Karthik Gopalratnam

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2ranked-venue papers

1as first author

0since 2021 · last 2005

—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-authorGraphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-authorHuman-computer interaction and ubiquitous computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence 1 paper		Probabilistic and Bayesian machine learning · 100%
Human-computer interaction and pervasive computing 1 paper		Ubiquitous computing and smart environments · 100%

Topics — the 3 heaviest of 4, each with the papers that count most for it

Topic	Weight	Papers	Last	Evidence papers
Machine learning › Probabilistic and Bayesian machine learning › structured models › graphical models › bayesian network continuous time bayesian networks	0.1	1	2005	Extending Continuous Time Bayesian Networks · AAAI 2005
Machine learning › Probabilistic and Bayesian machine learning › structured models graphical models	0.1	1	2005	Extending Continuous Time Bayesian Networks · AAAI 2005
Ubiquitous computing and smart environments smart home	0.0	1	2003	MavHome: An Agent-Based Smart Home · PerCom 2003

Methods — techniques the papers use, named apart from their topics

prediction algorithms · 0.0meta-predictor · 0.0

Year	Publication	Venue	Position
2005	Extending Continuous Time Bayesian Networks Karthik Gopalratnam, Henry A. Kautz, Daniel S. Weld	AAAI	1
2003	MavHome: An Agent-Based Smart Home abstract The goal of the MavHome (Managing An Intelligent Versatile Home) project is to create a home that acts as an intelligent agent. In this paper we introduce the MavHome architecture. The role of prediction algorithms within the architecture is discussed, and a meta-predictor is presented which combines the strengths of multiple approaches to inhabitant action prediction. We demonstrate the effectiveness of these algorithms on smart home data. Diane J. Cook, G. Michael Youngblood, Edwin O. Heierman III, Karthik Gopalratnam, Sira Rao, Andrey Litvin, Farhan Khawaja	PerCom	4