INA Research Group

INA research group pursues innovative ideas in/for Internet services and applications, cloud infrastructure, and systems that support artificial intelligence. We identify and anticipate new problems that arise from the evolution of Internet-/Cloud-based services and the development of new hardware, provide novel solutions for challenging problems in the real-world, design and implement the solutions in a way that reaches out for real-world impact. INA research group is part of the Computer Division of the School of Electrical Engineering.

INA research group provides a self-motivated atmosphere that allows for efficient research through flexible working hours. In the doctoral program, our research group provides the necessary training to become an independent researcher and become an expert in a particular field. There is also active exchange with foreign researchers, and Lab alumni have a strong network and receive many offers upon graduation, entering the industry, research lab, or academia as illustrated in Alumni@INA.

Open Position: We are looking for graduate students and undergraduate research interns to join the INA lab (상시 채용). We have several interesting topics such as large-scale deep learning, neural volumetric streaming, and retrospective video analytics. You can find more information on Join Us page.

Research

INA has published 14 papers at top-tier systems conferences, ranking 1st in Korea (refer to link). Currently, INA Lab is conducting research in three different directions, which are listed below. You can find a full list of projects and publications on Project@INA and Publication@INA.

Systems for AI / AI for Systems

• Accelerating training for large-scale deep neural networks [ICML’22]
• Accelerating DNA sequencing by learned index [Bioinformatics’22]
• Optimizing microservice auto-scaling by graph neural networks [CoNEXT’21]

AI-augmented Media Delivery

• Incorporating neural enhancement with adaptive video streaming [OSDI’18, SIGCOMM’20]
• Accelerating neural enhancement at scale [MobiCom’20, SIGCOMM’22]
• Optimizing traditional image codecs for neural enhancement [CVPR’23]

Networked Systems (Data center, 5G) / Systems Security

• Integrating trusted execution environment with conventional networked systems [NSDI’17,ToN’20,ToN’22]
• Optimizing network resource and wireless spectral efficiency under 5G, 6G environments [CoNEXT’22]
• Improving congestion control schemes for datacenter networking [SIGCOMM’17, EuroSys’21, EuroSys’23]