I study how people engage and make decisions with data and AI.
I design interactive visualizations to make complex data and algorithms interpretable and actionable.
I immerse myself in high-stakes domains where AI is transforming consequential decisions.

This shapes three interconnected lines of research:

Improving Healthcare with Visualization. I study how visualization can bridge the gap between what AI systems know and what clinicians and patients can actually use at the point of care, from PHORA, our NIH-funded clinical decision support system for pulmonary arterial hypertension, to studies with critical care physicians revealing how AI recommendations shape clinical reasoning (CHI 2023, CHI 2026), to MedSyn, a generative AI that produces anatomically accurate 3D CT scans from radiology reports.
Improving AI Development with Visualization. AI models are easy to train but hard to understand. My group builds interactive tools that bring human judgment into every phase of AI development: specifying the right model with domain experts (Tempo, Divisi), continuously profiling data during development (AutoProfiler, Texture), and systematically evaluating model behavior at scale (Zeno, Vipera).
Improving Human-AI Collaboration with Visualization. Through deployments in child welfare, the opioid crisis, and wildlife conservation, I study how practitioners make sense of, trust, and act on AI, and how explanations can either support or actively mislead high-stakes decisions.

Selected papers across these lines — view all publications

Vipera: Blending Visual and LLM-Driven Guidance for Systematic Auditing of Text-to-Image Generative AI

CHI 2026 Yanwei Huang, Wesley Hanwen Deng, Sijia Xiao, Motahhare Eslami, Jason Hong, Arpit Narechania, Adam Perer

It's Not Just for Trust: Designing for Emerging Uses of Explainable AI in Clinical Decision-Making

ACM HEALTH 2026 Katelyn Morrison, Zexuan Li, Minsuk Kim, Chengqi (Malia) Hong, Jidapa Kraisangka, Priscilla Correa-Jaque, Charles Fauvel, Sandeep Sahay, Rebecca R. Vanderpool, Allen Everett, Shili Lin, Manreet Kanwar, Raymond Benza, Adam Perer

Intelligent Reasoning Cues: A Framework and Case Study of the Roles of AI Information in Complex Decisions

CHI 2026 Venkat Sivaraman, Eric Mason, Mengfan Ellen Li, Jessica Tong, Andrew King, Jeremy Kahn, Adam Perer

Tempo: Helping Data Scientists and Domain Experts Collaboratively Specify Predictive Modeling Tasks

CHI 2025 Venkat Sivaraman, Anika Vaishampayan, Xiaotong Li, Brian R Buck, Ziyong Ma, Richard D Boyce, Adam Perer

Static Algorithm, Evolving Epidemic: Understanding the Potential of Human-AI Risk Assessment to Support Regional Overdose Prevention

CSCW 2025 Venkat Sivaraman, Yejun Kwak, Courtney Kuza, Qingnan Yang, Kayleigh Adamson, Katie Suda, Lu Tang, Walid Gellad, Adam Perer

Transparency in the Wild: Navigating Transparency in a Deployed AI System to Broaden Need-Finding Approaches

FAccT 2024 Violet Turri, Katelyn Morrison, Katie Robinson, Collin Abidi, Adam Perer, Jodi Forlizzi, Rachel Dzombak

MedSyn: Text-Guided Anatomy-Aware Synthesis of High-Fidelity 3-D CT Images

IEEE Medical Imaging 2024 Yanwu Xu, Li Sun, Wei Peng, Shuyue Jia, Katelyn Morrison, Adam Perer, Afrooz Zandifar, Shyam Visweswaran, Motahhare Eslami, Kayhan Batmanghelich

The Impact of Imperfect XAI on Human-AI Decision-Making

CSCW 2024 Katelyn Morrison, Philipp Spitzer, Violet Turri, Michelle Feng, Niklas Kühl, Adam Perer

Zeno: An Interactive Framework for Behavioral Evaluation of Machine Learning

CHI 2023 Ángel Alexander Cabrera, Erica Fu, Donald Bertucci, Kenneth Holstein, Ameet Talwalkar, Jason I. Hong, Adam Perer

Ignore, Trust, or Negotiate: Understanding Clinician Acceptance of AI-Based Treatment Recommendations in Health Care

CHI 2023 Venkat Sivaraman, Leigh A. Bukowski, Joel Levin, Jeremy M. Kahn, Adam Perer

Evaluating the Impact of Human Explanation Strategies on Human-AI Visual Decision-Making

CSCW 2023 Katelyn Morrison, Donghoon Shin, Kenneth Holstein, Adam Perer

Improving Human-AI Collaboration with Descriptions of AI Behavior

CSCW 2023 Ángel Alexander Cabrera, Adam Perer, Jason I. Hong

Dead or Alive: Continuous Data Profiling for Interactive Data Science

VIS 2023 Will Epperson, Vaishnavi Gorantla, Dominik Moritz, Adam Perer

Leveraging Analysis History for Improved In Situ Visualization Recommendation

EuroVis 2022 Will Epperson, Doris Jung-Lin Lee, Leijie Wang, Kunal Agarwal, Aditya Parameswaran, Dominik Moritz, Adam Perer

Improving Human-AI Partnerships in Child Welfare: Understanding Worker Practices, Challenges, and Desires for Algorithmic Decision Support

CHI 2022 Anna Kawakami, Venkat Sivaraman, Hao-Fei Cheng, Logan Stapleton, Yanghuidi Cheng, Diana Qing, Adam Perer, Zhiwei Steven Wu, Haiyi Zhu, Kenneth Holstein

Discovering and Validating AI Errors With Crowdsourced Failure Reports

CSCW 2021 Ángel Alexander Cabrera, Abraham Druck, Jason I. Hong, Adam Perer

Designing Alternative Representations of Confusion Matrices to Support Non-Expert Public Understanding of Algorithm Performance

CSCW 2020 Hong Shen, Haojian Jin, Ángel Alexander Cabrera, Adam Perer, Haiyi Zhu, Jason I. Hong

Getting Playful with Explainable AI: Games with a Purpose to Improve Human Understanding of AI

CHI 2020 Laura Beth Fulton, Ja Young Lee, Qian Wang, Zhendong Yuan, Jessica Hammer, Adam Perer

Ablate, Variate, and Contemplate: Visual Analytics for Discovering Neural Architectures

VAST 2019 Dylan Cashman, Adam Perer, Remco Chang, Hendrik Strobelt

Seq2Seq-VIS : A Visual Debugging Tool for Sequence-to-Sequence Models

VAST 2018 Hendrik Strobelt, Sebastian Gehrmann, Michael Behrisch, Adam Perer, Hanspeter Pfister, Alexander Rush

Clustervision: Visual Supervision of Unsupervised Clustering

VAST 2017 Bum Chul Kwon, Ben Eysenbach, Janu Verma, Kenney Ng, Christopher deFilippi, Walter Stewart, Adam Perer

Supporting Iterative Cohort Construction with Visual Temporal Queries

VAST 2015 Josua Krause, Adam Perer, Harry Stavropoulos

The Longitudinal Use of SaNDVis: Visual Social Network Analytics in the Enterprise

IEEE TVCG 2013 Adam Perer, Ido Guy, Erel Uziel, Inbal Ronen, Michal Jacovi

Balancing Systematic and Flexible Exploration of Social Networks

IEEE InfoVis 2006 Adam Perer, Ben Shneiderman
Improving Network Analysis with Visualization. My dissertation and early publications focused on designing visualization tools and techniques for making sense of large-scale social networks.

Bio

Adam Perer is an Associate Professor at Carnegie Mellon University's Human-Computer Interaction Institute, where he co-directs the Data Interaction Group and is the incoming HCII PhD Program Director. His research combines interactive visualization with data science and AI to support decision-making in high-stakes domains, with a focus on clinical medicine. His work has been published in over 50 papers across CHI, IEEE VIS, CSCW, and IUI, and has been cited more than 8,000 times. His research is supported by the NIH, NSF, and numerous industry and government partners. He has served in leadership roles across the visualization and HCI communities, including General Chair of the IEEE Symposium on Visualization in Data Science, Subcommittee Chair for ACM CHI, and Area Papers Chair for IEEE VIS. Prior to CMU, he was a research scientist at IBM Research. He holds a Ph.D. in Computer Science from the University of Maryland, College Park.

I co-founded and co-direct the Data Interaction Group (DIG) with Dominik Moritz, and beginning Fall 2026, I serve as Director of the HCII PhD Program.