Abstract : Advances in electrochemistry at solid-liquid interfaces are vital for driving technological innovations that are needed to deliver reliable, affordable, and environmentally-friendly energy. In the first part of presentation, we highlight the key achievements in the development of new materials […]

Abstract : Of a range of materials that have been investigated in pursuit of greater utilization of solar energy, currently one of the most studied semiconductor photocatalysts is titanium dioxide, TiO2, which has an electronic band gap in the ultraviolet (UV) […]

Abstract : Recently force control has become a significant topic in robotics, as robots that interact with human while doing their task require safe and human friendly force control. In order to answer the requirement, high performance transparent force control has […]

Abstract : When people suffer paralysis of the hand and fingers in the wake of injuries or diseases, such as spinal cord injury, stroke, and cerebral palsy, they often cannot perform even the simplest activities of daily life. A polymer-based tendon-driven […]

Abstract : My research interest is in human-machine interaction. I enjoy investigating how humans interact with their environment and with each other. With my group, we develop new technical tools for this purpose, and take all opportunities to use our acquired […]

Abstract : Precise quantification of human motor performance is essential for effective rehabilitation and sports training. Among various aspects of motor performance, stability is one of the most important factors. Stability of rhythmic motor tasks including walking has been frequently assessed […]

Abstract : In this talk, I will present a novel framework of applying deep neural network (DNN) to discrete denoising problem. DNN has recently shown remarkable performance improvements in diverse applications, and most of the success are based on the supervised […]

Abstract : Image restoration or deconvolution that is to recover the original clean image from a noisy or corrupted image has long been an important and fundamental problem in image processing and computer vision. In this talk, a very effective deep […]

Abstract : Deep generative models are a rich class of models for density estimation which specify a generative process for observed data using a set of stochastic latent variables. Among those, of particular interest is the variational autoencoder (VAE) which is […]

Abstract : In this talk, we are concerned with large-scale machine learning problems in changing environment where a small part of the dataset which have been used for training a machine learning model is incrementally updated, and the effect of the […]