Bill Qian

Hi, I am a second-year PhD student in the Department of Computer Science at Rice University.

I am a member of the Human-Centered AI and Robotics Lab led by Prof. Vaibhav Unhelkar. My research focuses on leveraging multimodal information, such as language and images, to enable autonomous agents to learn task performance and collaborate more effectively with humans.

Previously, I worked on characterizing the pathogenicity of short nucleotide sequences with machine learning at Treangen Lab.

selected publications

HRI Companion

Measuring Variations in Workload during Human-Robot Collaboration through Automated After-Action Reviews

Qian, Z., Orlov Savko, L., Neubauer, C. and 2 more authors

In Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction 2024

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Human collaborator’s workload plays a central role in human-robot collaboration. Algorithms designed to minimize cognitive workload enhance fluent human-robot teamwork. Time series data of workload is vital for both designing and assessing these algorithms. However, accurately quantifying and measuring cognitive workload, particularly at high temporal resolution, poses a substantial challenge. Towards addressing this challenge, we explore the potential of after-action reviews (AARs) as a tool for gauging workload during human-robot collaboration. First, through a case study, we present and demonstrate AutoAAR for measuring human workload post-task at a high temporal resolution. Second, through a user study, we quantify the validity and utility of measurements derived using AutoAAR for human-robot teamwork. The paper concludes with guidelines and future directions to extend this method to measure other internal states, such as trust and intent.
HRI

RW4T Dataset: Data of Human-Robot Behavior and Cognitive States in Simulated Disaster Response Tasks

Orlov Savko, L., Qian, Z., Gremillion, G. and 3 more authors

In Proceedings of the 2024 ACM/IEEE International Conference on Human-Robot Interaction 2024

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To forge effective collaborations with humans, robots require the capacity to understand and predict the behaviors of their human counterparts. There is a growing body of computational research on human modeling for human-robot interaction (HRI). However, a key bottleneck in conducting this research is the relative lack of data of cognitive states – like intent, workload, and trust – which undeniably affect human behavior. Despite their significance, these states are elusive to measure, making the assembly of datasets a challenge and hindering the progression of human modeling techniques. To help address this, we first introduce Rescue World for Teams (RW4T): a configurable testbed to simulate disaster response scenarios requiring human-robot collaboration. Next, using RW4T, we curate a multimodal dataset of human-robot behavior and cognitive states in dyadic human-robot collaboration. This RW4T dataset includes state, action and reward sequences, and all the necessary data to replay a visual task execution. It further contains psychophysiological metrics like heart rate and pupillometry, complemented by self-reported cognitive state measures. With data from 20 participants, each undertaking five human-robot collaborative tasks, this dataset (comprising of 100 unique trajectories) accompanied with the simulator can serve as a valuable benchmark for human behavior modeling.
Genome Biology

SeqScreen: accurate and sensitive functional screening of pathogenic sequences via ensemble learning

Balaji, A., Kille, B., Kappell, A. and 11 more authors

Genome Biology Jun 2022

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The COVID-19 pandemic has emphasized the importance of accurate detection of known and emerging pathogens. However, robust characterization of pathogenic sequences remains an open challenge. To address this need we developed SeqScreen, which accurately characterizes short nucleotide sequences using taxonomic and functional labels and a customized set of curated Functions of Sequences of Concern (FunSoCs) specific to microbial pathogenesis. We show our ensemble machine learning model can label protein-coding sequences with FunSoCs with high recall and precision. SeqScreen is a step towards a novel paradigm of functionally informed synthetic DNA screening and pathogen characterization, available for download at www.gitlab.com/treangenlab/seqscreen.