Elias Hossain

I am a Ph.D. student at the University of Central Florida (UCF), advised by Dr. Niloofar Yousefi. My research focuses on advancing Large Language Models (LLMs) and Machine Learning for biomedical discovery and antimicrobial resistance (AMR) prediction. I am currently developing an AI-driven framework that integrates LLM-based reasoning with computational biology to predict resistance mechanisms and design digital twin systems capable of simulating host–pathogen interactions for drug discovery in bacterial pathogens. My work aims to enable trustworthy, interpretable, and data-efficient biomedical AI capable of bridging computational intelligence with clinical translation. With a professional background as a Software Engineer and Machine Learning Researcher, I bring expertise in backend system design, algorithm optimization, and scalable AI model deployment. This combination of computational and biomedical insight drives my goal of creating robust, explainable, and high-impact AI systems for healthcare and life sciences.

Recent News

  • Paper Accepted — 7th Molecular Machine Learning (MoML) Conference, Massachusetts Institute of Technology (MIT), Cambridge, USA (2025)

Education

University of Central Florida (UCF), United States

Doctor of Philosophy (Ph.D.) in Industrial Engineering and Management Systems
My research focuses on advancing Large Language Models (LLMs) for biomedical discovery, with applications in antimicrobial resistance (AMR) prediction, digital twin systems, and data-driven drug discovery for bacterial pathogens. I aim to develop interpretable and trustworthy AI frameworks that bridge computational modeling with biological understanding, contributing to the next generation of intelligent healthcare systems.
Aug 2025 – Dec 2028 (Expected)

Mississippi State University, United States

Master of Science (M.S.) in Computer Science
Thesis: “TopNet R1: A Multi-Stage AI Framework for Topic Discovery in Scientific Abstracts” (July 2025) — Download
Major Professor: Dr. Andy Perkins
My research centered on Natural Language Processing (NLP), Machine Learning, and scientific topic modeling. In particular, my work focused on developing advanced AI-driven frameworks for uncovering latent structures and thematic patterns in large-scale scientific corpora, contributing to more interpretable and efficient methods for knowledge discovery in research literature.
Jan 2024 – July 2025

Research & Insights

I occasionally write about my ongoing research, methodologies, and experiments at the intersection of Large Language Models (LLMs), Computational Biology, and Biomedical AI. This section highlights key ideas and progress from my recent work.

Building an Interpretable LLM Framework for AMR Prediction

Exploring how specialized biomedical embeddings and fine-tuned reasoning layers can help predict antimicrobial resistance patterns with greater interpretability and confidence.

Read More
Digital Twin Systems for Host–Pathogen Simulation

Introducing AI-driven digital twin architectures designed to simulate biological interactions and accelerate antibiotic discovery through predictive modeling and multi-agent reasoning.

Read More

More articles and technical discussions are available on my Medium and YouTube channels.

Selected Publications

For a complete list of my publications, visit my Google Scholar profile.

Conferences

During Ph.D. at University of Central Florida (2025–Present)

My doctoral journey at the University of Central Florida (UCF) began with the honor of presenting my research at the 7th Molecular Machine Learning (MoML) Conference held at the Massachusetts Institute of Technology (MIT), Cambridge, USA (2025). This prestigious event brought together global leaders in molecular AI and computational biology, where I presented my work on Large Language Models (LLMs) for antimicrobial resistance (AMR) prediction and drug discovery frameworks. It marked a defining milestone in my Ph.D. research on trustworthy, data-efficient biomedical AI systems.

Poster Presentation at MIT MoML

Presenting my poster at MoML Conference, MIT

Group Photo at MIT MoML

Labmates and advisors at MoML 2025, MIT

Discussion with Professors at MoML

With Dr. Yousefi discussing research at MIT

During M.S. at Mississippi State University (2024)

During my master's research at Mississippi State University (MSU), I presented my work on AI and health informatics at national conferences and graduate research symposia. These experiences strengthened my foundation in translational research and interdisciplinary collaboration within healthcare innovation.

MSHIC Presentation

Presenting research at MSHIC 2024 Conference

Discussion with Dr. David

In discussion with Dr. David, MSU Professor

Graduate Research Symposium

Spring 2024 Graduate Research Symposium

Contact

I’m always open to discussing research collaborations, conference invitations, or ideas in AI, Bioinformatics, and Computational Biology.

📧 elias DOT hossain one nine one AT gmail DOT com

University of Central Florida (UCF) · Orlando, FL, USA

  • Publication
  • Conference
  • Contact

    • Elias Hossain

    I am a Ph.D. student at the University of Central Florida (UCF), advised by Dr. Niloofar Yousefi, with mentorship from Dr. Ivan Garibay. My research focuses on advancing Large Language Models (LLMs) and Machine Learning for biomedical discovery and antimicrobial resistance (AMR) prediction. I am currently developing an AI-driven framework that integrates LLM-based reasoning with computational biology to predict resistance mechanisms and design digital twin systems capable of simulating host–pathogen interactions for drug discovery in bacterial pathogens. My work aims to enable trustworthy, interpretable, and data-efficient biomedical AI capable of bridging computational intelligence with clinical translation. With a professional background as a Software Engineer and Machine Learning Researcher, I bring expertise in backend system design, algorithm optimization, and scalable AI model deployment. This combination of computational and biomedical insight drives my goal of creating robust, explainable, and high-impact AI systems for healthcare and life sciences.

    Recent News

    • Paper Accepted — 7th Molecular Machine Learning (MoML) Conference, Massachusetts Institute of Technology (MIT), Cambridge, USA (2025)

    Education

    University of Central Florida (UCF), United States

    Doctor of Philosophy (Ph.D.) in Industrial Engineering and Management Systems
    My research focuses on advancing Large Language Models (LLMs) for biomedical discovery, with applications in antimicrobial resistance (AMR) prediction, digital twin systems, and data-driven drug discovery for bacterial pathogens. I aim to develop interpretable and trustworthy AI frameworks that bridge computational modeling with biological understanding, contributing to the next generation of intelligent healthcare systems.
    Aug 2025 – Dec 2028 (Expected)

    Mississippi State University, United States

    Master of Science (M.S.) in Computer Science
    Thesis: “TopNet R1: A Multi-Stage AI Framework for Topic Discovery in Scientific Abstracts” (July 2025) — Download
    Major Professor: Dr. Andy Perkins
    My research centered on Natural Language Processing (NLP), Machine Learning, and scientific topic modeling. In particular, my work focused on developing advanced AI-driven frameworks for uncovering latent structures and thematic patterns in large-scale scientific corpora, contributing to more interpretable and efficient methods for knowledge discovery in research literature.
    Jan 2024 – July 2025

    Research & Insights

    I occasionally write about my ongoing research, methodologies, and experiments at the intersection of Large Language Models (LLMs), Computational Biology, and Biomedical AI. This section highlights key ideas and progress from my recent work.

    Building an Interpretable LLM Framework for AMR Prediction

    Exploring how specialized biomedical embeddings and fine-tuned reasoning layers can help predict antimicrobial resistance patterns with greater interpretability and confidence.

    Read More
    Digital Twin Systems for Host–Pathogen Simulation

    Introducing AI-driven digital twin architectures designed to simulate biological interactions and accelerate antibiotic discovery through predictive modeling and multi-agent reasoning.

    Read More

    More articles and technical discussions are available on my Medium and YouTube channels.

    Selected Publications

    For a complete list of my publications, visit my Google Scholar profile.

    Conferences

    During Ph.D. at University of Central Florida (2025–Present)

    My doctoral journey at the University of Central Florida (UCF) began with the honor of presenting my research at the 7th Molecular Machine Learning (MoML) Conference held at the Massachusetts Institute of Technology (MIT), Cambridge, USA (2025). This prestigious event brought together global leaders in molecular AI and computational biology, where I presented my work on Large Language Models (LLMs) for antimicrobial resistance (AMR) prediction and drug discovery frameworks. It marked a defining milestone in my Ph.D. research on trustworthy, data-efficient biomedical AI systems.

    Poster Presentation at MIT MoML

    Presenting my poster at MoML 2025, MIT

    Group Photo at MIT MoML

    My labmates and advisors at MoML 2025, MIT

    Discussion with Professors at MoML

    With Dr. Yousefi discussing research at MIT

    During M.S. at Mississippi State University (2024)

    During my master's research at Mississippi State University (MSU), I presented my work on AI and health informatics at national conferences and graduate research symposia. These experiences strengthened my foundation in translational research and interdisciplinary collaboration within healthcare innovation.

    MSHIC Presentation

    Presenting research at MSHIC 2024 Conference

    Discussion with Dr. David

    In discussion with Dr. David, MSU Professor

    Graduate Research Symposium

    Spring 2024 Graduate Research Symposium

    Stay Updated with My Research

    Follow me on Medium to get notified whenever I publish new articles, research insights, or project updates.

    Visit My Medium Profile

    You can also explore older posts and in-depth essays on AI, bioinformatics, and reasoning systems.

    Contact

    I’m always open to discussing research collaborations, conference invitations, or ideas in AI, Bioinformatics, and Computational Biology.

    📧 elias DOT hossain one nine one AT gmail DOT com

    University of Central Florida (UCF) · Orlando, FL, USA