Mehmet Kerem Turkcan

I build and deploy AI systems for settings where latency, reliability, and real-world context matter, from city streets and surgical training workflows to complex scientific data. Across these domains, my work turns deep learning research into practical computer-vision, multimodal, and data systems that can be tested outside the lab.

At the Center for Smart Streetscapes (CS3) and Civil Engineering & Engineering Mechanics at Columbia University, I lead machine learning efforts that move from research prototypes to fielded systems, including real-time video analytics, open-vocabulary perception, edge and vision-language model workflows, street-scale data pipelines, synthetic data generation, urban digital twins, and foundation-model workflows for medical robotics.

DART Open-vocabulary detection at deployment speed UrbanOmniDetect View-agnostic urban detection Medical Robotics Open datasets and world models for training

Work and leadership

• Fielded AI systems: I lead multimodal perception projects for sensing models intended to scale across 900+ New York City intersections and CS3’s three urban testbeds: COSMOS PAWR in New York City, DataCity in New Brunswick, and FAU MobIntel in West Palm Beach.
• Work at leading venues: Publications and preprints across CVPR, ICML, ACM UIST, ACM/IEEE SEC, IEEE INFOCOM, IEEE PerCom, EDM, eLife, and medical AI venues.
• Open-source systems: Public AI systems including DART and generative-agents, with 1,200+ combined GitHub stars.
• Research leadership: Grants and challenge recognition from NVIDIA, EmpireAI, and INRIX x MetroLab, plus collaboration across CS3.
• Teaching and mentorship: Graduate deep-learning courses at Columbia, mentoring of Master’s and high-school researchers, and service as the main engineering instructor for the CS3 Research Experience for Teachers in 2024, 2025, and 2026.

My current systems include DART for real-time open-vocabulary detection, UrbanOmniDetect and UrbanOmniView for calibration-free monocular 3D perception, PAVE and bikeped for urban safety, city-scale traffic analysis, Loom for analytical neural computing, and world models and open datasets for medical robotics.

Earlier language-system work includes GPTune, a GPT-2 fine-tuning toolkit from the early public LLM era, and FlyBrainLab, an early prototype of today’s research-oriented AI workbenches. FlyBrainLab’s natural-language interface coordinated access to papers, programmable ontologies, and an OrientDB-backed connectome knowledge graph, linking retrieved evidence to large-scale queries, GPU simulation, and interactive 3D visualization. Although it predated general-purpose LLMs, the platform addressed the grounding, retrieval, tool-integration, and provenance problems now central to agentic scientific systems.

My independent game and film work also feeds back into my research practice. Through Wisedawn and KEDIKAT, I work with real-time engines, visual storytelling, and production constraints that inform simulation work such as Boundless. KEDIKAT’s film FLEA received four festival accolades in 2026, including Best Storytelling Runner-Up at the MetaMorph AI Award.