Experiences

Basics

Name Tiantian Feng
Label Postdoc Research Scholar
Email tiantiaf@usc.edu
Phone (310) 500-9047
Url https://tiantiaf0627.github.io/

Work

Awards

Languages

English
Professional
Chinese
Native speaker

Projects

  • 2023 - Present
    Detecting and mapping stress patterns across space and time: Multimodal modeling of individuals in real-world physical and social work environments
    Sponsored by: NSF Smart Connected Health. Through our work, we aim to generate new analytic models to uncover and map the patterns and pathways that influence work-related stress to understand the primary contributing factors to stress across space and time. The project will develop methods for integrating different data types from the physical and social environment (e.g., temperature, lighting, conversational tones), physiology (e.g., heart rate data, electrodermal activity, movement), and personal experiences (e.g., ecologic momentary assessment) to identify patterns that inform personalized solutions for improving self-awareness and managing work-related health and well-being. We will develop individually contextualized understandings of stress among office workers using machine learning methods that incorporate heterogeneous and noisy multimodal data streams at multiple temporal resolutions while enabling the unsupervised discovery of behavioral routines.
    • Wearables
    • Natural Human Understanding
  • 2022 - Present
    EDA and Sensor Fusion for Fatigue/Affective State Detection
    Sponsored by: Toyota Research Institute North America (TRINA). Sensor analysis and fusion for detecting human states and affect is a challenging open problem in ubiquitous computing research, as well as a popular milestone for the automobile industry. The use of driver assistance systems has become increasingly popular due to advances in Artificial Intelligence, with the aim of improving road safety and reducing the number of accidents caused by human error. Despite their great potential, the deployment of such technologies is still at infant stage, especially when considering the driver’s affective state, which can greatly impact driving performance. This project aims to address this issue by developing systems and improving the performance of affective state detection in driving with the use of multimodal biometric sensor information, such as EDA, ECG, PPG, and respiration.
    • Biosignal
    • Natural Human Understanding
  • 2024 - Present
    SFARI: Multimodal, objective assessment of the ASD phenotype: Longitudinal stability and change across contexts
    Sponsored by: Simons Foundations
    • Autism
    • Natural Human Understanding
    • Speech
  • 2021 - 2023
    Using Automated Methods to Classify Language Ability in Children with Autism
    Sponsored by: Apple
    • Autism
    • Natural Human Understanding
    • Speech
  • 2021 - 2023
    USC-Amazon Reseach Award on Trustworthiness Computing
    Sponsored by: Amazon. Explore trustworthiness of modern foundation models and on-device learning
    • Federated Learning
    • Generative AI
    • Foundation Model
  • 2017 - 2020
    TILES: Tracking Individual Performance using Sensors
    The Tracking Individual Performance with Sensors (TILES) is a project holding multimodal data sets for the analysis of stress, task performance, behavior, and other factors pertaining to professionals engaged in a high-stress workplace environments. Biological, environmental, and contextual data was collected from hospital nurses, staff, and medical residents both in the workplace and at home over time. Labels of human experience were collected using a variety of psychologically validated questionnaires sampled on a daily basis at different times during the day. The data sets are publicly available and we encourage researchers to use it for data mining and testing their own human behavior models.
    • Wearable Sensing
    • Natural Human Understanding