Biography
Tonni Das Jui is a Assistant Professor at Saint Leo University, FL, USA with research focus in Machine Learning. She previously worked at Leading University, Bangladesh as a Lecturer, where she distinguished herself by supervising undergraduate students and organizing programming contests. Tonni holds a PhD degree in Computer science from Baylor University, with a focus on robust and structure-aware graph representation learning and five years of experience as Graduate Teaching & Research assistant. She completed her Bachelor’s degree in Computer Science from BRAC University, Bangladesh. More information can be found in RESUME.
- Machine Learning
- Graph Representation Learning
- Fairness in AI Models
- Quantum Machine Learning
- Sign Language Analysis
PhD in Computer Science, 2026
Baylor University, USA
MSc in Computer Science, 2023
Baylor University, USA
BSc in Computer Science, 2018
BRAC University, Bangladesh
Experience
Independently taught undergraduate courses in Android Studio, Database Systems, and C++, designing rigorous theoretical and hands-on curricula that emphasized mobile app development, database design, and efficient problem-solving aligned with industry standards.
Mentored students through project-based learning, code reviews, and structured assessments, integrating interactive coding, real-world assignments, and technical skill development to strengthen practical implementation and critical thinking.
Led research in graph machine learning and representation learning within a bioinformatics lab, developing novel attention-based models and structural integration methods for node and graph classification on complex, real-world datasets.
Designed, implemented, and evaluated scalable machine learning pipelines (data preprocessing, model development, experimentation, and analysis), producing reproducible research outcomes and advancing methodological contributions at the intersection of AI and biomedical applications.
Projects: Topological graph embedding, Graph information influence, k-hop, k-hop_ARGA, P-value
Conducted interdisciplinary AI research, developing temporal CNN-based methods for sign language similarity analysis and evaluating fairness and performance of classical and quantum classifiers on real-world datasets.
Supported undergraduate instruction as a Teaching Assistant, facilitating labs, guiding student projects, and assisting with grading and conceptual reinforcement in core AI and machine learning topics.
Projects: SLSimilarity, QClassifiers, FairAI
- Taught courses: Artificial Intelligence, Data Structures, Discrete Mathematics etc.
- Mentored students on various android based mobile and desktop app.
- Affiliation: Faculty Profile
- Instructed classes, specially on Object-oriented programming (OOP) concepts.
- Guided students on intense programming in Python and Java
Skills
Research
Graph Representation Learning
- Actively working on graph information influence analysis under supervision of Dr. Erich Baker and Dr. Mary Lauren Benton in Bioinformatics lab.
- Developing methodology for generating graph’s topological embedding disregarding node feature information.
- Predicting p-values in Alzheimer’s disease gene graphs employing topological embedding.
- Detecting gene and genesets relations for multiple disease from GeneWeaver.
- Anlyzing edge feature and k-hopped topological and feature information for node attribute prediction with graph embedding.
- Published an articles and submitted two more (pending decision).
Sign Language Analysis
- Supervised by Dr. Pablo Rivas and collaborated with Dr. Gissella Bejarano in Baylor AI lab.
- Automated Sign Language (SL) similarity analysis with a 3D multi-stage convolutional neural network method.
- Verified method’s credibility comparing with SL similarity scores from Lexicostatistics (considering four features: Handshape, Orientation, Movement, Location) method.
- Employed annotations analysis with segmentation approach for automation.
- Published an article.
Quantum Classifiers Analysis
- Supervised by Dr. Pablo Rivas and Dr. Javier Orduz.
- Employed quantum classifiers with quantum kernals for analyzing classification performance compared to traditional classifiers.
- Employed quantum adversarial autoencoder for identigying DDoS attacks.
- Published an article and submitted another article (pending decision).
Fairness Evaluation in Models
- Supervised by Dr. Pablo Rivas in Baylor AI lab.
- Proposed classification of fairness issues and generalized the methods to solve them and their challanges according to the issue classes.
- Implemented systematic mapping study approach to analyze biases and the fairness of AI, ML methods
- Developed search queries (for IEEE, ACM, SpringerLink, Google Scholar, Science Directory) and research questions.
- Published a Book Chapter, submitted an article (pending decision) and collaborated in a NSF funding proposal write-up.
Projects
Health Insurance prediction
Predicting health insurance cost using supervised learning technique.
Machine Learning en Sistemas Embebidos
A spanish course for Machine Learning by Baylor university.
Quantum Classifiers
A Comparative analysis of quantum classifiers and corresponding classical classifiers.
Bear Go
A product delivery platform utilizing existing travelers
Car Tuning
A SpringBoot application for providing online orders of car parts.
Contest Registering
A SpringBoot application for organizing contests.
Hall of Fame
Similarity analysis between two objects of the same type.
Quantum Computing
A website for the public materials of Quantum computing.
SL similarity
Determining similarity between two sign languages using ML models.
Personal Website
A website developed with Hugo that represents academic and career and basic personal details.
Wireless Notice Board
To display a msg sent from faculty member to students and vise-versa without number sharing.
Payroll Management
Showing different employee names with their individual salaries
Color Sorter
A prototype device for separating RGB colored objects.
Publications
Achievements
Published 20+ peer-reviewed publications and a book chapter, accumulating 150+ citations within five years.
First authored a high-impact IJMLC publication with 50+ citations in its first year, placing it among top-cited works in the machine learning fairness domain.
Awarded merit scholarship (top academic performance, CGPA >3.70) from BRAC University throughout undergraduate studies.
Activities
Programming
Solved 500+ problems on Leetcode and similar on HackerRank.
Developed and contributed to Spring Boot–based backend applications, designing RESTful APIs and managing version-controlled collaboration via GitHub.
Leadership
Organized inter-university programming competitions and mentored student teams as a Computer Science Club advisor at Leading University.
Led teams as mentor of PENNTESTING CLUB at Saint Leo University.
Served as vice president of BRACU COMPUTER CLUB.
Contact
- tonnijuicse@gmail.com
- 882 N John Way, Chandler, AZ 85225