Research

In my research lab, we develop AI models and algorithms to solve problems across various disciplines. Our research is funded by NVIDIA, Google, the National Aeronautics and Space Administration (NASA), and the U.S. National Science Foundation. We have published our research findings in prestigious peer-reviewed journals including Scientific Reports, PLOS ONE, IEEE Frontiers in Education, and Education Sciences on topics such as visualizing students’ writing process and integrating AI into education with safeguards. Within the field of AI, our focus lies in explainable AI (XAI), a subfield dedicated to understanding and explaining why AI systems succeed or fail.

📃 Publications - Education and AI

Please see Google Scholar for a full list of publications.

• Thinking Beyond Chatbots’ Threat to Education: Visualizations to Elucidate the Writing or Coding Process
  Journal: Education Sciences, 2023
  Authors: Badri Adhikari
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• Engaging Students to Learn Coding in the AI Era with Emphasis on the Process
  Journal: Edukasiana: Jurnal Inovasi Pendidikan, 2023
  Authors: Kate Arendes, Shea Kerkhoff, and Badri Adhikari
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📃 Publications - Bioinformatics

• Scoring protein sequence alignments using deep learning
  Journal: Bioinformatics, 2022
  Authors: Bikash Shrestha and Badri Adhikari
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• DNCON2: improved protein contact prediction using two-level deep convolutional neural networks
  Journal: Bioinformatics, 2017 Authors: Badri Adhikari, Jie Hou, and Jianlin Cheng
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• CONFOLD: residue‐residue contact‐guided ab initio protein folding
  Journal: Proteins: Structure, Function, and Bioinformatics, 2015
  Authors: Badri Adhikari, Debswapna Bhattacharya, Renzhi Cao, Jianlin Cheng
  Read Online

🏆 Select Grants

• 2024, $100,000, CooperVision, Using Deep Neural Networks to Predict Success in Orthokeratology Lens Fitting, Role: Co-PI | News
• 2020, $256,496, National Aeronautics and Space Administration (NASA), STTR Phase1: Autonomous Environmental Monitoring and Management Platform for Remote Habitats, Role: Co-PI
• 2020, $163,535, National Science Foundation (NSF), CISE CRII: Deep Learning Methods for Protein Inter-residue Distance Prediction, Role: PI | News

For More Info

Please see our Github Projects page for open source tools we have developed.