VLDB 2026 Research / reviewers in the wild / expert
Shriya Kurpad
dblp:319/4018
· DBLP profile ↗
2ranked-venue papers
0as first author
2since 2021 · last 2023
0000-0001-7834-4530ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Human-computer interaction and ubiquitous computing · 2 · 2 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Human-computer interaction and pervasive computing
2 papers |
User interface design and tools · 35% Collaborative and social computing · 35% Accessibility and assistive technology · 30% | |
| Theoretical computer science
2 papers |
Mathematical optimization · 100% | |
| Computer graphics and multimedia
1 paper |
Image and video coding · 100% |
Topics — the 7 heaviest of 7, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Mathematical optimization
design optimization |
0.8 | 2 | 2023 | Maptimizer: Using Optimization to Tailor Tactile Maps to Users Needs · CHI 2022 OPTIMISM: Enabling Collaborative Implementation of Domain Specific Metaheuristic Optimization · CHI 2023 |
Collaborative and social computing
collaborative design |
0.7 | 1 | 2023 | OPTIMISM: Enabling Collaborative Implementation of Domain Specific Metaheuristic Optimization · CHI 2023 |
User interface design and tools
design tools |
0.7 | 1 | 2023 | OPTIMISM: Enabling Collaborative Implementation of Domain Specific Metaheuristic Optimization · CHI 2023 |
Accessibility and assistive technology › assistive technology for visual impairment
tactile maps |
0.6 | 1 | 2022 | Maptimizer: Using Optimization to Tailor Tactile Maps to Users Needs · CHI 2022 |
Mathematical optimization › design optimization
generative design |
0.6 | 1 | 2022 | Maptimizer: Using Optimization to Tailor Tactile Maps to Users Needs · CHI 2022 |
Mathematical optimization
metaheuristic optimization |
0.2 | 1 | 2023 | OPTIMISM: Enabling Collaborative Implementation of Domain Specific Metaheuristic Optimization · CHI 2023 |
Image and video coding › image compression
spatial coding |
0.2 | 1 | 2022 | Maptimizer: Using Optimization to Tailor Tactile Maps to Users Needs · CHI 2022 |
Methods — techniques the papers use, named apart from their topics
user study · 1.7two-stage optimization · 1.7metaheuristic optimization · 1.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | OPTIMISM: Enabling Collaborative Implementation of Domain Specific Metaheuristic OptimizationabstractFor non-technical domain experts and designers it can be a substantial challenge to create designs that meet domain specific goals. This presents an opportunity to create specialized tools that produce optimized designs in the domain. However, implementing domain-specific optimization methods requires a rare combination of programming and domain expertise. Creating flexible design tools with re-configurable optimizers that can tackle a variety of problems in a domain requires even more domain and programming expertise. We present OPTIMISM, a toolkit which enables programmers and domain experts to collaboratively implement an optimization component of design tools. OPTIMISM supports the implementation of metaheuristic optimization methods by factoring them into easy to implement and reuse components: objectives that measure desirable qualities in the domain, modifiers which make useful changes to designs, design and modifier selectors which determine how the optimizer steps through the search space, and stopping criteria that determine when to return results. Implementing optimizers with OPTIMISM shifts the burden of domain expertise from programmers to domain experts. Megan Hofmann, Nayha Auradkar, Jessica Birchfield, Jerry Cao, Autumn G. Hughes, Gene S.-H. Kim, Shriya Kurpad, Kathryn J. Lum, Kelly Mack, Anisha Nilakantan, Margaret Ellen Seehorn, Emily Warnock, Jennifer Mankoff, Scott E. Hudson |
CHI | 7 |
| 2022 | Maptimizer: Using Optimization to Tailor Tactile Maps to Users NeedsabstractTactile maps can help people who are blind or have low-vision navigate and familiarize themselves with unfamiliar locations. Ideally, tactile maps can be customized to an individual’s unique needs and abilities because of their limited space for representation. We present Maptimizer, a tool that generates tactile maps based on users’ preferences and requirements. Maptimizer uses a two stage optimization process to pair representations with geographic information and tune those representations to present that information more clearly. In a small user study, Maptimizer helped participants more successfully and efficiently identify locations of interest in unknown areas. These results demonstrate the utility of optimization techniques and generative design in complex accessibility domains. Megan Hofmann, Kelly Mack, Jessica Birchfield, Jerry Cao, Autumn G. Hughes, Shriya Kurpad, Kathryn J. Lum, Emily Warnock, Anat Caspi, Scott E. Hudson, Jennifer Mankoff |
CHI | 6 |