My research focuses on advanced machine learning algorithms, particularly reinforcement learning (RL), as evidenced by my ICLR publication. With over 8 years of experience in machine learning, I have a deep understanding of cutting-edge algorithms and their deployment in practical scenarios. My expertise extends across diverse domains, including NLP, CV, embedded ML, signal processing, bio-signal (e.g., EEG, EMG, ECG) processing, time-series classification/regression, low-power algorithm design, and sustainable AI.

I am actively seeking a full-time position with a strong desire to apply my expertise, enthusiasm, and track record of accomplishments to drive innovation in machine learning and artificial intelligence through involvement in cutting-edge projects.

Email me at tbasaklar@gmail.com if you are interested in talking to me!

Research Highlights

DTRL: Decision Tree-based Multi-Objective Reinforcement Learning for Runtime Task Scheduling in Domain-Specific System-on-Chips

Toygun Basaklar, A. Alper Goksoy, Anish Krishnakumar, Suat Gumussoy, Umit Y. Ogras. Oral and Poster Presentation at ESWEEK 2023 [Paper]

A Self-Sustained CPS Design for Reliable Wildfire Monitoring

Yigit Tuncel, Toygun Basaklar, Dina Carpenter-Graffy, Umit Y. Ogras. Oral and Poster Presentation at ESWEEK 2023 [Paper]

PD-MORL: Preference-Driven Multi-Objective Reinforcement Learning Algorithm

Toygun Basaklar, Suat Gumussoy, Umit Y. Ogras. Accepted in ICLR 2023 and Presented in Deep Reinforcement Learning Workshop NeurIPS 2022[Paper][Code]

GEM-RL: Generalized Energy Management of Wearable Devices using Reinforcement Learning

Toygun Basaklar, Yigit Tuncel, Umit Y. Ogras. Oral and Poster Presentation at DATE 2023 [Paper]

tinyMAN: Lightweight Energy Manager using Reinforcement Learning for Energy Harvesting Wearable IoT Devices

Toygun Basaklar, Yigit Tuncel, Umit Y. Ogras. Accepted in RL4RealLife Workshop NeurIPS 2022 and tinyML Research Symposium 2022[Paper]