Nathan Peter is the founder of Logits, an early-stage research startup focused on AI safety, alignment, and interpretability.
Nathan is a senior at North Garland High School, graduating in 2026 with a Distinguished Diploma on the Computer Science and Engineering track. He will begin his B.S. in Electrical and Computer Engineering at the University of Texas at Dallas in Fall 2026, with plans to pursue a PhD in machine learning afterward.
Nathan currently works as a Deep Learning Researcher with the Stanford University School of Medicine and Emory University School of Medicine, where he is developing a DenseNet121-based pneumonia classifier trained on the RSNA chest X-ray dataset, optimized for lightweight edge deployment in low-resource clinical settings. He has also worked as a Computer Vision Intern at the UT Dallas Intelligent Robotics and Vision Lab, contributing to monocular metric depth estimation and visual grounding research with ROS, Intel RealSense, and vision-language models. As an AI Product and QA Intern at PathSpark, he focused on adversarial testing of large language models and received a return offer.
Lightweight Pneumonia Detection — DenseNet121 classifier on RSNA chest X-rays, optimized for edge deployment in low-resource clinical settings.
EV Charging Load Prediction — benchmark study comparing classical regression and neural network approaches for forecasting electric vehicle charging demand.
Aemmz Auto Repair — full-stack Flask website for a local auto repair shop, deployed via Vercel with CI/CD through GitHub and DNS through Namecheap.
NeighborMesh — real-time collaboration platform co-built with a graduate engineer, currently being refactored as a distributed system under Logits AI.
Coming soon.
Stanford University School of Medicine — coming soon.
UT Dallas Intelligent Robotics and Vision Lab — coming soon.