Roni Khardon
Professor
Department of Computer Science
Luddy School
Indiana University, Bloomington

Research Interests and Projects: My interests are in the areas of Machine Learning and Data Mining, Artificial Intelligence, and Efficient Algorithms and my research explores theoretical questions, empirical questions, and applications. Most recently my work has focused on problems in two broad areas: Theory, Algorithms and Applications of Graphical models and Stochastic Planning and Reinforcement Learning and their Relation to Probabilistic Inference.

Please beware of phishing attempts: Multiple phishing attempts using my name have been circulating over the last few months. Typical examples inlcude fake job offers that ask you to communicate with me by text/phone or through an alternate non-IU email address - these are phishing attempts. Sophisticated variations and schemes are evolving. If you are reading this web page after seeing an odd offer it is likely a phishing attempt. Please help others by reporting these. Information about reporting phishing at IU is at: https://phishing.iu.edu/report/index.html

Research Group

Yadi Wei, PhD Student Working on algorithms and theory for approximate inference in Bayesian models.

Weizhe Chen, PhD Student

Co advised with Lantao Liu Working on machine learning for non-stationary spatial data and its use for informative planning in robotics.

Zhennan Wu, PhD Student Working on planning, reinforcement learning and their connection to probabislitic inference.

Palash Chatterjee, PhD Student Working on planning, reinforcement learning and their connection to probabislitic inference.

Santhosh M Patil , MS Student Working on prediction of tropical cyclones using deep learning models.

Alumni

Ashutosh Chapagain , MS, Indiana University, 2023. Topic: applications of planning and reinforcement learning to robotics.
Hao Cui, PhD, Tufts University, 2019. Topic: Stochastic Planning in Combinatorial Spaces.
Rishit Sheth, PhD, Tufts University, 2018. Topic: Algorithms and Error Guarantees for Variational Inference in Machine Learning.
Yuyang Wang, PhD, Tufts University, 2013. Topic: Bayesian models and algorithms for multi-task learning and time series data.
Saket Joshi, PhD, Tufts University, 2010. Topic: Structured Markov Decision Processes via First Order Logic Representations.
Gabriel Wachman, PhD, Tufts University, 2009. Topic: Kernel Methods, Uncertainty Calibration, Hypergraph Data and Time Series.
Chengang Wang, PhD, Tufts University, 2007. Topic: Structured Markov Decision Processes via First Order Logic Representations.
Marta Arias, PhD, Tufts University, 2004. Topic: Theory and Algorithms for Learning Logical Expressions.

	Yadi Wei, PhD Student	Working on algorithms and theory for approximate inference in Bayesian models.
	Weizhe Chen, PhD Student Co advised with Lantao Liu	Working on machine learning for non-stationary spatial data and its use for informative planning in robotics.
	Zhennan Wu, PhD Student	Working on planning, reinforcement learning and their connection to probabislitic inference.
	Palash Chatterjee, PhD Student	Working on planning, reinforcement learning and their connection to probabislitic inference.
	Santhosh M Patil , MS Student	Working on prediction of tropical cyclones using deep learning models.