Professor Dan Goldwasser

Dr. Goldwasser is an assistant professor at the department of computer science at Purdue university. Before that he was a postdoctoral researcher at the University of Maryland in College Park. He completed his Ph.D. studies at the University of Illinois at Urbana-Champaign in the department of Computer Science.
He is currently working on applying machine learning approaches to natural language processing problems. He is broadly interested in connecting natural language with real world scenarios, and using them to guide natural language understanding.

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Kristen Johnson

Kristen is a second year member of PurdueNLP. Her research focuses on the application of NLP methods and graphical models to the analysis of political discourse on Twitter. She is the CS representative for the Women in Science Program (WISP) and a founding member of the Purdue Women in CS group. In her free time she enjoys yoga, photography, gardening, watercolor painting, and baking.

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Xiao (Cosmo) Zhang

Cosmo is a 4th year Ph.D. student in CS at Purdue University. His research interests are connectionism inspired machine learning methods for natural language processing, including deep learning and reinforcement learning. He is also very interested in structural models that try to understand language as a structural cognitive representation.
Before joining the CS department, he obtained his master in psychology in Psychological Sciences at Purdue University. During that period, He received intensive quantitative and computational training, and also developped his interest in machine learning.

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Chang Li

Chang is a third year Ph.D. student in Computer Science at Purdue University. He is broadly interested in Natural Language Processing and Machine Learning. His current research focuses on incorporating information from multiple sources to form a better node embedding and deep relational learning for structural prediction.
Before joining Purdue, he received his bachelor degree in CS from Nanjing University in China.

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Maria Leonor Pacheco

Maria is a second year PhD student in the Department of Computer Science at Purdue University.
Her research interests are in the areas of Natural Language Processing and Machine Learning, particularly in modeling and learning semantic representations for language understanding and structured prediction. She is currently working with Professor Dan Goldwasser as part of the Purdue NLP Group.

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Mahak Goindani

Mahak is a First Year Ph.D. Student working under Dr. Dan Goldwasser in the Department of Computer Science at Purdue University. Before that she pursued an integrated Bachelor & Master of Technology in Computer Science & Engineering at Indian Institute of Technology Madras. Her research interests are in the fields of Machine Learning, Natural Language Processing and Data Mining.

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I-Ta Lee

I-Ta Lee is a PhD student in the Department of Computer Science at Purdue University. His research stands at the intersection of Natural Language Processing (NLP), and Machine learning. He aims to become a leading researcher of machine intelligence technology. He hopes to share his expertise through innovative works with engineers and information scientists all over the global.
I-Ta Lee received his Bachelors degree in Computer Science from Yuan Ze University in 2008, and a MS in Computer Science from National Tsing Hua University in 2010. From 2010 to 2014 I-Ta worked at Moxa, a world-class embedded networking device company, and later in Trend Micro, a leading IT security company. I-Ta was a senior software engineer specializing in embedded systems, Linux/Windows driver and application developments. During 2014 to 2015, he conducted the research in the fields of natural language processing and machine learning at Academia Sinica, one of the most renowned research institutes in Taiwan. Starting in 2015, I-Ta joined the department of Computer Science at Purdue University to begin his PhD journey. I-Ta is currently advised by Dr. Dan Goldwasser and researching in the area of natural language processing and machine learning, more specifically, with semantic embeddings and narrative script learning.

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