Resumé

Resumé

You can download my Resumé here.

Summary

Ph.D. candidate in Computer Science with expertise in Computer Vision, Deep Learning, and Generative AI. Proven track record in developing cutting-edge machine learning solutions and leading successful research projects. Seeking a research internship position to contribute to and learn from industry-leading AI teams.

Education

2023 - Current Virginia Tech PhD in Computer Science

2018 - 2021 Boğaziçi University MSc in Computer Engineering

2012 - 2017 Boğaziçi University BSc in Computer Engineering

Publications

2024 CLoRA: A Contrastive Approach to Compose Multiple LoRA Models. arXiv preprint arXiv:2403.19776.

2024 Conditional Information Gain Trellis. Pattern Recognition Letters Accepted to Pattern Recognition Letters

2023 CONFORM: Contrast is All You Need For High-Fidelity Text-to-Image Diffusion Models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition CVPR Accepted to CVPR 2024

2022 Unsupersived Routing Strategies for Conditional Deep Neural Networks. MSc Thesis. Boğaziçi University.

2020 BURST: Software and Simulation Solutions of an Autonomous Vehicle, 2020 28th Signal Processing and Communications Applications Conference

2018 Privacy-Preserving Intersection Management for Autonomous Vehicles, Proceedings of the Tenth International Workshop on Agents in Traffic and Transportation

Work Experience

Aug 2023 - Now Research Assistant and Lab Lead, Virginia Tech

• Lead research efforts on generative models, focusing on enhancing diffusion-based text-to-image and text-to-video models.
• Collaborate with Google to implement research findings in closed-source diffusion-based image generation models. The work has been published at CVPR 2024 and subsequent work submitted to ICLR 2025.
• Awarded research grant from Deloitte to work on mechanistic explanations of large language models.
• Developing video diffusion models to efficiently transfer motion from real videos to generated videos.

May 2024 - Oct 2024 Research Intern, Adobe

• Part of the Adobe Firefly Video Generative Model team.
• Conducted research on instruction-based video editing using video diffusion transformers and image-text editing datasets.
• Explored the temporal capabilities of video models to innovate video editing solutions.

Nov 2022 - Aug 2023 Machine Learning Engineer, Lyrebird Studio

• Developed and maintained image generation ML services handling 5 million daily requests.
• Architected robust machine learning CI/CD pipelines using GitHub Actions and utilized AWS CDK for building architecture as code, enabling seamless deployment of research team outputs as production-ready services.
• Implemented golden AMIs for GPU-accelerated instances using Packer, resulting in a remarkable reduction in boot-up time from tens of minutes to a few seconds, ensuring faster scale-up to meet high-volume demands.
• Led the design and deployment of diffusion-based model training and image generation services, effectively handling thousands of daily requests on GPU-accelerated instances with high performance and stability.
• Integrated deep learning-based generative solutions into existing applications, significantly enhancing their capabilities and user experience.

Aug 2021 - Nov 2022 Machine Learning Engineer, Vispera

• Spearheaded the automation of deep learning model training using Python and TypeScript, resulting in a tenfold increase in daily model deployments, significantly reducing development time and costs.
• Led the launch of a user-friendly VueJS front-end service for the operations department, empowering researchers to efficiently train and deploy new models by providing real-time monitoring of online and offline metrics, enhancing model observability and researchers’ productivity.
• Successfully coordinated the transition of the deep learning stack to TensorFlow 2, streamlining the adoption of state-of-the-art deep learning models for production, leading to improved performance and maintainability.
• Worked as a full-stack machine learning engineer, using VueJS for frontend services; Python for machine learning services; TypeScript, NodeJS, Go, PostgreSQL, MongoDB, and GraphQL for backend services; Docker and Argo Workflows for containerization and orchestration.

Oct 2019 - Aug 2021 Computer Vision Research Engineer, Vispera

• Led research and development for deep learning image recognition models, utilizing Python, TensorFlow, and OpenCV, to solve challenging problems related to out-of-distribution recognition and hierarchical classification.
• Successfully implemented state-of-the-art deep learning image recognition models, achieving exceptional classification accuracy above 95% on online measurements, ensuring the delivery of high-performance solutions to meet business requirements.
• Pioneered the formulation and implementation of a novel zero-shot learning-based image recognition model using PyTorch, which significantly reduced image annotation time by four times. This innovative approach recommends best matches without annotated data, optimizing the model development process.

Aug 2018 - Oct 2019 Computer Vision Research Engineer, İdea Technology Solutions

• Introduced a novel tree-based deep learning architecture and method based on sparse execution of neural networks using Python, TensorFlow, and TensorFlow Lite for a project funded by The National Scientific and Technological Research Institution.
• Proposed a k-centroids-based clustering algorithm to determine better anchor boxes for object detection models, increasing the model’s object detection performance by approximately 15%.

Awards

2021 Winner, Teknofest RoboTaksi Autonomous Vehicle Competition - The Most Original Software Prize

2020 Runner-up, Anadolu Sigorta Datathon Challenge

2020 Finalist, Teknofest RoboTaksi Autonomous Vehicle Competition

2020 Winner, Teknofest RoboTaksi Autonomous Vehicle Competition Simulation Phase

2019 Finalist, Teknofest RoboTaksi Autonomous Vehicle Competition

2019 Winner, Teknofest RoboTaksi Autonomous Vehicle Competition Simulation Phase

2018 Winner, Mercedes-Benz Hack.Istanbul Hackathon

2017 Winner, BSH Analytics for Production Excellence Hackathon

2017 Runner-up, Boğaziçi University Computer Engineering Senior Projects Competition

2016 Finalist, TUBITAK Undergraduate Software Project Competition

2012 Top %0.01, Turkish National University Entrance Exam

Additional Experiences

2021 Guide, inzva - METU ImageLab AI Labs Joint Program

• Conducted lectures for the review of probability, statistics, and graphical models for the Deep Generative Models course, organized in collaboration with Prof. Gokberk Cinbis from METU.

2018 - 2021 Head of Autonomous Vision Team, BURST (Boğaziçi University Autonomous Electrical Vehicle Team)

• Founded a team and laboratory for building an electric autonomous vehicle, creating autonomous driving R&D opportunities at Bogazici University.