Ting-Hao (Kenneth) Huang 黃挺豪

Tenure-Track Assistant Professor
College of Information Sciences and Technology (IST)
Pennsylvania State University (University Park)

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I combine AI with crowdsourcing to create systems that are more usable, robust, and intelligent.

News

Teaching

Selected Publications

  • Evorus is a crowd-powered conversational assistant built to automate itself over time. Our 5-month-long deployment shows that Evorus can automate itself without compromising conversation quality.
    Evorus: A Crowd-Powered Conversational Assistant Built to Automate Itself Over Time
    Ting-Hao K. Huang, Joseph Chee Chang, Jeffrey P. Bigham.
    In Proceedings of Conference on Human Factors in Computing Systems 2018 (CHI 2018), 2018, Montréal, Canada.
    Best Paper Honorable Mention Award (101/2500 = 5%)
  • Retainer is fast but costly, posting HIT is cheap but slow. Deployed Chorus uses Ignition to balance them.
    A 10-Month-Long Deployment Study of On-Demand Recruiting for Low-Latency Crowdsourcing
    Ting-Hao K. Huang, Jeffrey P. Bigham.
    In Proceedings of The fifth AAAI Conference on Human Computation and Crowdsourcing (HCOMP 2017), 2017, Quebec City, Canada.
  • Chorus was launched to public in May, 2016. The system details are described in our HCOMP 2016 paper.
    "Is there anything else I can help you with?": Challenges in Deploying an On-Demand Crowd-Powered Conversational Agent
    Ting-Hao K. Huang, Walter S. Lasecki, Amos Azaria, Jeffrey P. Bigham.
    In Proceedings of Conference on Human Computation & Crowdsourcing (HCOMP 2016), 2016, Austin, TX, USA.
    Media Coverage: [The Register]
  • We developed "Guardian" to convert an existent Web APIs to a dialog system by using crowdsourcing.
    Guardian: A Crowd-Powered Spoken Dialog System for Web APIs
    Ting-Hao K. Huang, Walter S Lasecki, Jeffrey P Bigham.
    In Proceedings of Conference on Human Computation & Crowdsourcing (HCOMP 2015), pages 62–71, November, 2015, San Diego, USA.
  • A group of crowd workers can reliably extract target entities on demand from a short dialog in an average of ~8 seconds.
    Real-time On-Demand Crowd-powered Entity Extraction
    Ting-Hao K. Huang, Yun-Nung Chen, Jeffrey P. Bigham.
    In Proceedings of the 5th Edition Of The Collective Intelligence Conference (CI 2017, oral presentation), 2017, New York University, NY, USA.
  • I developed the Visual Storytelling Dataset during my summer internship at Microsoft Research in 2015.
    Visual Storytelling
    Ting-Hao K. Huang*, Francis Ferraro*, Nasrin Mostafazadeh, Ishan Misra, Jacob Devlin, Aishwarya Agrawal, Ross Girshick, Xiaodong He, Pushmeet Kohli, Dhruv Batra, Larry Zitnick, Devi Parikh, Lucy Vanderwende, Michel Galley and Margaret Mitchell. (*: Co-first author.)
    In Proc. NAACL 2016, June, 2016, San Diego, CA, USA.

Honors

  • Best Paper Honorable Mention Award (101/2500 = 5%), CHI 2018.
  • Best Paper Honorable Mention Award (14/281 = 5%), CHI LBW 2016.
  • Yahoo! Fellowship of the InMind project at CMU, 2014 – Present.
  • Best Poster Award, LTI Student Research Symposium 2013.