Andrew Kuznetsov, NRP

PhD Student
Carnegie Mellon University

I'm a 6th year PhD student in the School of Computer Science at Carnegie Mellon University and nationally registered paramedic. My PhD research focuses on building technology (such as AI agents and software tools) to support collecting, organizing, and sharing collections of information snippets - with the goal of bridging the gap between individual sensemaking and distributed cognition (e.g. teamwork)

Currently, I work on AI tools for home healthcare teams through the NSF AI-CARING AI Institute. Previously, I've worked on tools for architects at Autodesk Research, readers at Wikipedia, and spent some time at Amazon Mechanical Turk prototyping crowdsourcing workflows/chains.

At CMU, I work with Dr. Niki Kittur (Human Computer Interaction Institute) and Dr. Anita Woolley (Tepper School of Business, Organizational Behavior and Theory). I've been fortunate to collaborative with and learn from faculty at the University of Pittsburgh School of Health and Rehabilitation Sciences, Center for Emergency Medicine, and Department of Emergency Medicine.

[kuz] at cmu (dot) edu
Resume →
Google Scholar →


Fuse: In-Situ Sensemaking Support in the Browser
Andrew Kuznetsov, Joseph Chee Chang, Nathan Hahn, Napol Rachatasumrit, Bradley Breneisen, Julina Coupland, Aniket Kittur.
ACM Symposium on User Interface Software and Technology (UIST), 2022.
[Summary] [ACM DL] [arxiv] [Paper PDF]
A browser extension that externalizes users’ working memory by combining low-cost collection with lightweight organization of content in a compact card-based sidebar that is always available. Fuse helps users simultaneously extract key web content and structure it in a lightweight and visual way. We discuss how these affordances help users externalize more of their mental model into the system (e.g., saving, annotating, and structuring items) and support fast reviewing and resumption of task contexts. Our 22-month public deployment (N=89) and follow-up interviews provide longitudinal insights into the structuring behaviors of real-world users conducting information foraging tasks.
Wigglite: Low-cost Information Collection and Triage
Michael Liu, Andrew Kuznetsov, Yongsung Kim, Joseph Chee Chang, Aniket Kittur, Brad A. Myers.
ACM Symposium on User Interface Software and Technology (UIST), 2022.
[Summary] [ACM DL] [arxiv] [Paper PDF] [Talk Video]
In this work, we explore a new interaction technique called “wiggling,” which can be used to fluidly collect, organize, and rate information during early sensemaking stages with a single gesture. Wiggling involves rapid back-and-forth movements of a pointer or up-and-down scrolling on a smartphone, which can indicate the information to be collected and its valence, using a single, light-weight gesture that does not interfere with other interactions that are already available. Through implementation and user evaluation, we found that wiggling helped participants accurately collect information and encode their mental context with a 58% reduction in operational cost while being 24% faster compared to a common baseline.
Templates and Trust-o-meters: Towards a widely deployable indicator of trust in Wikipedia.
Andrew Kuznetsov, Margeigh Novotny, Jessica Klein, Diego Saez-Trumper, Aniket Kittur
ACM CHI Conference on Human Factors in Computing Systems (CHI), 2022.
In this work we identify and address three key challenges: empirically determining which metrics from prior and existing community approaches most impact reader trust; 2) validating indicator placements and designs that are both compact yet noticed by readers; and 3) demonstrating that such indicators can not only lower trust but also increase perceived trust in the system when appropriate. By addressing these, we aim to provide a foundation for future tools that can practically increase trust in user generated content and the sociotechnical systems that generate and maintain them.
An open repository of real-time COVID-19 indicators
Alex Reinhart, Logan Brooks, Maria Jahja ... Andrew Kuznetsov, Ryan Tibshirani.
Proceedings of the National Academy of Sciences (PNAS), 2021.
[Summary] [PNAS] [Paper PDF] [Project Website]
Operational since April 2020, the COVIDcast API provides open access to both traditional public health surveillance signals (cases, deaths, and hospitalizations) and many auxiliary indicators of COVID-19 activity, such as signals extracted from deidentified medical claims data, massive online surveys, cell phone mobility data, and internet search trends. These are available at a fine geographic resolution (mostly at the county level) and are updated daily. The COVIDcast API also tracks all revisions to historical data, allowing modelers to account for the frequent revisions and backfill that are common for many public health data sources. All of the data are available in a common format through the API and accompanying R and Python software packages. This paper describes the data sources and signals, and provides examples demonstrating that the auxiliary signals in the COVIDcast API present information relevant to tracking COVID activity, augmenting traditional public health reporting and empowering research and decision-making.
LIFT: Integrating Stakeholder Voices into Algorithmic Team Formation
Emily M. Hastings, Albatool Alamri, Andrew Kuznetsov, Christine Pisarczyk, Karrie Karahalios, Darko Marinov, Brian P. Bailey.
ACM CHI Conference on Human Factors in Computing Systems (CHI), 2020.
[Summary] [ACM DL] [Paper PDF] [Project Website]
A novel learner-centered workflow where students propose, vote for, and weigh the criteria used as inputs to the team formation algorithm. We conducted an experiment (N=289) comparing LIFT to the usual instructor-led process, and interviewed participants to evaluate their perceptions of LIFT and its outcomes. Learners proposed novel criteria not included in existing algorithmic tools, such as organizational style. They avoided criteria like gender and GPA that instructors frequently select, and preferred those promoting efficient collaboration. LIFT led to team outcomes comparable to those achieved by the instructor-led approach, and teams valued having control of the team formation process. We provide instructors and designers with a workflow and evidence supporting giving learners control of the algorithmic process used for grouping them into teams.
It's just a matter of perspective (s): Crowd-Powered Consensus Organization of Corpora
Ayush Jain, Joon Young Seo, Karan Goel, Andrew Kuznetsov, Aditya Parameswaran, Hari Sundaram.
arXiv, 2016.
[Summary] [arxiv]
We develop cost-efficient, accurate algorithms for identifying the consensus organization (i.e., the organizing perspective most workers prefer to employ), and incorporate these algorithms into a cost-effective workflow for organizing a collection of objects, termed ORCHESTRA. We compare our algorithms with other algorithms for clustering, on a variety of real-world datasets, and demonstrate that ORCHESTRA organizes items better and at significantly lower costs.

Non-Academic Projects

Outside of full-stack web development, I maintain a wide range of prototyping experiences, including mobile development, AR/VR , hardware and IoT devices, as well as some more esoteric stuff like Solidity (Ethereum).
Left 4 Virtual Reality (2015)
Re-purposing Consumer Toys as VR Input Devices
Nerf toy // Wii controller // Microsoft Kinect // Hardware flex sensors // Particle, Arduino micro-controller board.
QuickMed (2015)
Prototype Mobile Platform for Field Delivery of Medication
Map web app // iOS Mobile application
StreamPoint (2016)
Prototype Meeting Software to Generate Real-time Slides During Presentation
Presentation web app // Bing API // iOS Mobile application // NLP // Voice-to-Text.
Search3 (2018)
Prototype Data Network for Search and Rescue Robotics
Ethereum smart contract // Computer vision embeddings // Camera-equipped drone // iOS mobile application.
Exponent Network (2018)
Prototype Data Marketplace for Crowdsourcing Search Leads
Ethereum smart contact // Modified query incentive network mechanism.
PhD Positions Dashboard (2023)
Deployed System for Collecting Open CS/HCI PhD Positions, ∼15,000 yearly users.
Multi-agent LLM orchestration // Google sheets API // Image-to-text