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Andreea Sistrunk

dblp:252/8104 · DBLP profile ↗
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5ranked-venue papers
3as first author
3since 2021 · last 2024
0009-0004-9539-2736ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Human-computer interaction and ubiquitous computing · 3 · 3 first-author · 3 since 2021Artificial intelligence and machine learning · 2Databases, data management, data science and information retrieval · 1Graphics, computer vision, multimedia, augmented reality and games · 1Applied, interdisciplinary, general and emerging computing · 1

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.

Databases, data mining, and information retrieval
1 paper
Data mining · 100%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Smart cities and intelligent transportation · 100%
Theoretical computer science
1 paper
Algorithms and data structures · 100%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Data mining
clustering
0.412020
Geospatial Clustering for Balanced and Proximal Schools · AAAI 2020
Data mining › clustering
spatial clustering
0.412020
Geospatial Clustering for Balanced and Proximal Schools · AAAI 2020
Algorithms and data structures › clustering
constrained clustering
0.112020
Geospatial Clustering for Balanced and Proximal Schools · AAAI 2020

Methods — techniques the papers use, named apart from their topics

geokmeans · 1.3
YearPublicationVenuePosition
2024 Redistrict: Online Public Deliberation Support that Connects and Rebuilds Inclusive Communities
abstract
Public deliberations are often a staple ingredient in community decision-making. However, traditional, time-constrained, in-person debates can become highly polarized, eroding trust in authorities, and leaving the community divided. This is the case in redistricting deliberations for public school zoning. Seeking alternative ways of support, we evaluated the potential introduction of an online platform that combines multiple streams of data, visualizes school attendance boundaries, and enables the manipulation of representations of land parcels. To capture multiple stakeholders' values about the potential to enhance public engagement in school rezoning decision-making through an online platform, we conducted interviews with 12 participants with previous experiences in traditional, in-person deliberations. Insights from the interviews highlight the several roles an online platform could take, especially as it provides alternative means of participation (online, synchronous, and asynchronous). Additionally, we discuss the potential for technology to increase the visibility and participation of multiple community actors in public deliberations and present implications for the design of future tools to support public decision-making.
Andreea Sistrunk, Nathan Self, Subhodip Biswas, Kurt Luther, Nervo Verdezoto, Naren Ramakrishnan
Proc. ACM Hum. Comput. Interact.1
2023 CSCW & Redrawing Public School Boundaries: An Intersection of Computer Science, Education Policy, and Geography Research
Andreea Sistrunk, Subhodip Biswas, James Egenrieder, William Glenn, Kurt Luther, Naren Ramakrishnan
ECSCW1
2022 Redistricting Practices in Public Schools: Social Progress or Necessity?
Andreea Sistrunk, Subhodip Biswas, Nathan Self, Kurt Luther, Naren Ramakrishnan
ECSCW1
2020 Geospatial Clustering for Balanced and Proximal Schools
abstract
Public school boundaries are redrawn from time to time to ensure effective functioning of school systems. This process, also called school redistricting, is non-trivial due to (1) the presence of multiple design criteria such as capacity utilization, proximity and travel time which are hard for planners to consider simultaneously, (2) the fixed locations of schools with widely differing capacities that need to be balanced, (3) the spatial nature of the data and the need to preserve contiguity in school zones, and (4) the difficulty in quantifying local factors that may arise. Motivated by these challenges and the intricacy of the process, we propose a geospatial clustering algorithm called GeoKmeans for assisting planners in designing school boundaries such that students are assigned to proximal schools while ensuring effective utilization of school capacities. The algorithm operates on polygonal geometries and connects them into geographically contiguous school boundaries while balancing problem-specific constraints. We evaluate our approach on real-world data of two rapidly growing school districts in the US. Results indicate the efficacy of our approach in designing boundaries. Additionally, a case study is included to demonstrate the potential of GeoKmeans to assist planners in drawing boundaries.
Subhodip Biswas, Fanglan Chen, Andreea Sistrunk, Sathappan Muthiah, Zhiqian Chen, Nathan Self, Chang-Tien Lu, Naren Ramakrishnan
AAAI3
2019 REGAL: A Regionalization framework for school boundaries
abstract
Due to constant shifts in population and changing demographics, school boundary processes take place to make adjustments to school attendance zones. This spatial problem has multiple criteria like locations of schools, their capacity utilization, proximity, presence of geographical/ man-made barriers, etc. In this paper, we formulate the problem of designing school boundaries as a spatially-constrained clustering/ regionalization problem and propose an automated approach called REGAL for solving it. REGAL is two-stage framework that starts by creating a candidate solution with regard to domain constraints such as school locations and spatial contiguity. Then a local search method improves the quality of the candidate solution by optimizing population balance and compactness of school zones while satisfying problem constraints. Experimentally, we demonstrate the efficacy of the REGAL framework on actual datasets from two school districts in the US.
Subhodip Biswas, Fanglan Chen, Zhiqian Chen, Andreea Sistrunk, Nathan Self, Chang-Tien Lu, Naren Ramakrishnan
SIGSPATIAL/GIS4