Arjun Mukherjee Joins Computer Science Faculty
Area of Expertise: Data Mining, Web Mining, Natural Language Processing

We are pleased to welcome Dr. Arjun Mukherjee to the Department of Computer Science at the University of Houston. He joins the department as an assistant professor at the start of fall 2014.

Arjun received his Ph.D. from the University of Illinois at Chicago (UIC) and his bachelor’s degree from the Sikkim Manipal Institute of Technology, India. Previously, he was a research intern fellow at Microsoft Research and Indian Statistical Institute. He is the recipient of several highly competitive fellowships from UIC, such as the Dean’s Scholar Award, Chancellor’s Graduate Research Fellowship, and the Provost’s and Deiss Award for Graduate Research.

Arjun’s addition enhances the department’s growing research strength in data sciences and big-data analytics. His research spans areas such as Bayesian inference, statistical data mining, machine learning, natural language processing, and social and information sciences with a particular emphasis on solving big-data problems in social media and on the web.

His works have addressed a wide variety of social computing problems including (1) modeling opinion spam, deception and user behaviors; (2) fine-grained sentiment analysis; (3) modeling social conversations; and (4) knowledge induction in graphical models for aspect extraction.

“The tremendous growth of the web and the surge of social media render massive amounts of data that present new forms of actionable intelligence to corporate, government, healthcare, political and crisis management sectors,” Arjun said. “The vast amount of user-generated content in the web today has valuable implications for the future. Public sentiments in online debates, discussions, blogs and news comments are crucial to governmental agencies for passing new bills/policy, gauging social unrest/upheaval, predicting elections, managing disasters, etc.”

Arjun is interested in working with faculty and students to enhance technologies in data sciences and developing state-of-the-art frameworks for a viable end-to-end actionable web data mining infrastructure.