Artificial Intelligence, Robotics, and Transportation Lab
Prof. Tsz-Chiu Au
“Agents and Robotic Transportation (ART) Lab (a.k.a. AI, Robotics, and Transportation Lab) is a research lab dedicated to Artificial Intelligence (AI) Research. Our goal is to scientifically investigate the foundations of AI systems for decision making and problem solving, using techniques such as planning, machine learning, and automated reasoning. Apart from traditional AI topics, our lab also focuses on AI/robot systems in transportation and logistics domains. Current research topics in the lab are autonomous vehicles, intelligent transportation systems, automated planning, game theory, and multiagent systems.”
Computer Architecture and Systems Lab
Prof. Woongki Baek
The Computer Architecture and Systems Lab. (CASL) investigates innovative hardware and software techniques that significantly improve the performance, efficiency, security, and reliability of computer systems. CASL takes a vertically integrated research approach to maximize the synergistic effects across the entire computer system hierarchy including computer architecture, system software, runtimes, and applications.
Cryptography and Secure Computation Lab
Prof. Aaram Yun
Cryptography studies mathematical algorithms for securing communication and computation. We are interested in designing new cryptographic schemes, developing rigorous methods for security proofs, and finding new applications. Cryptography is an area where ideas from both computational mathematics and theoretical computer science are often actively used together, and has many applications including Internet commerce and cloud computing.
Data Intensive Computing Lab
Prof. Beomseok Nam
Data Intensive Computing Lab (DICL) focuses on managing and analyzing truly large datasets. Efficient indexing techniques are essential when processing large volumes of data, typically referred to as Big Data. Parallel and distributed computation is another key component to divide and conquer large scale problems. DICL pushes high performance technology to the limit by providing novel and innovative technologies in the field of distributed and parallel query processing research.
Embedded Computing Lab(SPL)
Prof. Jongeun Lee
Internet of things is a great vision where everything embeds a tiny computer so that we can listen to them and even talk to them if they have the capacity to understand and act on our words. Undoubtedly there are many challenges to this, but a crucial one is how to make things very very energy-efficient and error-tolerant. In the Embedded Computing Lab at UNIST we are deeply interested in this question of how to make things efficient and resilient, and are exploring solutions across different boundaries (eg., hardware vs software, digital vs analog vs stochastic) encompassing multiple levels of abstraction from application specification to circuit-level design. Though our team has a limited man-power, we tend to be very effective and productive, generating many exciting results and publications primarily in the domain of architecture and compiler for embedded systems since 2009 when it was first established.
High-performance Visual Computing Lab
Prof. Won-Ki Jeong
High-performance Visual Computing Lab (HVCL) at UNIST focuses on developing novel visual computing algorithms and visualization systems for scientific discoveries. Specifically, our research interests lie across diverse research fields such as Bio-medical Image Analysis, Scientific and Information Visualization, GPU Computing, and Computer Graphics.
Machine Learning and Visual Recognition Lab
Prof. Sung Ju Hwang
We are a group focusing on the research of machine learning and visual recognition, at Ulsan National Institute of Science and Technology (UNIST). We are primarily interested in solving visual recognition problems with novel machine learning models, and are currently focusing on the following three problems.
1) Learning semantics: How can we learn deeper knowledge from the given visual (and textual) data, such that machine’s understanding of the world aligns well with how we humans understand the world?
2) Interactive learning: How can we utilize human feedback to better guide the learning process, while also minimizing human supervision effort?
3) Transfer learning: How can we effectively transfer the knowledge learned from one task to another, to the degree that humans do?
While these are our current problems at hand, our general research topics span more widely across the areas of both machine learning and visual recognition. In machine learning, we primarily focus on the transfer / multitask learning, structured prediction, learning with structured sparsity, active / interactive learning, and learning with human in the loop. In visual recognition, we focus on the problem of object / scene recognition, object localization, and image annotation.
Mobile smart networking lab
Prof. Kyunghan Lee
Mobile smart networking (MSN) lab aims at developing core technologies ranging from networking to computing for future mobile networks. MSN lab’s long term vision is on enabling ‘stream computing’ where most intelligences of mobile devices exist in the cloud and local transactions on mobile devices including sensing, touching, media capturing are sent to the cloud and then returned to the mobile devices in ‘real time’ as ‘streaming’ after going through high-level analysis, interpretation, and processing.
In order to enable the vision, MSN lab is mainly focusing on the following topics as of now (but not limited to these):
1) Extremely low latency transport layers including TCP, UDP, and HTTP.
2) Mobile intelligence with understandings of human behaviors, mobile contexts, and energy-efficient sensing.
3) Radical new ways of encoding/decoding data and sensing information for real-time delivery.
4) Novel mathematical frameworks with knowledges borrowed from optimization theory, queueing theory, information theory, game theory, graph theory, and machine learning
5) Clean-slate Internet and computer architectures for instant data handling
Network Research Lab(CSDL)
Prof. Changhee Joo
Network Research Lab. aims to advance the state-of-the-art networking technology with broad scientific and economic impact. We are interested in understanding the fundamental aspects of networking that can improve our everyday life, and in designing practical solutions for seamless interconnection toward the Internet of Things.
Probabilistic Artificial Intelligence Lab
Prof. Jaesik Choi
The primary research goal of the PAI-Lab is to build new models and algorithm to achieve human-level artificial intelligence. The main research areas are statistical relational learning, statistical inference with large-scale graphical models, predicting events in dynamic systems, spatio-temporal video data analysis and learning robot actions. Specifically, we focus on building efficient inference algorithms for accurate prediction of future events in large real-world data.
System Software Laboratory
Prof. Young-ri Choi
The main focus of System Software Laboratory is to design, develop, and evaluate system software technologies that can support the diversity of emerging applications, including high performance scientific applications and big data applications, and new computer architectures. Our laboratory is interested in virtualization, cloud computing, scientific and data-intensive computing.