EDBT 2026 Demo / reviewers in the wild / expert
Jiexin Ding
dblp:317/0642
· DBLP profile ↗
3ranked-venue papers
1as first author
3since 2021 · last 2025
0009-0008-9599-1586ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Human-computer interaction and ubiquitous computing · 3 · 1 first-author · 3 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Human-computer interaction and pervasive computing
2 papers |
Learning and educational technologies · 40% Interaction techniques and input · 26% Wearable and physiological sensing · 26% | |
| Artificial intelligence
1 paper |
Language models and text generation · 100% |
Topics — the 4 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Learning and educational technologies
language learning |
0.9 | 1 | 2025 | Unknown Word Detection for English as a Second Language (ESL) Learners using Gaze and Pre-trained Language Models · CHI 2025 |
Wearable and physiological sensing › earable sensing
earphone-based sensing |
0.6 | 1 | 2022 | FaceOri: Tracking Head Position and Orientation Using Ultrasonic Ranging on Earphones · CHI 2022 |
Natural language and speech › Language models and text generation
pre-trained language model |
0.3 | 1 | 2025 | Unknown Word Detection for English as a Second Language (ESL) Learners using Gaze and Pre-trained Language Models · CHI 2025 |
Ubiquitous computing and smart environments › context-aware computing
context-aware interaction |
0.2 | 1 | 2022 | FaceOri: Tracking Head Position and Orientation Using Ultrasonic Ranging on Earphones · CHI 2022 |
Methods — techniques the papers use, named apart from their topics
pre-trained language model · 1.7gaze tracking · 1.7user study · 0.6ultrasonic ranging · 0.6acoustic sensing · 0.6
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Unknown Word Detection for English as a Second Language (ESL) Learners using Gaze and Pre-trained Language Models
Jiexin Ding, Bowen Zhao 0004, Yuntao Wang 0001, Xinyun Liu, Ishan Chatterjee, Yuanchun Shi |
CHI | 1 |
| 2025 | FlowRing: Integrated Microgesture and Surface Interaction Ring for Versatile XR Input MHCI010abstractAs Extended Reality (XR) advances, a device has the potential to be used across contexts from immersive productivity at a desk to on-the-go, public scenarios. Existing input solutions lack the versatility to provide both high-throughput, mouse-grade input and subtle, ergonomic interaction. We introduce FlowRing, a novel ring-form device that combines microgestures with precise 2D mouse-like input on surfaces. FlowRing supports five microgestures for discreet interaction and 2D input for richer tasks, using an optical flow sensor, skin-contact microphone, and IMU at the base of the finger. In a study with 11 participants, FlowRing achieved 93.6% microgesture recognition accuracy across sessions and 85.2% across unseen users, rising to 90.1% with just four gesture set examples from a new user. A separate 2D Fitts’ law study demonstrated its effectiveness for continuous input on various surfaces. FlowRing emerges as a versatile, user-friendly solution for the future of interactive technology. Ishan Chatterjee, Jiexin Ding, Anandghan Waghmare, Joseph Breda, Yuquan Deng, Bo Liu 0091, Yuntao Wang 0001, Shwetak N. Patel |
Proc. ACM Hum. Comput. Interact. | 2 |
| 2022 | FaceOri: Tracking Head Position and Orientation Using Ultrasonic Ranging on EarphonesabstractFace orientation can often indicate users’ intended interaction target. In this paper, we propose FaceOri, a novel face tracking technique based on acoustic ranging using earphones. FaceOri can leverage the speaker on a commodity device to emit an ultrasonic chirp, which is picked up by the set of microphones on the user’s earphone, and then processed to calculate the distance from each microphone to the device. These measurements are used to derive the user’s face orientation and distance with respect to the device. We conduct a ground truth comparison and user study to evaluate FaceOri’s performance. The results show that the system can determine whether the user orients to the device at a 93.5% accuracy within a 1.5 meters range. Furthermore, FaceOri can continuously track user’s head orientation with a median absolute error of 10.9 mm in the distance, 3.7° in yaw, and 5.8° in pitch. FaceOri can allow for convenient hands-free control of devices and produce more intelligent context-aware interactions. Yuntao Wang 0001, Jiexin Ding, Ishan Chatterjee, Farshid Salemi Parizi, Yuzhou Zhuang, Yukang Yan, Shwetak N. Patel, Yuanchun Shi |
CHI | 2 |