Ongoing Research Directions

Human–AI Collaboration for Neurodivergent Employment (2023-present, NSF sponsored)

Problem Statement

Neurodivergent adults, including individuals with autism and Attention Deficit Hyperactivity Disorder (ADHD), possess unique strengths such as hyperfocus, creativity, attention to detail, and persistence. However, they continue to face significant barriers in employment and workplace success. For instance, the unemployment rate for autistic adults has been over 80% for a decade, while adults with ADHD experience higher levels of workplace instability, such as being 2.1 times more likely to be unemployed, 60% more likely to be fired, and 3 times more likely to quit jobs impulsively compared to neurotypical workers. Besides both autistic and ADHD workers often encounter challenges related to sustained attention, task planning and organization, missed deadlines, miscommunication or frequent mistakes. These challenges are not solely individual limitations but often arise from a mismatch between workplace expectations and neurodivergent cognitive styles. There remains a critical need for technology-driven approaches that support neurodivergent individuals across the full employment lifecycle, from vocational search and preparation to workplace productivity and safety.

Vision and Approaches

This research area envisions human–AI collaborative systems to support neurodivergent adults across the employment journey, from job acquisition to workplace performance. One of the directions investigates how AI-driven tools can assist individuals and their social surroundings (e.g., peers, coaches, or collaborators) in navigating job search processes together, preparing for interviews, and improving communication gap between autistic and neurotypicals. Another direction explores how Virtual Reality (VR) and AI systems can support ADHD attention, productivity, and safety in complex work environments. Through empirical studies, participatory design, and experimental research, this work develops intelligent systems to support neurodivergent adults in their employment journey. Particularly area is focused on enabling collaborative sensemaking during vocational search and providing adaptive workplace supports such as body-doubling, real-time performance monitoring, and context-aware stimuli to help sustain attention during tasks.

Impact

The successful implementation of this research will provide practical support for autistic and ADHD adults struggling with the challenges of job-seeking and being productive at the workplace. By fostering collaboration through innovative interventions, this proposal seeks to help autistic adults overcome these barriers and secure employment. On the other hand, employing AI-based adaptive stimuli in VR can significantly support ADHD workers' cognitive state aiming to reduce distractions, sustain attention, and improve overall task performance and productivity.

Collaborator(s) and Partner(s):

Elizabeth Foster (Melwood Inc.), Elizabeth Green (LinkTalent), Dave Caudel (Frist Center for Autism and Innovation at Vanderbilt University), Dasha Peppard (Center for Student Professional Development, Temple University), Andrew Hundt (Carnegie Mellon University)

Outcome(s):

[CHI2026] Lost in Translation: Understanding Autistic–Neurotypical Communication Style Differences in Job Postings
[CHI2026 EA] Rethinking Productivity Support for Workers with ADHD in the Construction: Preliminary Insights
[CHI2024] Collaborative Job Seeking for People with Autism: Challenges and Design Opportunities
[NWRC2024] Collaborative Design for Job-Seekers with Autism: A Conceptual Framework for Future Research

LLM-Driven Agentic System for MCI and Early-Stage Dementia Support

Developing an LLM-Driven Agentic System to Support Individuals with Mild Cognitive Impairment (MCI) or Early-Stage Dementia (2024-Present)

Problem Statement

The global population is aging, and a growing share of older adults live with mild cognitive impairment (MCI), which affects roughly 22.7% of people in the United States. Despite technological advances, individuals and their caregivers struggle to obtain personalized, context-aware guidance for unique behaviors and social environments.

Vision and Approaches

The long-term vision is to create an agentic system that supports individuals with MCI or early-stage dementia by blending state-of-the-art AI with continuous sensing and caregiver collaboration. The system combines three components: (1) LLM-Driven Conversational Intelligence—An LLM serves as the system's conversational core, drawing on physiological data, living environment, dietary habits, and social engagement to tailor advice, personalize reminders and encouragement (e.g., gentle exercise, social interactions), and maintain an empathetic dialogue; (2) Wearable-Based Behavioral Sensing—Wearables continuously monitor physiological signals and daily behaviors (heart rate, heart rate variability, stress levels, sleep quality, activity data via smartwatches), capturing a real-time picture of the user's state and triggering timely in-app responses or alerts; (3) Caregiver-AI Collaboration Loop—The system will incorporate caregiver observations and preferences into an adaptive intervention loop, providing insights and reducing monitoring burden.

Impact

By integrating personalized conversational support, continuous behavioral sensing, and caregiver collaboration, the system aims to deliver timely, context-aware interventions that enhance daily routines, promote brain-healthy behaviors, and support long-term cognitive and emotional well-being, laying the foundation for scalable, human-centered digital health solutions for aging populations.

Collaborator(s) and Partner(s):

N/A

Outcome(s):

[JApplGerontol2025] Utilizing Conversational AI Technology for Social Connectedness Among Older Adults: A Systematic Review

Multi-Robot and Multi-Video Sensemaking for Public Safety and Disaster Recovery

Multi-robot + Multi-video Sensemaking for Public Safety and Disaster Recovery (2023-present)

Problem Statement

Ground robots are increasingly available and autonomous, generating videos that can enhance situational awareness for public servants (e.g., police officers for public safety, disaster response for damaged infrastructure or people and animals in need). Robots can perform surveillance in areas where aerial images are unavailable. The challenge is that analyzing vast amounts of video from multiple robots and providing commands to them requires significant human attention. Current visual evidence collection through robots is increasing, but dedicated designs and technologies for supporting human reasoning in this context are ill-defined.

Vision and Approaches

The project aims to design, develop, deploy, and evaluate datasets, scenarios, and interactive systems that facilitate multi-robot + multi-video sensemaking for public sector workers to achieve situational awareness for both real-time decision-making and post-event investigations. Situational awareness applications include public safety (e.g., addressing assault, vandalism) and disaster recovery (e.g., clearing roads after a hurricane).

Impact

The ultimate goal is to improve the efficiency and effectiveness of group robot operations for public safety and disaster recovery, minimize officers' effort and risk, and foster seamless collaboration between humans, videos, and robots. This will help society leverage robots and videos for situational awareness and contribute to HCI, Robotics, and Computer Vision communities through a conceptual framework of multi-robot multi-videos for scenarios for social good, benchmark datasets and tasks, and advances in video understanding and interactive human-group robot control.

Collaborator(s) and Partner(s):

Steve Peterson (NIH), Michael Lighthiser (GMU)

Outcome(s):

Scalable Human-in-the-Loop and Actionable Explainable AI

Connecting AIs and Humans through Scalable Human-in-the-Loop and Actionable XAI (2021-present)

Problem Statement

An unexpected AI failure can have severe consequences, affecting human lives (safety, productivity, trust, ethics). Understanding AI's vulnerability is essential but challenging and resource-costly for Machine Learning engineers.

Vision and Approaches

The goal is to develop novel human-AI collaboration designs to help ML engineers investigate and fix AI vulnerabilities more efficiently. Two main methodological aspects: (1) Scalable Human-In-The-Loop—maintain a reasonable level of human input in the modeling process, especially with large-scale data; (2) Actionable XAI (explainable AI)—enable humans to convert learned insights into direct actions that update the AI model after assessing its decision-making using XAI techniques.

Impact

The proposed solution can enhance human-AI interaction, provide ML engineers with better capability of "communicating" with future AI models, and empower ML engineers across diverse datasets (images, videos, text) to use more reliable and controllable AI.

Collaborator(s) and Partner(s):

Liang Zhao (Emory University), Young-Ho Kim (Naver)

Outcome(s):

[ICDM2021] GNES: Learning to Explain Graph Neural Networks
[KDD2022] RES: A Robust Framework for Guiding Visual Explanation
[CSCW2022] Aligning Eyes between Humans and Deep Neural Network through Interactive Attention Alignment
[CSCW2023] Designing a Direct Feedback Loop between Humans and Convolutional Neural Networks through Local Explanations
[CSCW2024] 3DPFIX: Improving Remote Novices' 3D Printing Troubleshooting through Human-AI Collaboration

COMPLETED RESEARCH DIRECTIONS