I am Mohammad Masudur Rahman, a Ph.D. candidate in Computer Science at the University of Louisiana at Lafayette, where I am currently working as a Graduate Research Assistant under the supervision of Dr. Beenish M. Chaudhry at the School of Computing & Informatics. My doctoral research focuses on developing a multimodal clinical natural language processing (NLP) framework for medical relational agents, with the overarching goal of improving healthcare informatics and patient engagement through intelligent, explainable, and trustworthy AI systems. Maintaining a perfect CGPA of 4.0/4.0 throughout my Ph.D. coursework, I have built a strong foundation in advanced neural networks, machine learning for health informatics, image processing, human-computer interaction, and advanced algorithms.
Prior to starting my Ph.D., I completed my M.Sc. in Computer Science at the same university, where my thesis investigated user feedback analysis in mobile app reviews in natural language processing, sentiment analysis, and large-scale text mining. I hold a B.Sc. in Computer Science and Engineering from United International University (UIU), Bangladesh, graduating with honors (Cum Laude). My undergraduate thesis proposed a corpus-based n-gram hybrid approach for Bengali-to-English machine translation, reflecting my longstanding interest in computational linguistics and multilingual NLP.
My academic and professional journey combines research, teaching, and industry experience. I have over five years of teaching experience as a Lecturer in Bangladesh, where I taught a wide range of computer science courses, including Algorithms, Data Structures, Artificial Intelligence, and Operating Systems. As a Senior Research Engineer at the AIMS Lab (UIU), I coordinated multiple machine learning and NLP projects, such as Onubad, Smart Reception, and AIMScribe, and supervised junior researchers. I also bring industry experience as a Software Engineer, with a strong background in backend development, API integration, database optimization, and applied machine learning.
My research contributions include 8 peer-reviewed conference papers and 5 journal/book chapter publications, with recent work appearing in Diabetes Research and Clinical Practice (Q1, IF 5.1) and Engineering Applications of Artificial Intelligence. These publications span topics such as federated learning for type-2 diabetes detection, explainable AI for gestational diabetes diagnosis, healthcare chatbots for COVID-19 service delivery, neural style transfer for artistic image generation, and cyber threat intelligence via topic modeling. Beyond academia, I am the author of Math-magic, a popular science book published at the Amar Ekushey Book Fair 2022.
I am deeply passionate about natural language processing, machine learning, healthcare informatics, and federated learning. My long-term goal is to design AI-driven systems that are not only accurate but also fair, interpretable, and accessible empowering both clinicians and patients in data-driven decision-making.
If you have any question or concern please feel free to contact me via email masudurism@gmaill.com