Doctor of Philosophy, Computer Science

University of Illinois Chicago, USA

• GPA (Current): 4.00/4.00
• Advisor: Fabio Miranda
• Research area: Visualization and Visual analytics, Big Data Analysis, Applied Machine Learning

Bachelor of Science, Computer Science & Engineering

Military Institute of Science and Tehcnology, Bangladesh

• GPA: 3.71/4.00 (Top 10th percentile)
• Thesis Advisor: Muhammad Nazrul Islam

Graduate Research Assistant

University of Illinois at Chicago, USA

• Advisor: Fabio Miranda
• Research on Visual Analytics and Machine Learning focused on Urban Planning and Health Informatics.

Graduate Teaching Assistant

University of Illinois Chicago, USA

• Instructors: Fabio Miranda, Andruid Kerne.
• CS 422: User Interface Design and Programming
• CS 424: Visualization and Visual Analytics
• CS 425: Computer Graphics

Lecturer

Uttara University, Bangladesh

• Courses Taught: Discrete Mathematics, Computer Peripheral Interfacing and Maintenance, Digital Logic Design, Computer Graphics, Object Oriented Programming, Design and Analysis of Algorithms, Data Structures.

Software Developer Intern

Solution Art Ltd, Bangladesh

• Designed database schema for a hotel management system.
• Frontend development of the system.

Decision Support Tools for Sustainable Urban Planning and Public Health

• Development of a decision support tool integrating visual analytics for sustainable urban planning, transportation, energy, and public health.
• Flexible design framework adaptable to various decision-support scenarios, focusing on environmental parameters like carbon footprint and air quality.

Visual Gait and Motion Analysis
(Paper under review at IEEE TVCG)

• Developed VIGMA, an open-access visual analytics system for gait and motion analysis, integrating computational notebooks and a Python library.
• Analytical capabilities for disease progression analysis and multi-patient group comparisons, validated through expert usage scenarios.

Bi-GRU Model for Automatic Gait Event Detection in Older Adults
(Paper under review at Journal of NeuroEngineering and Rehabilitation)

• Developed an automatic gait event detection method using Bi-GRU models, specifically tailored for perturbed walking scenarios in older adults, leveraging marker, angle, and GRF data.
• Demonstrated that kinematic-based approaches, as opposed to traditional GRF methods, offer promising accuracy and efficiency, reducing the need for manual cross-validation in clinical gait analysis.

Navigating Large Dining Hall Spaces Considering Dietary Restrictions

• Developed a React Native mobile application to assist students with dietary restrictions in navigating UIC's dining hall, featuring personalized meal planning, detailed dish information, and interactive maps.
• Conducted user research and comparative testing, resulting in positive feedback on the app's usability and planning future improvements like custom dietary restrictions and enhanced image matching.

Generative Model for Global Sunlight Access and Shadows
(Paper)

• Developed Deep Umbra, a novel computational framework utilizing a generative adversarial network to quantify sunlight access and shadows in urban environments at a global scale.
• Created a comprehensive dataset with sunlight access information for over 100 cities, demonstrating the model's low RMSE and extensibility across different urban contexts.

Trustworthiness of OpenStreetMap Sidewalk Data in US
(Paper)

• Conducted a preliminary study on the availability and trustworthiness of OpenStreetMap (OSM) sidewalk data across over 50 major U.S. cities, addressing the scarcity of open sidewalk data.
• Analyzed the completeness of sidewalk data in Seattle, Chicago, and New York City, and developed a trustworthiness index using historical OSM sidewalk data.

COVID-19 Impact Analysis in Chicago Neighborhoods

• Modeled the impact of COVID-19 in Chicago neighborhoods using sociodemographic and COVID-19 data across ZIP codes, performing extensive data wrangling, geospatial analysis, and correlation analysis to identify key patterns.
• Developed a Random Forest Regression model to predict COVID-19 death rates based on sociodemographic variables, achieving a training error rate of 0.29 and a test error rate of 0.78 deaths per thousand people, with key insights into the influence of income, housing value, and public transit usage on COVID-19 outcomes.

Chicago Taxi Ridership Visualization Tool

• Developed a comprehensive visualization tool using Python, R, and Shiny to analyze and display trends in 2019 Chicago taxi ridership data, optimized for large-screen displays at UIC's EVL lab.
• Implemented features including detailed filtering by community areas, taxi companies, and time intervals, with findings highlighting patterns in ridership behavior across different neighborhoods and times of the day.

Mobility-Flow Query Approximation using NeuralCubes

• Developed an in-memory model using NeuralCubes to approximate spatiotemporal mobility-flow queries with high accuracy.
• Achieved less than 2% absolute error in query approximation while maintaining a minimal memory footprint of 114 KB.

Autism Spectrum Disorder Prediction Model and Mobile Application
(Paper 1, Paper 2)

• Undergraduate thesis.
• Developed a mobile application and a merged random forest prediction model to classify autism traits across all age groups, achieving over 92% accuracy with the AQ-10 dataset.
• Evaluated the model using both AQ-10 and 250 real-world datasets, demonstrating superior performance in accuracy, specificity, sensitivity, precision, and false positive rate (FPR) compared to existing models.

IoT-based Assistive Tool for Alzheimer’s Patients
(Paper)

• Undergraduate research project.
• Proposed an assistive tool for Alzheimer’s patients and their caregivers, offering features such as health monitoring, medication reminders, item tracking, and location monitoring.
• Conducted a light-weighted evaluation study with 15 participants, demonstrating the system's effectiveness and usability for both patients and caregivers.

Visual Analytics Approaches for Facilitating Explainability of Graph Neural Networks

• Ph.D. Qualifier Exam, 2023

Crowdsourcing and Sidewalk Data: A Preliminary Study on the Trustworthiness of OpenStreetMap Data in the US

• Paper presented at ASSETS’22 Workshop on The Future of Urban Accessibility, 2022.

An Intelligent Assistive Tool for Alzheimer’s Patient

• Paper presented at IEEE ICASERT conference, 2019.

A Machine Learning Approach to Predict Autism Spectrum Disorder

• Paper presented at IEEE ECCE conference, 2019.

• Merit Scholarship for academic performance, Military Institute of Science and Technology

• Dean’s List Award (two consecuctive years), Military Institute of Science and Technology

• Paper reviewer for PacificVis 2024, EuroVis 2023-2024, IEEE VIS 2022-2024.

• Volunteer for CAVE3 demos hosted by EVL, UIC.

• Vice President of Media of Bangladeshi Student Association at UIC.

• IEEE VIS Satelite Event (2021) volunteer. Held at EVL, UIC.

• Class Representative, Department of CSE at Military Institute of Science and Technology.