Prasoon Patidar

schoolCarnegie Mellon Universitybar_chart#2680 Global Rank

Total Papers

HCI Rank

#2680

Research Areas

Years Active

Recent Publications

Scaling Context-Aware Task Assistants that Learn from Demonstration and Adapt through Mixed-Initiative Dialogue

Daily tasks such as cooking, machine operation, and medical self-care often require context-aware assistance, yet existing systems are hard to scale due to high training costs and unpredictable and imperfect performance. This work introduces the PrISM framework, which streamlines the process of creating an assistant f…

UBICOMP '241st AuthorIntelligent Tutoring Systems & Learning Analytics +2

ClassID: Enabling Student Behavior Attribution from Ambient Classroom Sensing Systems

Patidar 等人开发ClassID环境课堂感知系统，通过多模态传感器和机器学习实现学生行为自动归因，帮助教师实时了解课堂动态与学生参与度。

UBICOMP '232nd AuthorHuman Pose & Activity Recognition +1

TAO: Context Detection from Daily Activity Patterns Using Temporal Analysis and Ontology

"Translating fine-grained activity detection (e.g., phone ring, talking interspersed with silence and walking) into semantically meaningful and richer contextual information (e.g., on a phone call for 20 minutes while exercising) is essential towards enabling a range of healthcare and human-computer interaction applic…

UBICOMP '231st AuthorHuman Pose & Activity Recognition +2

VAX: Using Existing Video and Audio-based Activity Recognition Models to Bootstrap Privacy-Sensitive Sensors

"The use of audio and video modalities for Human Activity Recognition (HAR) is common, given the richness of the data and the availability of pre-trained ML models using a large corpus of labeled training data. However, audio and video sensors also lead to significant consumer privacy concerns. Researchers have thus e…

DIS '233rd AuthorHonorable MentionIntelligent Tutoring Systems & Learning Analytics +1

"An Instructor is [already] able to keep track of 30 students": Students’ Perceptions of Smart Classrooms for Improving Teaching & Their Emergent Understandings of Teaching and Learning

Multi-modal classroom sensing systems can collect complex behaviors in the classroom at a scale and precision far greater than human observers to capture learning insights and provide personalized teaching feedback. As students are critical stakeholders in the adoption of smart classrooms for the improvement of teachi…

Quick Actions

Share this page

ios_share

X in mail

https://hci.top/en/authors/24445?source=all