Hi, I'm Md Tanvir Rouf Shawon
Ph.D. student researching how to make conversational AI safer for mental-health care, with work in natural language processing, explainable AI, and computer vision.
About Me
I am a 3rd-year Ph.D. student in Computer Science at George Mason University, advised by Dr. Kevin Lybarger. My research centers on health informatics, building and evaluating conversational AI systems for safety in mental-health care, particularly how these systems behave around vulnerable patients before they ever reach a real clinical setting.
My path here started in natural language processing. As Co-Principal Investigator on a CASR-funded project at AUST, I helped build one of the early benchmark datasets for detecting fake reviews in Bengali, alongside published work spanning NLP and computer vision. That line of research is what pulled me toward safety-critical applications of AI, which is why my Ph.D. work now focuses on patient-simulation frameworks for evaluating conversational agents used in mental-health care.
I was formerly a Lecturer at Ahsanullah University of Science and Technology (AUST), Dhaka, Bangladesh, in the Department of CSE, where I also completed my B.Sc. in Computer Science and Engineering, graduating first in my class. Between 2021 and 2024 I taught across much of the undergraduate CS curriculum there, from introductory and assembly-language programming to algorithms, pattern recognition, and soft computing, and later trained government-sponsored cohorts of teachers in Python and AI as part of a national digital-literacy initiative in Bangladesh.
Teaching taught me as much as it let me teach: explaining complex concepts and engaging with students' questions continually challenged me to deepen my own understanding, which is part of what drew me toward a Ph.D.
Currently working on: a PCORI-funded project developing an advanced AI system for depression management, as a Graduate Research Assistant under PIs Dr. Farrokh Alemi and Dr. Kevin Lybarger, alongside a related patient-simulation framework for evaluating conversational agents in mental-health care for suicidal patients. See the Research section below for details.
Research Interests
Full activity timeline
Sep 2026: Paper accepted at JMIR AI: A Patient Simulation Framework for Risk Assessment of Conversational Healthcare AI: Evaluation of an Antidepressant Decision Aid (preprint).
Apr 2025: Paper published at Multimedia Tools and Applications (link).
Aug 2024: Joined the CS department of George Mason University as a Ph.D. student.
Apr 2024: Paper published at Neurocomputing (link).
Mar 2024: Paper published at W-NUT 2024, collocated with EACL 2024 (link).
Dec 2023: Two papers presented at ICCIT 2023.
Oct 2023: Promoted to Senior Lecturer, Department of CSE, AUST.
Jul 2023: Four papers accepted at BIM 2023.
Mar 2023: Presented a paper at ICNLP 2023.
Oct 2022: Article accepted at the Iranian Journal of Computer Science.
Jul 2022: Presented a paper at ICICTD 2022.
May 2022: Paper accepted for the LNNS series (vol. 583) at DCAI 2022.
Jan 2022: Awarded funding for a project from CASR, AUST.
Jun 2021: Joined as a Lecturer, Department of CSE, AUST.
Feb 2021: Joined as an Adjunct Lecturer, Department of CSE, AUST.
Dec 2020: Successfully defended my B.Sc. Thesis.
Nov 2020: Paper accepted at ICCIT 2020.
Research Experience
Development of an Advanced AI System for Depression Management
Evaluating conversational AI for depression management: exploring how large language models can improve antidepressant recommendations and patient outcomes. The project develops patient simulations and risk-assessment methods to test whether conversational agents give trustworthy guidance on antidepressant selection.
Patient Simulation for Systematic Evaluation of Conversational Agents in Healthcare for Suicidal Patients
A Patient Simulation Framework for Risk Assessment of Conversational Healthcare AI
Bengali Fake Reviews: A Benchmark Dataset and Detection System
Conference Presentations
Bengali Fake Review Detection using Semi-supervised Generative Adversarial Networks
Effectiveness of Transformer Models on IoT Security Detection in StackOverflow Discussions
Journal Reviewing
Publications
Full list, synced live from Google Scholar (20+ publications, 250+ citations, h-index 11).
Education
Ph.D. in Computer Science
B.Sc. in Computer Science and Engineering
CGPA: 3.934 / 4.00. Graduated 1st in merit position. Supervisor: Mr. Mohammad Imrul Jubair.
Higher Secondary Certificate (HSC)
GPA: 5.00 / 5.00
Secondary School Certificate (SSC)
GPA: 5.00 / 5.00
Achievements
- Dean's List of Honors, Ahsanullah University of Science and Technology
- Government Scholarship, SSC, Sylhet Board, 2013
- Government Scholarship, JSC, Sylhet Board, 2010
- I-Genius, Grameenphone Prothom Alo Internet Festival, 2013
- Participated in the 5th International Conference on Natural Language Processing, Guangzhou, China, 2023
- Participated in the 19th International Conference on Distributed Computing and Artificial Intelligence, L'Aquila, Italy, 2022
- Participated in the 23rd International Conference on Computer and Information Technology (ICCIT), Dhaka, Bangladesh, 2020
Teaching Experience
Graduate Teaching Assistant
Courses: Database Concepts, Introduction to Low-Level Programming.
Senior Lecturer
Lecturer
Part-Time Faculty
AI & Python Facilitator
Delivered government-sponsored training on Python and Artificial Intelligence.
Courses taught, by semester
Spring 2020
- Assembly Language Programming
Fall 2020
- Algorithms Lab
- Pattern Recognition Lab
Spring 2021
- Computer Programming
- Computer Programming Lab
- Pattern Recognition Lab
Fall 2021
- Object-Oriented Programming Lab
- Algorithms Lab
- Soft Computing Theory
Spring 2022
- Object-Oriented Programming Lab
- Soft Computing Theory
- Soft Computing Lab
Fall 2022
- Introduction to Computer System
- Soft Computing Theory
Course descriptions
Introduction to Computer System
Basic principles of analog and digital computation, number systems, computer architecture and organization, operating systems fundamentals, and computer security.
Object-Oriented Programming Lab
Laboratory work on OOP principles: classes and objects, encapsulation, inheritance, polymorphism, and class hierarchy design.
Computer Programming & Lab
Introductory programming: variables, control flow, functions, recursion, arrays, pointers, structures, and basic data structures.
Algorithms Lab
Algorithmic complexity analysis and design techniques: divide and conquer, greedy methods, dynamic programming, backtracking, and branch and bound.
Pattern Recognition Lab
Object classification and machine learning: regression, Bayesian classifiers, neural networks, decision trees, SVMs, and clustering.
Soft Computing & Lab
Fuzzy sets and logic, artificial neural networks, probabilistic reasoning, and genetic algorithms.
Assembly Language Programming
Assembly programming basics, instruction formats, interrupts, procedures, and hardware interfacing.
Get in Touch
The best way to reach me is by email. I'm always happy to talk about health informatics, NLP, and evaluating AI systems for safety-critical settings.