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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
Posts
Future Blog Post
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Blog Post number 4
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 3
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Blog Post number 2
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Blog Post number 1
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portfolio
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publications
Large-Scale Deep Learning–Enabled Infodemiological Analysis of Substance Use Patterns on Social Media: Insights From the COVID-19 Pandemic
Published in J Med Internet Res 2024, 2024
The COVID-19 pandemic intensified the challenges associated with mental health and substance use (SU), with societal and economic upheavals leading to heightened stress and increased reliance on drugs as a coping mechanism. Centers for Disease Control and Prevention data from June 2020 showed that 13% of Americans used substances more frequently due to pandemic-related stress, accompanied by an 18% rise in drug overdoses early in the year. Simultaneously, a significant increase in social media engagement provided unique insights into these trends. Our study analyzed social media data from January 2019 to December 2021 to identify changes in SU patterns across the pandemic timeline, aiming to inform effective public health interventions.
Recommended citation: Maharjan J, Zhu J, King J, Phan N, Kenne D, Jin R Large-Scale Deep Learning–Enabled Infodemiological Analysis of Substance Use Patterns on Social Media: Insights From the COVID-19 Pandemic JMIR Infodemiology 2025;5:e59076
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Differential Analysis of Age, Gender, Race, Sentiment, and Emotion in Substance Use Discourse on Twitter during the COVID-19 Pandemic: An NLP Approach
Published in JMIR (Journal of Medical Internet Research) 2024, 2024
User Demographics are often hidden in social media data due to privacy concerns. However, demographic information on Substance Use can provide valuable insights, allowing Public Health policymakers to focus on specific cohorts and develop efficient prevention strategies, especially during global crises like COVID-19.
Recommended citation: Maharjan J, Jin R, King J, Zhu J, Kenne D Differential Analysis of Age, Gender, Race, Sentiment, and Emotion in Substance Use Discourse on Twitter during the COVID-19 Pandemic: An NLP Approach JMIR Preprints. 08/10/2024:67333
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Do Large Language Models (LLMs) Really Understand Personality? A Test of Embeddings vs. Zero-Shot (Preprint)
Published in Journal of Medical Internet Research 2025, 2025
Recommended citation: Maharjan J Do Large Language Models (LLMs) Really Understand Personality? A Test of Embeddings vs. Zero-Shot JMIR Preprints. 01/04/2025:75347
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Intersection of Big Five Personality Traits and Substance Use on X: Insight from the COVID-19 Pandemic
Published in Journal of Medical Internet Research 2025, 2025
Recommended citation: Maharjan J, Jin R, Zhu J, Kenne D, Intersection of Big Five Personality Traits and Substance Use on X: Insight from the COVID-19 Pandemic
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talks
Big Data and Hadoop Training
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Conducted a workshop for 3rd-year Computer Science students at Deerwalk Institute of Technology, Kathmandu, Nepal. Trained students on Hadoop architecture, data processing, and distributed computing techniques.
Transformer-based LLMs
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teaching
Teaching Assistant for Computer Programming (C++)
Undergraduate course, Kent State University, Computer Department, 2019
- Assisted in teaching C++ programming to undergraduate students.
- Conducted lab sessions, guided students in debugging and optimizing code, and explained core data structures and algorithms.
- Provided one-on-one tutoring and graded programming assignments, ensuring students understood best coding practices.
Teaching Assistant – Data Structures
Workshop, Kent State University, Computer Department, 2020
- Assisted in teaching fundamental and advanced data structures to undergraduate students.
- Conducted lab sessions, helping students implement linked lists, trees, graphs, hash tables, and sorting algorithms.
- Provided one-on-one tutoring and graded assignments, ensuring students understood algorithm efficiency (Big O notation) and best coding practices.
- Guided students in debugging and optimizing C++/Java code, preparing them for real-world software engineering challenges.