Muhammad Naufil

Finetuned vision foundation models on a large-scale collection of deepfakes using clusters of 8×A100 GPUs. Conducted large-scale generation of synthetic deepfake datasets to expand training coverage and improve detection robustness.

Evaluated instruction-following and few-shot learning of the latest open- and closed-source LLMs. Built a personal AI companion using GPT-4o with function calling and RAG retrieval (Pinecone). Explored agentic frameworks including n8n and crewAI to build AI workflows and automation pipelines.

Built up expertise in diffusion models progressively: started with unconditional image generation, then conditioned models on class labels to understand classifier-free guidance. Graduated to latent diffusion models for monadic co-speech gesture synthesis — first unconditional, then conditioned on speech transcription to generate gestures that naturally accompany spoken language.

Empower your operators, engineers, and managers to dramatically boost the quality and productivity of your manual processes. Create digital mistake-proofing mechanisms for a variety of assembly and packing processes. Pathfinder tracks individual steps of an assembly process, and offers audible and visual alerts to help associates avoid mistakes.

Collects timing data for every cycle performed on a line, at a fine-grained level. It identifies non-value add work as well as most efficiently performed cycles. These capabilities directly aid industrial engineers in improving processes. Additionally, Pathfinder grades individual assembly sessions, versus ideal number of cycles that could have been performed and considering operator mistakes. This further helps engineers compare and contrast different sessions and work styles to rapidly improve processes.

Computer vision based "in-process" health and safety analytics. Analyzes videos recorded from ordinary phone cameras via a mobile app or web portal upload. Uses state-of-the-art computer vision technology to compute 3D skeletal poses and extract 3D joint angles, optimized for industrial use cases with robustness to partial obfuscation and extreme postures. Has significant advantages over wearables, goniometers, or Marker-Based Motion Capture Systems.