Biography
Yuuki Nishiyama is an Associate Professor with the Center for Spatial Information Science (CSIS), The University of Tokyo. He received the B.A. degree in Environment and Information Studies from Keio University, Japan, in 2012, and the M.S. and Ph.D. degrees in Media and Governance from Keio University, Japan, in 2014 and 2017, respectively. In 2018, he was a postdoctoral researcher at the Center for Ubiquitous Computing, University of Oulu, Finland. He joined the Institute of Industrial Science (IIS), The University of Tokyo, as an Assistant Professor in 2019, and moved to CSIS as a Lecturer in 2022. He has been in his current position since April 2025. He was a visiting research scholar at the Human-Computer Interaction Institute (HCII), Carnegie Mellon University, USA, in 2015, and at the University of Washington, Seattle, USA, in 2025. He is also a JST PRESTO Researcher. His research interests include ubiquitous computing, mobile and wearable sensing, IoT, just-in-time adaptive intervention, and mHealth. He has received awards including the IEEE HealthCom Best Paper Award (2014), the ACM MobileHCI Honorable Mention Demo Award (2018), Best Poster Awards at ACM UbiComp (2024, 2025), and multiple IPSJ SIG-UBI Outstanding Paper Awards (e.g., 2020, 2021, 2022, and 2024).
News
View All →- 2025-10-15 award Best Poster Award at ACM UbiComp 2025 — "Recognizing Hidden-in-the-Ear Private Key for Reliable Silent Speech Interface Using Multi-Task Learning"
- 2025-10-14 talk Presented "A-UVI" at Smart Sensing in Health session, ACM UbiComp 2025
- 2025-10-01 grant Research proposal selected for JST PRESTO program
- 2025-09-14 visit Started a research stay at University of Washington, Seattle (until Nov 2026, funded by KAKENHI 23KK0209)
- 2025-06-18 paper "A-UVI: GNSS-Assisted EO-based UV Index Estimation Method" published in Proc. ACM IMWUT
Research Topics
See Research Projects →Selected Publications
View All →A-UVI: GNSS-Assisted EO-based UV Index Estimation Method for Individual-level Precise UV Exposure Assessment
Yuuki Nishiyama, Subaru Atsumi, Kota Tsubouchi, Kaoru Sezaki
Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., 2025
Excessive or insufficient exposure to ultraviolet (UV) light can have adverse effects on health, including the development of skin cancer, cataracts, and osteoporosis. An Earth observation (EO)-based UV index can estimate area-level UV indexes without effort in open-sky environments but can not provide sufficient accuracy for shaded environments. In contrast, conventional methods for monitoring individual-level, i.e., personal, UV exposure, such as mobile and wearable UV sensors, face limitations in terms of measurement and usability, presenting challenges for practical long-term usage. To address these issues, we introduce A-UVI, a method that enhances the accuracy of the EO-based UV index by leveraging raw signals from global navigation satellite systems (GNSS). By integrating this EO-based UV index and an attenuation ratio estimated from raw GNSS signals, our method especially improves estimation accuracy in shady environments affected by obstructions. We evaluated our method on data collected by different GNSS receivers in different mobility scenarios encompassing a diverse range of contexts and observation areas over the course of three days. Our evaluation showed that A-UVI estimates the UV index with a precision exceeding existing methods by at least 44.25%, achieving 5.53 times higher estimation accuracy in forest environments. We also confirmed that A-UVI is compatible with GNSS receivers in consumer-grade smartphones and has an average accuracy that is 23% better than the baseline EO-based method. Our findings demonstrate that utilizing raw GNSS signals enables accurate estimation of the UV index in various conditions, including in shaded areas, without the need for particular measurement actions or devices. This marks a significant advancement in enabling passive individual-level UV exposure monitoring and adaptive UV exposure management beyond simple exposure tracking.
HeadMon+: Domain Adaptive Head Dynamic-based Riding Maneuver Prediction
Zengyi Han, En Wang, Mohan Yu, Jie Wang, Yuuki Nishiyama, Kaoru Sezaki
IEEE Transactions on Mobile Computing, 2025
Micro-mobility has become a vital means of transportation in recent years, however, it has also resulted in a rise in traffic incidents. Timely tracking and predicting riders' maneuvers hold the potential to ensure active protection and allow for sufficient time to avert accidents by issuing timely warnings and interventions. We contend that the rider's head dynamics can provide valuable information regarding their subsequent maneuvers. Riders' traveling habits, however diverse, not to mention the rapidly varying riding environment. The above factors contribute to significant disruptions in the data source, and various micro-mobility forms further exacerbate the issue. We accordingly present HeadMon+, which predicts the rider's subsequent maneuver by examining their head dynamics, and it can effectively adapt to various riding conditions and individuals. The system incorporates a deep learning framework with an advanced domain adversarial network. By single-time pre-training, HeadMon+ is capable of adapting to new data domains, including human subjects, and riding conditions for robust maneuver prediction. Based on our evaluation, we have found that the maneuver prediction of HeadMon+ has an overall precision of 94% with a prediction time gap of 4 seconds. HeadMon+'s low cost and rapid response capability make it easily deployed and then contribute to enhancing safe riding.
ReHEarSSE: Recognizing Hidden-in-the-Ear Silently Spelled Expressions
Xuefu Dong, Yifei Chen, Yuuki Nishiyama, Kaoru Sezaki, Yuntao Wang, Kenneth Christofferson, Alex Mariakakis
Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems, 2024
Silent speech interaction (SSI) allows users to discreetly input text without using their hands. Existing wearable SSI systems typically require custom devices and are limited to a small lexicon, limiting their utility to a small set of command words. This work proposes ReHEarSSE, an earbud-based ultrasonic SSI system capable of generalizing to words that do not appear in its training dataset, providing support for nearly an entire dictionary’s worth of words. As a user silently spells words, ReHEarSSE uses autoregressive features to identify subtle changes in ear canal shape. ReHEarSSE infers words using a deep learning model trained to optimize connectionist temporal classification (CTC) loss with an intermediate embedding that accounts for different letters and transitions between them. We find that ReHEarSSE recognizes unseen words with an accuracy of pmnice{89.3}{10.9}%.
RideGuard: Micro-Mobility Steering Maneuver Prediction with Smartphones
Zengyi Han, Xuefu Dong, Liqiang Xu, Zhen Zhu, En Wang, Yuuki Nishiyama, Kaoru Sezaki
2024 IEEE 44th International Conference on Distributed Computing Systems (ICDCS), 2024
Although micro-mobility has become a popular and indispensable mode of transportation in recent years, it has also introduced a large number of traffic accidents. Timely tracking and predicting the maneuvers hold the potential to prevent accidents through prompt warnings and interventions. However, the open and simple structure of micro-mobility makes it hard to install sophisticated infrastructures for maneuver prediction. In this paper, we argue that the micro-mobility body dynamics provide sufficient information for maneuver prediction. Our preliminary study suggests that micro-mobility body dynamic patterns appear beforehand and exhibit the correlation with steering maneuvers. We accordingly present RideGuard, which leverages a built-in Inertial Measurement Unit on smartphones to achieve the prediction of steering maneuvers. Through a dual- stream CNN deep learning architecture, RideGuard effectively captures complex patterns and feature relationships from the time and frequency domain. Our extensive real-traffic experi- ments involving 20 participants demonstrate the superiority of RideGuard: employing a 3s detection window, RideGuard attains a minimum of 94% precision in maneuver prediction with a 5s prediction time gap. The low-cost and rapid response feature of RideGuard enables feasible deployment and promotes safer riding practices. Additionally, we open-source our well-labeled dataset to facilitate further research.
Multi-label Classification Model for Infant Activity Recognition Using Single Inertial Sensor
Ayaka Onodera, Riku Ishioka, Yuuki Nishiyama, Kaoru Sezaki
IEEE Pervasive Computing, 2024
Recording and sharing childcare information is crucial for accurately assessing a child's health status and taking appropriate action in case of illness or other emergencies. Although numerous applications and systems have been proposed to assist in recording and sharing these records, the process is still performed manually, presenting a significant burden for parents. Therefore, automatic recording of infants' daily activities is required. In this study, we implement a machine learning model to recognize multi-labeled infant activities using a chest-mounted low-sampling rate accelerometer. We collected accelerometer data from twenty-four infants between 6 and 24 months as a dataset. Based on the data, we extracted 25 time- and frequency-domain features calculated from the single accelerometer and user features to recognize the fourteen daily activities. The performance evaluation considering multi-label classification showed that our proposed model reaches over 88% in the F1 score in the best case.
HeadMon: Head Dynamics Enabled Riding Maneuver Prediction
Zengyi Han, Liqiang Xu, Xuefu Dong, Yuuki Nishiyama, Kaoru Sezaki
2023 IEEE International Conference on Pervasive Computing and Communications (PerCom), 2023
Although micro-mobility brings convenience to modern cities, they also cause various social problems, such as traffic accidents, casualties, and substantial economic losses. Wearing protective equipment has become the primary recommendation for safe riding. However, passive protection cannot prevent the occurrence of accidents. Thus, timely predicting the rider's maneuver is essential for active protection and providing more time to avoid potential accidents from happening. Through the qualitative study, we argue that we can use the rider's head dynamic as an information source to predict the rider's following maneuvers. We accordingly present HeadMon, a riding maneuver prediction system for safe riding. HeadMon utilizes the head dynamics of a rider by installing an inertial measurement unit on the helmet. It uses the extracted head dynamics features as the input of the deep learning architecture to achieve prediction. We implemented the HeadMon prototype on Android smartphone as a proof of concept. Through comprehensive experiments with 20 participants, the result demonstrates the excellent performance of HeadMon: not only could it achieve an overall precision of at least 85% for maneuver prediction under a 4s prediction time gap, but it also could keep a high accuracy under a low sampling rate. The low-cost feature of HeadMon allows it to be readily deployable and towards more safety riding.
Deep Learning-Based Compressed Sensing for Mobile Device-Derived Sensor Data
Liqiang Xu, Yuuki Nishiyama, Kota Tsubouchi, Kaoru Sezaki
Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, 2024
As the capabilities of smart sensing and mobile technologies continue to evolve and expand, storing diverse sensor data on smartphones and cloud servers becomes increasingly challenging. Effective data compression is crucial to alleviate these storage pressures. Compressed sensing (CS) offers a promising approach, but traditional CS methods often struggle with the unique characteristics of sensor data-like variability, dynamic changes, and different sampling rates-leading to slow processing and poor reconstruction quality. To address these issues, we developed Mob-ISTA-1DNet, an innovative CS framework that integrates deep learning with the iterative shrinkage-thresholding algorithm (ISTA) to adaptively compress and reconstruct smartphone sensor data. This framework is designed to manage the complexities of smartphone sensor data, ensuring high-quality reconstruction across diverse conditions. We developed a mobile application to collect data from 30 volunteers over one month, including accelerometer, gyroscope, barometer, and other sensor measurements. Comparative analysis reveals that Mob-ISTA-1DNet not only enhances reconstruction accuracy but also significantly reduces processing time, consistently outperforming other methods in various scenarios.
Assessing environmental benefits from shared micromobility systems using machine learning algorithms and Monte Carlo simulation
Helinyi Peng, Yuuki Nishiyama, Kaoru Sezaki
Sustainable Cities and Society, 2022
Shared micromobility systems (SMSs) are paving the way for new, more convenient travel options while also lowering transportation-related greenhouse gas (GHG) emissions. However, few studies have used real-world trip data to estimate SMSs' environmental benefits, especially for dockless scooter-sharing services. To this end, we proposed a system to estimate the GHG emission reduction effected by SMSs. First, several machine learning (ML) algorithms were utilized to identify citizens' travel mode choice preferences, and then the mode substituted by each shared micromobility trip was estimated. We compared the ML algorithms' estimation results and selected those from the random forest, lightGBM, and XGBoost model for further estimating GHG reductions. Second, the Monte Carlo simulations were used to simulate the substituted mode at the trip level to improve the reliability of the final GHG reduction estimation. Finally, the environmental benefits were calculated based on the trip distances and the travel modes that were substituted. Instead of estimating a specific number, we obtained a probabilistic outcome for the environmental benefits while considering the level of uncertainty. Our results suggest that SMSs have positive environmental impacts and have the potential to facilitate the decarbonization of urban transport. According to these findings, implications and suggestions on extending SMSs' environmental benefits are proposed.
iOS Crowd-Sensing Won't Hurt a Bit!: AWARE Framework and Sustainable Study Guideline for iOS Platform
Yuuki Nishiyama, Denzil Ferreira, Yusaku Eigen, Wataru Sasaki, Tadashi Okoshi, Jin Nakazawa, Anind K Dey, Kaoru Sezaki
Distributed, Ambient and Pervasive Interactions, 2020
The latest smartphones have advanced sensors that allow us to recognize human and environmental contexts. They operate primarily on Android and iOS, and can be used as sensing platforms for research in various fields owing to their ubiquity in society. Mobile sensing frameworks help to manage these sensors easily. However, Android and iOS are constructed following different policies, requiring developers and researchers to consider framework differences during research planning, application development, and data collection phases to ensure sustainable data collection. In particular, iOS imposes strict regulations on background data collection and application distribution. In this study, we design, implement, and evaluate a mobile sensing framework for iOS, namely AWARE-iOS, which is an iOS version of the AWARE Framework. Our performance evaluations and case studies measured over a duration of 288 h on four types of devices, show the risks of continuous data collection in the background and explore optimal practical sensor settings for improved data collection. Based on these results, we develop guidelines for sustainable data collection on iOS.
Education
Ph.D. in Media and Governance
Graduate School of Media and Governance, Keio University
Apr. 2014 - Sep. 2017 · Ubiquitous Computing
Master of Media and Governance
Graduate School of Media and Governance, Keio University
Apr. 2012 - Mar. 2014 · Ubiquitous Computing
Bachelor of Arts in Environment and Information Studies
Faculty of Environment and Information Studies, Keio University
Apr. 2008 - Mar. 2012 ·
Work Experience
Visiting Researcher
Information School, University of Washington
Oct. 2025 - Present · Seattle, WA, USA
Associate Professor
Center for Spatial Information Science, The University of Tokyo
Apr. 2025 - Present · Tokyo, Japan
Lecturer (Assistant Professor)
Center for Spatial Information Science, The University of Tokyo
Apr. 2022 - Mar. 2025 · Tokyo, Japan
Research Associate
Institute of Industrial Science, The University of Tokyo
Sep. 2019 - Mar. 2022 · Tokyo, Japan
Postdoctoral Researcher
Faculty of Information Technology and Electrical Engineering, University of Oulu
Apr. 2018 - Aug. 2019 · Oulu, Finland
Postdoctoral Researcher
Graduate School of Media and Governance, Keio University
Oct. 2017 - Mar. 2018 · Kanagawa, Japan
Part-time Lecturer
Faculty of Environment and Information Studies, Keio University
Apr. 2017 - Mar. 2018 · Kanagawa, Japan
Part-time Assistant
College of Science and Engineering, Aoyama Gakuin University
Apr. 2017 - Sep. 2017 · Tokyo, Japan
Visiting Research Scholar
Ubicomp Lab, Human Computer Interaction Institute (HCII), School of Computer Science, Carnegie Mellon University
Sep. 2015 - Mar. 2016 · PA, USA
Research Assistant (RA)
Global Environmental System Leaders Program (GESL), Keio University
Apr. 2014 - Mar. 2017 · Kanagawa, Japan
Funding
PRESTO
Personalized AI Development and Evaluation Platform Using Structure-Mapped Synthetic Time-Series Behavioral Data
Japan Science and Technology Agency (JST) · PI
2025 - 2028
Grant-in-Aid for Scientific Research (C) (KAKENHI)
Development of an Evidence-Based Childcare Support Platform through the Detection and Utilization of Latent Childcare Information
Japan Society for the Promotion of Science (JSPS) · Co-Investigator
2025 - 2028
Research Grant (A)
Development of a Postural Sway Measurement Method Using an Ear-Worn Device with Inertial Sensors
Tateisi Science and Technology Foundation · PI
2025
Grant-in-Aid for Early-Career Scientists (KAKENHI)
A Platform for Detecting Postpartum Depression through Passive Mobile Sensing
Japan Society for the Promotion of Science (JSPS) · PI
2023 - 2025
Joint Research
Activity recognition technologies for infants using wearable devices
First-Ascent Inc. · PI
2022 - 2023
Joint Research
Sensor data processing and its utilization obtained from smartphones
Yahoo! JAPAN R&D · PI
2021 - Current
Beyond 5G R&D Promotion Project
Smart urban transportation infrastructure technology through behavior change and dynamic control of transportation infrastructure
National Institute of Information and Communications Technology (NICT) · Co-Investigator
2021 - 2023
Research and Development of ICT for Countermeasures against Viruses and Other Infectious Diseases
Diversity-driven transformation technologies to realize a hyper-diverse society in the post-corona era
National Institute of Information and Communications Technology (NICT) · Co-Investigator
2021 - 2023
Grant-in-Aid for Scientific Research (A) (KAKENHI)
Animal Wearable 2.0: Establishment of high-speed communication and high-reliability mechanisms for wildlife IoT
Japan Society for the Promotion of Science (JSPS) · Co-Investigator
2021 - 2023
Grant-in-Aid for Scientific Research (A) (KAKENHI)
Distributed Collaborative Learning Analytics for Developing Communities
Japan Society for the Promotion of Science (JSPS) · Co-Investigator
2020 - 2024
Grant-in-Aid for Early-Career Scientists (KAKENHI)
Platform for detecting and utilizing kinesthetics to promote motor learning using voice-based ESMs
Japan Society for the Promotion of Science (JSPS) · PI
2020 - 2021
Invited Talks
Measuring Human Well-Being in Cities Using Smartphone Data
Urban AI Symposium — Brooklyn, New York, USA
LinkPassive Mobile/Wearable Sensing for Human Mental and Physical Health
The 25th Japan Seattle AI Innovation Meetup — Seattle, USA
In-the-wild IoT: Lessons from real-world implementations
Mobile AI Systems (ACM MobiSys 2024 Adjunct Workshop) — Tokyo, Japan
LinkEncouraging human behavior change by using passive mobile sensing and lifelog sharing
SSE Invited Speaker Series, Stevens Institute of Technology — NJ, USA
LinkProjections for DPS Workshop Programs in 30 Years
30th Workshop on Multimedia Communications and Distributed Processing (DPSWS 2022) — Yonago, Japan
LinkMOCHA: Development and operation of a system for monitoring campus congestion, confirming contact, and recording stays using Bluetooth beacons
Location Business Japan — Tokyo, Japan
LinkSenbayKit: Introduction of an infrastructure for immediate archiving and re-streaming of multiple data streams using sensor data-integrated video
Workshop of SIG Smart Sensing System (SSS), SICE — Kumamoto, Japan
LinkHuman and Urban Sensing and Analysis Platform Using Mobile and Wearable Devices for Realization of Well-being Smart City
Joint Workshop of Regional IoT and Information Capability Consortium / Health Information Consortium — Tokyo, Japan
LinkAwards
Outstanding research on mobile and wearable sensing for human safety
"Recognizing Hidden-in-the-Ear Private Key for Reliable Silent Speech Interface Using Multi-Task Learning"
"ウェアラブルUWBの時系列CIRを用いた呼吸推定" (as a co-author)
"Expression Recognition Based on Ear Canal Shape Detection Using Earbud and Ultrasound" (as a co-author)
"Investigating Acceptable Voice-based Notification Timings through Earable Devices: A Preliminary Field Study"
"Toward Detecting Student-Athletes' Condition Using Passive Mobile and Wearable Sensing"
"GNSS衛星ごとの信号情報に対する点群ニューラルネットワークを用いたUVインデックス推定" (as a co-author)
"ハンズフリーのデバイス操作のための汎用イヤラブルデバイスのIMUセンサーを用いた表情認識手法" (as a co-author)
"スマートフォンのGNSSセンサを用いたUVインデックス推定" (as a co-author)
"腕時計型ウェアラブルデバイスを用いた会話時間計測手法の構築に向けて" (as a co-author)
"タクシー車両を用いたマイクロモビリティ再配置手法の検討" (as a co-author)
"イヤラブルデバイスを用いた身体感覚記録・利活用システムの構築に向けて"
"An Online Task Offloading Strategy in Vehicular Edge Computing" (as a co-author)
"GPS信号受信状態を用いた紫外線量推定手法の検討" (as a co-author)
"環境センサを用いたタクシー車室内における感染症リスク評価に関する一検討" (as a co-author)
"Non-Negative Tensor Factrization を用いたドックレス型マイクロモビリティの利用形態分類手法の検討" (as a co-author)
"Senbay: A Platform for Instantly Capturing, Integrating, and Restreaming of Synchronized Multiple Sensor-Data Stream"
"Aqua Mapping: 水槽を介した観賞魚とのインタラクションシステム" (as a co-author)
"Senbay : 活動促進のためのスマートフォンを利用したセンサデータ統合型動画記録・共有・分析プラットフォーム"
"A robot control system for video streaming services by using dynamic encoded QR codes" (as a co-author)
"Towards health exercise behavior change for teams using life-logging"
Academic Services
Conference Organizer
Chair
| Publicity Chair | ACM UbiComp 2025 | 2025 |
| Sponsorship Chair | ACM MobiSys 2025 | 2025 |
| Local Chair | ACM MobiSys 2024 | 2024 |
| Publicity Chair | IoT 2023 | 2023 |
| Publicity Chair | ACM MobiSys 2021 | 2021 |
| Web Chair | ICMU 2021 | 2021 |
| Publicity Chair | ACM SenSys 2020 | 2020 |
| Web Chair | IEEE RTCSA 2018 | 2018 |
Program Committee
| Program Committee | ABC | 2021, 2022, 2024, 2025 |
| Technical Program Committee | IEEE HealthCom | 2024 |
| Program Committee | ACM ICMI | 2024 |
| Technical Program Committee | IoT | 2024 |
| Technical Program Committee | IEEE WoWMoM | 2024 |
| Program Committee for Late Breaking Results | ACM MobileHCI | 2019 |
| Technical Program Committee | EAI MobiCASE | 2018 |
Associate Editor
| SICE Annual Conference | 2022-2024 |
Workshop Co-organizer
| Ubiquitous Mobile Sensing Workshop (ACM UbiComp 2018) | 2018 |
| UbiMI 2017 (ACM UbiComp 2017) | 2017 |
| UbiMI 2016 (ACM UbiComp 2016) | 2016 |
Editorial Board
| Editorial Board Member | Journal of Information Processing (JIP) - Special Issue on UBI | 2020, 2022-2024 |
| Associate Editor | Transactions of SICE | 2020, 2021, 2022 |
Journal Reviewer
| ACM IMWUT | 2018-2025 |
| ACM TIOT | 2025 |
| ACM TOCHI | 2024 |
| IJHCS (Elsevier) | 2021, 2022 |
| SICE JCMSI | 2021, 2022 |
| MDPI Sensors | 2021 |
Conference Reviewer
| ACM CHI | 2026 |
| ACM MM | 2025 |
| ACM ICMI | 2023-2025 |
| ACII | 2024, 2025 |
| IEEE WoWMoM | 2024-2026 |
| IEEE HealthCom | 2024, 2025 |
| IoT | 2023-2025 |
| ABC | 2022-2026 |
| ACM MobileHCI | 2022 |
| IEEE SMC | 2022 |
| ACM UbiComp (Poster & Demo) | 2021-2025 |
| ACM CHI (WiP) | 2022 |
| ACM SenSys (Poster & Demo) | 2019 |
| ACM MUM (Rising Stars Forum) | 2018 |
| ACM MobiSys (Rising Stars Forum) | 2018 |
| AppLens (ACM UbiComp Workshop) | 2018 |
| EmotionAware (IEEE PerCom Workshop) | 2018 |
| UbiMI (ACM UbiComp Workshop) | 2016, 2017 |
Teaching
Lecturer
| Autumn 2025 | Urban Computing Theory | Graduate School of Frontier Sciences, The University of Tokyo |
| Autumn 2024 | Urban Computing Theory | Graduate School of Frontier Sciences, The University of Tokyo |
| Autumn 2023 | Urban Computing Theory | Graduate School of Frontier Sciences, The University of Tokyo |
| Spring 2022 | Introduction to Socio-Cultural Environmental Studies | Graduate School of Frontier Sciences, The University of Tokyo |
| Autumn 2022 | University-wide Open Research Seminar (Introduction to Web Service and Application Design) | The University of Tokyo |
| Autumn 2022 | Urban Computing Theory | Graduate School of Frontier Sciences, The University of Tokyo |
| Autumn 2021 | University-wide Open Research Seminar (Introduction to Web Service and Application Design) | The University of Tokyo |
| Autumn 2021 | Urban Computing Theory | Graduate School of Frontier Sciences, The University of Tokyo |
| Spring 2021 | Network Architecture | Graduate School of Frontier Sciences, The University of Tokyo |
| Autumn 2020 | Spatial Information Design | Graduate School of Frontier Sciences, The University of Tokyo |
| Autumn 2018 | Mobile Computing | Faculty of Information Technology and Electrical Engineering, University of Oulu |
Invited Lecture
| Autumn 2017 | Data Acquisition (GIGA) - Human Activity Sensing using Mobile Phone (Invited Lecture) | Faculty of Environment and Information Studies, Keio University |
| Autumn 2014 | Data Acquisition (GIGA) - Human Activity Sensing using Mobile Phone (Invited Lecture) | Faculty of Environment and Information Studies, Keio University |
Teaching Assistant
| Autumn 2017 | Fundamentals of Information Technology 1&2 | Faculty of Environment and Information Studies, Keio University |
| Spring 2017 | Fundamentals of Information Technology 1&2 | Faculty of Environment and Information Studies, Keio University |
| Spring 2017 | System Construction Practical Training (TA) | College of Science and Engineering, Aoyama Gakuin University |
| Autumn 2014 | Fundamentals of Object-Oriented Programming (TA) | Faculty of Environment and Information Studies, Keio University |
| Autumn 2014 | Large-Scale Environmental Systems (TA) | Graduate School of Media and Governance, Keio University |
| Spring 2014 | Environmental Information System Architecture (TA) | Graduate School of Media and Governance, Keio University |
| Autumn 2013 | Introduction of Novel Fabrication (TA) | Faculty of Environment and Information Studies, Keio University |
| Autumn 2013 | Object-Oriented Programming (TA) | Faculty of Environment and Information Studies, Keio University |
| Autumn 2013 | Ubiquitous Information Service (TA) | Faculty of Environment and Information Studies, Keio University |
| Spring 2012 | Ubiquitous System Architecture (TA) | Faculty of Environment and Information Studies, Keio University |