Project Title: Applied Machine Learning and Computer Vision
David Langerman, SHREC PhD Student, Group Leader
Luke Kljucaric, SHREC PhD Student
Justin Goodwill, SHREC Master’s Student
Computer vision and machine learning are currently two of the most researched topics in computer engineering. In 2012, when AlexNet surpassed all human-tuned image classifiers at the ImageNet challenge, research on convolutional neural networks (CNNs) had been stalled for years due to a lack of computational power and sufficient training data necessary to train a CNN. Since then, however, research in this area has accelerated at an explosive rate. Unfortunately, this has resulted in an extremely steep learning curve for those seeking to do research and development in this area. The ML/CV group in SHREC-SURG will give students the tools necessary to bridge this gap. Students will be exposed to theoretical and practical applications of pattern recognition, basic models and uses for CNNs, as well as machine learning acceleration and training methods on GPUs and CPUs. Many individual projects both at SHREC and PSC are available for hands-on experience using these tools. These projects include network traffic analysis, neural pathway classification, neural architecture discovery, and many more!
Some coding experience in any language
Strong Python or C++ experience
OpenCV, TensorFlow, Torch, Caffe
Learning ML frameworks in the context of image and time-series data classification. Learning computer vision techniques and how they relate to modern ML models.
Student exposure to a wide variety of tools and techniques related to ML and CV in both an HPC and HPEC environment. Exceptional projects might be invited to work with SHREC student on academic paper submission.
Computer Engineering/Computer Science/Electrical Engineering
Students in this position will be volunteers.