Ozgur Kara

About Me

I am a 5th year Computer Science PhD student at the University of Illinois Urbana-Champaign (UIUC), advised by Prof. James M. Rehg. I am also fortunate to be mentored by Prof. David Forsyth.

I am currently a Student Researcher at Google (Pixel Biometrics AI Research) with Du Tran, working on efficient tokenization in generative modeling. Last summer I interned at Google with Du Tran, Vincent Chu, Yujia Chen, and Ming-Hsuan Yang on generative video composition, and in Summer 2024 at Adobe with Tobias Hinz, Krishna Kumar Singh, Feng Liu, and Duygu Ceylan on multi-shot video generation.

My research asks what stands between modern vision generative models and everyday use: can we control them, can we use them safely, and can we afford them? I treat these as one question asked three ways: how a generative model represents the visual world, and how that choice of representation determines how efficiently, and how safely, we can control it.

I am open to opportunities for collaboration and am always interested in discussing new research ideas. Please feel free to contact me via email.

!!! Always looking for motivated students to work with — if you're interested in research on generative modeling, or want to come with your own proposal, feel free to reach out!

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Health Care Engineering Systems Center

1206 W Clark St. UIUC

Urbana, IL, USA, 61801

Research Statement

My research asks what stands between modern vision generative models and everyday use: can we control them, can we use them safely, and can we afford them? I treat these as one question asked three ways: how a generative model represents the visual world, and how that choice of representation determines how efficiently, and how safely, we can control it.

My work so far has explored the right unit of generation for controlling video: a pixel, a token, a layer, or a shot? Treating frames jointly gave consistent editing from an image prior (RAVE); making the shot a first-class unit gave multi-shot generation with per-shot control (ShotAdapter); decomposing video into layers gave controllable composition without annotated data (Split-then-Merge). Modern generative models can also learn how humans perceive: modeling eye-tracking data generatively yields gaze trajectories and saliency that mimic human visual attention (DiffEye, ST-DiffEye). In parallel, I have studied the safety side of the same question. The models that make editing effortless also make misuse effortless: anyone's visual content can be manipulated without their consent. Protecting content means making it unusable to these models, which raises two questions: why are modern generative models so robust against such adversarial attacks (Immune2V), and how can protection be applied efficiently at scale (DiffVax)?

My current focus is efficiency, which I approach from two directions. From the model perspective: how do compression and distillation preserve plausible outputs while cutting compute, and what do they give up? From the representation perspective: given how redundant images and video are, how should the visual world be represented for a modern generative model, and what is the right unit of generation to build it on?

I believe these questions matter because cost is the most immediate barrier between generative models and everyday use. Efficiency, safety, and control are usually treated as separate engineering concerns bolted on after a model is trained. I believe getting the representation right is how generative models become part of our daily lives.

Some of my specific works include:


News— scroll down for more ↓
  • Aug 2026:📄 Our paper, ConvDeck, on conversational paper-to-slide generation has been accepted to EMNLP 2026 Findings!
  • Jun 2026:🎉 Split-then-Merge and ArcDeck have been accepted to ECCV 2026!
  • Jan 2026:📄 Thrilled to share that our paper, DiffVax, on optimization-free image immunization has been accepted to ICLR 2026!
  • Dec 2025:🎤 I was invited to a talk organized by Voxel51 on Visual AI for Video Use Cases (Feb 11, 2026).
  • Dec 2025:🗓️ 9th edition of our workshop, CVEU, has been accepted to CVPR 2026. Stay tuned!
  • Dec 2025:🎉 My Google project, StM, was featured on Official Google Channels in Instagram / Twitter.
  • Dec 2025:📄 My Google project, Split-then-Merge (StM), is now available. StM is a new video composition framework that improves control and handles data scarcity.
  • Sep 2025:🏆 I was recognized as an Outstanding Reviewer at ICCV 2025 (top 2%).
  • Sep 2025:📄 Thrilled to share that our paper, DiffEye, on generating continuous eye-tracking data has been accepted to NeurIPS 2025!
  • Aug 2025:🗓️ The 7th edition of our workshop, CVEU, was conducted at SIGGRAPH 2025, where I served as a co-organizer.
  • Jun 2025:🏆 I was recognized as an Outstanding Reviewer at CVPR 2025.
  • Jun 2025:📄 Our paper, ShotAdapter, was accepted to CVPR 2025.
  • May 2025:👨‍💻‍ I started my summer internship at Google (BAIR).
  • Dec 2024:🎉 The 6th edition of our workshop, CVEU, has been accepted for CVPR 2025, where I serve as the primary organizer.
  • Sep 2024:👨‍🎓‍ I transferred to the University of Illinois Urbana-Champaign to continue my PhD!
  • Aug 2024:🏆 I was recognized as an Outstanding Reviewer at ECCV 2024.
  • Jul 2024:📄 Our paper on Point Tracking was accepted to the ECCV 2024 ILR Workshop.
  • Jun 2024:📄 Our paper, RAVE, was accepted as a Highlight at CVPR 2024.
  • May 2024:👨‍💻‍ I started my summer internship at Adobe (FireflyTeam).
  • Mar 2024:📄 Our paper on Sign Language Recognition was accepted to FG 2024.
  • Jul 2023:🏫 I attended the International Computer Vision Summer School (ICVSS).
  • Jun 2023:🏫 I participated in the CIMPA Research School on Graph Structure.
  • Aug 2022:🎓 I started my PhD at Georgia Institute of Technology with Prof. James M. Rehg.
  • Jul 2022:📄 Our paper on Fair Affective Robotics was accepted to the LEAP-HRI Workshop.
  • Jun 2022:📄 Our paper, ISNAS-DIP, was accepted to CVPR 2022.
  • May 2022:👨‍💻‍ I started my summer research at EPFL in the VILAB.
  • Aug 2021:✅ I successfully completed the Google Summer of Code program.

Education
UIUC Logo
PhD, Computer Science 2024 - Present
University of Illinois Urbana-Champaign
Advisor: Founder Prof. James M. Rehg
  • Overall GPA: 4.00/4.00
Georgia Tech Logo
PhD (transferred) and MSc, Computer Science 2022 - 2024
Georgia Institute of Technology
Advisor: Founder Prof. James M. Rehg
  • Overall GPA: 4.00/4.00
  • ECE Departmental Fellowship (2022-2024)
  • Otto F. and Jenny H. Krauss Fellowship (2022-2023)
Bogazici University Logo
BSc, Electrical-Electronics Engineering 2018 - 2022
Bogazici University
Advisors: Prof. Lale Akarun, Prof. Murat Saraclar
Kadikoy Anadolu High School Logo
High School, Math and Science 2013 - 2018
Kadikoy Anadolu High School
  • Republic Honour Award (Awarded to one student in a graduating class of 340).

Research Experience
UIUC & Georgia Tech Logo
UIUC / Georgia Tech, Rehg Lab 2022 Fall - Present
Supervised by Founder Prof. James M. Rehg
EPFL Logo Supervised by Asst. Prof. Amir Zamir
Bogazici University Logo
Bogazici University, Perceptual Intelligence Lab 2021 Fall - 2022 Spring
Supervised by Prof. Lale Akarun
ETH Zurich Logo
ETH Zurich, Computer Vision Lab 2021 - 2022
Supervised by Assoc. Prof. Ender Konukoglu
University of Tübingen Logo
University of Tübingen, Explainable ML Group 2021 Spring
Supervised by Prof. Zeynep Akata and Dr. Yongqin Xian
University of Cambridge Logo
University of Cambridge, Affective Intelligence Lab 2020 - 2021
Supervised by Prof. Hatice Gunes
Bogazici University Logo
Bogazici University, Nanonetworking Research Group 2019 - 2020
Supervised by Prof. Ali Emre Pusane

Industry Experience
Google Logo [May 2026 – Present] Student Researcher
Google, Pixel Biometrics AI Research (BAIR)
Google Logo [May 2025 – April 2026] Research Intern
Google, Pixel Biometrics AI Research (BAIR)
  • Worked on generative video composition; a patent was filed and the paper was accepted to ECCV 2026.
  • Ran large-scale distributed training on a video dataset using a video diffusion transformer model.
  • Advisor: Du Tran Collaborators: Yujia Chen, Vincent Chu, & Prof. Ming-Hsuan Yang.
Adobe Logo [May 2024 – December 2024] Research Intern
Adobe, Firefly Team
  • Worked on diffusion transformer-based long-term multi-shot video generation.
  • A CVPR 2025 paper was published and a patent has been filed for this work.
  • Experienced large-scale distributed training on an internal Adobe video model.
  • Advisor: Tobias Hinz Collaborators: Krishna Kumar Singh, Prof. Feng Liu, & Duygu Ceylan.

Publications (* denotes equal contribution)

Exact Posterior Score Estimation for Solving Linear Inverse Problems
Exact Posterior Score Estimation for Solving Linear Inverse Problems
Abbas Mammadov*, Ozgur Kara*, Kaan Oktay, Iskander Azangulov, Adil Kaan Akan, Hyungjin Chung, James M. Rehg, Yee Whye Teh
Under Review, 2026
Paper
TL;DR: EPS solves linear inverse problems by replacing the standard isotropic query with a measurement-aware posterior query, leveraging anisotropic covariance to denoise with the right geometry and produce high-quality reconstructions in as few as a single step.
Layer-Aware Video Composition via Split-then-Merge
Layer-Aware Video Composition via Split-then-Merge
Ozgur Kara, Yujia Chen, Ming-Hsuan Yang, James M. Rehg, Wen-Sheng Chu*, Du Tran*
ECCV 2026 (European Conference on Computer Vision)
Project Webpage / Paper
Media: UIUC News / Official Google Channels in Instagram / Twitter
TL;DR: Split-then-Merge (StM) is a new video composition framework that improves control and handles data scarcity by splitting unlabeled videos into foreground and background layers, and then self-composing them.
ST-DiffEye: Diffusion-Based Continuous Gaze Generation via Joint Scanpath-Trajectory Modeling
ST-DiffEye: Diffusion-Based Continuous Gaze Generation via Joint Scanpath-Trajectory Modeling
Brian Nlong Zhao, Ozgur Kara, Junho Kim, James M. Rehg
Under Review, 2026
Project Webpage / Paper
TL;DR: ST-DiffEye is a unified diffusion framework that jointly generates continuous gaze trajectories and discrete scanpaths by coupling both modalities as raw input channels, achieving state-of-the-art performance on task-driven visual search and free-viewing benchmarks.
Immune2V: Image Immunization Against Dual-Stream Image-to-Video Generation
Immune2V: Image Immunization Against Dual-Stream Image-to-Video Generation
Zeqian Long*, Ozgur Kara*, Haotian Xue*, Yongxin Chen, James M. Rehg
Under Review, 2026
Project Webpage / Paper / Code
TL;DR: Immune2V is an image immunization framework that protects images against unauthorized dual-stream image-to-video generation, preventing illicit video synthesis from protected content.
A Survey on Adversarial Attacks and Defenses for Diffusion Models Across Multiple Modalities
A Survey on Adversarial Attacks and Defenses for Diffusion Models Across Multiple Modalities
Ozgur Kara, Tarik Can Ozden, Furkan Horoz, Zeqian Long, Haotian Xue, Yipu Chen, Oguzhan Akcin, Yongxin Chen, James M. Rehg
LifeGenIP Workshop, ECCV 2026 (Life-Cycle Intellectual Property Governance of Visual Generative Models)
Paper (coming soon)
TL;DR: A survey unifying adversarial attacks and defenses for diffusion models across image, video, and 3D modalities, covering how protective perturbations are built and how generative pipelines can be made to fail on protected content.
ConvDeck: Conversational Paper-to-Slide Generation via Stage-Specific User Feedback
ConvDeck: Conversational Paper-to-Slide Generation via Stage-Specific User Feedback
Tarik Can Ozden*, Sachidanand VS*, Furkan Horoz*, Ozgur Kara, Dilek Hakkani-Tür, Junho Kim, James M. Rehg
EMNLP 2026 Findings (Findings of the Association for Computational Linguistics)
Paper
TL;DR: ConvDeck distributes user feedback across the generation pipeline rather than collecting it only after a full deck exists, refining both the slide outline and the rendered deck through stage-specific conversational rounds.
Narrative-Driven Paper-to-Slide Generation via ArcDeck
Narrative-Driven Paper-to-Slide Generation via ArcDeck
Tarik Can Ozden*, Sachidanand VS*, Furkan Horoz*, Ozgur Kara, Junho Kim, James M. Rehg
ECCV 2026 (European Conference on Computer Vision)
Project Webpage / Paper / Code
TL;DR: ArcDeck is an agentic system for narrative-driven paper-to-slide generation that transforms academic papers into structured, visually coherent presentation slides.
DiffVax: Optimization-Free Image Immunization Against Diffusion-Based Editing
DiffVax: Optimization-Free Image Immunization Against Diffusion-Based Editing
Tarik Can Ozden*, Ozgur Kara*, Oguzhan Akcin, Kerem Zaman, Shashank Srivastava, Sandeep P. Chinchali, James M. Rehg
ICLR 2026 (The Fourteenth International Conference on Learning Representations)
Project Webpage / Paper / Code
TL;DR: DiffVax is an optimization-free image immunization framework that effectively protects against diffusion-based editing, generalizes to unseen content, is robust against counter-attacks, and shows promise in safeguarding video content.
CoherentHand: Temporally Consistent 3D Hand Trajectory Synthesis with Semantic Motion Priors
CoherentHand: Temporally Consistent 3D Hand Trajectory Synthesis with Semantic Motion Priors
Bikram Boote, Junho Kim, Ozgur Kara, Sangmin Lee, James M. Rehg
CVPR Findings 2026 (IEEE/CVF Conference on Computer Vision and Pattern Recognition)
Paper
TL;DR: CoherentHand synthesizes temporally consistent 3D hand trajectories using semantic motion priors, enabling realistic and coherent hand motion generation.
DiffEye: Diffusion-Based Continuous Eye-Tracking Data Generation Conditioned on Natural Images
DiffEye: Diffusion-Based Continuous Eye-Tracking Data Generation Conditioned on Natural Images
Ozgur Kara*, Harris Nisar*, James M. Rehg
NeurIPS 2025 (The Thirty-ninth Annual Conference on Neural Information Processing Systems)
Project Webpage / Paper / Code
TL;DR: We propose DiffEye, a diffusion-based generative model for creating realistic, raw eye-tracking trajectories conditioned on natural images, which outperforms existing methods on scanpath generation tasks.
ShotAdapter: Text-to-Multi-Shot Video Generation with Diffusion Models
ShotAdapter: Text-to-Multi-Shot Video Generation with Diffusion Models
Ozgur Kara, Krishna Kumar Singh, Feng Liu, Duygu Ceylan, James M. Rehg, Tobias Hinz
CVPR 2025 (IEEE/CVF Conference on Computer Vision and Pattern Recognition)
Project Webpage / Paper
TL;DR: ShotAdapter enables text-to-multi-shot video generation with minimal fine-tuning, providing users control over shot number, duration, and content through shot-specific text prompts, along with a multi-shot video dataset collection pipeline.
RAVE: Randomized Noise Shuffling for Fast and Consistent Video Editing with Diffusion Models
RAVE: Randomized Noise Shuffling for Fast and Consistent Video Editing with Diffusion Models
Ozgur Kara, Bariscan Kurtkaya, Hidir Yesiltepe, James M. Rehg, Pinar Yanardag
CVPR 2024, Highlight (IEEE/CVF Conference on Computer Vision and Pattern Recognition)
Project Webpage / Paper / Code / HuggingFace Demo / Video
Media: Voxel51 Talk / NewGenAI / creativaier / JanRT / Future Thinker @Benji
TL;DR: RAVE is a zero-shot, lightweight, and fast framework for text-guided video editing, supporting videos of any length utilizing text-to-image pretrained diffusion models.
Towards Social AI: A Survey on Understanding Social Interactions
Towards Social AI: A Survey on Understanding Social Interactions
Sangmin Lee, Minzhi Li, Bolin Lai, Wenqi Jia, Fiona Ryan, Xu Cao, Ozgur Kara, Bikram Boote, Weiyan Shi, Diyi Yang, James M. Rehg
In Submission, 2025
Paper
TL;DR: This is the first survey to provide a comprehensive overview of machine learning studies on social understanding, encompassing both verbal and non-verbal approaches.
Transfer Learning for Cross-dataset Isolated Sign Language Recognition in Under-Resourced Datasets
Transfer Learning for Cross-dataset Isolated Sign Language Recognition in Under-Resourced Datasets
Alp Kindiroglu*, Ozgur Kara*, Ogulcan Ozdemir, Lale Akarun
FG 2024 (IEEE International Conference on Automatic Face and Gesture Recognition)
Paper / Code
TL;DR: This study provides a publicly available cross-dataset transfer learning benchmark from two existing public Turkish SLR datasets.
Leveraging Object Priors for Point Tracking
Leveraging Object Priors for Point Tracking
Bikram Boote, Anh Thai, Wenqi Jia, Ozgur Kara, Stefan Stojanov, James M. Rehg, Sangmin Lee
ECCV 2024 ILR Workshop, Oral (Instance-Level Recognition Workshop at European Conference on Computer Vision)
Paper / Code
TL;DR: We propose a novel objectness regularization approach that guides points to be aware of object priors by forcing them to stay inside the the boundaries of object instances.
Domain-Incremental Continual Learning for Mitigating Bias in Facial Expression and Action Unit Recognition
Domain-Incremental Continual Learning for Mitigating Bias in Facial Expression and Action Unit Recognition
Nikhil Churamani, Ozgur Kara, Hatice Gunes
IEEE TAC 2022 (IEEE Transactions on Affective Computing)
Paper / Code
TL;DR: we propose the novel use of Continual Learning (CL), in particular, using Domain-Incremental Learning (Domain-IL) settings, as a potent bias mitigation method to enhance the fairness of Facial Expression Recognition (FER) systems.
Molecular Index Modulation using Convolutional Neural Networks
Molecular Index Modulation using Convolutional Neural Networks
Ozgur Kara, Gokberk Yaylali, Ali Emre Pusane, Tuna Tugcu
Nano Communication Networks 2022
Paper / Code
TL;DR: We propose a novel convolutional neural network-based architecture for a uniquely designed molecular multiple-input-single-output topology, aimed at mitigating the detrimental effects of molecular interference in nano molecular communication.
ISNAS-DIP: Image-Specific Neural Architecture Search for Deep Image Prior
ISNAS-DIP: Image-Specific Neural Architecture Search for Deep Image Prior
Metin Ersin Arican*, Ozgur Kara*, Gustav Bredell, Ender Konukoglu
CVPR 2022 (IEEE/CVF Conference on Computer Vision and Pattern Recognition)
Paper / Code / Video
TL;DR: ISNAS-DIP is an image-specific Neural Architecture Search (NAS) strategy designed for the Deep Image Prior (DIP) framework, offering significantly reduced training requirements compared to conventional NAS methods.
Towards Fair Affective Robotics: Continual Learning for Mitigating Bias in Facial Expression and Action Unit Recognition
Towards Fair Affective Robotics: Continual Learning for Mitigating Bias in Facial Expression and Action Unit Recognition
Ozgur Kara, Nikhil Churamani, Hatice Gunes
LEAP-HRI 2021 (Workshop on Lifelong Learning and Personalization in Long-Term Human-Robot Interaction)
Paper / Code
TL;DR: We propose the novel use of Continual Learning (CL) as a potent bias mitigation method to enhance the fairness of Facial Expression Recognition (FER) systems.
Neuroweaver: a platform for designing intelligent closed-loop neuromodulation systems
Neuroweaver: a platform for designing intelligent closed-loop neuromodulation systems
Parisa Sarikhani, Hao-Lun Hsu, Ozgur Kara, Joon Kyung Kim, Hadi Esmaeilzadeh, Babak Mahmoudi
Brain Stimulation: Basic, Translational, and Clinical Research in Neuromodulation, Elsevier, 2021
Paper / Code
TL;DR: Our interactive platform enables the design of neuromodulation pipelines through a visually intuitive and user-friendly interface. (Google Summer of Code 2021 project)

Service & Recognition

Professional Service

  • Workshop Organizer: AI for Visual Content Generation, Editing & Understanding (CVEU) — CVPR 2026 (Lead), SIGGRAPH 2025 (Co-organizer), CVPR 2025 (Lead)
  • Outstanding Reviewer: ICCV 2025 (top 2%), CVPR 2025 (top 5%), ECCV 2024 (top 10%)
  • Program Committee / Reviewer: CVPR 2024–2026, ICCV 2025, ECCV 2024/2026, NeurIPS 2023/2025, ICML 2024, ICLR 2024/2025, AAAI 2025; IEEE Transactions on Affective Computing, ACM Transactions on Graphics

Invited Talks

  • Voxel51 Computer Vision Meetup, Visual AI for Video Use Cases 2026
  • Google Computer Vision Reading Group, ShotAdapter: Text-to-Multi-Shot Video Generation with Diffusion Models 2025
  • UIUC Computer Vision Group Lunch, RAVE: Randomized Noise Shuffling for Fast and Consistent Video Editing with Diffusion Models 2024
  • Voxel51 Computer Vision Meetup, GenAI for Video: Diffusion-Based Editing and Generation, [Recording] 2024
  • More Than 101, Review of Basic Probability and Introduction to Diffusion Models 2023

Teaching

  • CS444 Deep Learning for Computer Vision, UIUC 2026 Spring
  • ECE2026 Introduction to Signal Processing, Georgia Institute of Technology 2022 Fall

Mentorship

  • Amreen Tejani (UIUC, 1st Year Chemistry Pre-Med) Spring 2026
    Synthesizing expert trajectories on medical scans to assist clinical interpretation training. Mentored under the departmental URAP program.
  • Furkan Horoz (METU, BS CS) Fall 2025-Present
    Narrative-driven multi-agentic slide generation (ArcDeck, ECCV 2026; ConvDeck, EMNLP 2026 Findings).
    Characterizing adversarial attacks on diffusion modeling.
  • Emir Kisa (Bogazici University, BS EE) Spring 2026-Present
    Generative video transition. Visiting in-person at UIUC during Summer 2026.
  • Zeqian (Richard) Long (UIUC, BS CS) Spring 2025 - Spring 2026
    Adversarial attacks on video diffusion models (Immune2V).
  • Aditya Ved, Nalin Kundu (UIUC, BS CS) Spring 2025
    Developed deepfake prevention mobile app (URSA, Awarded 1st Place in Research Symposium).
  • Miguel Aenlle (UIUC, BS CS) Fall 2024 - Fall 2025
    Google Summer of Code (GSoC) 2025 Contributor. Real-time video editing mobile app (URSA).
  • Tarik Can Ozden (Bogazici University BS CS → UIUC MS CS) Spring 2024-Present
    Immunization model against deepfake attacks (DiffVax, ICLR 2026).
    Narrative-driven multi-agentic slide generation (ArcDeck, ECCV 2026; ConvDeck, EMNLP 2026 Findings).
  • Peter Guan (UIUC, BS CS) Fall 2024
    Real-time video editing mobile app (URSA).
  • Sarper Yurtseven (YTU BS Maths. → Polimi MS Comp. Maths.) Summer 2024
    Google Summer of Code (GSoC) 2024 Contributor. Universal controller diffusion models.
  • Ioannis Valasakis (King's College London, PhD) Summer 2023
    Google Summer of Code (GSoC) 2023 Contributor.
  • Mehul Sinha (Manipal University, B.Tech → MS CS @ USC) Summer 2022
    Google Summer of Code (GSoC) 2022 Contributor.

Scholarships

  • UIUC CS PhD Fellowship Addendum 2024-2026
  • Georgia Tech ECE Departmental Fellowship 2022-2024
  • Georgia Tech Otto F. and Jenny H. Krauss Fellowship 2022-2023
  • Summer@EPFL Fully Funded Summer Internship Program 2022
  • 2205 TUBITAK Undergraduate Scholarship Holder 2022
  • Outstanding Success Scholarship Holder from Turkish Educational Foundation (TEV). 2019-2022
  • 2247-C TUBITAK Research Internship Scholarship 2021-2022

Awards & Achievements

  • Served as a mentor in the Google Summer of Code program. 2022-2025
  • Attended the 2023 International Computer Vision Summer School (ICVSS), ranked among the top 25% of 614 applicants (approximately 154 individuals), [Project] 2023
  • Participated in the CIMPA Research School on Graph Structure and Complex Network Analysis. 2023
  • Attended the highly competitive Summer@EPFL program, with a 2% acceptance rate. 2022
  • Placed among the top 50 teams worldwide in the Google Developer’s Solution Challenge, selected from over 5,000 teams, [Project] 2022
  • Placed 3rd in the Yildiz Bootcamp and was directly invited to the Yildiz Technopark Pre-Incubation Program. 2022
  • Successfully completed Google Summer of Code with the project "Graphical User Interface for OpenAI Gym," selected among 1,205 students from 6,991 applicants (17% acceptance rate), [Project] 2021
  • Placed 3rd out of 172 projects (top 1.7%) in the TUBITAK Undergraduate Research Project Competition. 2021
  • Ranked among the top 10 teams regionwide in the Google Solution Challenge with the project titled "Torch in Darkness." 2020
  • Placed 1st out of 100 projects (top 1%) in the TUBITAK Undergraduate Research Project Competition for the project "Joint Depth Estimation and Object Detection Software," [Project] 2020
  • Placed 3rd out of 15 projects (top 20%) in the IEEE METU Pixery Hackathon with the project "Mobile Application for Blind People," [Project] 2020
  • Finalist among 81 teams in the Turkish Airlines Travel Datathon. 2019
  • Ranked 180th out of 2 million (top 0.009%) in the Turkish National University Entrance Exam. 2018
  • Received the Republic Honour Award at Kadikoy Anadolu High School, awarded to one student out of 340 annually. 2018
  • Placed 3rd nationwide in the TUBITAK High School Research Project Competition for the project "Drone for Landmine Detection Using GPS," [Project] 2018
  • Placed 1st regionwide in the TUBITAK High School Research Project Competition for the project "An Autonomous Hexapod for Helping Search Teams After Earthquake," [Project] 2017
  • Accepted into the CS Bridge program, a two-week programming course led by Stanford TAs. 2016

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