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Texas A&M University - Team Howdy Y'All.jpg
Location: College Station, TX, USA
Faculty advisor: James Caverlee

Howdy Y'All

We are a mix of graduate and undergraduate students with expertise in natural language processing, machine learning, and systems development.

By Texas A&M University
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We are the Howdy Yall team from Texas A&M University. We are a mix of graduate and undergraduate students with expertise in natural language processing, machine learning, and systems development. Our vision of Howdy Yall is an expert task-oriented platform that is both wide (providing high coverage of tasks) and deep (providing high confidence in the tasks that it does cover). Inspired by the official greeting of Texas A&M ("Howdy"), our taskbot is designed to embody this fundamental friendliness so users feel satisfaction, trust, and a core value of respect.

Majid A. - Team leader

I work on social media analytics and user behavior modeling using big data technologies. (Hadoop / Spark / Kafka / Flume / HBase / SolrCloud). In addition, I am interested in studying online user evolution and interaction patterns both behaviorally and linguistically.

Aditya P.

I am a computer science and applied mathematics double major at Texas A&M University. In the past, I've used time series data to train predictive models at Deephaven Data Labs. I also helped build a dynamic web-assistance chatbot using Azure's cognitive services to assist visually impaired users. My broad research interests include recommender systems, natural language processing, and applications of machine learning in finance. I also organize TAMU Datathon, a data science focused hackathon at Texas A&M University.

Xiangjue D.

Xiangjue Dong is a first year PhD student working with Prof. James Caverlee.

Karthic M.

Karthic was awarded his B.E in Mechatronics Engineering from Anna University, India in 2005, earned a Masters degree in Industrial Engineering from Texas A&M University, College Station in 2009 and a Masters degree in Computer Science from Texas A&M University in 2014. Mr. Madanagopal joined Knowledge Based Systems, Inc. in January 2010, as a Programmer Analyst and has been the principal architect and technology development lead on several federally-funded research and advanced technology development projects. Karthic is now pursuing a doctorate in Computer Science from TAMU with a dissertation research focus on semantic information processing and Language bias analysis.

Maria T.

Maria is a senior majoring in Computer Science at Texas A&M University, with minors in Mathematics and Electrical Engineering. Her research interests include natural language processing, social media, and health. She wrote the RetailMeNot Alexa Skill while interning on their data science team, and gave a Sandbox Talk on skill development to both the RetailMeNot and Valassis data science teams. She also wrote Friendly Frog, the Alexa Skill for the Hi, How Are You Project in Austin, Texas.

Zhuoer W.

Zhuoer is a Ph.D. student and member of the Infolab led by Prof. James Caverlee in the Computer Science & Engineering Department at Texas A&M University. His research interests include Natural Language Understanding, Natural Language Generation, Application of NLP.

Shuo L.

Coming soon!

Timothy F.

My name is Timothy Feldman and I am a Sophomore Computer Science student at Texas A&M University. I am a National Merit Scholar as well as a member of the Engineering Honors program here at A&M. I have a strong foundation in data structures and algorithms, and I have some experience working with neural networks. I am currently interested in machine learning and parallel computing and I'm excited to learn more in my study of computer science.

Ziwei Z.

I am a final-year Ph.D. student, advised by Prof. James Caverlee, at Computer Science and Engineering Department at Texas A&M University. I am broadly interested in data mining, machine learning, and information retrieval, with a special focus on augmenting responsibility in search and recommendation systems. Specifically. My past research has contributed to identify, analyze and alleviate various bias and unfairness issues in search and recommendation systems.

James Caverlee - Faculty advisor

Coming soon!

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Texas A&M University

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