About Me

I am a 5th year Ph.D. student in the Robotics Department at the Georgia Institute of Technology. I am conducting my studies under the supervision of Prof. Omer Inan at Inan Research Lab. Broadly, I am interested in decision-making problems involving data-driven methodologies such as machine and deep learning. My current research focuses on developing predictive models to address challenges in wearable health and physiological sensing. This includes leveraging advanced methodologies such as deep learning-based time series representation learning (e.g., supervised, unsupervised and self-supervised learning, contrastive learning, multi-modal data fusion), signal processing, traditional machine learning, anomaly detection, state-space modeling, computer vision, and reinforcement learning. My research is/was supported by fundings from the Defense Advanced Research Projects Agency (DARPA), the Office of Naval Research (ONR), and the National Institutes of Health (NIH).

Prior to my graduate studies, I was an undergraduate student in the Department of Electrical and Electronics Engineering at Bilkent University. I was fortunate to be advised by Prof. Yildiray Yildiz in Systems Laboratory for developing behavioral human driver models using game theory, control theory and reinforcement learning.

In the summer of 2025, I joined Goldman Sachs in Dallas as a Quantitative Strategist Summer Associate in the Credit Risk team. My work aimed at developing and validating a mathematical model to evaluate credit risk for structured finance products backed by commercial real estate. My role involved exploring various time series models for scenario generation and conducting quantitative risk analysis to support robust risk assessment frameworks.

Publications

1. C. O. Yaldiz et al., “Short-Term Physiological Forecasting with Adaptive Covariance Matrix Estimation”, IEEE-EMBS International Conference on Body Sensor Networks, Accepted for publication (2025).
2. D. Tangolar et al., “Enabling Intelligent Resuscitation: Non-Invasive Cardiac Output Monitoring via Physiological Sensing and Machine Learning”, IEEE-EMBS International Conference on Body Sensor Networks, Accepted for publication (2025).
3. C. O. Yaldiz et al., “Real-Time Autoregressive Forecast of Cardiac Features for Psychophysiological Applications”, IEEE Journal of Biomedical and Health Informatics, 2025.
4. S.Karimi et al., “Prescreening Depression Using Wearable Electrocardiogram and Photoplethysmogram Data from a Psycholinguistic Experiment”, Physiological Measurement, 2025.
5. O.S. Kilic et al., “Heart rate informed detection of cardiac events using the Kalman filter”, Computers in Biology and Medicine, 2025.
6. C. O. Yaldiz and Y. Yildiz, “Driver Modeling Using a Continuous Policy Space: Theory and Traffic Data Validation”, IEEE Transactions on Intelligent Vehicles, 2023.
7. D. J. Lin et al., “Predicting Soldier Performance on Structured Military Training Marches with Wearable Accelerometer and Physiological Data”, IEEE Sensors Journal, 2023.
8. C. O. Yaldiz et al., “Early Prediction of Impending Exertional Heat Stroke With Wearable Multimodal Sensing and Anomaly Detection”, IEEE Journal of Biomedical and Health Informatics, 2023.
9. C. O. Yaldiz, N. Sebkhi, A. Bhavsar, J. Wang, and O. T. Inan, “Improving Reliability of Magnetic Localization Using Input Space Transformation”, IEEE Sensors Journal, 2023.
10. C. O. Yaldiz and Y. Yildiz, “Driver modeling using continuous reasoning levels: A game theoretical approach”, in 2022 IEEE 61st Conference on Decision and Control (CDC), 2022, pp. 5068–5073.