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PhD Doctor of Philosophy candidate

Artificial Intelligence, Computer Vision, Machine Learning, Autonomous Vehicles, Human-Robot (and Human-Computer) Interaction, Music, Intelligent Systems, Image Processing
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Project ideas

Project ideas are meant to help inspire student thinking about their own project. Students are in the driver seat of their research and are free to use any or none of the ideas shared by their mentors.

Computer Recognition of Directed Gestures

After spending a year in virtual classrooms, what are some of the small, nonverbal interactions you miss from in-person conversations? Eye contact & directed gestures are two modalities of communication we usually take for granted, but over Zoom, it can be very hard to tell to whom someone is waving or pointing. What if a computer vision system could help in determining & communicating this information? In this project, you can research methods in computer vision and machine learning that will allow for gesture recognition. Beginning from a standard desktop workstation, you can further experiment with your methods, extending to other poses and contexts.

Machine Learning for Autonomous Vehicles

There are many, many tasks involved in the realm of self-driving vehicles, which involve analysis of activities happening inside the vehicle, outside the vehicle, and even from a bird's-eye-view. Some projects you may want to explore: Driver activity analysis using inside-facing cameras, exploring a driver's eyes, attention, posture, hand activity, etc. Detecting pedestrians and other vehicles in the outside scene using machine learning. Path planning and trajectory prediction for intelligent agents. If your idea involves an intelligent vehicle, I'd love to connect and help guide your research!

Enhanced Transfer Learning for Convolutional Neural Networks

Transfer learning is a popular mechanism to improve performance and reduce training time of Convolutional Neural Network (CNN) models. However, when a pretrained model well-suited for a task operates on a different scale than the original training images, it may be beneficial to change the size of the convolutional kernel. But, this requires retraining the network, making transfer learning impossible. In this project, you will develop a method for initializing kernels that maintain the effects of learned weights while having flexibility to accommodate new kernel sizes in convolutional layers.

Coding skills

Python, Pytorch, Tensorflow, Keras, OpenCV, MATLAB, Java, C, C++, ROS