Research Students

Graduate Student

Project Bio: 

The early diagnosis of Vascular Dementia is a pressing research area. Some of the earliest indicators of vascular dementia onset, come in the form of gait and cognitive impairment variation. In this project we seek to use machine learning techniques to identify and analyze the variations found within an individuals’ gait pattern and executive functioning. Concretely, the objective of this work is to: (1) implement a system that can collect gait data, including force and moments measured off a force treadmill and muscle activation measured from on-body EMG sensors, simultaneously with cognition data in a way in which the two can be incorporated, (2) aggregate measures of gait such as stride length, lateral movement in gait, single support time, double support time, and the center of gravity at various key points in the stride, and (3) analyze the correlates these measures using classification models. Through this analysis we hope gain insight into the applicability of machine learning to gait disturbance detection. 

Personal Bio: 

Janna is a second year graduate student in the Department of Computer Science. Her academic interests include artificial intelligence, machine learning, logistics and quality management. In her free time, she enjoys reading, camping, mountain biking, playing hockey and baking bread. 

Graduate Student

Biography

My name is Yumna Anwar. I am currently a second-year graduate student at University of Minnesota Duluth and will be graduating spring 2019. I am also a Computer Science Graduate Teaching Assistant at UMD. I did my undergraduate in Computers science from Institute of Business administration, Karachi, Pakistan. For my Ms Thesis I am working with Dr. Khan on mobile applications and wearable sensors for improvement in mental health care, especially bipolar disorder. 

Currently we are investigating how psychological changes, affect physiological responses specifically HRV and Skin conductance, which is pivotal for a preliminary study on a sensor based predictive model for bipolar disorder. The data gathered, and the results of this study would not only be useful for Bipolar Disorder, but for developing many assistive technologies for mental healthcare.  

Publications:

Graduate Student

Biography

An Nguyen Thai is a computer science graduate student who works with Professor. Khan on developing a mobile app for human face recognition. I graduated at Ho Chi Minh national university - the University of Information Technology in 2015. Next two years, I worked as a software developer and solution architect for a big project related to the Ultra Sound Machine from Hitachi Corporation in Vietnam. I have been studying in the graduate program at the University of Minnesota Duluth since 2017.

A face recognition application is developed on mobile platforms which are iOS and Android. It is applied machine learning algorithms to detect human faces on image, video, or output recorded by the mobile camera. The main algorithm named Viola-John or AdaBoost is explored to warrant that the speed of detecting process, and it will be improved by modern approaches as multi-task CNN and small face detections. That is key points to enhance the detecting speed and result.