• Student at the University of Chicago majoring in Computer Science, Physics and Mathematics.
• Focused on neural network training, machine learning infrastructure, and autonomous systems.
• Passionate about bridging research with real world applications, from model design to deployment pipelines.

• Short term: Become an AI Engineering intern on the Tesla FSD or Waymo Driver team.
• Long term: Build developer-first platforms and found tech companies that blend deep engineering with user simplicity.

• Developing a modular Mixture-of-Experts (MoE) architecture for autonomous driving within the CARLA simulator.
• Combines multiple specialized perception experts (object detection, drivable area segmentation, etc.) using a learned gating network to handle diverse driving contexts.
• Built high-performance data pipelines and multi-GPU training scripts (DistributedDataParallel) for large autonomous driving datasets including BDD100K, nuScenes and CARLA.

• Large-scale multimodal dataset (~365 GB, ≈82k frames) with synchronized RGB images, semantic segmentation, LiDAR point clouds, 2D bounding boxes, ego-vehicle states, and rich environment metadata.
• Designed for research in object detection, segmentation, sensor fusion, imitation learning, and reinforcement learning.
• Built on CARLA with varied weather, maps, and controllable traffic; packaged for Hugging Face Datasets with train/val/test splits and reproducible pipelines.

• Open, multi-camera dataset (~188 GB, ≈68k frames) with synchronized RGB images, ego pose/velocity, control signals, traffic density, and collision logs.
• Collected in CARLA using synchronous stepping (Δt = 0.05 s), variable weather, and controllable NPC traffic; fixed extrinsics for front, front-left 45°, front-right 45°, and rear cameras.
• Packaged for Hugging Face Datasets with stable splits (56.2k/4.8k/7.2k) and a reproducible collection pipeline derived from AutoMoE. Suitable for imitation learning, vision-to-control, and sensor-fusion benchmarks.

• Full-stack application for semantic image retrieval powered by OpenAI’s CLIP model.
• Next.js frontend (TypeScript & Tailwind CSS) provides a responsive interface for text-based search.
• FastAPI backend indexes images and computes CLIP embeddings to find and return similar images.

• A terminal-based LLM chat tool with infinite memory through FAISS-powered local vector search.
• Designed to turn your terminal into a Claude/GPT-like chat interface with persistent, searchable memory.
• 100% local and privacy-respecting.

Visit my digital homepage at ipeter.tech for my resume, academic progress, and evolving journey in AI, software engineering and entrepreneurship. You can also interact with ImmanuelAI, a chatbot that answers questions on my behalf.