I am an experienced researcher in many areas of machine learning, with a proven publication record in leading venues. I am proud of my multidisciplinary background and experience in the humanities, which complement my technical expertise to provide unique perspectives. I have hands-on experience with many statistical machine learning techniques and approaches, including but not limited to neural networks and deep learning. I also have a strong background in statistics and engineering experiment design, with a focus on obtaining repeatable and interpretable results. I am also proud of my strong communication skills, and my ability to discuss advanced concepts to a wide range of audiences of all levels.RESEARCH PUBLICATIONS• G. Dawson and R. Polikar, “OpinionRank: extracting ground truth labels from unreliable expert opinions with graph-based spectral ranking,” in International Joint Conference on Neural Networks, 2021.• M. Umer, G. Dawson, and R. Polikar, “Targeted forgetting and false memory formation in continual learners through adversarial backdoor attacks,” in International Joint Conference on Neural Networks, 2020.• C. Frederickson, M. Moore, G. Dawson, and R. Polikar, “Attack strength vs. detectability dilemma in adversarial machine learning,” in International Joint Conference on Neural Networks, 2018.• G. Dawson and R. Polikar, “Contributor-aware defenses against adversarial backdoor attacks,” Under review.• G. Dawson and R. Polikar, “Beyond instance-dependent noise: generalizing noisy label modeling to multiple labeler dependence,” Under review.TECHNICAL PROFICIENCIES AND EXPERIENCES• Python (incl. PyTorch, TensorFlow, Keras, OpenCV, Scikit-Learn, NumPy, SciPy, Pandas, Matplotlib)• MATLAB (incl. Deep Learning, Stats & Machine Learning Toolboxes)• Machine Learning (incl. Convex Optimization, Linear and Logistic Regression, Kernel Methods, Random Forests, XGBoost, Ensemble Methods, Evolutionary Algorithms, Expectation Maximization, Time Series Modeling, Reinforcement Learning, Transfer Learning, Graphical Models)• Deep Learning (incl. Computer Vision, Convolutional Neural Networks, Deep Reinforcement Learning, Long Short-Term Memory, Variational Autoencoders, Adversarial Deep Learning)• Mathematics (incl. Statistics, Bayesian Statistics, Probability Theory, Abstract Algebra, Linear Algebra, Mathematical Logic, Operations Research, Game Theory, Real Analysis, Fourier Analysis)• Research (incl. Experiment Design, Hypothesis Testing, A/B Testing, Technical Writing, LaTeX)