Adam Perer · Carnegie Mellon HCII | Adam Perer is an Associate Professor at Carnegie Mellon University, where he is a member of the Human-Computer Interaction Institute.

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.

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.

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.

Selected Publications (View all publications)

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

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

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

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
ACM HEALTH 2026

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

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

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

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

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

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

Divisi: Interactive Search and Visualization for Scalable Exploratory Subgroup Analysis

Venkat Sivaraman , Zexuan (Zx) Li , Adam Perer
CHI 2025

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

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

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

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

Zeno: An Interactive Framework for Behavioral Evaluation of Machine Learning

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

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

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

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

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

Eye into AI: Evaluating the Interpretability of Explainable AI Techniques through a Game With a Purpose

Katelyn Morrison , Mayank Jain , Jessica Hammer , Adam Perer
CSCW 2023

Improving Human-AI Collaboration with Descriptions of AI Behavior

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

Dead or Alive: Continuous Data Profiling for Interactive Data Science

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

Leveraging Analysis History for Improved In Situ Visualization Recommendation

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

Emblaze: Illuminating Machine Learning Representations through Interactive Comparison of Embedding Spaces

Venkat Sivaraman , Yiwei Wu , Adam Perer
IUI 2022

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

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

Discovering and Validating AI Errors With Crowdsourced Failure Reports

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

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

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

Associate Professor

Co-director, Data Interaction Group

Human-Computer Interaction Institute

School of Computer Science

Carnegie Mellon University

adamperer@cmu.edu

Newell-Simon Hall 2504E

5000 Forbes Avenue

Pittsburgh PA 15213

Admin: Meagan Bulisco

412-268-8416

Curriculum vitae

Teaching

Interactive Data Science Spring 2026
Interactive Data Science Fall 2025
Interactive Data Science Spring 2025
Data Visualization Fall 2024
Data Science for Product Managers Spring 2023
HCI for Product Managers Spring 2023
Interactive Data Science Spring 2022
Data Visualization Fall 2021
Interactive Data Science Spring 2021
Interactive Data Science Fall 2020
Data Science and Visualization Spring 2019
Interpretable Machine Learning Spring 2019

Office hours: TBD

Advising

PhD Students

Undergraduate Students

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

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

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

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

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

Divisi: Interactive Search and Visualization for Scalable Exploratory Subgroup Analysis

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

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

Zeno: An Interactive Framework for Behavioral Evaluation of Machine Learning

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

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

Eye into AI: Evaluating the Interpretability of Explainable AI Techniques through a Game With a Purpose

Improving Human-AI Collaboration with Descriptions of AI Behavior

Dead or Alive: Continuous Data Profiling for Interactive Data Science

Leveraging Analysis History for Improved In Situ Visualization Recommendation

Emblaze: Illuminating Machine Learning Representations through Interactive Comparison of Embedding Spaces

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

Discovering and Validating AI Errors With Crowdsourced Failure Reports

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

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

SearchLens: Composing and Capturing Complex User Interests for Exploratory Search

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

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

Coping with Volume and Variety in Temporal Event Sequences: Strategies for Sharpening Analytic Focus

Clustervision: Visual Supervision of Unsupervised Clustering

Interacting with Predictions: Visual Inspection of Black-box Machine Learning Models

Supporting Iterative Cohort Construction with Visual Temporal Queries

Progressive Visual Analytics: User-Driven Visual Exploration of In-Progress Analytics

INFUSE: Interactive Feature Selection for Predictive Modeling of High Dimensional Data

Frequence: Interactive Mining and Visualization of Temporal Frequent Event Sequences

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

Data-Driven Exploration of Care Plans for Patients

Orion: A System for Modeling, Transformation and Visualization of Multi-dimensional Heterogeneous Networks

Search, Show Context, Expand on Demand: Supporting Large Graph Exploration with Degree-of-Interest

Systematic Yet Flexible Discovery: Guiding Domain Experts Through Exploratory Data Analysis

Integrating Statistics and Visualization: Case Studies of Gaining Clarity During Exploratory Data Analysis

Balancing Systematic and Flexible Exploration of Social Networks

Teaching

Advising