麻豆精选

• PhD in Electrical Engineering, University of Maryland, 2022
• MSc in Electrical Engineering, University of Maryland, 2020
• BSc in Electrical and Electronics Engineering, Bilkent University, 2016

Dr Baturalp Buyukates is an Assistant Professor at the University 麻豆精选, where he is part of the Socio-Technical Systems group within the School of Computer Science. Previously, he was a postdoctoral research associate at the University of Southern California, working with Salman Avestimehr. He earned his MSc and PhD degrees in Electrical Engineering from the University of Maryland, College Park, in 2020 and 2022, respectively, under the supervision of Sennur Ulukus. Prior to that, he completed his BSc in Electrical and Electronics Engineering at Bilkent University, Turkey, in 2016.

His research interests span machine learning, generative artificial intelligence, human-centred computing, distributed systems, wireless communications, networks, and information theory. His current research focuses on reliable and explainable large language models (LLMs), privacy-preserving machine learning, responsible data economics for collaborative machine learning, timely information exchange in distributed systems, and the semantics of information.

He received the 2024 CTTC Andrea Goldsmith Young Scholars Award for his contributions to age of information, low-latency communications, distributed computation, and learning. His work has received multiple best paper recognitions, and he was awarded the George Harhalakis Outstanding Graduate Student Award from the Institute for Systems Research at the University of Maryland in 2021 for his doctoral research on timely information delivery in large networks, distributed computation, and machine learning.

In his highly interdisciplinary research, Dr Buyukates utilizes tools and techniques from optimization, machine learning, statistics, applied cryptography, and information and coding theories.

• LC Artificial Intelligence 1, Computer Science

Accepting PhD applications in the general areas of Machine Learning (ML) Safety, Trustworthy Generative Artificial Intelligence (AI), Human-Centred Computing, Distributed Systems, Wireless Communications, Networks, and Information Theory. See the recent projects in the Research tab.

Key Focus Areas:

1. Reliable and Explainable LLMs/VLMs – Ensuring large language and vision language models are robust, transparent, and trustworthy.
2. Continual Learning for LLMs – Developing models that adapt to new information without forgetting prior knowledge.
3. Multi-Agent AI Systems – Enhancing coordination, routing, and learning among multiple AI agents in dynamic, heterogeneous environments.
4. Privacy-Preserving Training/Finetuning of Foundation Models – Protecting user data while training large models.
5. Trustworthy Federated/Collaborative Learning – Building frameworks that ensure integrity, verifiability, and security for collaborative learning.
6. Responsible Data Economics – Exploring ethical ways to assess data value and ownership in AI ecosystems.
7. AI-Driven Goal-Oriented Communication Networks – Applying AI tools to enhance the efficiency and adaptability of future time-sensitive networks.