About

Vipin Kumar is a Regents Professor and the William Norris Land Grant Chair in Large-Scale Computing at the University of Minnesota's Department of Computer Science & Engineering. He joined the department in 1989 and has been promoted to full professor, holding his current titles since 2005 and 2015 respectively. His research interests encompass data mining, high-performance computing, and their applications in climate/ecosystems and healthcare. Kumar's work has led to the development of the isoefficiency metric for evaluating the scalability of parallel algorithms, as well as highly efficient parallel algorithms and software for sparse matrix factorization and graph partitioning. His current major research focus is on leveraging big data and machine learning to understand the impact of human-induced changes on the Earth and its environment. Kumar's contributions to the field have been widely recognized, with over 127,000 citations, and he has received numerous awards including the IEEE Technical Achievement Award, the ACM SIGKDD Innovation Award, and the IEEE Fellow distinction. He is also the director of the Data Science Initiative at the university and has been actively involved in advancing climate modeling and environmental predictions through artificial intelligence and machine learning.

Research topics

• Computer Science
• Artificial Intelligence
• Machine Learning
• Climatology
• Geography
• Environmental science
• Data Mining
• Cartography
• Engineering
• Geology
• Management science
• Physics
• Remote sensing
• Statistical physics
• Data science
• Risk analysis (engineering)
• Mathematics

Selected publications

• Remote Work and Employee Well-being: A Study on the Role of HR Policies in Promoting Work-Life Balance

  International Journal For Multidisciplinary Research · 2026-03-16

  articleOpen access1st authorCorresponding

  The rapid global adoption of remote work has reshaped organizational landscapes, presenting both opportunities and challenges for employee well-being and work-life balance. This report undertakes a review of these dynamics, drawing upon established psychological theories such as the Job Demands-Resources (JD-R) model, Conservation of Resources (COR) theory, and Self-Determination Theory (SDT). The analysis presents the impact of remote work across psychological, physical, and social health domains. It highlights both its benefits and drawbacks. Central to this is the role of Human Resources (HR) policies in mediating these effects. The study identifies key HR policy areas like flexible work arrangements, mental health and social support initiatives, ergonomic provisions, communication guidelines, and professional development, as mechanisms for fostering a healthier and sustainable remote work environment. Some of the challenges are also explored, alongside the evolving leadership competencies required for effective remote team management.

  PublisherOA PDFDOI

• ADVANCED PREDICTION MODEL FOR EARLY DETECTION OF LUMPY SKIN DISEASE USING DEEP LEARNING AND IMAGE PROCESSING

  JOURNAL OF MECHANICS OF CONTINUA AND MATHEMATICAL SCIENCES · 2026-01-13

  articleOpen access

  The comprehensive study investigates the application of cutting-edge machine learning algorithms and advanced image processing techniques for the early detection of lumpy skin disease in cattle. The proposed robust analytical framework that evaluates multiple predictive models using comprehensive performance metrics, including F1 scores ranging from 0.87 to 0.97, precision up to 0.984, recall up to 0.963, and accuracy peaking at 97.77%. The novel approach incorporates pixel-level analysis to quantify disease severity through the ratio of affected to healthy tissue, complemented by processing speed delays between 5.54ms and 20.95ms. The research demonstrates significant improvements over traditional diagnostic methods, with particular emphasis on the model's ability to identify high-risk cases requiring immediate intervention. These findings have substantial implications for veterinary medicine, agricultural technology development, and livestock management policies, potentially revolutionizing disease surveillance systems in the agricultural sector.

  PublisherDOI

• Neural Network-Based Smart Detection of Skin Cancer Using Radial Basis Function Networks

  Information systems engineering and management · 2025-09-30

  book-chapter
  PublisherDOI

• KDD 2025 Panel on AI for Science

  2025-08-03

  articleOpen access1st authorCorresponding

  Artificial Intelligence (AI) is rapidly reshaping the landscape of scientific discovery by enabling the development of novel models that tackle complex, data- and computation-intensive problems. Scientific challenges, in turn, provide rich, use-inspired settings that push the boundaries of AI research. This virtuous cycle is increasingly driven by cross-disciplinary collaboration, where advances in AI and domain sciences co-evolve to accelerate innovation. In this plenary panel, we will examine the opportunities and challenges in designing cutting-edge AI models for scientific discovery, and high- light the transformative potential of cross-disciplinary partnerships in shaping the future of both AI and science.

  PublisherOA PDFDOI

• Recent Advancements and Innovations in Post-harvest Handling, Storage, and Technology for Vegetables: A Review

  Archives of Current Research International · 2025-02-08 · 7 citations

  reviewOpen access

  In a broad sense, Vegetables are highly perishable and experience significant qualitative and quantitative losses after harvest. Advances in post-harvest handling and storage technologies have become critical interventions for maintaining quality, extending shelf life, and reducing waste. This review explores recent developments in post-harvest management, including precision harvesting tools, innovative storage solutions, and smart packaging technologies. It also examines the challenges, such as infrastructural deficiencies, and highlights future opportunities for creating more efficient and sustainable vegetable post-harvest systems. These innovations are vital for sustaining vegetable quality, improving food security, and enhancing economic viability. Recent developments in post-harvest handling and storage technologies have been crucial in addressing this issue by curing, drying, and grading, rapid cooling and refrigeration, Processing and value addition. Those innovations play crucial roles in sustaining vegetable quality and shelf life extension, thus aiding in the process of economic viability and food security. This paper examines recent trends in post-harvest management such as precision harvesting tools (such as controlled atmosphere storage, modified atmosphere packaging), cutting-edge storage systems, and smart packaging technologies. The article identifies potential areas for further research to optimize post-harvest systems worldwide.

  PublisherDOI

• Raman–gene integration provides a novel space of information to explore metabolism and gene function

  Elsevier eBooks · 2025-01-01

  book-chapterSenior author
  PublisherDOI

• Availability of N.P.K under low intensity of soil survey of Northern District of Madhya Pradesh (India)

  International Journal of Research in Agronomy · 2025-02-01

  articleOpen access

  This study investigates the soil macronutrient composition across various land use types (agricultural, forest, and riverside) in five northern districts of Madhya Pradesh, India (Shivpuri, Gwalior, Morena, Bhind, and Datia). A total of 30 soil samples were collected from both surface (0-15 cm) and subsurface (15-30 cm) depths to assess key soil parameters, including pH, Electrical Conductivity (EC), Organic Carbon (OC), Nitrogen (N), Phosphorus (P), and Potassium (K). The results revealed that the soil pH ranged from neutral to slightly alkaline across the districts, with the highest pH values observed in the Datia district. EC levels were found to be low to medium, with the highest values recorded in Shivpuri. Organic carbon content was generally low to medium, with Gwalior and Datia exhibiting higher organic carbon concentrations in forest and riverside areas. Nitrogen content varied from low to medium, with Datia exhibiting the highest nitrogen levels, particularly in forested areas. Phosphorus was found to be low to medium, with the highest concentrations in the Morena district under agricultural use. Potassium levels also showed variation, with the highest concentrations in Bhind district across all land use categories. This study provides a comprehensive assessment of the soil nutrient status in the region, contributing valuable insights for sustainable land management practices and soil fertility optimization.

  PublisherOA PDFDOI

• AI and Science Day

  2025-08-03

  articleOpen access

  The past decade has been an inspiring time for artificial intelligence (AI) research. AI systems have transformed norms and practices across industries and have permeated the fabric of human society. Moreover, AI is ushering in a transformative technological age by making remarkable breakthroughs in a number of scientific fields such as protein structure prediction and medical imaging. There is increasing consensus in the wider scientific community that AI is poised to disrupt science by unlocking entirely new approaches, driving new scientific inquiry, and enabling greater scientific leaps with far-reaching social consequences. However, there are substantial barriers preventing science from realizing that potential, and addressing these barriers will require support for advances in AI methods and the adoption of these methods in routine scientific research. In this special day at KDD 2025, we host a series of talks by distinguished researchers on AI for science.

  PublisherOA PDFDOI

• Classical invariants for some time-dependent anharmonic potentials using Struckmeier and Riedel approach

  Indian Journal of Physics · 2025-01-10

  article1st author
  PublisherDOI

• Exploring process variables and their impact on water treatment: an in-depth review of biomass derived hydrochar

  International Journal of Environmental Science and Technology · 2025-05-24

  article
  PublisherDOI

Recent grants

• BIGDATA: F: Advancing Deep Learning to Monitor Global Change

  NSF · $1.5M · 2018–2025

• III-CTX: Collaborative Research: Spatio-Temporal Data Mining For Global Scale Eco-Climatic Data

  NSF · $298k · 2007–2011

• Collaborative Research: Knowledge Guided Machine Learning: A Framework for Accelerating Scientific Discovery

  NSF · $664k · 2019–2023

• ITR: COLLABORATIVE RESEARCH: A Data Mining and Exploration Middleware for Grid and Distributed Computing

  NSF · $611k · 2003–2007

• Data Mining for Rare Class Analysis

  NSF · $300k · 2003–2007

Frequent coauthors

• Philip S. Yu

  University of Illinois Chicago

  246 shared

• Rakesh Agrawal

  Purdue University West Lafayette

  244 shared

• Rao Kotagiri

  University of Melbourne

  243 shared

• Michael Steinbach

  University of Minnesota System

  164 shared

• Xiaowei Jia

  University of Pittsburgh

  120 shared

• Ankush Khandelwal

  University of Minnesota System

  83 shared

• Xin Yao

  82 shared

• Xin Yao

  Southern Medical University

  81 shared

Labs

• Vipin KumarPI

Awards & honors

• IEEE Computer Society Taylor L. Booth Education Award (2025)
• AAAI Fellow (2023)
• ACM/IEEE Supercomputing Conference Test of Time Award (2021)
• Institute on the Environment Fellow (2016)
• Regents Professorship (2015)