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Contribute to Artificial Super Intelligence
Hopefully a frontier lab. Beyond that, I'm deeply fascinated by space and satellite technology—I believe reliable global connectivity is foundational to our future. As the world accelerates toward autonomous vehicles, distributed data centers, and AI-everywhere infrastructure, ubiquitous low-latency connectivity isn't just convenient—it's critical. The systems that power tomorrow's autonomy will demand seamless, resilient communication networks spanning every corner of the planet.
Conquer K2 Mountain
8,611m. Other mountains are passively dangerous, but K2 actively tries to kill you.
Education & Research Background
I hold both MS and BS degrees in Computer Science from UT Austin, where I conducted research in the RobIN Laboratory (Robotic Interactive Intelligence) on transfer learning, multi-task RL using the MetaWorld manipulation benchmark suite, and return-conditioned sequence modeling with Decision Transformers on robomimic datasets, advised by Dr. Roberto Martin-Martin and visiting scholar Dr. Fernando Fernández Rebollo.
I also worked in the AMRL (Autonomous Mobile Robotics Laboratory) with Dr. Joydeep Biswas on inverse kinodynamics for autonomous vehicle drifting. My work was selected for presentation at an Amazon AI Symposium.
I served as a teaching assistant for three semesters under Dr. Chand John, who has been a mentor for quite some time and continues to inspire me.
The Dunning-Kruger Journey
My journey through reinforcement learning has been non-linear. Starting with robotic manipulation and policy learning in the RobIN lab, I built intuition for how agents learn from interaction, credit assignment, and the exploration-exploitation tradeoff. Those fundamentals from training robotic arms now inform how I think about LLMs as RL agents, applying insights from offline RL and sequence modeling to distributed training infrastructure, model optimization, and efficient inference at scale. I'm somewhere past the Valley of Despair on the Dunning-Kruger curve, where the derivative is finally positive again, climbing toward actual competence one paper at a time.
Research Interests
Training & Scaling Frontier Models
Distributed training, optimization algorithms, systems challenges of scaling to AGI.
AI Safety & Alignment
How frontier models work and ensuring they do what we want.
RL from Human Feedback
Applying robotic RL insights to align LLMs with human values.
ML Infrastructure at Scale
Multi-GPU orchestration, efficient inference, production deployment.
Current Work
Co-founder • 2025 - Present • Seattle, WA
The operating system for satellite constellation management. AI-powered platform that ingests 100K+ telemetry messages/second, predicts network failures with 90%+ accuracy, and autonomously reroutes traffic in under 2 seconds with zero data loss.
From Scratch Podcast
Co-founder • January 2025 - Present
Long-form conversations exploring first-principles thinking in AI and systems design.
Independent Research
Self-Directed • Ongoing
Efficient ML systems papers—distributed training, quantization, inference acceleration. Implementing techniques from scratch.
Selected Publications & Research
arXiv:2504.01266 • 2025
User-space API for multi-GPU programming. Abstracts CUDA complexities for parallel systems.
UT Austin AMRL • 2024 • Amazon AI Symposium
Data-driven kinodynamic model learning for high-speed autonomous drifting.
2024 • Building on IKD Work
End-to-end deep RL with SAC. 49% faster completion, outperforming model-based control.
2024 • Open Source
Research-grade Gymnasium environment with Pacejka tire dynamics and curriculum learning.
UT Austin RobIN Lab • 2023
Return-conditioned imitation learning on mixed-quality robomimic datasets.
Technical Projects
Deep RL algorithms from scratch for Tetris. DQN, policy gradients, reward shaping.
Satellite communication simulator with RF physics and real-time link analysis.
PyTorch chatbot with self-attention. 1.32 perplexity.
User-space paging system. OS internals and virtual memory management.
FUSE-based distributed file system with remote synchronization.
Adversarial search algorithms. Minimax, alpha-beta pruning, game tree optimization.
Featured In
Y Combinator W26 • 2026
AI for space mission assurance. Building the operating system for satellite constellation management.
Y Combinator Launch • 2026
Our official YC launch announcement for Constellation Space.
Hacker News • 2026
Featured on Hacker News front page for our YC W26 launch.
Y Combinator LinkedIn • 2026
"Congrats on the launch Kamran Majid, Raaid K., Omeed Tehrani, Laith Altarabishi!"
Founders Brief VC • 2026
Featured as co-founder alongside the Constellation founding story.
Grokipedia • 2025
Encyclopedia entry on my background, research, and entrepreneurship.
Grokipedia • 2025
Encyclopedia entry on Constellation Space Corporation.
GeekWire • 2025
Featured in GeekWire's spotlight on emerging Seattle tech startups.
UT Austin Computer Science • 2025
Profile on my journey through graduate research, startups, and finding purpose in AI engineering.
UT Austin Computer Science • 2021
Recognized for academic excellence with W.D. Blunk Endowed Presidential Scholarship.
UT Austin Amazon Science Hub • 2024
Selected for presentation on autonomous vehicle drifting research.