Dr. Calvin Lin and Dr. Lorenzo Alvisi)

  • M 427L - Vector Calculus (Dr. Bart Goddard)

  • M 340L - Matrices and Matrix Calculations (Dr. Eric Korman)

  • M 362K - Probability 1 (Dr. Dave Rusin)

Semester 3

  • CS 439H - Principles of Operating Systems: Honors (Dr. Ahmed Gheith)

  • CS 377P - Programming for Performance (Dr. Sreepathi Pai)

  • CS 378 - Embedded Systems (Dr. Robert Oshana)

  • M 378K - Intro to Mathematical Statistics (Dr. Stephen Walker)

Semester 4

  • CS 378H - Distributed Computing: Honors (Dr. Lorenzo Alvisi)

  • CS 331H - Algorithms and Complexity: Honors (Dr. Eric Price)

  • GOV 312L - Introduction to US Foreign Policy (Dr. Robert Moser and Dr. Patrick Mcdonald)

  • SDS 325H - Honors Statistics (Dr. James G. Scott)

  • CS 109 - Competitive Programming (Jaime Rivera and Arnav Sastry)

Semester 5 (In progress)

  • CS 356 - Computer Networks (Dr. Simon Lam)

  • CS 345H - Programming Languages: Honors (Dr. Thomas Dillig)

  • M 365C - Real Analysis (Dr. Hector Lomeli)

  • EE 381K - Convex Optimization [Graduate] (Dr. Constantine Caramanis)

Semester 6

A few of my projects


A parallel implementation in the MultiBoost library for the Adaptive Boosting (AdaBoost.MH) algorithm. This project was my final project in the "Programming for Performance" class.

Keywords: C++, Pthreads, Machine Learning, AdaBoost.MH, Parallel Boosting

Source code

A program to automatically generate music notes using a Markov chain trained on a sequence of notes. Uses music language ChucK to play generated notes.

Keywords: Java, ChucK, Discrete-time Markov Chain

Source code

A program to cluster your Gmail inbox into various topics based on the body text of the e-mails. Uses topic modeling algorithm LDA (Latent Dirichlet Allocation) to perform this clustering.

Keywords: Python, Latent Dirichlet Allocation

Source code

A shell script to aide in testing parallelized code. Compares the serialized and parallel version of code and outputs speedup gains from using different number of threads.

Keywords: Bash, Parallel Programming

Source code

Exploration of conflict graphs to parallelize stochastic subgradient descent in the context of Support Vector Machines (SVMs). This project was my final project in the "Convex Optimization" class.

Keywords: Python, Machine Learning, SVM, Stochastic Gradient Descent

View full github

Research Interests

My research interests include Artificial Intelligence, Machine Learning and particularly, Neural Networks and Deep Learning. Check in a few months to see more about this!