Update papers.

static/featured-venues.json

@@ -9,7 +9,7 @@
     "desc": "IUI 2023 - Sydney, Australia",
     "venue_nickname": "IUI",
     "year": 2023,
-    "visible": true
+    "visible": false
   },
   {
     "desc": "VIS 2023 - Melbourne, Australia",
@@ -40,5 +40,23 @@
     "venue_nickname": "EMNLP",
     "year": 2024,
     "visible": true
+  },
+  {
+    "desc": "Creativity & Cognition 2025",
+    "venue_nickname": "C&C",
+    "year": 2025,
+    "visible": true
+  },
+  {
+    "desc": "UIST 2025 - Busan, South Korea",
+    "venue_nickname": "UIST",
+    "year": 2025,
+    "visible": true
+  },
+  {
+    "desc": "VIS 2025 - Vienna, Austria",
+    "venue_nickname": "VIS",
+    "year": 2026,
+    "visible": true
   }
 ]

static/news.json

@@ -282,32 +282,32 @@
   {
     "text": "Our [Narrative Visualization](papers/narrative/) paper won an [InfoVis 10-Year Test-of-Time Award](http://ieeevis.org/year/2020/info/awards/test-of-time-awards).",
     "date": "2020-10-20",
-    "visible": true
+    "visible": false
   },
   {
     "text": "IDL welcomes (back) [Leilani Battle](https://homes.cs.washington.edu/~leibatt/bio.html) as a UW CSE professor and lab co-director!",
     "date": "2021-09-27",
-    "visible": true
+    "visible": false
   },
   {
     "text": "[D3](papers/d3) wins an [InfoVis 10-Year Test-of-Time Award](http://ieeevis.org/year/2021/info/awards/test-of-time-awards)!",
     "date": "2021-10-15",
-    "visible": true
+    "visible": false
   },
   {
     "text": "[Tisane](papers/tisane) is a CHI'22 Honorable Mention. Congrats [Eunice](https://eunicemjun.com/)!",
     "date": "2022-05-10",
-    "visible": true
+    "visible": false
   },
   {
     "text": "Our [interview study of data analysts](papers/enterprise-analysis-interviews) received a [10-Year Test-of-Time Award](https://ieeevis.org/year/2022/info/awards/test-of-time-awards).",
     "date": "2022-10-16",
-    "visible": true
+    "visible": false
   },
   {
     "text": "[ScatterShot](papers/scattershot) earns an IUI'23 Honorable Mention.",
     "date": "2023-03-27",
-    "visible": true
+    "visible": false
   },
   {
     "text": "Six new papers at VIS 2023!",
@@ -333,5 +333,20 @@
     "text": "IDL alum [Dominik Moritz](https://www.domoritz.de/) won a [2024 VGTC Significant New Researcher Award](https://ieeevis.org/year/2024/program/awards/awards)!",
     "date": "2024-10-16",
     "visible": true
+  },
+  {
+    "text": "Our [Mosaic demo](papers/mosaic-sigmod-demo) received a SIGMOD 2025 Best Demo Honorable Mention.",
+    "date": "2025-06-27",
+    "visible": true
+  },
+  {
+    "text": "[Publish-Time Optimizations for Web-Based Visualization](papers/publish-time-optimizations) won a VIS 2025 Best Short Paper Honorable Mention.",
+    "date": "2025-10-01",
+    "visible": true
+  },
+  {
+    "text": "[Voyager](papers/voyager) won a VIS 10 Year Test-of-Time Award!",
+    "date": "2025-10-03",
+    "visible": true
   }
 ]

static/papers/data-augmentation-for-visualization.json

@@ -0,0 +1,37 @@
+{
+  "doi": "10.48550/arXiv.2508.02216",
+  "web_name": "data-augmentation-for-visualization",
+  "title": "Data Augmentation for Visualization Design Knowledge Bases",
+  "venue": "VIS",
+  "year": 2026,
+  "note": "",
+  "start_page": null,
+  "end_page": null,
+  "volume": null,
+  "issue": null,
+  "editors": "",
+  "publisher": "IEEE",
+  "location": "",
+  "pdf": "https://www.arxiv.org/pdf/2508.02216",
+  "abstract": "Visualization knowledge bases enable computational reasoning and recommendation over a visualization design space. These systems evaluate design trade-offs using numeric weights assigned to different features (e.g., binning a variable). Feature weights can be learned automatically by fitting a model to a collection of chart pairs, in which one chart is deemed preferable to the other. To date, labeled chart pairs have been drawn from published empirical research results; however, such pairs are not comprehensive, resulting in a training corpus that lacks many design variants and fails to systematically assess potential trade-offs. To improve knowledge base coverage and accuracy, we contribute data augmentation techniques for generating and labeling chart pairs. We present methods to generate novel chart pairs based on design permutations and by identifying under-assessed features -- leading to an expanded corpus with thousands of new chart pairs, now in need of labels. Accordingly, we next compare varied methods to scale labeling efforts to annotate chart pairs, in order to learn updated feature weights. We evaluate our methods in the context of the Draco knowledge base, demonstrating improvements to both feature coverage and chart recommendation performance.",
+  "thumbnail": "images/thumbs/data-augmentation-for-visualization.png",
+  "figure": "images/figures/data-augmentation-for-visualization.png",
+  "caption": "Our work introduces data augmentation techniques for updating a visualization design knowledge base using example design pairs, in which one chart is deemed preferable to another. Given (A) an original set of design pairs obtained from empirical studies or example cases, (B) primitive augmentation produces variations of the original designs by enumerating pairs that exhibit the same differences in low-level design primitives as an original design pair (e.g., the use of the x channel); (C) feature augmentation extends the original pairs by adding new pairs that exhibit high-level design features (e.g., binning on the color channel) that the original pairs do not cover; lastly, (D) seed augmentation enumerates design pairs that the current knowledge base reasons about well.",
+  "visible": true,
+  "pub_date": "2026-01-01",
+  "mod_date": "2026-01-01",
+  "authors": [
+    {
+      "first_name": "Hyeok",
+      "last_name": "Kim",
+      "url": "https://hyeok.me/"
+    },
+    {
+      "first_name": "Jeffrey",
+      "last_name": "Heer",
+      "url": "http://homes.cs.washington.edu/~jheer/"
+    }
+  ],
+  "materials": [],
+  "tags": []
+}

static/papers/from-pen-to-prompt.json

@@ -0,0 +1,52 @@
+{
+  "doi": "10.1145/3698061.3726910",
+  "web_name": "from-pen-to-prompt",
+  "title": "From Pen to Prompt: How Creative Writers Integrate AI into their Writing Practice",
+  "venue": "C&C",
+  "year": 2025,
+  "note": "",
+  "start_page": null,
+  "end_page": null,
+  "volume": null,
+  "issue": null,
+  "editors": "",
+  "publisher": "ACM",
+  "location": "",
+  "pdf": "https://dl.acm.org/doi/pdf/10.1145/3698061.3726910",
+  "abstract": "Creative writing is a deeply human craft, yet AI systems using large language models (LLMs) offer the automation of significant parts of the writing process. So why do some creative writers choose to use AI? Through interviews and observed writing sessions with 18 creative writers who already use AI regularly in their writing practice, we find that creative writers are intentional about how they incorporate AI, making many deliberate decisions about when and how to engage AI based on their core values, such as authenticity and craftsmanship. We characterize the interplay between writers’ values, their fluid relationships with AI, and specific integration strategies—ultimately enabling writers to create new AI workflows without compromising their creative values. We provide insight for writing communities, AI developers and future researchers on the importance of supporting transparency of these emerging writing processes and rethinking what AI features can best serve writers.",
+  "thumbnail": "images/thumbs/from-pen-to-prompt.png",
+  "figure": "",
+  "caption": "",
+  "visible": true,
+  "pub_date": "2025-06-23",
+  "mod_date": "2025-06-23",
+  "authors": [
+    {
+      "first_name": "Alicia",
+      "last_name": "Guo",
+      "url": "https://www.aliciaguo.com/"
+    },
+    {
+      "first_name": "Shreya",
+      "last_name": "Sathyanarayanan"
+    },
+    {
+      "first_name": "Leijie",
+      "last_name": "Wang",
+      "url": "https://homes.cs.washington.edu/~leijiew/"
+    },
+    {
+      "first_name": "Jeffrey",
+      "last_name": "Heer",
+      "url": "http://homes.cs.washington.edu/~jheer/"
+    },
+    {
+      "first_name": "Amy",
+      "last_name": "Zhang",
+      "display_name": "Amy X. Zhang",
+      "url": "https://homes.cs.washington.edu/~axz/"
+    }
+  ],
+  "materials": [],
+  "tags": []
+}

static/papers/llm-chains-crowdsourcing.json

@@ -1,25 +1,25 @@
 {
-  "doi": "10.48550/arXiv.2312.11681",
+  "doi": "10.1145/3716134",
   "web_name": "llm-chains-crowdsourcing",
   "title": "Designing LLM Chains by Adapting Techniques from Crowdsourcing Workflows",
-  "venue": "arXiv",
-  "year": 2024,
+  "venue": "ACM TOCHI",
+  "year": 2025,
   "note": "",
   "start_page": null,
   "end_page": null,
-  "volume": null,
-  "issue": null,
+  "volume": 32,
+  "issue": 3,
   "editors": "",
-  "publisher": "arXiv",
+  "publisher": "Association for Computing Machinery (ACM)",
   "location": "",
-  "pdf": "https://arxiv.org/pdf/2312.11681",
-  "abstract": "LLM chains enable complex tasks by decomposing work into a sequence of subtasks. Similarly, the more established techniques of crowdsourcing workflows decompose complex tasks into smaller tasks for human crowdworkers. Chains address LLM errors analogously to the way crowdsourcing workflows address human error. To characterize opportunities for LLM chaining, we survey 107 papers across the crowdsourcing and chaining literature to construct a design space for chain development. The design space covers a designer's objectives and the tactics used to build workflows. We then surface strategies that mediate how workflows use tactics to achieve objectives. To explore how techniques from crowdsourcing may apply to chaining, we adapt crowdsourcing workflows to implement LLM chains across three case studies: creating a taxonomy, shortening text, and writing a short story. From the design space and our case studies, we identify takeaways for effective chain design and raise implications for future research and development.",
+  "pdf": "https://dl.acm.org/doi/pdf/10.1145/3716134",
+  "abstract": "LLM chains enable complex tasks by decomposing work into a sequence of subtasks. Similarly, the more established techniques of crowdsourcing workflows decompose complex tasks into smaller tasks for human crowdworkers. Chains address LLM errors analogously to the way crowdsourcing workflows address human error. To characterize opportunities for LLM chaining, we survey 107 papers across the crowdsourcing and chaining literature to construct a design space for chain development. The design space covers a designer’s objectives and the tactics used to build workflows. We then surface strategies that mediate how workflows use tactics to achieve objectives. To explore how techniques from crowdsourcing may apply to chaining, we adapt crowdsourcing workflows to implement LLM chains across three case studies: creating a taxonomy, shortening text, and writing a short story. From the design space and our case studies, we identify takeaways for effective chain design and raise implications for future research and development.",
   "thumbnail": "images/thumbs/llm-chains-crowdsourcing.png",
   "figure": "images/figures/llm-chains-crowdsourcing.png",
   "caption": "We contribute (1) a design space, (2) case studies, and (3) a discussion of techniques for LLM chains informed by crowdsourcing workflows. This scaffolding can help designers navigate the large possible space of LLM chains. For example, (Left) given a task of shortening text, as in Soylent, our design space aids an LLM chain designer in identifying relevant high-level objectives. These objectives incorporate elements of creativity and accuracy i.e., creatively shortening input text while verifying its faithfulness to the original. To support these objectives, the designer can lean upon concrete strategies, such as validation and user guidance. These strategies in turn point to lower-level tactics, such as allowing users to transform outputs. (Right) The designer can produce LLM chains to support the objectives by implementing strategies using tactics, as outlined in our design space. For example, generating diverse responses with the parallel generation tactic employed by Soylent creates an LLM chain that gives users control over the length of the shortened text via a directly manipulable slider.",
   "visible": true,
-  "pub_date": "2024-05-09",
-  "mod_date": "2024-05-09",
+  "pub_date": "2025-06-14",
+  "mod_date": "2025-06-14",
   "authors": [
     {
       "first_name": "Madeleine",
@@ -48,11 +48,6 @@
       "url": "http://homes.cs.washington.edu/~jheer/"
     }
   ],
-  "materials": [
-    {
-      "name": "Preprint",
-      "link": "https://arxiv.org/abs/2312.11681"
-    }
-  ],
+  "materials": [],
   "tags": []
 }

static/papers/mosaic-selections.json

@@ -0,0 +1,50 @@
+{
+  "doi": "10.48550/arXiv.2507.19690",
+  "web_name": "mosaic-selections",
+  "title": "Mosaic Selections: Managing and Optimizing User Selections for Scalable Data Visualization Systems",
+  "venue": "VIS",
+  "year": 2026,
+  "note": "",
+  "start_page": null,
+  "end_page": null,
+  "volume": null,
+  "issue": null,
+  "editors": "",
+  "publisher": "IEEE",
+  "location": "",
+  "pdf": "https://arxiv.org/pdf/2507.19690v1",
+  "abstract": "Though powerful tools for analysis and communication, interactive visualizations often fail to support real-time interaction with large datasets with millions or more records. To highlight and filter data, users indicate values or intervals of interest. Such selections may span multiple components, combine in complex ways, and require optimizations to ensure low-latency updates. We describe Mosaic Selections, a model for representing, managing, and optimizing user selections, in which one or more filter predicates are added to queries that request data for visualizations and input widgets. By analyzing both queries and selection predicates, Mosaic Selections enable automatic optimizations, including pre-aggregating data to rapidly compute selection updates. We contribute a formal description of our selection model and optimization methods, and their implementation in the open-source Mosaic architecture. Benchmark results demonstrate orders-of-magnitude latency improvements for selection-based optimizations over unoptimized queries and existing optimizers for the Vega language. The Mosaic Selection model provides infrastructure for flexible, interoperable filtering across multiple visualizations, alongside automatic optimizations to scale to millions and even billions of records.",
+  "thumbnail": "images/thumbs/mosaic-selections.png",
+  "figure": "images/figures/mosaic-selections.png",
+  "caption": "Pre-aggregation and querying for standard deviation and bivariate measures. Each sufficient statistic is included as a column in a materialized view, alongside grouping dimensions. The symbol x̂ indicates the average value of x across the full dataset; it is included to mean-center the data to prevent floating point error.",
+  "visible": true,
+  "pub_date": "2026-01-01",
+  "mod_date": "2026-01-01",
+  "authors": [
+    {
+      "first_name": "Jeffrey",
+      "last_name": "Heer",
+      "url": "http://homes.cs.washington.edu/~jheer/"
+    },
+    {
+      "first_name": "Dominik",
+      "last_name": "Moritz",
+      "url": "http://domoritz.de/"
+    },
+    {
+      "first_name": "Ron",
+      "last_name": "Pechuk"
+    }
+  ],
+  "materials": [
+    {
+      "name": "Examples",
+      "link": "https://idl.uw.edu/mosaic"
+    },
+    {
+      "name": "Software",
+      "link": "https://github.com/uwdata/mosaic"
+    }
+  ],
+  "tags": []
+}

static/papers/mosaic-sigmod-demo.json

@@ -0,0 +1,50 @@
+{
+  "doi": "10.1145/3722212.3725117",
+  "web_name": "mosaic-sigmod-demo",
+  "title": "Mosaic: An Architecture for Linking Databases and Scalable Interactive Visualizations",
+  "venue": "SIGMOD-Demo",
+  "year": 2025,
+  "note": "Best Demo Honorable Mention",
+  "start_page": null,
+  "end_page": null,
+  "volume": null,
+  "issue": null,
+  "editors": "",
+  "publisher": "ACM",
+  "location": "",
+  "pdf": "https://dl.acm.org/doi/pdf/10.1145/3722212.3725117",
+  "abstract": "Real-time interaction and visualization over large data volumes requires careful coordination of data queries and visual updates. Mosaic is an architecture for optimizing scalable and interoperable visualizations backed by a database, providing a platform for developing and deploying optimizations that span both visualization clients and backing databases. Mosaic applications consist of data-consuming clients that publish data needs as declarative queries, parameterized by shared filtering selections. These queries are managed and automatically optimized by a coordinator that proxies access to a scalable data store. For example, by analyzing selection predicates and client queries, the coordinator automatically constructs materialized views to perform selection updates over pre-aggregated data at interactive rates. Given only a high-level specification, Mosaic automatically enables orders-of-magnitude performance improvements over standard update queries.",
+  "thumbnail": "images/thumbs/mosaic-sigmod-demo.png",
+  "figure": "images/figures/mosaic-sigmod-demo.png",
+  "caption": "Interactive maps of 1.3B taxi pick-ups and drop-offs in New York City, cross-filtered by pickup time and location.",
+  "visible": true,
+  "pub_date": "2025-06-22",
+  "mod_date": "2025-06-22",
+  "authors": [
+    {
+      "first_name": "Jeffrey",
+      "last_name": "Heer",
+      "url": "http://homes.cs.washington.edu/~jheer/"
+    },
+    {
+      "first_name": "Dominik",
+      "last_name": "Moritz",
+      "url": "http://domoritz.de/"
+    },
+    {
+      "first_name": "Ron",
+      "last_name": "Pechuk"
+    }
+  ],
+  "materials": [
+    {
+      "name": "Examples",
+      "link": "https://idl.uw.edu/mosaic"
+    },
+    {
+      "name": "Software",
+      "link": "https://github.com/uwdata/mosaic"
+    }
+  ],
+  "tags": []
+}

static/papers/preserving-writer-values.json

@@ -0,0 +1,48 @@
+{
+  "doi": "10.1145/3690712.3690727",
+  "web_name": "preserving-writer-values",
+  "title": "Preserving Writer Values in AI Writing Assistance Tools",
+  "venue": "In2Writing",
+  "year": 2024,
+  "note": "",
+  "start_page": 58,
+  "end_page": 61,
+  "volume": null,
+  "issue": null,
+  "editors": "",
+  "publisher": "ACM",
+  "location": "",
+  "pdf": "https://dl.acm.org/doi/pdf/10.1145/3690712.3690727",
+  "abstract": "Many creative writers see writing as a deeply personal, human endeavor rather than a means to an end. As LLMs stand to transform how we conduct and perceive writing, how can AI writing tools assist creative writers without conflicting with the values they hold dear? We interview 8 creative writers who extensively use AI writing tools to understand their core writing values and how these shape their use of AI. Our preliminary findings indicate writers prioritize personal values of authentic self-expression and love of process when deciding if and how to employ AI writing aids. We conclude by proposing design implications for AI assistants that uphold writers’ values.",
+  "thumbnail": "images/thumbs/preserving-writer-values.png",
+  "figure": "",
+  "caption": "",
+  "visible": true,
+  "pub_date": "2024-10-15",
+  "mod_date": "2024-10-15",
+  "authors": [
+    {
+      "first_name": "Alicia",
+      "last_name": "Guo",
+      "url": "https://www.aliciaguo.com/"
+    },
+    {
+      "first_name": "Leijie",
+      "last_name": "Wang",
+      "url": "https://homes.cs.washington.edu/~leijiew/"
+    },
+    {
+      "first_name": "Jeffrey",
+      "last_name": "Heer",
+      "url": "http://homes.cs.washington.edu/~jheer/"
+    },
+    {
+      "first_name": "Amy",
+      "last_name": "Zhang",
+      "display_name": "Amy X. Zhang",
+      "url": "https://homes.cs.washington.edu/~axz/"
+    }
+  ],
+  "materials": [],
+  "tags": []
+}