Mehmet Kerem Turkcan

Columbia University

New York, NY

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 qualitative detections in a crowded street scene

DART Open-vocabulary detection at deployment speed

UrbanOmniView examples across urban camera viewpoints

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.
Sponsored research: Proposal development, technical reporting, sponsor reviews, and engineering delivery across approximately $30M in institutional research supported by NSF, DARPA, AFOSR, and Con Edison, with additional project support from NVIDIA and EmpireAI.
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 work on language systems 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.

News

Jul 10, 2026	Our paper, Digital Eyes on the Road: Using Street Cameras to Verify Traffic Integrity and Detect Sybil Attacks at City Scale, will appear at USENIX VehicleSec 2026. A public preprint is available for download.
Jul 2, 2026	I will head the engineering curriculum for the Center for Smart Streetscapes (CS3) Research Experience for Teachers program in 2026, continuing my role as its main engineering instructor for a third year.
Jun 26, 2026	KEDIKAT’s film FLEA has received several festival accolades, including Best Storytelling Runner-Up at the MetaMorph AI Award 2026.
Jun 8, 2026	Our UrbanOmniDetect paper is now available in the CVPR 2026 Workshops Open Access proceedings.
May 18, 2026	Our paper, Worst-Case Attacks in Reactive Edge-Cloud Systems: Expected Latency and SLA Violation, appeared at IEEE INFOCOM 2026. The DOI is available online.
May 1, 2026	At the annual NSF site visit for the Center for Smart Streetscapes on May 12-15, I presented several applied AI demonstrations, including DART and UrbanOmniDetect.
Apr 30, 2026	Our paper, Calibration-Free View-Agnostic Monocular 3D Object Detection for Urban Scenes, has been accepted to the CVPR 2026 DriveX Workshop as an oral presentation.
Apr 22, 2026	Our Open-H-Embodiment dataset paper for medical robotics foundation models is now available on arXiv.

Selected Publications

Calibration-Free View-Agnostic Monocular 3D Object Detection for Urban Scenes

Mehmet Kerem Turkcan, Devika Gumaste, and Zoran Kostic

In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, Jun 2026

HTML PDF Code Dataset Model

UrbanOmniDetect is a calibration-free monocular 3D object detection framework for heterogeneous urban cameras across ego-vehicle, infrastructure, and aerial viewpoints.
Open-H-Embodiment: A Large-Scale Dataset for Enabling Foundation Models in Medical Robotics

Nigel Nelson, Juo-Tung Chen, Jesse Haworth, Xinhao Chen, Lukas Zbinden, Dianye Huang, Mattia Ballo, Filippo Filicori, Mehmet Kerem Turkcan, and others

Apr 2026

arXiv DOI HTML Code Dataset Model

Open-H-Embodiment is a multi-institution open dataset for medical robot learning with synchronized video and kinematics across healthcare robotic platforms, enabling foundation models and world models for medical robotics.
Detect Anything in Real Time: From Single-Prompt Segmentation to Multi-Class Detection

Mehmet Kerem Turkcan

Mar 2026

arXiv DOI Code Model

DART is a training-free framework that converts a promptable frontier vision model into a real-time multi-class open-vocabulary detector by sharing class-agnostic visual backbone computation, batching decoding, and optimizing inference for deployment.
Real-Time Video Analytics for Urban Safety: Deployment over Edge and End Devices

Mahshid Ghasemi, Yongjie Fu, Xinyu Ouyang, Peiran Wang, Mehmet Kerem Turkcan, Jhonatan Tavori, Sofia Kleisarchaki, Thomas Calmant, Levent Gurgen, Zoran Kostic, Xuan Di, Gil Zussman, and Javad Ghaderi

In Proceedings of the Tenth ACM/IEEE Symposium on Edge Computing, 2025

Best Paper Award

DOI HTML

PAVE is a privacy-preserving real-time video analytics system that uses edge and end-device processing to support pedestrian safety at urban intersections.
Accelerating with FlyBrainLab the discovery of the functional logic of the Drosophila brain in the connectomic and synaptomic era

Mehmet Kerem Turkcan, Aurel A. Lazar, Tingkai Liu, and Yiyin Zhou

Elife, Mar 2021

DOI HTML Code Dataset Website

In recent years, a wealth of Drosophila neuroscience data have become available including cell type and connectome/synaptome datasets for both the larva and adult fly. To facilitate integration across data modalities and to accelerate the understanding of the functional logic of the fruit fly brain, we have developed FlyBrainLab, a unique open-source computing platform that integrates 3D exploration and visualization of diverse datasets with interactive exploration of the functional logic of modeled executable brain circuits. FlyBrainLab’s User Interface, Utilities Libraries and Circuit Libraries bring together neuroanatomical, neurogenetic and electrophysiological datasets with computational models of different researchers for validation and comparison within the same platform. Seeking to transcend the limitations of the connectome/synaptome, FlyBrainLab also provides libraries for molecular transduction arising in sensory coding in vision/olfaction. Together with sensory neuron activity data, these libraries serve as entry points for the exploration, analysis, comparison, and evaluation of circuit functions of the fruit fly brain.