Dr. Duygu Sarikaya

Dr. Duygu Sarikaya


My research interests span defining the technologies of future, artificial intelligence powered, healthcare applications. More specifically, I work on surgical vision and perception and medical image computing.


I received my MS and PhD from Computer Science and EngineeringUniversity at Buffalo, State University of New York. I am a Fulbright alumna.

Current Students:

  • Lucy Fothergill,  6 DoF Pose Estimation in Surgical Videos (supervising with Dominic Jones and Pietro Valdastri)
  • Yushi Guo, Surgical Video Understanding and Anticipation (supervising with Pietro Valdastri)
  • Nanzhu Chen, Representation Learning for Human Action Recognition (supervising with Brandon Bennett and He Wang)
  • George Baker, Medical Image Understanding through Vision-Language Models (supervising with Nishant Ravikumar, Marc De Kamps, and Kieran Zucker)
  • Arpita Saggar, A Virtual Patient to Support Medical Training (supervising with David Hogg, Vania Dimitrova, and Jonathan Darling)
  • Xin Ci Wong,  How much ‘Real Data’ do you need Really? (supervising with Nishant Ravikumar, Marc De Kamps, and Kieran Zucker)
  • Sevde Kutuk, Surgical Video Captioning (MS, supervising with Tuba Caglikantar)
  • Ali Coban, Summarization of Ultrasound Cardiac Videos (MS, supervising with Ceren Guzel Turhan)

* in order of supervision and starting date

MIRAI Research group

MIRAI Research group

Formerly I was:



Research interests


  • July 2024:  I will deliver a lecture at the UCL Medical Image Computing Summer School (MedICSS) 2024.
  • March 2024: I will start my Aurora Women’s Leadership Development Programme.
  • March 2024: AE-CAI | CARE | OR 2.0 Joint MICCAI Workshop is accepted. See you in Morocco! (latest!)
  • Feb 2023:  SpectraSpace is awarded by the CLIRPath-AI Network funded by EPSRC.
  • Jan 2024:  I delivered a talk at Robotics at Leeds Workshop




I organized the First and Second International Workshops on Context-Aware Operating Theaters in conjunction with MICCAI, the second International Surgical Data Science Workshop in conjunction with CARS, MIcro-Surgical Anastomose Workflow Recognition on Training Sessions (MISAW) Challenge, part of the Endovis Grand Challenge in conjunction with MICCAI, Computer Vision and Augmented Reality in Surgery Workshop  in conjunction with Hamlyn Symposium on Medical Robotics, and a series of the Joint AE-CAI, CARE and OR 2.0 Workshops in conjunction with MICCAI.

I am Vice President of University at Buffalo, State University of New York Turkey Network – Alumni. I served as a board member of Women in MICCAI, and formerly was a member of the Student Board and the Educational Initiative at MICCAI.


Public Datasets:

ATLAS Dione Dataset for Robot-Assisted Surgery Video Understanding
ATLAS Dione dataset provides video data (86 full subject study videos (~910 action clips)) of ten surgeons from Roswell Park Cancer Institute (RPCI) (Buffalo, NY) performing six different surgical tasks on the daVinci Surgical System® (dVSS) with annotations of robotic tools per frame (for a subset of 99 action clips), actions taking place and their timestamps. It also provides information on the surgeon expertise levels based on the Dreyfus model.

MISAW (MIcro-Surgical Anastomose Workflow recognition on training sessions)
The MISAW data set is composed of 27 sequences of micro-surgical anastomosis on artificial blood vessels performed by 3 surgeons and 3 engineering students. The dataset contained video, kinematic, and procedural descriptions synchronized at 30Hz. The procedural descriptions contained phases, steps, and activities performed by the participants.

I am a reviewer for Proceedings of IEEE, IEEE Transactions on Medical Imaging, Medical Image Analysis, ArtifiĀ€cial Intelligence in Medicine, The International Journal of Computer Assisted Radiology and Surgery, Computer Methods and Programs in Biomedicine, Computer Assisted Surgery, Medical Image Computing and Computer Assisted Interventions Conference (MICCAI), Computer Assisted Radiology and Surgery Conference (IJCARS), International Conference on Information Processing in Computer-Assisted Interventions (IPCAI). 


Fully-funded EPSRC Doctoral Training PhD Studentships are available at the School of Computing, University of Leeds: *Open to all nationalities * Deadline: 19 Feb 2024

Potential projects I am advertising:

I am always looking for students with a good knowledge of fundamental topics in computer vision, machine learning and deep learning, along with strong coding skills. Experience with advanced deep learning topics such as generative learning, deep reinforcement learning will be preferred. 

<h4>Research projects</h4> <p>Any research projects I'm currently working on will be listed below. Our list of all <a href="https://eps.leeds.ac.uk/dir/research-projects">research projects</a> allows you to view and search the full list of projects in the faculty.</p>


  • PhD in Computer Science and Engineering, University at Buffalo, State University of New York
  • MS in Computer Science and Engineering, University at Buffalo, State University of New York
  • BS in Computer Engineering, TOBB University of Economics and Technology

Student education

Data Science (Fall 2023), Intelligent Systems and Robotics (Spring 2024)

Past: Data Science, Deep Learning, Computer Vision, Image Processing, Machine Learning, Artificial Intelligence in Medicine, Expert Systems

Data Science Module 2023-2024 Cohort

Data Science Module 2023-2024 Cohort

Current postgraduate researchers

<h4>Postgraduate research opportunities</h4> <p>We welcome enquiries from motivated and qualified applicants from all around the world who are interested in PhD study. Our <a href="https://phd.leeds.ac.uk">research opportunities</a> allow you to search for projects and scholarships.</p>
    <li><a href="//phd.leeds.ac.uk/project/1858-effectively-using-hyperspectral-imaging-for-tissue-characterisation">Effectively Using Hyperspectral Imaging for Tissue Characterisation</a></li> <li><a href="//phd.leeds.ac.uk/project/1859-learning-to-automate-surgical-tasks-from-demonstration">Learning to Automate Surgical Tasks from Demonstration</a></li> <li><a href="//phd.leeds.ac.uk/project/1857-medical-vision-language-models-for-visual-question-answering">Medical Vision-Language Models for Visual Question Answering</a></li> <li><a href="//phd.leeds.ac.uk/project/1651-modelling-surgical-motion-and-activities">Modelling Surgical Motion and Activities</a></li>