Hi! I'm Abhi, if you're here to find my CTF writeups you should probably take a look here or look through my repositories for solutions to previous challenges. If you're here to learn a little bit more about me keeping reading this!

1. Learning Competitive Programming with C++ - repository
2. Learning Rust - repository
3. Interview prep - repository

1. My Blog - link
2. diff-sentry a comprehensive solution to OSS security - link
3. A transcript timestamp annotator tool built for busy researchers - repository
4. A full pipeline built in Python to extract symptoms from natural language using Apache cTAKES and SciSpaCy and validate the results against pre-determined ground truth - repository.
5. A named entity recognition analysis pipeline built off of state-of-the-art research in NLP validation - repository.
6. A collection of writeups for PicoCTF 2025 (got over 300 unique viewers) - repository
7. A web GUI for a popular CLI habit tracking tool - website
8. A website you can use to quickly query Pokémon weaknesses - website

This semester (Spring 2025):

1. CS 4790 - Cryptocurrency: An introduction to cryptocurrency and some security implications (51% attack, insecure contract code, etc.)
2. CS 4630 - Defense Against the Dark Arts: Modern techniques for binary exploitation, reverse engineering, secure programming, and some web exploitation stuff (lower level though, i.e. sandboxing, browser vulnerabilities, etc.)
3. CS 3120 - Theory of Computation: Finite State Automata, Circuits, Context-free languages, Turing machines, just the usual.
4. CS 4501 - Algorithmic Economics: How modern economics combines techniques in machine learning and statistics with economic knowledge to create efficient markets. Mostly an introduction where we learn about the basics of how reinforcement learning techniques, game theory, and tools like linear programming can be applied in an economic context.
5. CS 3240 - Software Engineering: A semester-long project course essentially. We learn about modern techniques in Software Engineering at scale (i.e. requirements elicitation, communicating with stakeholders, and problems associating with developing large software systems).

1. CS 3130 - Computer Systems and Organization II: Virtual memory, caches, pipelining, and some other low-level stuff including how attacks like Meltdown and Spectre work behind the hood.
2. CS 3100 - Data Structures and Algorithms II: An upper-level course on algorithms with an emphasis on problem-solving techniques with graphs, divide-and-conquer algorithms, greedy algorithms, dynamic programming, reductions, and some basic machine learning.
3. CS 4501 - Reinforcement Learning: An introduction from bandit algorithms to deep-Q-learning meant to teach you about the principles of Reinforcement Learning without getting too nitty-gritty with proofs and the mathematics behind the algorithms. We worked through chapters 1-13 of "Reinforcement learning : an introduction / Richard S. Sutton and Andrew G. Barto" skipping a few minor things along the way.
4. ECE 2410 - Machine Learning: An introduction to some of the principles of machine learning beginning with unsupervised vs. supervised learning and ending with hyperparameter optimization and neural networks.