Welcome to my website!
Hello! I’m Julina Maharjan, Ph.D. candidate in Computer Science (Computational Social Science) at Kent State University, graduating Summer 2025. Specializes in Transformer-based Large Language Models (LLMs), NLP, and deep learning. Experienced in developing scalable AI solutions using big data technologies. Strong research background in pretraining, finetuning, and optimization of LLMs, with four years of industry experience working onmachine learning pipelines and large-scale AI models. Passionate about advancing AI research to solve real-world language challenges.
About Me
I have a strong academic background in Deep Learning/Natural Language Processsing and have been involved in projects that aim to Decoding the concept of Deep Learning/Natural Language Processsing to understand how machine learns to understand human language. My journey in research has led me to collaborate with interdisciplinary teams, publish impactful papers, and contribute to advancing knowledge in my area of expertise.
When I’m not researching, you’ll often find me reconnecting with nature or staying energized through fitness activities. I believe in balancing intellectual curiosity with personal well-being.
Research Interests
- Transformer Models
- Deep Learning/Natural Language Processsing
- Reinforcement Learning
- Large Language Models (LLM)
- LLaMA(Large Language Model Meta AI)
Recent Publications
Large-Scale Deep Learning–Enabled Infodemiological Analysis of Substance Use Patterns on Social Media: Insights From the COVID-19 Pandemic
J Med Internet Res, 2025
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.Differential Analysis of Age, Gender, Race, Sentiment, and Emotion in Substance Use Discourse on Twitter during the COVID-19 Pandemic: An NLP Approach
JMIR, 2025
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.Do Large Language Models (LLMs) Really Understand Personality? A Test of Embeddings vs. Zero-Shot
JMIR, 2025
Recent advancements in Large Language Models (LLMs) have sparked interdisciplinary interest in their ability to assess psychological constructs, particularly Personality. While prior machine learning research has focused on evaluating LLMs’ capability to infer personality traits, often via zero-shot or few-shot learning, few studies have systematically examined the applicability of LLM embeddings for Personality Prediction within a robust psychometric validity framework or explored their correlation with psychological and linguistic features. Addressing this gap, we investigate performance of LLM embeddings on a well-labeled PANDORA dataset (Big Five Personality traits from Reddit).
Contact Me
Feel free to connect if you’re interested in collaborating or learning more about my work. You can reach me at [email protected] or connect with me on LinkedIn.
Thank you for visiting my website!