Research focus areas

What the research
has found

Each focus area below is a short narrative review of research by Narasimha Rao Vajjhala and co-authors: what the studies examined, what they found, and how the findings fit together — with an APA citation attached to every claim and links to each publication’s own page. For individual publications, see the publication summaries; for longer reviews that place the studies in the wider literature, see the evidence overviews; for definitions of key terms, see the glossary.

Research synthesis
Machine learning & data science
Research synthesis
Cybersecurity
Research synthesis
Healthcare informatics
Research synthesis
ESG & sustainability measurement
Research synthesis
Sustainable software engineering
Research synthesis
Project management & project success
Research synthesis
Computing education

In brief

Questions and answers

What has Narasimha Rao Vajjhala’s research found about machine learning and data science?

Vajjhala and colleagues show that in applied machine learning — from weed classification and weather prediction to medical imaging, software defects and project analytics — multi-seed statistical comparison, metrics beyond accuracy and leakage checks matter more than headline scores, and that simple models often match complex ones.

  • Differences among high-performing deep learning models for weed classification were not consistently statistically significant across random seeds (Kumar et al., 2026).
  • Linear regression matched random forest for hyperlocal temperature prediction and outperformed it for humidity (Gigov et al., 2025).
  • A perfect in-sample classifier (AUC = 1.000) on organizational records was an artefact of label leakage (Strang & Vajjhala, 2026b).
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What has Narasimha Rao Vajjhala’s research found about cybersecurity?

Vajjhala and colleagues treat cyber risk as a problem of decision-making, organizational behaviour and education as much as technology: most major attacks exploit missed warnings, unpatched systems and insecure contractors, and awareness and compliance depend on social, cultural and organizational factors.

  • Of 25 major cyberattacks in 2020–2022, 44% were extortion, 32% service disruption and 24% data theft; 36% involved bitcoin ransom (Strang & Vajjhala, 2023b).
  • Unsupervised machine learning on hypertext from about 848 US higher-education website records can score breach likelihood (Strang & Vajjhala, 2024b).
  • Low information security awareness in sub-Saharan African SMEs is only partly explained by infrastructure, literacy and cost (Nasir & Vajjhala, 2020).
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What has Narasimha Rao Vajjhala’s research found about healthcare informatics?

Vajjhala and colleagues argue that AI in healthcare succeeds only when it is transparent, accountable and rigorously validated, and they document how machine learning, IoT, 5G and efficiency analysis can support patient care and health-system management.

  • Liability concerns, lack of explainability and unclear accountability are the key barriers to healthcare AI adoption (Eappen et al., 2026c).
  • A lightweight EfficientNetV2B0 model detected paediatric pneumonia with AUC 0.967 and recall 0.98 (Haveri & Vajjhala, 2026).
  • Data envelopment analysis supports efficiency measurement and benchmarking in healthcare but is limited by input/output selection, outlier sensitivity and statistical noise (Vajjhala & Eappen, 2024a).
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What has Narasimha Rao Vajjhala’s research found about ESG and sustainability measurement?

Strang and Vajjhala show that ESG compliance can be measured reliably at project level with a validated seven-item instrument, but that firm-level ESG ratings, organizational evaluation records and supply chain risk perceptions are much weaker measures than they appear.

  • ESG ratings from MSCI, Sustainalytics and CSRhub were unrelated to the misconduct and arbitration records of 14 trillion-dollar asset managers (Strang & Vajjhala, 2024a).
  • A seven-item, two-factor instrument measures project-level ESG compliance with excellent fit (CFI = 0.99, RMSEA = 0.052) (Strang & Vajjhala, 2026c).
  • Supply chain decision-makers rated labor and environmental risks lowest of six regulatory risks during record forced-labor enforcement (Strang & Vajjhala, 2026b).
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What has Narasimha Rao Vajjhala’s research found about sustainable software engineering?

Vajjhala and colleagues address sustainability in software engineering in two senses — whether software projects can be assessed against ESG criteria, and whether software and IT projects are built to last through defect prediction, sound programming education and evidence on why IT projects fail.

  • In financial software engineering projects, social and governance ratings tracked the overall project score almost perfectly while the environmental factor was unrelated to it (Strang & Vajjhala, 2026a).
  • An ensemble CatBoost model gave outstanding software defect prediction performance when judged on AUC, F-measure and MCC rather than accuracy alone (Saheed et al., 2021).
  • Project manager experience, budget and in-house versus outsourced management predict IT project failure (Strang & Vajjhala, 2023a).
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What has Narasimha Rao Vajjhala’s research found about project management and project success?

Strang and Vajjhala show, with machine learning on project big data and controlled experiments, that IT project outcomes depend on the project manager — experience, risk competency, resistance to incentive bias and commitment — and on team transparency during procurement.

  • Random forest analysis of about 17,430 US government IT projects identified seven failure indicators, led by project manager experience (Strang & Vajjhala, 2023a).
  • A 400% bonus incentive significantly lowered experienced project managers’ likelihood of cancelling a failing project (Cohen’s d = 0.826) (Strang & Vajjhala, 2022a).
  • Willingness to disclose past performance raised the explained variance in project success from 9.6% to 18.8% (Strang & Vajjhala, 2025a).
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What has Narasimha Rao Vajjhala’s research found about computing education?

Vajjhala and colleagues study how computing is taught and accepted — functional programming with Haskell, computational thinking across disciplines, blockchain within cybersecurity curricula, and student acceptance of mandatory learning technology.

  • Functional programming taught with Haskell brings consistent benefits for mathematical reasoning and abstract thinking, with selective transfer to object-oriented languages (Fonkam & Vajjhala, 2026).
  • Among students required to use a new learning management system, behavioral intention predicted actual course grade (Strang & Vajjhala, 2017).
  • Blockchain can be taught along two dimensions — stakeholder role and teaching ideology (Strang et al., 2020a).
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Longer reviews

Evidence overviews

Detailed mini-reviews that compare the studies in a theme and set them against the literature they cite.

ESG and sustainability measurement
Can ESG compliance be measured reliably? Evidence from ESG ratings, project-level instruments, machine learning and supply chain records
Project management and project success
What predicts IT project failure and success? Evidence on project managers, bias and team transparency from machine learning and experiments
Agriculture, food security and farm information systems
What is holding back Nigerian agriculture and farm technology adoption? Evidence from extension workers and farmer surveys
Technology adoption and digital consumers
What drives technology adoption and online purchase intention? Evidence from Indian consumers, SMEs and a mandatory LMS
Culture, knowledge management and transition economies
How does national culture shape knowledge sharing and collaboration in transition economies? Evidence from Albania