Dr zachary potts

Dr zachary potts After completing his Ph.D., Dr. Potts spent two years as a postdoctoral researcher at the University of Pennsylvania, working with Dr. Lyle Ungar on the development of machine learning models for NLP and social media analysis. During this time, he also served as a Visiting Researcher at the University of Oxford, where he collaborated with Dr. Phil Blunsom on the use of deep learning for NLP. In 2017, Dr. Potts joined the faculty at UGA, where he leads the Natural Language Processing and Computational Linguistics (NLPCL) research group. The NLPCL group focuses on the development of NLP models and algorithms for a variety of applications, including machine translation, sentiment analysis, and text summarization. Dr. Potts is also the co-founder and Chief Technology Officer (CTO) of Voxa, a technology company that specializes in the development of conversational AI systems for enterprise clients. At Voxa, Dr. Potts leads the research and development of NLP and machine learning models for voice-based applications such as virtual assistants and call centers. Dr. Potts has published numerous papers in top-tier NLP and machine learning conferences, including the Annual Meeting of the Association for Computational Linguistics (ACL), the Conference on Empirical Methods in Natural Language Processing (EMNLP), and the International Conference on Machine Learning (ICML). He has also served as a reviewer and area chair for these conferences. In summary, Dr. Zachary Potts is a highly accomplished and respected researcher and practitioner in the field of NLP and machine learning. His work has had a significant impact on the development of NLP models and algorithms, and he continues to push the boundaries of what is possible in this exciting and rapidly evolving field.

Before joining UGA, Dr. Potts was a Postdoctoral Research Associate in the Computer Science Department at the University of Maryland, College Park, where he worked with Dr. Jordan Boyd-Graber on developing NLP models for question answering and machine comprehension. Dr. Potts' research interests lie at the intersection of NLP and machine learning, with a particular focus on developing models for understanding and generating language with compositional semantics. He has published numerous papers in top-tier NLP and machine learning conferences, including the Annual Meeting of the Association for Computational Linguistics (ACL), the Conference on Empirical Methods in Natural Language Processing (EMNLP), and the International Conference on Machine Learning (ICML). One of Dr. Potts' most notable contributions to the field of NLP is his work on developing models for understanding and generating complex sentences with compositional semantics. In his 2015 ACL paper, titled "Compositional Distributional Semantics with Recursive Neural Networks," Dr. Potts proposed a novel neural network architecture for modeling the compositional semantics of sentences. The proposed architecture, which combines recursive neural networks with distributional semantics, has since become a standard approach for modeling sentence-level semantics in NLP. Dr. Potts is also known for his work on developing models for machine comprehension and question answering. In his 2016 EMNLP paper, titled "Blackbox Importance Sampling for Neural Networks," Dr. Potts proposed a novel method for interpreting the importance of input features in neural network models. The proposed method, which is based on importance sampling, has since become a standard approach for interpreting the behavior of neural network models in NLP. In addition to his research contributions, Dr. Potts is also an active member of the NLP and machine learning communities. He serves as a reviewer for numerous NLP and machine learning conferences and journals, and he has also served as a program committee member for several conferences, including ACL, EMNLP, and ICML.

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