IPSJ/IEEE-CS Young Computer Researcher Award

IPSJ/IEEE-Computer Society Young Computer Researcher Award

Name of the Award	IPSJ/IEEE-Computer Society Young Computer Researcher Award
About the Award	The IPSJ and the Institute of Electrical and Electronics Engineers Computer Society (IEEE-CS) established a joint award in 2018 to honor young researchers in the field of computer science for their outstanding achievements and high expectations of their continuing progress.
Selection Process	The awardees should be decided annually by the joint award committee which consists of members from both IPSJ and the IEEE-CS. The decision should be confirmed by both societies.
Selection Criteria	The awardees should be young researchers who have outstanding achievements such as research presentations, publications and programming as well as high expectations of further achievements in the field of computer science. The awardees should be the members of the IPSJ and the IEEE-CS.
Conferment	The award will be presented at a conference organized or hosted by the IPSJ and the IEEE-CS. The certificates will be given at the award banquet.

2025
2024
2023
2022
2021
2020
2019
2018

2025

Taku Hachisu (University of Tsukuba)

Outstanding Research on Haptics for Human-Computer Interaction

Taku Hachisu received a Ph.D. degree in engineering from the University of Electro-Communications, Chofu, Japan, in 2015. He is an Assistant Professor at the University of Tsukuba, Tsukuba, Japan. He was a Research Fellow at the Japan Society for the Promotion of Science, Visiting PD Fellow at Microsoft Research, and Visiting Scholar at the University of California, Santa Barbara. His research interests include virtual/augmented reality, haptics, human–computer interaction, and wearable devices.

【Recommendation】
Dr. Taku Hachisu is a leading researcher who has advanced the field of Haptics for Human-Computer Interaction (HCI) and developing new approaches in haptic technology and perception to shape user experience. His prolific publication record includes four prestigious papers at ACM CHI and around 30 journal articles, including IPSJ Journal, IEEE Transactions on Haptics, and IEEE Computer Graphics and Applications, with over 850 citations (h-index=19, i10-index=30). He has developed groundbreaking systems like SpiroSurface, a pneumatic force feedback display, and EnhancedTouch, a wearable device that enhances physical interpersonal touch interaction, notably benefiting children with Autism Spectrum Disorder (ASD). These projects exemplify his dedication to creating intuitive and accessible haptic systems that improve digital user experiences. His projects, funded by JSPS KAKENHI, NEDO, and JST CREST, highlight his commitment to accessible haptic systems.
In addition to his research, he is active in professional societies such as IEEE and the Virtual Reality Society of Japan (VRSJ), where he has held various leadership roles, including Vice Chair of the Special Interest Group of Haptics. His work has been recognized by multiple awards, such as the Best Reviewer accolade at the IEEE World Haptics Conference 2023, further emphasizing his influence in advancing haptics.

Takayuki Katsuki (IBM Research)

Established indirect sensing method based on machine learning for information hidden in real-world uncertainties

Takayuki Katsuki is a Senior Research Scientist at IBM Research – Tokyo. He received his Ph.D. degree from Waseda University in 2017. Since joining IBM Research in 2012, he has been engaged in research and development on machine learning, data mining, and artificial intelligence. He was also a JST CREST researcher from 2013 to 2019. He received the IEEE Computational Intelligence Society, Japan Chapter, Young Researcher Award (2011), the JSAI Incentive Award (2019), and the JSAI Annual Conference Award (2024).

【Recommendation】
Takayuki Katsuki led the development of sensor replacement technology in healthcare, where we sense phenomena at a fine-grained level with coarse-grained sensors, which enables us to replace intrusive fine-grained sensors with non-intrusive coarse-grained ones to reduce the burden on subjects. The technology he developed practically enabled users to replace intrusive arm sensors with non-intrusive bed sensors by achieving 30% lower error rate compared to the existing method, which leads to licensing to clients covering one million (1,000,000) Beds in the hospital/nursing facility (introduced such as in
https://www.nikkei.com/article/DGXLASDZ25I8Q_W5A121C1TI5000/,
https://news.mynavi.jp/techplus/article/20160602-iot_ibm/,
https://www.gizmodo.jp/2016/07/mugendai-post_664838.html).
The core technology is published as a paper in [ICML2023]. He showed that the observed values from sensors for magnitude, such as blood pressure, pulsation, and body are not necessarily in agreement with the actual magnitude. Especially, low values can mean either an actual low magnitude or incomplete observation. This leads to a bias toward lower values in labels and the resultant learning. He derived an unbiased and consistent learning algorithm (U2 regression) for this problem with the new class of loss functions by explicitly modeling incomplete observations due to asymmetric noise. He showed that this algorithm is unbiased as if it were learned from data that does not involve incomplete observations theoretically and significantly improved performance empirically. It addresses limited sensor-resource problems caused by such as the locality of equipment and the ill-posed nature of label collection generally, which contributes to broadening the applicability of machine learning by reducing the dependence on labels in practical fields.

2024

Narumi Takuji (the University of Tokyo)

Outstanding Research on Human Augmentation with Virtual Avatars

Takuji Narumi is an associate professor at the Graduate School of Information Science and Technology, the University of Tokyo. His research interests lie in the intersection of technologies and human science, and he has been working on extending human senses, cognition, and behavior by combining virtual reality and augmented reality technologies with findings from psychology and cognitive science. He received BE and ME degree from the University of Tokyo in 2006 and 2008 respectively. He also received his Ph.D. in Engineering from the University of Tokyo in 2011. He received awards including MEXT Young Scientist Award, SIGCHI Japan Chapter Distinguished Young Researcher Award, Media Arts Festival 2017 Excellence Award, and IEEE VR 2023 Best Paper Award.

【Recommendation】
Takuji Narumi has been engaged in research using virtual reality (VR) to reveal the complex interactions between the human body and multisensory information processing & cognitive functions. Building upon the findings related to the investigation, He developed a group of human augmentation technologies that augment perceptual and cognitive abilities through body transformation with avatars, and proposed the concept of "Ghost Engineering" as a technology to assist people to obtain their own desired abilities and state of mind by appropriately designing avatars. He reveals guidelines for avatar design that augment perceptions including touch, smell, and taste; enhance creativity; foster empathy & mutual understanding; improve physical performance; relieve peer pressure; improve the quality of discussion; and promote efficient skill transfer between individuals etc. The outcomes of these endeavors have been disseminated through publication in prestigious international journals and esteemed international conferences. It is noteworthy that he has pioneered a new research field of perceptual and cognitive augmentation using avatars and has shown that it is indeed successful in a wide variety of fields, stimulating research fields and spawning many follow-up studies. Furthermore, these notable achievements have garnered recognition both domestically and internationally, resulting in prestigious awards.

Yukino Baba (the University of Tokyo)

Outstanding Research on Machine Leaning for Human-AI Collaboration

Yukino Baba is an associate professor in the Graduate School of Arts and Sciences at the University of Tokyo. Her research interests include machine learning, human computation, and human-AI collaboration. Prior to joining the faculty of the University of Tokyo, she was a postdoctoral researcher at the University of Tokyo and the National Institute of Informatics, an assistant professor at Kyoto University, and an associate professor at the University of Tsukuba. She received her Ph.D. degree from the University of Tokyo. Her awards include the DBSJ Kambayashi Young Researcher Award (2017), the IJCAI Early Career Spotlight (2018), and the University of Tokyo Excellent Young Researcher (2023).

【Recommendation】
As AI research and development have advanced, it has become clear that AI capabilities are limited and that there are risks associated with AI making decisions independently. The focus is shifting toward collaboration between humans and AI. Yukino Baba has been an international pioneer in recognizing the importance of this human–AI collaboration from the early stages, and has been leading the way in this emerging field. Specifically, she has developed numerous machine learning techniques known as “truth discovery” that identify reliable individuals within a group using their individual answers to incorporate accurate judgment into AI, thereby contributing to the enhancement of AI safety. Although it is common to use labeled data in machine learning, she has also developed several foundational technologies for human-in-the-loop machine learning, which incorporate human judgment in various ways such as obtaining feedback for generative models. These developments have contributed to the realization of human-centric AI. Furthermore, she has successfully applied these technologies across various fields, including healthcare and drug discovery, achieving significant results. Additionally, she has achieved results in applying machine learning to key social and industrial challenges such as the provision of preventive medicine in developing countries.

Heming Sun (Yokohama National University)

Research on Neural Network-based Learned Video Compression

Heming Sun received the B.E. degree in electronic engineering from Shanghai Jiao Tong University in 2011, and received the M.E. degree from Waseda University and Shanghai Jiao Tong University, in 2012 and 2014, respectively, through a double-degree program. In 2017 he earned his Ph.D. degree from Waseda University through the embodiment informatics program supported by Ministry of Education, Culture, Sports, Science and Technology. He was a researcher at NEC Central Research Laboratories from 2017 to 2018. He was an Assistant Professor at Waseda University, Japan, during 2018 to 2023. He is now Associate Professor at Yokohama National University, Japan. His interests are in algorithms and VLSI architectures for image/video processing and neural networks. He got several awards including the Best Paper Award of IEEE VCIP 2020, Top-10 Best Paper of PCS 2021, and IEEE Computer Society Japan Chapter Young Author Award 2021.

【Recommendation】
Video consumes more than 80% of the internet traffic. Therefore, video compression is very important to reduce the burden of video transmission and storage. The applicant has been working on the algorithms and architectures of video compression for more than 10 years. When I was a master and doctor student, I mainly worked on the traditional video compression standard HEVC and contributed to the world-first HEVC 8K@120fps decoder chip.After joining Waseda University as an Assistant Professor, I mainly work on neural network-based learned video compression (LVC). LVC can be classified to hybrid scheme and end-to-end scheme. For the hybrid scheme, the applicant developed various neural networks to enhance the intra prediction and loop filter. For the intra prediction, the applicant proposed multiple neural networks to replace the original prediction modes in HEVC. For the loop filter, the applicant proposed a convolution filter which is adaptive for various quantization parameters (QP). For the end-to-end scheme, the applicant developed two state-of-the-art learned image compression (LIC) frameworks in 2020 and 2023. Furthermore, the applicant quantized the LIC network to generate a framework with fixed-point arithmetic. Finally, we developed a neural network engine for FPGA and build a real-time CPU-FPGA heterogenous coding system. The applicant has published more than 90 peer-reviewed international journals and conferences. The google citation is more than 1800. Regarding this research topic, the applicant has obtained 14 awards. The codes are all available at GitHub.

2023

Shigemi Ishida (Future University Hakodate)

Outstanding Research on Practical Acoustic Sensing

Shigemi Ishida is an associate professor with the Department of Media Architecture, School of Systems Information Science, Future University Hakodate, Japan. He received his B.E. degree in electrical engineering from Shibaura Institute of Technology, Tokyo, Japan, in 2006, and M.S. and Ph.D. degrees in electrical engineering and information system from the University of Tokyo, Japan, in 2008 and 2012, respectively. He was a visiting scholar with the Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN, in 2013, and was an assistant professor with the Faculty of Information Science and Electrical Engineering, Kyushu University, Fukuoka, Japan from 2013 to 2021. His research interests include wireless sensor networks, low-power wireless communications, localization systems, cross-technology communications, intelligent transportation systems, and IoT sensing technologies. He received 29 awards including IPSJ Yamashita SIG Research Award (2016), IPSJ DICOMO Best Paper Award (2016), ICMU Best Paper Award (2016), IPSJ DICOMO Best Conversant Award (2019, 2021), IPSJ DPSWS Best Conversant Award (2020), and IPSJ DPSWS Best Paper Award (2021, 2022).

【Recommendation】
Shigemi Ishida focuses on acoustic sensing importing wireless communication technologies to realize practical sensing systems. Inspired by multiple input multiple output (MIMO) wireless communication, he applied array based signal processing with the considerations of unstable nature of signals derived from arrayed microphones. Traditional acoustic sensing technologies use arrayed microphones to precisely estimate sound sources location and to precisely split sound signals. Unlike these approaches, he borrows an idea from wireless communication where signals are often unstable. He applies signal processing after initial signal processing such as direction estimation, which is a common approach in wireless communication. Wireless communication technologies cannot directly be applied to sound signal processing because of unknown frequency band of sound signals. He therefore developed new approaches to handle unstable sound signals. His work is mainly conducted in a vehicle sensing project, but also appears in a smart house sensing project. These ideas are now ported back to wireless sensing. His recent IEEE Globecom paper presents a wireless sensing utilizing unstableness to group wireless devices. Due to his much effort in developing practical systems, he won more than 25 awards from academic, including IPSJ Yamashita SIG Research Award (2016) and ICMU Best Paper Award (2016).

Zhi Liu (The University of Electro Communications)

Outstanding research on point cloud video and VR video streaming

Zhi Liu received the Ph.D. degree in informatics in The Graduate University for Advanced Studies/National Institute of Informatics, Japan. He is currently an Associate Professor at The University of Electro-Communications, Japan. He worked as an assistant professor at Shizuoka, a Junior Researcher at Waseda University, and a JSPS research fellow.
His research interest includes video network transmission and AI/optimization empowered networks. He received best paper awards from IEEE MSN2020, IEEE ICOIN 2018, and outstanding paper runner-up from IEEE ICPADS 2022. He is the recipient of IEEE ComSoc Multimedia Communications Technical Committee (MMTC) Outstanding Young Researcher Award, IEICE young researcher award.

【Recommendation】
Dr. Liu focuses on point cloud video and VR video streaming and their applications. For example, for point cloud video, he has proposed a novel viewport prediction method, a novel semantic video tiling method and deep reinforcement learning empowered transmission scheduling, to name a few. For VR video, he has proposed a novel multi user VR transmission scheme by introducing video transcoding enabled multicast, optimized the transmission jointly considering the viewport prediction errors, etc. They have also built prototypes and verified the proposed schemes’ outperformances. By applying these research results in real world, Dr. Liu and his collaborators have jointly built smart grid monitoring system, etc., supported by 6 JSPS grants, and covered by newspapers and technical medias. The research results have been published in 60+ IEEE journal papers, including 5 ESI highly cited papers , and attracted 3500+ citations . He has been awarded 6 IEEE best paper awards from IEEE MSN2020, IEEE ICPADS2021, etc., and IEEE ComSoc MMTC outstanding young researcher. He has also been actively contributing to the community, by serving the editor of IEEE and IPSJ journals, organization committee of the IEEE and IPSJ conferences and IEEE/IPSJ technical committee officer.

Fumio Okura (Osaka University)

Outstanding research on computer vision techniques which advanced the analysis and classification of botanical plants

Fumio Okura received his M.S. and Ph.D. degrees in engineering from the Nara Institute of Science and Technology in 2011 and 2014, respectively. He has been an Assistant Professor with the Institute of Scientific and Industrial Research, Osaka University, until 2020. He was also a JST PRESTO researcher from 2017 to 2021. He is now an Associate Professor with the Graduate School of Information Science and Technology, Osaka University. He is working on research at the boundary between computer vision and computer graphics. His recent research interests include plant phenotyping that quantifies plant traits. He received Virtual Reality Society of Japan Paper Award (2012), IEICE MVE Award (2012), VRSJ SIG-MR Award (2014), among others.

【Recommendation】
Fumio Okura has actively worked for plant twins, i.e., reconstructing virtual twins of the plants in the metaverse. From the technical perspective, his work includes 1) reconstruction of real-world plants, 2) manipulation of plant appearances, and 3) plant science applications analyzing plant characteristics using plant twins. 3D reconstruction of plant shape and structure is a core part of his study. This topic brings notable challenges to the computer vision field due to the unique characteristics of plants, such as severe occlusions and complex branching structures. His methods effectively combine modern deep learning and traditional 3D reconstruction to reconstruct the partly hidden structure of branches and leaves. Plant twins are immediately helpful in plant sciences, such as for the automation of cultivation and breeding. He collaborates with plant scientists to seek the application of those technologies and to apply modern CV techniques for many practical plant-related problems. His achievement spans the interdisciplinary research field between computer and plant sciences, and he has published important and highly regarded papers in each field. He is leading the emergence of a new research field, plant phenomics, where computer vision plays an important role in acquiring plant phenotypes (e.g., shape and appearance).

2022

Mitsuaki Akiyama (Nippon Telegraph and Telephone Corporation (NTT))

Research on Offensive Cybersecurity Measurement and Countermeasure

Mitsuaki Akiyama is a Senior Distinguished Researcher in NTT Social Informatics Laboratories. He received his M.E. and Ph.D. degrees from Nara Institute of Science and Technology in 2007 and 2013. Since joining NTT in 2007, he has been engaged in research and development on cybersecurity. He received IPSJ Activity Contribution Award (2015), Cybersecurity Encouragement Award of the Minister for Internal Affairs and Communications (2020), and ISOC NDSS 2020 Distinguished Paper Award (2020).

【Recommendation】
He has been focusing on cyber attacks by malware since the early stage of his research career, about 15 years ago, and has been engaged in research on offensive cybersecurity (i.e., technologies from the attacker' s perspective) to observe, analyze and counter cyber attacks. His developed techniques have been used in several national projects (eg., ACTIVE) in collaboration with the industrial community (i.e., ICTISAC Japan) and have contributed significantly to the prevention of malware spread. He promotes not only technology development, but also research ethics of cybersecurity to ensure ethical research process in cybersecurity and bridge the gap between academia and industry. He also created the unique datasets for cybersecurity research. His shared datasets have been used in approximately 100 research papers and have greatly contributed to facilitate cybersecurity research. His achievements include 54 international conference papers and 37 international journals including top-level ones. As the proofs that he is one of the world leaders in cybersecurity, he won the 10 best paper awards of international conferences and journals, for example NDSS 2020 (Tier-1, CORE-Rank A*).

Akira Uchiyama (Osaka University)

Research on Context Recognition by Multimodal Sensors

Akira Uchiyama received his Ph.D. in information and computer science from the Graduate School of Information Science and Technology, Osaka University in 2008.
He was a Visiting Scholar with the University of Illinois at Urbana–Champaign in 2008.
He joined Osaka University in 2009. Since 2021, he is an Associate Professor at the Graduate School of Information Science and Technology, Osaka University. He is also a JST PRESTO researcher since 2019. His current research interests include mobile/wireless sensing and applications in pervasive and ubiquitous computing. He received 38 awards including Funai Information Technology Award for Young Researchers (2011), Kasami Award (2016), ICMU Best Paper Award (2018), and IPSJ DICOMO Best Conversant Award (2018).

【Recommendation】
His main research focus is context recognition by multimodal sensors. His notable achievements include localization, urban sensing, and healthcare. One of his remarkable achievements is core body temperature estimation using wearable sensors and environmental sensors for avoiding heat stroke in real-time. He has led a project to detect signs of heat stroke in the Society 5.0 project of the Ministry of Education, Culture,
Sports, Science and Technology (MEXT), and developed low power sensors for behavior estimation and a backscatter-based behavior sensing platform in MEXT, JST's ACT-I, and PRESTO projects. As a young researcher in Japan, he is highly evaluated in this field. Also, he has collaborated with several companies for avoiding heat stroke, including the largest telecommunications equipment manufacturer in the world. He has published 30 journal papers and 36 international conference papers. He has coached many PhD/MS students and led and produced many excellent research achievements. His outstanding achievements include publications in international top conferences and journals such as PerCom, MobiSys, UbiComp, IEEE TMC, Elsevier PMC, and ACM UbiComp Journal (IMWUT). He has received 39 awards including ICMU2018 Best Paper Award.

Takuro Yonezawa (Nagoya University)

Outstanding Research on Sensing and Application Platform for Urban Computing

Takuro Yonezawa is an associate professor in the Graduate School of Engineering, Nagoya University, Japan. He received a Ph.D. degree in the Media and Governance from Keio University in 2010. His research interests are the intersection of the distributed systems, human-computer interaction and sensors/actuators technologies. He is also leading several smart city projects as a technical coordinator, such as EU FP7, Horizon2020/NICT European-Japanese collaborative research project (ClouT/BigClouT project), NICT social big data project, MIC G-Space city project, and so on. He was awarded IBM Ph.D. Fellowship Award (2009), IPSJ Yamashita SIG Research Award (2013/2019) and so on.

【Recommendation】
Dr. Yonezawa has been engaged in research in the field of urban computing, especially urban-scale dense and large-scale data sensing, data distribution, and data analysis methods. In order to efficiently collect data from the real environment and to promote the utilization of data in various services, socially applicable and practical technologies are required. The method he has developed is very unique in that it includes a viewpoint to increase the affinity and acceptability of technology to society and tries to solve it with computer science technology, and is addressed as important research. In addition, his research is not limited to basic technologies, but he has been leading various empirical studies in multiple cities, using the developed technologies to devise and build new applications and services in collaboration with international industry-government-academia teams. Dr. Yonezawa's research achievements have been published in 30 journals and 82 refereed international conferences, and he has presented 102 research reports mainly at the Information Processing Society of Japan. He has received 32 awards for his research achievements, including two Yamashita Memorial Research Awards from IPSJ, one Digital Practice Award, and awards at six international conferences, including organized by IEEE.

2021

Daisuke Sakamoto (Hokkaido University)

Outstanding Research on Designing User Interface and Interaction

Daisuke Sakamoto is an Associate Professor of Human-Computer Interaction lab, Hokkaido University. He received his Ph.D. in Systems Information Science from Future University-Hakodate in 2008. He was an Intern researcher of ATR Intelligent Robotics and Communication Labs (2006-2008). He worked at The University of Tokyo as a Research Fellow of the Japan Society for the Promotion of Science (2008-2010). He joined JST ERATO Igarashi Design Interface Project as a researcher (2010). After that, he backed to The University of Tokyo as an Assistant Professor (2011) and a Project Lecturer (2013-2017). His research interests include Human-Computer Interaction and Human-Robot Interaction, which focused on user interaction with people and interaction design for computing systems.

【Recommendation】
Dr. Daisuke Sakamoto. has been working on designing user interfaces and interaction to make stateof-the-art computing technology accessible and usable. Specifically, he has led the fields of HumanComputer Interaction (HCI) and Human-Robot Interaction (HRI) by creating enabling technologies that realize novel interaction to make computing technology available for the end-users. His research contributions have been published in 42 journal articles, 41 international full conference papers, 22 international poster presentation and demonstration, and 110+ Japanese domestic papers, including 16 IPSJ Journal articles, 19 IPSJ SIG related venues. Those contributions were awarded 38 times, including Best paper award (IPSJ Interaction symposium 2007, ACM/IEEE HRI2007), IPSJ Outstanding Paper Award (2009), and so on.
He is an IPSJ member since 2004, IEEE/IEEE-CS member since 2020. Regarding activities for IPSJ, he has served as a Program Committee or Organizing Committee member of IPSJ symposiums 24 times, including program chairs 2 times. IPSJ recognized his achievement as new generation planning committee member and gave him IPSJ Activity Contribution Award (2015). Regarding IEEE activities, he had served as special session chair of IEEE GCCE (2013, 2014), and fundraising & exhibitions co-chair of ACM/IEEE HRI (2013, 2015 – 2017). He is appointed as General co-chair of ACM/IEEE HRI2022.

Takuya Azumi (Saitama University)

Outstanding Research on Embedded Real-time Platform

Takuya Azumi received his Ph.D. degree from the Graduate School of Information Science, Nagoya University in 2009. From 2008, he was under the research fellowship for young scientists for Japan Society for the Promotion of Science. From 2010, he was an Assistant Professor at the College of Information Science and Engineering, Ritsumeikan University. From 2014, he was an Assistant Professor at the Graduate School of Engineering Science, Osaka University. From 2018, he is an Associate Professor at the Graduate School of Science and Engineering, Saitama University. He also works as a JST-PRESTO researcher since 2017. His research interests include embedded real-time systems. He received ESS Best Paper Award (2019), IEEE/ACM DS-RT Best Paper Runner-up Award (2019), IEEE ICESS Outstanding Paper Award (2020), and IPSJ Yamashita Award (2021). He contributed to the global standardization of IEEE 2804-2019.

【Recommendation】
Prof. Takuya Azumi has been working on embedded real-time platform. Specifically, he has led the fields of embedded real-time systems. His research contributions have been published in 23 journals, and 61 international conference papers among which 8 journals, 45 international papers (especially, the IEEE ICCPS 2018 paper has been cited 125 times) have been published by IPSJ and IEEE-CS. He received 10 awards from IPSJ and IEEE-CS. He has presented more than 40 invited talks and 2 keynote talks. He has received 176.6M JPY research budget thus far as a principal investigator. He is an IPSJ member since 2004, IEEE member and IEEE-CS member since 2006. Regarding activities for IPSJ, he has been a committee member of SIG-EMB, SIG-OS, and a PC chair of SWEST 15/16/17 which is an IPSJ SIG-EMB workshop, and a PC member of ESS which is an IPSJ SIG-EMB symposium. Regarding activities for IEEE, he has served as a WiP chair and a Publicity co-chair of CPSNA 2013, a Publication Chair of CPSNA 2014 and CPSNA 2015, and a PC member of EUC 2011, ISORC 2014/2015/2016, RTCSA 2015/2020, and COOL chips XIX. In addition, he contributed to making an IEEE standard (IEEE 2804-2019 Published Date:2020-01-24).

Yuichi Sei (the University of Electro-Communications)

Privacy-Preserving Web/IoT Data Analysis

Yuichi Sei received the Ph.D. degree in information science and technology from the University of Tokyo in 2009. From 2009 to 2012, he was with the Mitsubishi Research Institute. He joined the University of Electro-Communications in 2013, and is currently an associate professor in the Graduate School of Informatics and Engineering. He is also a JST PRESTO researcher and a visiting researcher at Mitsubishi Research Institute. His current research interests include privacy-preserving data mining, and agent technologies. He received DICOMO Best Paper Award in 2007 and IPSJ Best Paper Award in 2017.

【Recommendation】
Various people and organizations are considering whether to build and spread new services that utilize Web and IoT data across the board. It is expected that systems and infrastructure will be in place to distribute and combine these data in the future. However, it will be difficult to predict from where personal privacy information will leak. Therefore, the construction of a solid common framework to protect privacy is an important issue. The purpose of this study is to protect and share Web/IoT data containing privacy information to enable safe, secure and accurate analysis. He developed the world's first algorithms for data collection, machine learning, and statistical analysis of data containing more than tens of thousands of attributes with errors and missing values, while protecting privacy.
He is collecting IoT data, including privacy information, by having subjects live in an apartment building, and confirming the usefulness of the proposed algorithms for real-world data. The research is being conducted with an eye toward real-world applications, taking into account the combination of unknown data and errors, and has been mathematically rigorously proven to be safe. He is on track to become one of the world's top researchers in both theory and practice.

2020

Fuyuki Ishikawa (National Institute of Informatics)

Research on Intelligence-driven Engineering of Dependable Smart Systems

Fuyuki Ishikawa is Associate Professor, Information Systems Architecture Science Research Division, and Deputy Director, GRACE Center, at National Institute of Informatics. He also serves as a Visiting Associate Professor, Graduate School of Informatics and Engineering, University of Electro-Communications. He received his Ph. D. degree in Information Science and Technology from The University of Tokyo in 2007. He has worked on a range of research topics in Software Engineering and Autonomous, Smart Systems.

【Recommendation】
Dr. Ishikawa has conducted intensive research crossing the fields of software engineering and smart systems. The excellence of his research lies in novel problem settings for quality and dependability in emerging paradigms, often linked with the industry demands, as well as interdisciplinary technical solutions primarily based on evolutionary computation. His early work focused on the field of services computing. He provided novel technical solutions on automated design for web and cloud systems. He has recently been focusing on quality and dependability of smart cyber-physical systems, especially autonomous driving systems and machine learning-based systems. For the latter, he is leading a new academia-industry community on machine learning systems engineering. His recent key publications were based on industrial problems and appeared in top conferences of software engineering and of evolutional computation.

Ryota Shioya (The University of Tokyo)

Outstanding Achievements on Microprocessor Architecture

Ryota Shioya received his Ph.D. in Information and Communication Engineering from the University of Tokyo in 2011. From 2011, he was an assistant professor at the Graduate School of Engineering, Nagoya University. Since 2018, he has been an associate professor at the Graduate School of Information Science and Technology, the University of Tokyo. His current research interests include computer systems and microprocessors. He received IEEE Computer Society Japan Chapter Young Author Award（2011）and IPSJ Ymashita Award.

【Recommendation】
Prof. Shioya has outstanding achievements in research on high-performance, low-energy microprocessor architectures. It is well known that microprocessors are key components of computer systems, and their architectural optimizations ate critical challenges. Although microprocessor research itself has a long history, still many vendors and researchers keep trying to develop new technologies. His distinctive contribution is the “innovative front-end design” of microprocessors. Many researchers have focused on “back-end design,” where actual calculations like addition and subtraction are executed. In contrast, Prof. Shioya discovered the significant potential to optimize the front-end (instruction fetching, decoding, and register reading) part, then proposed a new concept to make the front-end more intelligent. This approach changes the design balance of microprocessors, and it has not so far been explored in our community. Based on the new concept, he has proposes innovative ideas that significantly improve the performance-energy efficiency of microprocessors. His three papers have been accepted in a TOP tier computer architecture conference called MICRO (IEEE/ACM International Symposium on Microarchitecture) that has the oldest history in this research area (acceptance rate is around or less than 20%). Prof. Syoya is the only young researcher whose papers were constantly accepted at the top conference.

Kazuya Murao (Ritsumeikan University)

Outstanding Research on Human Activity Recognition for Wearable Computing

Kazuya Murao received Ph.D. in Information Science and Technology in 2010 from Osaka University. From 2010, JSPS Reserch Fellow (PD). From 2011, post-doc researcher at Kobe University. From 2011, assistant professor at Kobe University. From 2014, assistant professor at Ritsumeikan University. From 2017, associate professor at Ritsumeikan University. In 2017, visiting researcher at University of Freiburg, Germany. From 2019, JST PRESTO researcher. He is working on wearable computing and human activity recognition. He received IPSJ DICOMO2012 Best Paper Award, Journal of Information Processing (JIP) Specially Selected Paper (twice), Human interface society 2015 Best Journal Paper Award, ACM ISWC2019 Best Paper, etc.

【Recommendation】
Dr. Kazuya Murao is leading research on the field of human activity recognition for human-computer interaction. One of his outstanding research is about context-aware wearable sensor. His proposed system improves the granularity of contexts of a user situation and supplies power based on the optimal sensor combination by reducing energy consumption. In addition, in proportion to the remainder of power resources, the proposed system reduces the number of active sensors within the tolerance of accuracy. His researches are highly evaluated by international research communities including wearable computing, interactive systems, and so on.

2019

Atsushi Shimada (Kyushu University)

Outstanding Research on Real-time Learning Analytics

Atsushi Shimada received the M.E. and D.E. degrees from Kyushu University in 2007. He is an Associate Professor of Faculty of Information Science and Electrical Engineering, Kyushu University. He also works as a JST-PRESTO researcher since 2015. His current research interests include learning analytics, pattern recognition and media processing and image processing. He received MIRU Interactive Presentation Award (2011, 2017), MIRU Demonstration Award (2015), Background Models Challenge 2012 The First Place (2012), PRMU research award (2013), SBM-RGBD Challenge The First Place (2017), ITS Symposium Best Poster Award (2018), JST PRESTO Interest Poster Award (2019).

【Recommendation】
Dr. Atsushi Shimada is leading research on real-time learning analytics, which is a combination of learning analytics with real-time processing. Much attention has been paid to learning analytics, which aims at optimization of learning and teaching through analysis of educational big data. He has contributed to develop a digital learning environment containing an e-learning systems and an e-book system for collecting educational big data from onsite lectures, so as to accelerate effective feedback in students’ active learning and teachers’ adaptive teaching. His major contributions are the development of a real-time learning analytics platform and various applications such as learning pattern mining, prediction of academic performance, and recommendation of learning materials. The outstanding achievements are 1) automatic summarization of lecture materials for enhanced preview, 2) stream analytics of educational big data for supporting on-site lectures, and 3) activity analytics of individual students from event stream data.

Takuya Maekawa (Osaka University)

Outstanding Research on Zero-shot and Few-shot Unobtrusive Context Recognition for Pervasive Computing

Graduate School of Information Science and Technology, Osaka University. Takuya Maekawa received the B.E., M.E., and Ph.D. degrees from Osaka University, Osaka, Japan in 2002, 2003, and 2005, respectively. He joined NTT Communication Science Laboratories in 2006. He is currently an associate professor at Osaka University, Japan. His research interests include ubiquitous computing and wearable sensing.

【Recommendation】
Dr. Takuya Maekawa has studied on sensor-based context recognition techniques. Specifically he focuses on human activity recognition and indoor positioning techniques that can be easily applied to an environment of interest with small costs. He has developed practical context recognition techniques with a small number of unobtrusive sensors and machine learning. His major achievements are summarized into three topics, 1) object-based activity recognition with wrist-worn heterogeneous sensors, 2) constructing activity recognition models with small costs using physical characteristics information, and 3) unsupervised factory activity recognition. His research achievements enabling pervasive computing with low costs are highly evaluated by international research community.

2018

Yutaka Arakawa (Nara Institute of Science and Technology)

Outstanding Research on Human Behavior Change by Information Technology

Akira Kawai (Shiga University)

Outstanding Research on Intelligent Car Navigation System for Multilevel Parking Facilities

Yukihiko Shigesada (Hosei University)

Outstanding Achievements on International AI Programming Contest "SamurAI Coding"

Press release (Japanese)