Nakorn "ICE" Kumchaiseemak

About ME(s) :

Hello, I am Mr. Nakorn (Ice) Kumchaiseemak, and I am thrilled to introduce myself. I am a Ph.D. student and also kitesurfer. In 2014, I received my B.Sc. degree from King Mongkut's University of Technology North Bangkok (KMUTNB) in Thailand, and in 2017, I completed my M.S. degree from Kasetsart University in Bangkok. During my M.S. studies, I worked as a research assistant at Kasetsart University in the Excitable Media Lab, supervised by Assoc. Prof. Chaiya Luengviriya (Dr.rer.nat.). In this lab, I focused on computer simulations for reaction-diffusion systems and conducted research on electronic devices for agriculture, such as FDR soil moisture sensors and long-term thermometers.

To further enhance my skills in Deep Learning, I have decided to pursue a Ph.D. degree at the School of Information Science at VISTEC and involved in research at both the Vision & Learning Lab and the Brain Lab, under the supervision of Supasorn Suwajanakorn (Ph.D.) and Assoc. Prof. Theerawit Wilaiprasitporn, (D.Eng., SMIEEE). Currently, in 2023, I working at the Microwave, Signals and Systems (MS3) group in Delft University of Technology, The Netherlands as a guest Ph.D. researcher, under the supervision of Assoc. Prof. Francesco Fioranelli (Ph.D., SMIEEE). My research interests lie at the intersection of RADAR, Human-Computer Interaction (HCI), and Deep Learning, where I am actively exploring cutting-edge advancements in these areas

Academic Education(s) & Experience(s)

Guest Ph.D. Researcher, Microwave Sensing, Signals and System (MS3) group, Delft University of Technology, The Netherlands.

Ph.D. Information science and Technology (International program), VISTEC, Thailand

M.S. Physic, Kasetsart University, Thailand

B.Sc Industrial Physics and Medical Instrumentation, KMUTNB, Thailand

Selected Project(s)

DipSAR (IEEE Sensors Conference 2023)

We present a deep learning-based approach called DipSAR for reconstructing millimeter-wave synthetic aperture radar (SAR) images from sparse samples. The primary challenge lies in the requirement of a large training dataset for deep learning schemes. To overcome this issue, we employ the deep image prior (DIP) technique, which eliminates the need for a large dataset and instead utilizes only the sparse sample itself. 

RA-CNN (IEEE Transactions on Geoscience and Remote Sensing)

We propose a deep learning-based approach to localizing a small moving object with a single millimeter-wave frequency-modulated continuous-wave (FMCW) radar. This pilot study establishes a new baseline for small-object tracking using FMCW and can enable tracking of small animals, such as ants inside the colony for behavior studies. 

SleepPoseNet (IEEE Journal of Biomedical and Health Informatics)

This study investigates the performance of an off-the-shelf single antenna UWB in a novel application of sleep postural transition (SPT) recognition. The proposed Multi-View Learning, entitled SleepPoseNet.


Research interest(s)


Wangchan Valley 555 Moo 1 Payupnai, Wangchan, Rayong 21210 Thailand 

Email :


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