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AI and Robotics
1: Deep Learning Series
Content: Interested in learning how ChatGPT and DALLE work? Join us for weekly presentations/projects covering exciting areas like neural networks, CNNs, transformers, and diffusion models! We will cover fundamentals and applications, giving you the skills to tackle real-world deep learning challenges in vision and language. Expect to spend 1 hour per week attending meetings, with extra time if you want to complete the projects. Co-hosted by MLDS
Required Prerequisites: None
Recommended Prerequisites: Basic programming experience
Group Type: Hybrid (Presentations with Optional Projects)
Level: Beginner
Mentors: Harshal Bharatia
Curriculum: https://docs.google.com/document/d/1pP16NnsfbNBBltKu0lz5rgWnEvgUAI1wIbdEZ6ppz34/edit?usp=sharing
2: Introduction to Reinforcement Learning
Content: Introduction to Reinforcement Learning! We will cover the basics of RL, ranging from the fundamentals to various real-world applications (e.g. robotics, LLMs). We will build motivation and intuition for RL, and learn when and how to apply RL to a problem.
Required Prerequisites: Any programming experience
Recommended Prerequisites: Basic familiarity with ML and neural networks
Group Type: Reading
Level: Beginner
Mentor: Akaash Reddy, Alex Lu
3: Advanced Reinforcement Learning
Content: Explore the newest ideas in Reinforcement Learning including post training, continual learning, representation learning, and applications. We will discuss modern RL theory and practice, building up the intuition to work on some of the most difficult problems in AI.
Required Prerequisites: Some RL theory (textbook), high-level understanding of LLMs and modern AI developments.
Recommended Prerequisites: Introduction to Reinforcement Learning DiRP group
Group Type: Reading
Level: Advanced
Mentor: Sarthak Dayal
4: Autonomous and Social Navigation
Content: How can robots navigate safely and intelligently through environments shared with people? In this reading group, we will explore the fundamentals of autonomous robot navigation, including perception, mapping, localization, path planning, and obstacle avoidance, before diving into social navigation and human-robot interaction. We will read and discuss foundational and recent papers on how robots can understand, predict, and navigate around humans in socially appropriate ways.
Required Prerequisites: Basic understanding of how MLP/ML works
Recommended Prerequisites: Some familiarity with transformers and encoders
Group Type: Reading
Level: Beginner
Mentor: Pengen Zheng
5: Emergence in Computational Systems, Open-ended Evolutionary Algorithms
Content: Simple rules often lead to very complex behavior. In hardware, simple arrangements of many transistors lead to powerful computers which can execute extremely complex algorithms. In biology, collections of (simple) amino acids give rise to all sorts of remarkable machinery that operates in our cells. In computer simulation games like the Game of Life, a simple environment transition rule supports emergent complex structures, like glider guns, spaceships, etc. We want to understand which environments support emergence of complex behavior, and what kind of complex behavior comes about! The plan is to do this with an emphasis on simulations, at scale! In particular: which physical environments support complex properties, like self-replication? We'll need lots of ideas from many domains (computer science, physics, math, biology, statistics, and more!) to make it happen!
Required Prerequisites: None
Recommended Prerequisites: None
Group Type: Project
Level: Advanced
Mentors: George Tsoukalas
6: Agentic Engineering
Content: "Claude, Make no mistakes." Does this really work?
It seems that every day, there are new ways to better leverage agents to help us execute work. With this trend, if we don't keep up and figure out how to leverage the newest techniques, we might be left behind in the dust.
In this reading group, we will read together what people are doing to get a concrete understanding of the techniques that work and don't work, and figure out how to improve our productivity.
Required Prerequisites: None
Recommended Prerequisites: Basic understanding of software, system design, and what constitutes of high quality code
Group Type: Hybrid (Reading and Project)
Level: Beginner
Mentors: Johnny Liu, Edward Mei
Curriculum: https://docs.google.com/document/d/1MZUzV7vk-cGedIcAl_3naKiceefoZ_KN3MbQ-KiB2aY/edit?usp=sharing
7: Human-Centric Vision
Content: My research aims to build human-centered AI systems that treat human communication and interaction as first-class capabilities, grounded in embodiment and real-world context, enabling physically and socially meaningful interactions between people and AI.
Required Prerequisites: PyTorch, basics of vision, 3D, or robotics
Recommended Prerequisites: Some research experience
Group Type: Project
Level: Advanced
Mentor: Hezhen Hu
8: LLM Reasoning
Content: LLMs may just be stochastic parrots using next-token prediction, but they are somehow able to perform shockingly well in a variety of tasks and demonstrate complex reasoning. In this group we will read some landmark papers that have brought us from mere text auto-complete bots to powerful AI agents. We may potentially explore reasoning epistemically and advancements of AI reasoning beyond just text.
Required Prerequisites: None
Recommended Prerequisites: Usage of an LLM chatbot
Group Type: Reading
Level: Beginner
Mentor: Eve Feng
9: Technical AI Safety
Content: AI safety is becoming increasingly important as AI gets more and more capable. This reading group will help you learn more about introductory concepts related to technical AI safety work. This includes subjects like alignment, monitoring, evals, mechanistic interpretability, and AI control. We provide hands-on labs for each subfield to get a feel for what the work / research would look like. ML experience is nice to have, but open to people of all experience levels.
Required Prerequisites: Coding
Recommended Prerequisites: ML experience
Group Type: Reading
Level: Beginner
Mentor: Aarushi Lakhi
10: Philosophy of Artificial Intelligence
Content: The "Philosophy of Artificial Intelligence," studies Artificial Intelligence from a human-first perspective. We will read history's great thinkers in order to investigate the origins and mechanisms of human intelligence. By examining the historical debates surrounding dualism, physicalism, and functionalism, we will explore the question if artificial systems can reproduce human intellect and how philosophical grounding informs the limits of the artificially intelligent systems we build.
Required Prerequisites: None
Recommended Prerequisites: None
Group Type: Reading
Level: Beginner
Mentor: Nathan Tsoi
Systems and Theory
11: Technical Foundations of Systems + ML Research
Content: This reading group gives you the practical skills required to start contributing to ML + Systems research. You’ll learn how to implement models from scratch in PyTorch, how the training pipeline works in practice, and how to work with different types of data. We’ll also explore how and when to use ML to optimize various systems operations, and get some hands-on experience with toy projects! By the end, you'll be ready to start contributing to ML- and Systems-related research. This group is part of the Learning Directed Operating System (LDOS) research expedition, and motivated students can get connected to research opportunities through the program.
Required Prerequisites: None
Recommended Prerequisites: Python, basic understanding of data structures
Group Type: Reading
Level: Beginner
Mentor: Sanika Nandpure
12: Systems behind Modern AI
Content: We hear constantly about bigger models and better GPUs, but what actually makes an AI system fast? We’ll look under the hood of modern AI systems and explore how GPUs, memory, batching, scheduling, and inference engines work together to serve models efficiently. Along the way, we’ll read real systems research and experiment with some of these ideas ourselves, including how systems can adapt their resource decisions to different workloads.
Required Prerequisites: None
Recommended Prerequisites: Basic CS principles
Group Type: Reading + Project (for those interested)
Level: Beginner
Mentor: Mahin Naveen
Curriculum: https://docs.google.com/document/d/1zrCou1nYtiOoR2nzSHBMRr_RcTlRgCbr
13: Introduction to Computer Architecture
Content: We’ll explore how a computer’s instruction set and the machine’s hardware design affect how programs execute, and how tools like compilers interface with these lower-level constructs to present the programmer with a clean model for writing, building, and running code. Each week, we’ll work together through a bit of code that highlights an important facet of computer architecture. We’ll also sprinkle in a few readings (papers and other resources) for the experience.
Required Prerequisites: None
Recommended Prerequisites: Any coding experience in some programming language
Group Type: Project
Level: Beginner
Mentor: Michael Jennings
Curriculum: https://docs.google.com/document/d/1O_vyT3sF_jffT2E5bzOAmWhs-Kp3aC1UybmHO8JFVLw/edit?usp=sharing
14: Introduction to Operating Systems
Content: Operating Systems are a bridge between hardware and user software that is magical because we, as developers, almost never have to think about what the OS is doing on our behalf. In this group we will dive into what is actually happening behind the scenes and explore the magical things Operating Systems do to abstract away the hardware resources of a computer.
Required Prerequisites: Data Structures (CS 314), CS 429 or some knowledge of computer architecture
Recommended Prerequisites: C++, Access to UT Lab Machines
Group Type: Project
Level: Beginner
Mentor: Aditya Madhan
Curriculum: https://docs.google.com/document/d/1QIFphFHIMIHInoJSyHl11C2w08JKShzaat-tBR4yZcQ/edit?usp=sharing
15: Introduction to Security
Content: Exploring the world of computer systems security. We will be taking a look at the history of security, including attack mechanisms, mitigations, and defenses. I plan to cover topics such as memory safety, control flow integrity, JavaScript/JIT/browsers, software fault isolation/sandboxing, and hardware security. Time permitting, we may explore other topics (open to suggestions) and/or current security news. In collaboration with ISSS
Required Prerequisites: None
Recommended Prerequisites: CS 429 and CS439 or equivalent
Group Type: Reading
Level: Beginner
Mentor: Caleb Eden
Curriculum: https://docs.google.com/document/d/1TESwm6A74X3AyKnE-_LhWDSP8xGRkuppCTXVLNWM2MM/edit?usp=sharing
16: Search via Quantum Walk
Content: We will start with an explanation of Markov processes and random walks, and an introduction to quantum computing concepts. We introduce quantum random walks and explore how the concept can be used for search problems, including within the amplitude amplification framework.
Required Prerequisites: Basics of probability and computational complexity (Big-O, etc)
Recommended Prerequisites: Understanding of Markov chains and basic quantum computing (qubits, superpositions, states)
Group Type: Reading
Level: Beginner
Mentor: Aditya Patwardhan
Miscellaneous
17: Catch-All Group
Content: The Catch-All group is a space to explore brand-new papers and emerging ideas at the frontiers of computer science research. Members help shape what we read by proposing research they find exciting and leading discussions about its ideas, contributions, and implications. By bringing together different interests and perspectives, we aim to broaden everyone’s exposure to CS research beyond their own areas of focus. Inspired by YC Paper Club.
Required Prerequisites: None
Recommended Prerequisites: None
Group Type: Reading
Level: Beginner
Mentor: Sumedh Rao
18: Deep Learning for Proteins
Content: Explore the latest deep learning advances for protein discovery, engineering, and design. Learn about AlphaFold / other protein folding models, inverse folding, protein language models, and binder design pipelines. Half the time will be spent studying deep learning models, the other half will be spent learning how to create protein data engineering pipelines and training the models. This is a hybrid reading/project based DiRP, and we welcome people from any background (computer science, biology, chemistry, math, etc) - but prior deep learning experience is necessary to understand content.
Required Prerequisites: Prior deep learning experience (core concepts such as neural networks, gradient-based optimization/backpropagation, loss functions, training/evaluating models); basic programming proficiency
Recommended Prerequisites: Familiarity with Python and PyTorch, transformers, graph neural networks, or computational biology
Group Type: Project
Level: Advanced
Mentor: Ravi Shah
Curriculum: https://docs.google.com/document/d/1riUcJncm_0j6cj8XPQTxEUpYmBdK1QSfY0SOhFaJARg/edit?usp=sharing
19: Long-Range Deep Genomic Modeling
Content: Have you ever felt depressed about how AI is going to take all of our jobs? What if you wanted to watch it suffer for a change?
Then join this DiRP! We will investigate deep learning architectures designed to process megabase-scale genomic sequences.
Standard state of the art LLMs usually break down at these massive context lengths, and you will get a front row seat to watching ostensibly infallible models break under the harsh reality of biological data.
If you want to see where modern AI hits its limits, then this DiRP is for you!
Required Prerequisites: Familiarity with deep learning
Recommended Prerequisites: Knowledge of basic high school biology
Group Type: Reading
Level: Advanced
Mentor: Vishal Thyagarajan
20: Technology's Impact on Society
Content: This group will focus on ways that technology has impacted society, including how we communicate with each other and societal culture. We will be exploring various technologies' impacts, including artificial intelligence and social media.
Required Prerequisites: None
Recommended Prerequisites: None
Group Type: Reading
Level: Beginner
Mentor: Sasi Kondru
Curriculum: https://docs.google.com/document/d/1az067oSKP_asSVnBShQztndWYzth2MHf4l8alKu1qKA/edit?usp=sharing
21: Sequences of Rationality
Content: A cognitive science and philosophical look into examining one's own beliefs. When was the last time you examined where your information comes from and why you believe the things you believe? The Sequences is a series of tools that you can use to understand and Bayesianly update your map of the world, uncover biases and thinking traps, and problem solve more efficiently. This group will be a discussion group on samplers of the following topics and their applications in general reasoning and problem solving:
-Probability theory and bayesian inference
-Cognitive Science and heuristics and biases research
-Decision theory and game theory
-Algorithmic information theory and occam’s razor
-Philosophy of science and epistemology
-Evolutionary biology and evolutionary psychology
-Ai alignment theory
-Philosophy of mind (how it applies to AI)
Required Prerequisites: None
Recommended Prerequisites: None
Group Type: Reading
Level: Beginner
Mentor: Hunter Hoinkes
22. Technologies in the Market (Maybe)
Content: Learn about technologies used in quantitative finance like microwave transmissions and FPGAs
Required Prerequisites: None
Recommended Prerequisites: None
Group Type: Reading
Level: Beginner
Mentor: Hudson River Trading (HRT)
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