Senior Member, IEEE
Senior Lecturer, Department of Computer Science , University of Maryland, College Park.
I received Ph.D. and M.S. degrees in Computer Science in the fields of Computer Vision and Machine Learning from Colorado State University in 2013 and 2007, respectively, and B.E. degree in Electronics and Communications from National Institute of Technology, Srinagar, Kashmir. From July 2015 - 2017, I was an Assistant Professor of Computer and Information Science at Harrisburg University, Harrisburg, PA and from 2014 - July, 2015, I was an Assistant Professor of Computer Science & Engineering at the National Institute of Technology, Srinagar. I have also worked as a Software Engineer at Hewlett-Packard from 2003-2005, and at Wipro from 2000-2003. My research interests center on improving and design of systems employing the use of Computer Vision, Machine Learning, and Data Science algorithms.
In this project we are studying the use of a Generative Adversarial Network (GAN) to generate target depth images from a public LiDAR dataset. This depth information would be used to augment the intensity dataset from a disaster area whose building depth is not available. This is used to train a Convolution Neural Network (CNN) for damage classification.
We examine the extent to which distracted driving can be measured from facial expressions, body activity, and eye movement. Through the use of several cameras, machine learning algorithms will take real-time data and detect whether a user is distracted: reaching out to the back, talking to the passengers, fiddling with the radio, texting, talking on the phone, fidgeting with hair or makeup, yawning, and / or feeling drowsy. In order to develop these algorithms, we need your help in obtaining this data.
This is a collaborative project between the Departments of Computer Science and Agriculture at University of Maryland, College Park. The goal of this research is to understand the behavior of chickens using Computer Vision algorithms. The different behaviors we are currently tracking include: sitting, standing, feeding, pecking, accessing water and dust bowl, and social pecking. Such behavior analysis would enable us to understand the well being of the chickens. Understanding the behavior of chickens would help identify any medical or other conditions of the chicken in a similar or relatively similar setting. There are many challenges in this work.The immediate and the most important one is the similarity of the chickens which makes tracking them a challenging problem.