Add Jackson's profile, CHI/CSCW papers

_people/ziyong.md

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+---
+name: Ziyong Ma
+image: /assets/people/ziyong.jpg
+role: Undergraduate Student
+advisors:
+  - Adam Perer
+---

_publications/2025-divisi.html

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+---
+layout: publication
+year: 2025
+title: "Divisi: Interactive Search and Visualization for Scalable Exploratory Subgroup Analysis"
+authors:
+  - Venkat Sivaraman
+  - Zexuan (Zx) Li
+  - Adam Perer
+venue: CHI
+venue_location: Yokohama, Japan
+venue_url: https://chi2025.acm.org
+type:
+  - Conference
+tags:
+  - Machine Learning
+  - Visualization
+  - Interpretability
+pdf: https://arxiv.org/pdf/2502.10537
+arxiv: https://arxiv.org/abs/2502.10537
+link: https://github.com/cmudig/divisi-toolkit
+---
+
+Analyzing data subgroups is a common data science task to build intuition about a dataset and identify areas to improve model performance. However, subgroup analysis is prohibitively difficult in datasets with many features, and existing tools limit unexpected discoveries by relying on user-defined or static subgroups. We propose exploratory subgroup analysis as a set of tasks in which practitioners discover, evaluate, and curate interesting subgroups to build understanding about datasets and models. To support these tasks we introduce Divisi, an interactive notebook-based tool underpinned by a fast approximate subgroup discovery algorithm. Divisi's interface allows data scientists to interactively re-rank and refine subgroups and to visualize their overlap and coverage in the novel Subgroup Map. Through a think-aloud study with 13 practitioners, we find that Divisi can help uncover surprising patterns in data features and their interactions, and that it encourages more thorough exploration of subtypes in complex data.

_publications/2025-pdmp.html

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+---
+layout: publication
+year: 2025
+title: "Static Algorithm, Evolving Epidemic: Understanding the Potential of Human-AI Risk Assessment to Support Regional Overdose Prevention"
+authors:
+  - Venkat Sivaraman
+  - Yejun Kwak
+  - Courtney Kuza
+  - Qingnan Yang
+  - Kayleigh Adamson
+  - Katie Suda
+  - Lu Tang
+  - Walid Gellad
+  - Adam Perer
+venue: CSCW
+venue_location: Bergen, Norway
+venue_url: https://cscw.acm.org
+type:
+  - Conference
+tags:
+  - Machine Learning
+  - AI-Assisted Decision Making
+  - Interpretability
+pdf: https://arxiv.org/pdf/2502.10542
+arxiv: https://arxiv.org/abs/2502.10542
+---
+
+Drug overdose deaths, including those due to prescription opioids, represent a critical public health issue in the United States and worldwide. Artificial intelligence (AI) approaches have been developed and deployed to help prescribers assess a patient's risk for overdose-related death, but it is unknown whether public health experts can leverage similar predictions to make local resource allocation decisions more effectively. In this work, we evaluated how AI-based overdose risk assessment could be used to inform local public health decisions using a working prototype system. Experts from three health departments, of varying locations and sizes with respect to staff and population served, were receptive to the potential benefits of algorithmic risk prediction and of using AI-augmented visualization to connect across data sources. However, they also expressed concerns about whether the risk prediction model's formulation and underlying data would match the state of the overdose epidemic as it evolved in their specific locations. Our findings extend those of other studies on algorithmic systems in the public sector, and they present opportunities for future human-AI collaborative tools to support decision-making in local, time-varying contexts.

_publications/2025-tempo.html

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+---
+layout: publication
+year: 2025
+title: "Tempo: Helping Data Scientists and Domain Experts Collaboratively Specify Predictive Modeling Tasks"
+authors:
+  - Venkat Sivaraman
+  - Anika Vaishampayan
+  - Xiaotong Li
+  - Brian R Buck
+  - Ziyong Ma
+  - Richard D Boyce
+  - Adam Perer
+venue: CHI
+venue_location: Yokohama, Japan
+venue_url: https://chi2025.acm.org
+type:
+  - Conference
+tags:
+  - Machine Learning
+  - AI-Assisted Decision Making
+  - Interpretability
+pdf: https://arxiv.org/pdf/2502.10526v2
+arxiv: https://arxiv.org/abs/2502.10526v2
+link: https://github.com/cmudig/tempo
+---
+
+Temporal predictive models have the potential to improve decisions in health care, public services, and other domains, yet they often fail to effectively support decision-makers. Prior literature shows that many misalignments between model behavior and decision-makers' expectations stem from issues of model specification, namely how, when, and for whom predictions are made. However, model specifications for predictive tasks are highly technical and difficult for non-data-scientist stakeholders to interpret and critique. To address this challenge we developed Tempo, an interactive system that helps data scientists and domain experts collaboratively iterate on model specifications. Using Tempo's simple yet precise temporal query language, data scientists can quickly prototype specifications with greater transparency about pre-processing choices. Moreover, domain experts can assess performance within data subgroups to validate that models behave as expected. Through three case studies, we demonstrate how Tempo helps multidisciplinary teams quickly prune infeasible specifications and identify more promising directions to explore.