略歴
東京大学空間情報科学研究センター准教授。2012年慶應義塾大学環境情報学部卒業、2014年同大学 大学院 政策・メディア研究科修士課程修了、2017年同研究科後期博士課程修了。博士（政策・メディア）。2017年同研究科特任助教、2018年オウル大学（フィンランド）Center for Ubiquitous Computing ポスドク研究員、2019年東京大学生産技術研究所助教を経て、2022年同大学空間情報科学研究センター講師。2025年より現職。2015年カーネギーメロン大学（米国）Human-Computer Interaction Institute（HCII）訪問研究員、2025年ワシントン大学（米国）訪問研究員、JSTさきがけ研究員。ユビキタスコンピューティング、モバイル・ウェアラブルセンシング、IoT、Just-in-Time介入、mHealthに関する研究に従事。IEEE HealthCom Best Paper Award（2014）、ACM UbiComp Best Poster Award（2024、2025）、情報処理学会SIG-UBI優秀論文賞、IPSJ/IEEE Computer Society Young Computer Researcher Award（2026）など、受賞多数。
ニュース
すべて見る →
研究トピック
研究プロジェクトを見る →- ユビキタスコンピューティング
- モバイル・ウェアラブルセンシング
- IoT・エッジコンピューティング
- ヒューマン・コンピュータインタラクション
- モバイルヘルス (mHealth)
- 適時適応型介入 (JITAI)
主要業績
すべて見る →
From a Point to Hundreds: Embracing LiDAR on Commodity Smartphones for Fine-Grained Pulmonary Function Sensing
Xuefu Dong, Wenwei Li, Minhao Cui, Zilong Wang, Lupeng Zhang, Akihito Taya, Yuuki Nishiyama, Kaoru Sezaki, Lili Qiu, Jie Xiong
Proceedings of the 2026 ACM/IEEE International Conference on Embedded Artificial Intelligence and Sensing Systems, 2026
Wireless sensing is an emerging technology with a wide range of applications, but most existing systems capture only the motion of a single point, such as in respiration monitoring. This limitation is critical for tasks requiring multi-point data, such as respiratory volume measurement, where different body points provide distinct information, and a single point cannot represent them all. In this paper, we propose LiSen, a smartphone-integrated LiDAR system for multi-point wireless sensing, and demonstrate its contact-free capability for measuring respiratory volume. LiSen uses smartphone LiDAR to track multiple chest and abdominal points, enabling the first ranging-based spirometer system that captures the full volume curve without new-user calibration. We leverage the unique feature of multi-point sensing to address challenges such as body interference, diverse breathing patterns, and pressure differences. Tests with 35 examinees show that LiSen accurately estimates both instantaneous forced expiratory and inspiratory volume, achieving mean absolute errors below 0.24 L and 0.30 L, respectively, and an 8.93\% error for four common pulmonary function indices.
AEDHunter: Investigating AED Retrieval in the Real World via Gamified Mobile Interaction and Sensing
Helinyi Peng, Akihito Taya, Yuuki Nishiyama, Kaoru Sezaki
Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., 2026
Early defibrillation significantly improves survival rates in cases of out-of-hospital cardiac arrest. However, limited public awareness of Automated External Defibrillator (AED) locations constrains their effective use. Existing solutions, such as static 2D maps, often fall short in urgent or complex real-world scenarios. To address this challenge, we developed AEDHunter, a gamified, location-based mobile application designed to transform AED retrieval into an engaging and repeatable practice experience. Leveraging smartphone sensors to analyze participants' movement and learning patterns, and using low-cost Bluetooth tags to verify arrivals at AED locations, AEDHunter guides users through multiple sessions of AED discovery. In a real-world evaluation study, participants significantly reduced their AED retrieval times after repeated practice sessions and reported increased confidence in locating AEDs. Additionally, we employ a two-state activity detector to identify “exploratory pauses”, which are then used as a behavioral learning signal to quantify hesitation and its progressive reduction through practice. Our findings suggest that gamified applications like AEDHunter can improve AED retrieval performance through repeated, in-situ training and enhance self-reported preparedness, offering design insights for technology-supported learning and public safety applications.
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}%.
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.
学歴
博士（政策・メディア）
慶應義塾大学 大学院 政策・メディア研究科
2014年4月 - 2017年9月 · Cyber Informatics
修士（政策・メディア）
慶應義塾大学 大学院 政策・メディア研究科
2012年4月 - 2014年3月 · Cyber Informatics
学士（環境情報学）
慶應義塾大学 環境情報学部
2008年4月 - 2012年3月 ·
職歴
客員研究員
Information School, University of Washington
2025年10月 - 現在 · シアトル、アメリカ合衆国
准教授
東京大学 空間情報科学研究センター
2025年4月 - 現在 · 千葉県柏市
講師
東京大学 空間情報科学研究センター
2022年4月 - 2025年3月 · 千葉県柏市
助教
東京大学 生産技術研究所
2019年7月 - 2022年3月 · 東京都
ポスドク研究員
Faculty of Information Technology and Electrical Engineering, University of Oulu
2018年4月 - 2019年6月 · オウル・フィンランド
特任助教
慶應義塾大学 大学院 政策・メディア研究科
2017年10月 - 2018年3月 · 神奈川県
非常勤講師
慶應義塾大学 環境情報学部
2017年4月 - 2018年3月 · 神奈川県
非常勤助手
青山学院大学 理工学部
2017年4月 - 2017年9月 · 東京都
客員研究員
Human-Computer Interaction Institute, School of Computer Science, Carnegie Mellon University
2015年9月 - 2016年3月 · ペンシルベニア州、アメリカ
Research Assistant (RA)
慶應義塾大学大学院 グローバル環境システムリーダープログラム (GESL)
2014年4月 - 2017年3月 · 神奈川県
研究費
研究代表者 (PI)
研究助成(A)
慣性センサを搭載した耳装着デバイスによる姿勢動揺計測手法の開発
公益財団法人立石科学技術振興財団 · PI
2025年
科研費 国際共同研究加速基金（国際共同研究強化）
Passive Mobile Sensingを用いた産後うつ症状検出基盤の国際的な拡張と透明性の向上
日本学術振興会 (JSPS) · PI
2024年 - 2026年
企業共同研究
ウェアラブルデバイスを用いた乳児のコンテキスト検知に関する研究
株式会社ファーストアセント · PI
2022年 - 2023年
企業共同研究
スマートフォンから得られるセンサデータ処理およびその活用に関する研究
Yahoo! JAPAN 研究所 · PI
2021年 - 現在
研究分担者
Beyond 5G研究開発促進事業
行動変容と交通インフラの動的制御によるスマートな都市交通基盤技術の研究開発
国立研究開発法人情報通信研究機構 (NICT) · Co-Investigator
2021年 - 2023年
ウイルス等感染症対策に資する情報通信技術の研究開発
アフターコロナ時代の超多様性社会を実現するダイバーシティ・ドリブン・トランスフォーメーション技術の研究開発
国立研究開発法人情報通信研究機構 (NICT) · Co-Investigator
2021年 - 2023年
科研費 基盤研究(A)
アニマルウェアラブル2.0：野生動物IoTの高速通信・高信頼機構の確立
日本学術振興会 (JSPS) · Co-Investigator
2021年 - 2023年
科研費 基盤研究(A)
開発途上コミュニティのための分散協調型学習アナリティクス
日本学術振興会 (JSPS) · Co-Investigator
2020年 - 2024年
招待講演
A-UVI：個人レベルの精密な紫外線曝露評価に向けた地球観測データに基づくGNSS支援型の紫外線指数推定手法
FIT2026 第25回情報科学技術フォーラム トップコンファレンス6-2 ユビキタス・モバイルコンピューティング — 北九州学術研究都市、北九州
Linkパッシブモバイルセンシングを用いた持続可能な人の心身の状態推定基盤
文部科学省と国立大学附置研究所・センター 個別定例ランチミーティング 第142回 東京大学 空間情報科学研究センター — 東京
LinkWorkshop on Ubiquitous Computing and Spatial Data
ユビキタスコンピューティングと空間データ利用に関するワークショップ、ヘルシンキ大学 — ヘルシンキ、フィンランド
Measuring Human Well-Being in Cities Using Smartphone Data
Urban AI Symposium — ニューヨーク、アメリカ
LinkPassive Mobile/Wearable Sensing for Human Mental and Physical Health
The 25th Japan Seattle AI Innovation Meetup — シアトル、アメリカ
In-the-wild IoT: Lessons from real-world implementations
Mobile AI Systems (ACM MobiSys 2024 Adjunct Workshop) — 東京
LinkEncouraging human behavior change by using passive mobile sensing and lifelog sharing
SSE Invited Speaker Series, Stevens Institute of Technology — ニュージャージー、アメリカ
LinkProjections for DPS Workshop Programs in 30 Years
第30回マルチメディア通信と分散処理ワークショップ (DPSWS 2022) — 米子
LinkMOCHA: Bluetoothビーコンを用いたキャンパス混雑モニタリング・接触確認・滞在記録システムの開発と運用
Location Business Japan — 東京
LinkSenbayKit: センサデータ統合映像を用いた複数データストリームの即時アーカイブ・再配信基盤の紹介
計測自動制御学会 スマートセンシングシステム部会 (SSS) ワークショップ — 熊本
Linkウェルビーイングスマートシティの実現に向けたモバイル・ウェアラブルデバイスを用いた人間・都市センシング・分析プラットフォーム
地域IoT・情報能力コンソーシアム / ヘルス情報コンソーシアム 合同ワークショップ — 東京
Link
受賞
"GNSS信号情報と点群ニューラルネットワークを活用したUVインデックス推定における季節・地理的汎化性能の評価"（共著）
Outstanding research on mobile and wearable sensing for human safety
"A Mechanical Wi-Fi Antenna Device for Automatic Orientation Tuning with Bayesian Optimization"（共著）
"Recognizing Hidden-in-the-Ear Private Key for Reliable Silent Speech Interface Using Multi-Task Learning"
"Expression Recognition Based on Ear Canal Shape Detection Using Earbud and Ultrasound"（共著）
"Investigating Acceptable Voice-based Notification Timings through Earable Devices: A Preliminary Field Study"
"Toward Detecting Student-Athletes' Condition Using Passive Mobile and Wearable Sensing"
"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"（共著）
"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"（共著）
"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"（共著）
"An Online Task Offloading Strategy in Vehicular Edge Computing"（共著）
"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"（共著）
"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"（共著）
"Senbay: A Platform for Instantly Capturing, Integrating, and Restreaming of Synchronized Multiple Sensor-Data Stream"
"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" (共著)
"Towards health exercise behavior change for teams using life-logging"
学術活動
学会運営
チェア
| Publicity Chair | ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) 2025 | 2025 |
| Sponsorship Chair | ACM International Conference on Mobile Systems, Applications, and Services (MobiSys) 2025 | 2025 |
| Local Chair | ACM International Conference on Mobile Systems, Applications, and Services (MobiSys) 2024 | 2024 |
| Publicity Chair | International Conference on the Internet of Things (IoT) 2023 | 2023 |
| Publicity Chair | ACM International Conference on Mobile Systems, Applications, and Services (MobiSys) 2021 | 2021 |
| Web Chair | International Conference on Mobile Computing and Ubiquitous Networking (ICMU) 2021 | 2021 |
| Publicity Chair | ACM Conference on Embedded Networked Sensor Systems (SenSys) 2020 | 2020 |
| Web Chair | IEEE International Conference on Real-Time Computing Systems and Applications (RTCSA) 2018 | 2018 |
プログラム委員
| Program Committee | Activity and Behavior Computing (ABC) | 2021-2026 |
| Technical Program Committee | IEEE International Conference on E-health Networking, Application & Services (HealthCom) | 2024-2026 |
| Program Committee | ACM International Conference on Multimodal Interaction (ICMI) | 2024, 2025 |
| Technical Program Committee | International Conference on the Internet of Things (IoT) | 2024 |
| Technical Program Committee | IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM) | 2024-2026 |
| プログラム委員 (Late Breaking Results) | ACM International Conference on Mobile Human-Computer Interaction (MobileHCI) | 2019 |
| Technical Program Committee | EAI International Conference on Mobile Computing, Applications and Services (MobiCASE) | 2018 |
Associate Editor
| Associate Editor | SICE Annual Conference | 2022-2024 |
ワークショップ共同主催
| Co-organizer | Ubiquitous Mobile Sensing Workshop (ACM UbiComp 2018) | 2018 |
| Co-organizer | Workshop on Ubiquitous Mobile Instrumentation (UbiMI) (ACM UbiComp 2017) | 2017 |
| Co-organizer | Workshop on Ubiquitous Mobile Instrumentation (UbiMI) (ACM UbiComp 2016) | 2016 |
編集委員
| Associate Editor | Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT) | 2026 |
| 編集委員 | 情報処理学会論文誌 - UBI特集号 | 2020, 2022-2024 |
| 編集委員 | 計測自動制御学会論文集 | 2020, 2021, 2022 |
論文誌査読
国際会議査読
教育活動
講師
| Autumn 2026 | アーバンコンピューティング論 | 東京大学 新領域創成科学研究科 |
| Autumn 2025 | アーバンコンピューティング論 | 東京大学 新領域創成科学研究科 |
| Autumn 2024 | アーバンコンピューティング論 | 東京大学 新領域創成科学研究科 |
| Autumn 2023 | アーバンコンピューティング論 | 東京大学 新領域創成科学研究科 |
| Spring 2022 | 社会文化環境学概論 | 東京大学 新領域創成科学研究科 |
| Autumn 2022 | 全学自由研究ゼミナール（Webサービス・アプリデザイン概論） | 東京大学 |
| Autumn 2022 | アーバンコンピューティング論 | 東京大学 新領域創成科学研究科 |
| Autumn 2021 | 全学自由研究ゼミナール（Webサービス・アプリデザイン概論） | 東京大学 |
| Autumn 2021 | アーバンコンピューティング論 | 東京大学 新領域創成科学研究科 |
| Spring 2021 | Network Architecture | 東京大学 新領域創成科学研究科 |
| Autumn 2020 | 空間情報デザイン | 東京大学 新領域創成科学研究科 |
| Autumn 2018 | Mobile Computing | オウル大学 情報技術・電気工学部 |
招待講義
| Autumn 2017 | データ取得 (GIGA) - モバイルフォンを用いた人間行動センシング（招待講義） | 慶應義塾大学 環境情報学部 |
| Autumn 2014 | データ取得 (GIGA) - モバイルフォンを用いた人間行動センシング（招待講義） | 慶應義塾大学 環境情報学部 |
ティーチングアシスタント
| Autumn 2017 | 情報技術基礎 1&2 | 慶應義塾大学 環境情報学部 |
| Spring 2017 | 情報技術基礎 1&2 | 慶應義塾大学 環境情報学部 |
| Spring 2017 | システム構築実習（TA） | 青山学院大学 理工学部 |
| Autumn 2014 | オブジェクト指向プログラミング基礎（TA） | 慶應義塾大学 環境情報学部 |
| Autumn 2014 | 大規模環境システム（TA） | 慶應義塾大学 大学院 政策・メディア研究科 |
| Spring 2014 | 環境情報システムアーキテクチャ（TA） | 慶應義塾大学 大学院 政策・メディア研究科 |
| Autumn 2013 | ノベルファブリケーション入門（TA） | 慶應義塾大学 環境情報学部 |
| Autumn 2013 | オブジェクト指向プログラミング（TA） | 慶應義塾大学 環境情報学部 |
| Autumn 2013 | ユビキタス情報サービス（TA） | 慶應義塾大学 環境情報学部 |
| Spring 2012 | ユビキタスシステムアーキテクチャ（TA） | 慶應義塾大学 環境情報学部 |