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The Indian Institute of Information Technology, Hyderabad (IIIT-H) is going to host the 10th Edition of Human-Computer Interaction Conference in Hyderabad. The conference will take place from 1st November to 3rd November 2019 in Hyderabad.
With the theme ‘Agents and Agency: Human-Computer Interaction in the Age of Machine Intelligence’, the conference will focus on examining the interplay of machine agency and human in a world that is becoming increasingly complex due to AI (Artificial Intelligence).
With the intention of popularising user interaction models to promote start-ups and industries by deliberating with researchers, the conference is being organised in cooperation with SIGCHI & ACM Academic Researchers, innovators, and designers.
The 10th edition of the conference i.e., this year’s edition, is being sponsored by Wadhwani AI, Google, and Pitney Bowes. The conference will comprise six keynote sessions that include Maya Indira, a Technology Researcher at Leuphana University; Jeffrey Scudder, Digital Artist at Southern Oregon University; Amol Gurwara, Country Manager at Kindle - Amazon Singapore; Dr. Ashok Jhunjhunwala, Professor at IIT Madras, Partner, PwC and Leader at Jagriti. It will also include two special sessions with Uzramma and Pete Draper, Co-Founder at MaKuta.
Talking about the conference, Professor Ramesh Loganathan and Nimmi Rangaswamy said that the collaboration with ACM (Association for Computing Machinery) will ensure the position of Hyderabad on the Global map of Design research and Human-Computer Interaction.
The HCI 2019 is inviting participants with research and industry backgrounds to register for the conference. Candidates who are interested can find the link to registration on the official website of India HCI 2019.
The conference will serve as a very useful platform to explore the overlap between human and computer interaction and the sense of agency. In the community of intelligent machine interfaces and technology users, the conference will help in exploring and discussing the potential inference of self-learning systems.
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