Data-centric solutions for Responsible AI: fair data labeling and acquisition   Responsible AI problems like fairness need to be solved in a data-centric fashion as AI is only as good as its data. A fundamental solution is proposed by using fair data labeling and acquisition.   Article | Spring 2023     As AI has become more sophisticated, people may have felt nervous knowing that companie...

AI self-learns to discover the structure of the world like humans   One of the long-desired goals of machine learning is to develop a human-like self-supervised learning algorithm that can learn the object-centric world structure just by seeing the pixels of complex scenes. A collaboration project between KAIST and Rutgers University succeeded in this challenge for the first time in this line of research. &nb...

Battery-free Communication System for Over 100,000 Simultaneous Internet Connectivity   Massive connectivity is a key to Internet of Things (IoT). However, IoT is unable to live up to the scale due spectrum shortage and high energy consumption. This research is the first battery-free communication system supporting hundreds of thousands of simultaneous Internet connectivity.   Article | Spring 2023...

Quantum Jumping Quantum Mechanical 2D Device Simulations   Developing an alternative to the standard theory of quantum transport, a new simulation approach has been established for atomically-thin two-dimensional (2D) nanodevices in operando conditions. The approach for the first time allows faithful, first-principles modeling of graphene electrode-based 2D tunneling devices.   Article | S...

Acoustofluidic microreactor: altering cellular functions with contact-free dynamic cell culture   Acoustical actuation of microfluid enables dynamic cell culture in a controllable and contact-free manner, which applies mechanical stimulations to the cells and alters cellular functions.   Article | Spring 2023     When an external stimulus is transmitted to cells in the body, cells feel mec...

Multi-modal medical data analysis for Alzheimer’s disease prediction   The research introduces a framework for multi-modal medical data analysis aiming at predicting Alzheimer’s disease based on graph neural networks (GNNs). Combining image and non-image medical data offers complementary views of the same patient, which eventually leads to more accurate clinical decisions.   Article | Sp...