Murat Onur Yildirim

Hi! I am a Doctoral Researcher in the AMOR/e lab at the Data and AI Cluster of TU Eindhoven under the supervision of Joaquin Vanschoren.

My research focuses on continual adaptation across the machine learning lifecycle. I develop neuroscience-inspired methods for enabling models to acquire, retain, and update knowledge under non-stationary environments, with applications spanning continual pre-training, parameter-efficient post-training, and large-scale foundation model adaptation.

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Murat Onur Yildirim

News

  • 02/2026, "Automated Machine Learning for Unsupervised Tabular Tasks" has been accepted to MLJ!
  • 02/2026, "Unlocking [CLS] Features for Continual Post-Training" has been accepted to TMLR!
  • 01/2026, "Pruned Adaptation Modules: A Simple yet Strong Baseline for Continual Foundation Models" has been accepted to CPAL!
  • 05/2025, "Self-Regulated Neurogenesis for Online Data-Incremental Learning" has been accepted for an oral presentation at CoLLAs!
  • 04/2025, I have joined the General Assembly of the EU-project SYNERGIES in Paris, France.
  • 03/2025, I have participated in an inspiring spring school on "Learning Over Time" at Certosa di Pontignano, Italy.
  • 06/2024, I have attended an amazing summer school on Generative Modelling "GeMMS" at TU Eindhoven.
  • 11/2023, "Continual Learning with Dynamic Sparse Training: Exploring Algorithms for Effective Model Updates" has been accepted for an oral presentation at CPAL!
  • 09/2023, "AdaCL: Adaptive Continual Learning" has been accepted for an oral presentation at the CLAI!
  • 04/2023, "Condition Monitoring and Predictive Maintenance in Railways" is granted to international patent.
  • 10/2022, "Automated Machine Learning Approach in Material Discovery of Hole Selective Layers for Perovskite Solar Cells" has been published in the Energy Technology.
  • 11/2021, "Predicting Perovskite Bandgap and Solar Cell Performance with Machine Learning" has been published in the Solar RRL.
  • 05/2021, "A Machine Learning Approach for Metal Oxide Based Polymer Composites as Charge Selective Layers in Perovskite Solar Cells" published in the ChemPlusChem.
  • 08/2020, "Comparison of Machine Learning Models on Performance of Single- and Dual-Type Electrochromic Devices" has been published in the ACS Omega.
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Research

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Unlocking [CLS] Features for Continual Post-Training.
Murat Onur Yildirim, Elif Ceren Gok Yildirim, Joaquin Vanschoren.
TMLR, 2026 - NeurIPS, 2025 CCFM Workshop.
paper  -  github  -  blog

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Pruned Adaptation Modules: A Simple yet Strong Baseline for Continual Foundation Models.
Elif Ceren Gok Yildirim, Murat Onur Yildirim, Joaquin Vanschoren.
CPAL, 2026 - NeurIPS, 2025 CCFM Workshop.
paper  -  github  -  blog

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Self-Regulated Neurogenesis for Online Data-Incremental Learning.
Murat Onur Yildirim, Elif Ceren Gok Yildirim, Decebal Constantin Mocanu, Joaquin Vanschoren.
CoLLAs, 2025 (oral).
paper  -  github  -  blog

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AdaCL: Adaptive Continual Learning.
Elif Ceren Gok Yildirim, Murat Onur Yildirim, Mert Kilickaya, Joaquin Vanschoren.
ContinualAI, 2024 (oral).
paper  -  github  -  blog

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Continual Learning with Dynamic Sparse Training: Exploring Algorithms for Effective Model Updates.
Murat Onur Yildirim, Elif Ceren Gok Yildirim, Ghada Sokar, Decebal Constantin Mocanu, Joaquin Vanschoren.
CPAL, 2024 (oral).
paper  -  github

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Automated Machine Learning in Material Discovery of Hole Selective Layers for Perovskite Solar Cells.
Murat Onur Yildirim, Elif Ceren Gok Yildirim, Esin Eren, Peng Huang, Muhammed Haris, Samrana Kazim, Joaquin Vanschoren, Aysegul Uygun Oksuz, Shahzada Ahmad.
Energy Technology, 2023.
paper

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Predicting Perovskite Bandgap and Solar Cell Performance with Machine Learning.
Elif Ceren Gok, Murat Onur Yildirim, Muhammed Haris, Esin Eren, Meenakshi Pegu, Naveen Harindu Hemasiri, Peng Huang, Samrana Kazim, Aysegul Uygun Oksuz, Shahzada Ahmad.
Solar RRL, 2022.
paper

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A Machine Learning Approach for Metal Oxide Based Polymer Composites as Charge Selective Layers in Perovskite Solar Cells.
Murat Onur Yildirim, Elif Ceren Gok, Naveen Harindu Hemasiri, Esin Eren, Samrana Kazim, Aysegul Uygun Oksuz, Shahzada Ahmad.
ChemPlusChem, 2021.
paper

Publication Image 1

Comparison of Machine Learning Models on Performance of Single-and Dual-Type Electrochromic Devices.
Elif Ceren Gok, Murat Onur Yildirim, Esin Eren, Aysegul Uygun Oksuz.
ACS Omega, 2020.
paper

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Supervision

If you are excited about exploring machine learning through the lens of efficient adaptation and post-training strategies, I would love to brainstorm ideas and support your journey in a collaborative, low-pressure environment. I have been lucky to work with such talented and curious people:

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Danushkumar Venkadesh
Clover: Revisit-Aware Benchmark Suite for Continual Post-Training of Foundation Models — 2026 (ongoing).
paper

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Shreya Sajid
Muting Tokens: Exploring Token Sparsification for Efficient Audio Transformers — co-supervised by NXP, 2025.
paper

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Athira Kulampurath
Reliability and Uncertainty in Foundation Model-Based Class-Incremental Learning — 2025.
paper

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Coen Schouten
On-the-Fly Curriculum Design for Online Class-Incremental Learning — cum laude, 2024.
paper