Pankaj K. Agarwal

About

Pankaj K. Agarwal is the RJR Nabisco Distinguished Professor of Computer Science in Trinity College of Arts and Sciences at Duke University. His academic appointments include being a Professor of Computer Science since 1998 and a Professor of Mathematics since 2009 within the same college. He has held the Bass Fellowship since 2005. His research focuses on geometric algorithms, discrete geometry, geometric data analysis, data structures, database systems, data mining, robotics algorithms, and geographic information systems. Agarwal has made significant contributions to these fields through his research, publications, and professional activities, establishing himself as a leading figure in computational geometry and related areas.

Research topics

• Computer Science
• Mathematics
• Algorithm
• Data Mining
• Artificial Intelligence
• Information Retrieval
• Machine Learning
• Geometry
• Statistics
• Mathematical optimization

Selected publications

• Efficient Indexes for Diverse Top-k Range Queries

  2020 · 12 citations

  1st authorCorresponding

  Let P be a set of n (non-negatively) weighted points in Rd. We consider the problem of computing a subset of (at most) k diverse and high-valued points of P that lie inside a query range, a problem relevant to many areas such as search engines, recommendation systems, and online stores. The diversity and value of a set of points are measured as functions (say average or minimum) of their pairwise distances and weights, respectively. We study both bicriteria and constrained optimization problems.…

  DOI

• Durable Top-K Instant-Stamped Temporal Records with User-Specified Scoring Functions

  2022 IEEE 38th International Conference on Data Engineering (ICDE) · 2021 · 8 citations

  A way of finding interesting or exceptional records from instant-stamped temporal data is to consider their "durability, " or, intuitively speaking, how well they compare with other records that arrived earlier or later, and how long they retain their supremacy. For example, people are naturally fascinated by claims with long durability, such as: "On January 22, 2006, Kobe Bryant dropped 81 points against Toronto Raptors. Since then, this scoring record has yet to be broken." In general, given a…

  DOI

• Computing A Well-Representative Summary of Conjunctive Query Results

  Proceedings of the ACM on Management of Data · 2024-11-04 · 6 citations

  article1st authorCorresponding

  Data summarization is a powerful approach to deal with large-scale data analytics, which has wide applications in web search, recommendation systems, approximate query processing, etc. It computes a small, compact summary that preserves vital properties of the original data. In this paper, we study the data summarization problem of conjunctive query results, i.e., computing a k-size subset of a conjunctive query output, for any given k>0, that optimizes a certain objective. More specifically,…

  PublisherDOI

• Near-Optimal Min-Sum Motion Planning for Two Square Robots in a Polygonal Environment

  Society for Industrial and Applied Mathematics eBooks · 2024-01-01 · 2 citations

  book-chapter1st authorCorresponding

  Let W ⊂ ℝ2 be a planar polygonal environment (i.e., a polygon potentially with holes) with a total of n vertices, and let A, B be two robots, each modeled as an axis-aligned unit square, that can translate inside W. Given source and target placements sA,tA,sB, tB ∈ W of A and B, respectively, the goal is to compute a collision-free-motion plan π*, i.e., a motion plan that continuously moves A from sA to tA and B from sB to tB so that A and B remain inside W and do not collide with each other dur…

  PublisherDOI

• PAR2QO: Parametric Penalty-Aware Robust Query Optimization

  Proceedings of the VLDB Endowment · 2025-07-01 · 1 citations

  article

  Parametric Query Optimization (PQO) is an important problem in database systems, yet existing approaches suffer from high training costs, sensitivity to estimation errors, and vulnerability to severe performance regressions. This paper introduces PAR 2 QO (PARametric Penalty-Aware Robust Query Optimization), a system that integrates robust query optimization into PQO. PAR 2 QO strategically obtains plans from a well-balanced set of probe locations informed by the workload, and caches them as pla…

  PublisherDOI

Recent grants

• AF:Medium:Collaborative Research: Uncertainty Aware Geometric Computing

  NSF · $316k · 2012–2016

• Collaborative Proposal: Motion -- Models, Algorithms, and Complexity

  NSF · $267k · 2002–2006

• AF: Large: Collaborative Research: Compact Representations and Efficient Algorithms for Distributed Geometric Data

  NSF · $449k · 2010–2015

Frequent coauthors

• Micha Sharir

  Tel Aviv University

  236 shared

• Jiřı́ Matoušek

  Brno University of Technology

  51 shared

• Boris Aronov

  42 shared

• Sariel Har-Peled

  University of Illinois Urbana-Champaign

  39 shared

• Lars Arge

  Aarhus University

  34 shared

• Alon Efrat

  Alexandru Ioan Cuza University

  29 shared

• Subhash Suri

  University of California, Santa Barbara

  26 shared

• Ke Yi

  Hong Kong University of Science and Technology

  24 shared

Labs

• Pankaj K. Agarwal's LabPI

  Not provided

Awards & honors

• RJR Nabisco Distinguished Professor of Computer Science in T…
• Bass Fellow (2005 - Present)

Similar researchers at Duke University

• John H. Reifh-68
• Alberto Bartesaghih-56
• Cynthia D. Rudinh-56