Elif Ceren Gok Yildirim

Research Interests

Continual Learning, Computer Vision, Deep Learning.

I am highly interested in Continual Learning. Continual Learning is a specific field of AI where we teach our models with sequential data. What I mean by 'sequential data' is this: instead of assuming we have access to lots of data at once, we assume that the data comes to us part by part. We generally name this streaming data as "task". Our goal is to keep training our model with upcoming data (yes basically finetuning! 🤭). But there is a big problem here! As humans we also have this problem. Can you guess what that is? It is of course the forgetting issue 🙃. When we keep learning the new things as we grow, we unfortunately might forget some stuff. In continual learning we try to solve this forgetting issue while continue to learn new things. By saying "we" I mean many enthusiastics in this field. If you find this problem interesting, like I do, and would like to chat and even collaborate on this, I am looking forward to meet with you 🤓. You can reach me via the social links. See ya!

News

05/2025, Our paper called SERENA:"Self-Regulated Neurogenesis For Online Data-Incremental Learning" has accepted to CoLLAs 2025!

03/2025, I have attended LOT: Learning Over Time Spring School in Sienna.

01/2025, I have participated in 1 week F+Cube program as a mentee and had a chance to get mentorship about surviving in academia. I have summarized my PhD research and presented during the program.

10/2024, I have attended the Alice&Eve event women in computing in Leiden and presented our work called “Continual Learning on a Data Diet” and got a chance to network with women in the field.

07/2024, I have attended a summer school on Generative Modelling and here is my high level notes: GeMMS.

11/2023, Our paper "Continual Learning with Dynamic Sparse Training: Exploring Algorithms for Effective Model Updates" has been accepted for oral presentation at the CPAL.

10/2023, We have presented AdaCL in virtual: AdaCL:Adaptive Continual Learning

09/2023, Our paper "AdaCL: Adaptive Continual Learning" has been accepted for oral presentation at the CLAI.

04/2023, Our model "Condition Monitoring and Predictive Maintenance in Railways" is granted to international patent.

10/2022, Our paper "Automated Machine Learning Approach in Material Discovery of Hole Selective Layers for Perovskite Solar Cells" has been published in the Energy Technology.

11/2021, Our paper "Predicting Perovskite Bandgap and Solar Cell Performance with Machine Learning" has been published in the Solar RRL.

05/2021, Our paper "A Machine Learning Approach for Metal Oxide Based Polymer Composites as Charge Selective Layers in Perovskite Solar Cells" has been published in the ChemPlusChem.

08/2020, Our paper "Comparison of Machine Learning Models on Performance of Single- and Dual-Type Electrochromic Devices" has been published in the ACS Omega.

Publications

Gok Yildirim, E. C., Yildirim, M. O., Kilickaya, M., Vanschoren, J. AdaCL:Adaptive Continual Learning. In PMLR, 2024. paper

Yildirim, M. O., Gok Yildirim, E. C., Sokar, G., Mocanu, D. C., Vanschoren, J. Continual Learning with Dynamic Sparse Training: Exploring Algorithms for Effective Model Updates. In CPAL, 2024. paper

Yildirim, M. O., Gok Yildirim, E. C., Eren, E., Huang, P., Haris, M. P., Kazim, S., Vanschoren, J., Oksuz, A.U., Ahmad, S. Automated Machine Learning Approach in Material Discovery of Hole Selective Layers for Perovskite Solar Cells. In Energy Technology, 2023. paper

Gok, E. C., Yildirim, M. O., Haris, M. P., Eren, E., Pegu, M., Hemasiri, N. H., Huang, P., Kazim, S., Oksuz, A.U., Ahmad, S. Predicting perovskite bandgap and solar cell performance with machine learning. In Solar RRL, 2022. paper

Yildirim, M. O., Gok, E. C., Hemasiri, N. H., Eren, E., Kazim, S., Oksuz, A. U., Ahmad, S. A Machine Learning Approach for Metal Oxide Based Polymer Composites as Charge Selective Layers in Perovskite Solar Cells. In ChemPlusChem, 2021. paper

Gök, E.C., Olgun, M.O. SMOTE-NC and gradient boosting imputation based random forest classifier for predicting severity level of COVID-19 patients with blood samples. In Neural Computing & Applications, 2021. paper

Gok, E. C., Yildirim, M. O., Eren, E., Oksuz, A. U. Comparison of Machine Learning Models on Performance of Single-and Dual-Type Electrochromic Devices. In ACS Omega, 2020. paper