Griffin Dean Kent
Ph.D. Candidate in the Industrial and Systems Engineering (ISE) Department at Lehigh University
Member of the Optimization and Machine Learning Research Group at Lehigh University
President of the Lehigh INFORMS Student Chapter
Member of the Lehigh ISE Student Council and an Ambassador of the Lehigh ISE Department
A Brief Bio
Hi, my name is name is Griffin D. Kent and here's a little bit about me.
I am currently a Ph.D. candidate in the Industrial and Systems Engineering (ISE) Department of Lehigh University under the supervision of the department chair Prof. Luis N. Vicente. I hold a M.S. in Management Science and Engineering from Lehigh University and a B.S. in Statistics with a Minor in Computer Science from Utah Valley University. I am the current President of the Lehigh INFORMS Student Chapter and am also a member of the Optimization and Machine Learning (OptML) research group at Lehigh University.
My research largely focuses on the fields of optimization and machine learning. In the field of optimization, I have worked / currently am working on topics such as general nonlinear multi-level and multi-objective optimization, stochastic optimization, and global optimization. In the field of machine learning, I have worked / currently am working on topics such as neural architecture search (NAS), hyperparameter tuning, adversarial learning, and continual learning. While earning my undergraduate degree, I also did some research in the field of statistics on the problem of causal inference in the presence of confounding variables; work which I presented in the Netherlands.
Outside of strictly defined research, I have spent much time working on related topics such as deep learning, text analysis with natural language processing (NLP), applied data mining, and statistical analysis. I have also participated in several Kaggle competitions and have delivered several hour-length lectures on machine learning and data science at both Lehigh University and Utah Valley University; specifically on topics such as classical fully-connected feed-forward neural networks, convolutional neural networks (CNNs), random forests, and as well as a full walkthrough of a top-5% data mining perspective of the Titanic dataset. For a more thorough description of my technical skills, see my resume:
When I'm not working on research, I enjoy spending time with my amazing wife exploring the quaint coffee shops and bookstores around the old steel town of Bethlehem, Pennsylvania. In my spare time, I also enjoy playing the piano, exercising, and studying the core branches of philosophy: logic, epistemology, metaphysics, and axiology. I may keep an updated journal / blog of this journey in the "Ethos" tab.
