Education

Doctor of Philosophy in Computational Sciences

Marquette University, USA (May 2019)

Department: Mathematics, Statistics and Computer Science (MSCS)
Cumulative GPA: 4.00 (Out of 4.00)
Thesis: BEst (Biomarker Estimation): Health Bio-marker Prediction Noninvasively, Ubiquitously and Frequently
Advisor: Professor Dr. Sheikh Iqbal Ahamed

Research Student

Kyushu University, Japan (Sep 2014 - Dec 2014)

Department: Advanced Information Technology
Faculty: Information Science and Electrical Engineering, Kyushu University, Japan
Theme of Study: Study on Improvement of Mobile Health Consultancy for Unreached Communities

Laurea Specialistica in Computer Sciences

University of Trento, Italy (Oct 2009)

Specialization: System and Networks (Two years program)
Department: Department of Information Engineering and Computer Science
Thesis: Integrating Ontological Representation of Process in Collaborative Environment
Advisor: Professor Dr. Paolo Bouquet, Department of Information Engineering and Computer Science, University of Trento, Italy and Dr. Luciano Serafini, Data and Knowledge Management Research Unit, Fondazione Bruno Kessler (FBK), Trento, Italy

Bachelor of Science in Computer Science and Engineering

Khulna University of Engineering & Technology (KUET), Bangladesh (Sep 2003)

Department: Computer Science and Engineering (Four years program)
Thesis: Swarm Intelligence: Modification and application of Ant Colony System (ACS) Algorithm
Advisor: Professor Dr. MMA Hashem , Department of Computer Science and Engineering (CSE), KUET

Doctoral Research

I am interested in understanding how we can reliably estimate blood hemoglobin level noninvasively and explore other blood constituent levels applying medical image processing techniques, advanced machine learning algorithms, and complex data analysis procedures. To that end, I'm particularly interested in developing low-cost medical and wellness applications leveraging the sensing and computing power of a smartphone, the best candidate for a point of care tool. Besides hemoglobin project, I have a couple of collaborative research engagements which are briefly illustrated here. (details...)

Hemoglobin Estimation (primary focus)

Hemoglobin level estimation process focuses on the fingertip video and lower eyelid image of an individual, inspired by the new computational imaging paradigm to develop a smartphone-based low-cost and reliable blood diagnosis tool. Our both fingertip and eyelid-based methods effectively estimate the hemoglobin level as well as significantly outperform existing methods applying advanced machine learning algorithm such as the deep convolutional neural network. (BEst and SHeA)

Pain Level Measurement

Since patients have limitations to provide self-reported pain symptoms, we have investigated how mobile phone application can play the vital role to help the patients to detect their pain level using their facial images captured by a smart phone. The proposed algorithm has been used to classify faces, which is represented as a weighted combination of Eigenfaces. We apply angular distance and support vector machines (SVMs) on longitudinal and cross-sectional pain images collected from three different countries: Bangladesh, Nepal and the United States. (details...)

Activity Detection

For physical activities detection, we have used a smartphone with built-in accelerometers and gyroscopes and developed a multimodal system using pressure sensor data from shoes. We also considered the sensor data as time series shapelets, used our algorithms to differentiate the shapelets, and applied Gaussian Mixture Models with time-delay embedding for detecting different activities. (details...)

Preemie Baby

In high-risk pregnancies, knowledge of prematurity is important for critical decisions such as the timing and mode of delivery, whether or not to resuscitate the newborn if delivered in the periviable (22 - 24 weeks) period, and whether neonatal intensive care treatment should be stopped if significant complications arise. Here, we approach a smartphone-based educational program that provides anticipatory guidance to parents with prematurity-associated risk factors and gives the opportunity to improve on current prematurity care and parental decision-making. The project was desinged and developed in collaboration with Neonatal-Perinatal experts at the Children Hospital of Wisconsin at Medical College of Wisconsin. (details...)