Umut Özyurt
I specialize in building AI at the unique intersection of academic research and industrial application. For over two years, I have thrived by concurrently balancing rigorous undergraduate studies with hands-on development roles. This demanding synthesis has forged a singular perspective focused on creating AI that is both powerful in principle and effective in production.
In practice, my background includes engineering robust systems for controllable generative AI and efficient object recognition. This foundation now fuels my current research mission, that is advancing Vision-Language Models (VLMs) beyond simple labels, equipping them with the spatial awareness needed to reason about the physical world.
I am currently seeking a remote Research Engineer / Researcher position (part-time until Feb 2026, transitioning to full-time afterward) and fully funded direct PhD opportunities for the 2026–2027 academic year.
Education
Academic background and qualifications in computer science
Middle East Technical University (METU / ODTÜ)
B.Sc. in Computer Science (Senior Year)
09/2020 - 06/2026 (Expected) | Ankara, Turkey
CGPA: 3.86 / 4.00
Leadership
Technical Lead, METU Artificial Intelligence Society — Led technical workshops, organized events, and guided student projects in computer vision and machine learning.
Relevant Coursework (all completed with 4.0/4.0)
Honors & Awards
Recognitions of academic and research excellence
INSAIT SURF 2025
Selected for prestigious 3-month Summer Undergraduate Research Fellowship at INSAIT, with world-renowned ETH Zürich/EPFL faculty.
4000+ applicants from 150+ countries
UIUC Regh Lab Research Position
Offered summer research position to work with Ozgur Kara at the University of Illinois Urbana-Champaign. Declined due to INSAIT SURF commitment.
ICVSS 2025 Acceptance
Accepted to the prestigious 19th International Computer Vision Summer School (34% acceptance rate, primarily Master/PhD applicants). Declined due to INSAIT commitment.
Erasmus+ Traineeship Grant
Awarded funding support for research position at the University of Cambridge.
Top Project Recognition
Acknowledged for the most complex and successful project in Deep Generative Models graduate course.
High Honor Student
Recognized for 7 consecutive semesters of academic excellence.
METU Development Foundation Scholarship
Awarded for ranking in the Top 1000 among over 2.5 million applicants.
Selected Publications
Research contributions in computer vision and deep learning
Meta-LoRA: Meta-Learning LoRA Components for Domain-Aware ID Personalization
A novel approach using meta-learning for Low-Rank Adaptation (LoRA) components in diffusion models, enhancing identity preservation in text-to-image generation.
GRACE: Generating Socially Appropriate Robot Actions Leveraging LLMs and Human Explanations
A framework generating contextually appropriate robot behaviors by combining large language models with human social explanations for improved human-robot interaction.
Enhanced Thermal Human Detection with Fast Filtering for UAV Images
An approach optimizing thermal human detection on UAV platforms using efficient filtering techniques for real-time performance on edge devices.
Peer Review & Academic Service
Experience & Skills
Research and engineering experience in computer vision and deep learning
Research Experience
INSAIT (Institute for Computer Science, AI and Technology)
Summer Undergraduate Research Fellow (SURF)
06/2025 - 09/2025 (Expected)
Mentors: Dr. Danda Pani Paudel, Dr. Jan-Nico Zaech.
Working on enhancing Vision-Language Models (VLMs) by creating new questions that can scale and enhance VLM's 3D perceptions. Part of the prestigious SURF program (selected among 4000+ applicants with a %0.25 ratio) at this ETH Zürich/EPFL-founded institute which is supported by Google, AWS, and DeepMind.
METU ImageLab
Computer Vision & Deep Learning Researcher (Remote)
09/2024 - Present
Advisor: Assoc. Prof. R. Gökberk Cinbiş.
Conducting research on state-of-the-art deep learning models for computer vision tasks, including generative models and diffusion techniques for personalized image generation, aiming for high-impact publications.
University of Cambridge (AFAR Lab)
Visiting Researcher / Computer Vision Engineer
07/2024 - 09/2024
Advisor: Prof. Hatice Güneş.
Contributed significantly to research on uncertainty prediction in computer vision systems. Involved in all project phases: experimental design, implementation, analysis, and manuscript preparation (second author on ICRA 2025 paper).
METU Intelligent Systems Lab
Undergraduate Researcher / Candidate Engineer
07/2023 - 07/2024
Advisor: Assoc. Prof. Seyda Ertekin.
Researched, developed, and evaluated computer vision methods for thermal human detection using UAV imagery. Focused on real-time processing via edge computing (NVIDIA Jetson). Contributed to IISEC 2023 publication as the first author.
Professional Experience
Syntonym
Computer Vision & Generative AI Researcher (Remote)
09/2024 - Present
Researching deep learning models, particularly diffusion models, for high-fidelity face anonymization. Integrating techniques like ControlNet for fine-tuning and exploring text-to-image personalization (SD1.5, SDXL, FLUX).
Infodif
Computer Vision Engineer / Researcher
01/2024 - 07/2024
Developed and optimized a face recognition pipeline for the Turkish National Police using computer vision techniques, multi-attribute recognition, and custom deep learning architectures.
AsisGuard
Candidate Computer Vision Engineer / Researcher
03/2023 - 12/2023
Led computer vision projects from inception, implementing solutions including thermal imaging analysis and object detection/tracking, optimized for edge devices (NVIDIA Jetson, custom AI accelerators). Guided interns on integration tasks.
Technical Expertise
Research Skills
Frameworks & Libraries
Programming & Tools
Computer Vision & Deep Learning
Exploring visual understanding and generation through research
My research focuses on advancing computer vision by investigating and applying innovative deep learning methodologies. I explore diverse domains such as object detection, recognition, tracking, and the analysis of visual data like thermal imagery. A key research interest lies in generative models, especially diffusion techniques, for tasks including controllable image/video synthesis. My goal is to contribute to the development of robust, efficient, and interpretable AI systems through rigorous research.
Core Deep Learning Research
Researching and refining deep neural network architectures (CNNs, Transformers, etc.) for fundamental computer vision tasks and exploring novel optimization techniques.
Generative Vision Models Research
Investigating generative models (GANs, VAEs, Diffusion) for realistic data synthesis, image editing, personalization, and exploring challenges in video generation.
Visual Recognition & Analysis Research
Researching systems for object detection, tracking, face recognition, and semantic understanding in images/videos, including specialized domains like thermal vision.
Advanced Style Transfer Implementation
Re-implemented the model and training pipeline of the CVPR 2023 paper "Master: Meta Style Transformer for Controllable Zero-Shot and Few-Shot Artistic Style Transfer," creatively resolving critical ambiguities. Acknowledged as the most complex and successful work of the term among graduate submissions.
View on GitHubGet In Touch
Open to research collaborations in computer vision and deep learning
Location
Ankara, Turkey
Hobbies & Interests
Creative pursuits and recreational activities beyond academia
Music
A passionate self-taught pianist with piano as my instrument of choice, I also play violin and viola. Music serves as my creative outlet where I compose original pieces, exploring diverse genres and expressing emotions through melody and harmony.
Billiards
I deeply enjoy both playing and watching cue sports, especially three-cushion billiards, along with American pool and snooker.
Physical Activities
I maintain an active lifestyle through swimming, which I find meditative and excellent for endurance, and amateur tennis, which challenges my reflexes and strategic thinking. Both sports provide the perfect balance of physical exertion and mental focus.
Chess
I enjoy playing chess as an amateur, finding it a fascinating mental exercise that challenges my strategic thinking and pattern recognition. I'm particularly drawn to blitz and rapid formats, where the time pressure adds excitement and forces quick decision-making while still allowing for tactical depth.