Tamal Krishna Dey

About

Tamal Krishna Dey is a Professor of Computer Science at Purdue University, having joined the department in Fall 2020. His primary research areas include Computational Geometry and Topology, with applications to topological data analysis, geometric modeling, computer graphics, and mesh generation. Dey has authored two books: 'Curve and Surface Reconstruction: Algorithms with Mathematical Analysis' published by Cambridge University Press and 'Delaunay Mesh Generation' published by CRC Press. He recently coauthored another book titled 'Computational Topology for Data Analysis,' scheduled for publication by Cambridge University Press in 2022. With over 200 scientific articles to his name, Dey is an IEEE and ACM Fellow and has been inducted as a Fellow by the Solid Modeling Association. His academic background includes a PhD in Computer Science from Purdue University, a Masters from the Indian Institute of Science, and a Bachelor of Engineering from Jadavpur University. Prior to Purdue, he was a faculty member at Ohio State University from 1999 to 2020, where he led the Jyamiti research group and headed the NSF-sponsored TGDA TRIPODS Phase I Institute. Dey serves on various editorial and executive boards and is a sought-after speaker at academic forums.

Research topics

• Computer science
• Mathematics
• Algorithm
• Combinatorics
• Artificial intelligence

Selected publications

• Unusual Hypermucoviscous Clinical Isolate of Klebsiella pneumoniae with No Known Determinants of Hypermucoviscosity

  Microbiology Spectrum · 2022-06-01 · 31 citations

  articleOpen access1st author

  Hypermucoviscosity is a characteristic of hypervirulent Klebsiella pneumoniae strains, which are capable of causing invasive disease in community settings. This study reports phenotyping and genomic analysis of an unusual clinical isolate of Klebsiella pneumoniae , P34, which exhibits hypermucoviscosity and yet does not harbor rmp ( r egulator of m ucoid p henotype) genes, which are known determinants of hypermucoviscosity ( rmpA and rmpD ).

  PublisherDOI

• Topological Deep Learning: Going Beyond Graph Data

  arXiv (Cornell University) · 2022-06-01 · 27 citations

  preprintOpen access

  Topological deep learning is a rapidly growing field that pertains to the development of deep learning models for data supported on topological domains such as simplicial complexes, cell complexes, and hypergraphs, which generalize many domains encountered in scientific computations. In this paper, we present a unifying deep learning framework built upon a richer data structure that includes widely adopted topological domains. Specifically, we first introduce combinatorial complexes, a novel typ…

  PublisherOA PDFDOI

• Computing Connection Matrices via Persistence-Like Reductions

  SIAM Journal on Applied Dynamical Systems · 2024-01-04 · 5 citations

  articleOpen access1st authorCorresponding

  .Connection matrices are a generalization of Morse boundary operators from the classical Morse theory for gradient vector fields. Developing an efficient computational framework for connection matrices is particularly important in the context of a rapidly growing data science that requires new mathematical tools for discrete data. Toward this goal, the classical theory for connection matrices has been adapted to combinatorial frameworks that facilitate computation. We develop an efficient persis…

  PublisherOA PDFDOI

• Decomposing Multiparameter Persistence Modules

  ArXiv.org · 2025-01-01 · 1 citations

  articleOpen access1st authorCorresponding

  Dey and Xin (J.Appl.Comput.Top., 2022) describe an algorithm to decompose finitely presented multiparameter persistence modules using a matrix reduction algorithm. Their algorithm only works for modules whose generators and relations are distinctly graded. We extend their approach to work on all finitely presented modules and introduce several improvements that lead to significant speed-ups in practice. Our algorithm is fixed-parameter tractable with respect to the maximal number of relations of…

  PublisherOA PDFDOI

• Limit Computation Over Posets via Minimal Initial Functors

  arXiv (Cornell University) · 2026-01-01

  preprintOpen access1st authorCorresponding

  It is well known that limits can be computed by restricting along an initial functor, and that this often simplifies limit computation. We systematically study the algorithmic implications of this idea for diagrams indexed by a finite poset. We say an initial functor $F\colon C\to D$ with $C$ small is \emph{minimal} if the sets of objects and morphisms of $C$ each have minimum cardinality, among the sources of all initial functors with target $D$. For $Q$ a finite poset or $Q\subseteq \mathbb N^…

  PublisherDOI

Recent grants

• AF: Small: Expanding the Reach of Topological Data Analysis

  NSF · $350k · 2020–2024

• MCS: Reconstructing and Inferring Topology and Geometry from Point Cloud Data

  NSF · $462k · 2009–2013

• Collaborative Research: Non-smoothness in Meshing and Reconstruction

  NSF · $429k · 2006–2010

Frequent coauthors

• Amitava Akuli

  119 shared

• Abhra Pal

  Centre for Development of Advanced Computing

  118 shared

• Nabarun Bhattacharyya

  Symbiosis International University

  115 shared

• Gopinath Bej

  Centre for Development of Advanced Computing

  115 shared

• Sabyasachi Majumdar

  108 shared

• Tapas Sutradhar

  Centre for Development of Advanced Computing

  104 shared

• Rishin Banerjee

  Centre for Development of Advanced Computing

  103 shared

• Moumita Naskar

  100 shared

Education

• PhD, Computer Science

  Purdue University

  1991

Awards & honors

• IEEE Fellow
• ACM Fellow
• Fellow by Solid Modleing Association

Similar researchers at Purdue University

• Edward J. Delph-69
• Bharat Bhargavah-52
• Walid G. Arefh-51