jana-pic     Janardhan Rao ( Jana ) Doppa
Assistant Professor
    School of EECS, Washington State University
    Adjunct Faculty, Oregon State University
    Office: EME 133
    Office hours: by appointment in Summer
    Voice: 1-509-335-1846 
    Email: jana [AT] eecs.wsu.edu
                           LATEST UPDATES

My general research interests are in the broad field of artificial intelligence (AI), where I mainly focus on sub-fields of machine learning, and data-driven science and engineering. Current focus of my work is:
I did my PhD with the Artificial Intelligence group at Oregon State University, where I was wisely advised by Prof. Prasad Tadepalli and Prof. Alan Fern . I'm fortunate to work with Prof. Alan Fern and I enjoy working with him a lot.

Note for Prospective Students: I'm always looking for strong, self-motivated, and ambitious PhD students. You can find more details here.
For undergrad students at WSU: If you are interested in working with me for research experience and/or honors thesis, please take my machine learning class and we can discuss the details along the way. Please read this article for some useful advice.

Undergrad / MS Non-Thesis Advising Meetings: Please drop by my office hours. DON'T send me emails for appointments unless my office hours don't work for you. Thanks!

Quick Links:   [  Research  ]   [  Publications  ]   [  Teaching  ]   [ Students ]  [  Awards and Honors  ]   [  Professional Service  ]   [  Reading Groups  ]   [  Personal  ]


Ph.D., Computer Science, Oregon State University, 2014.
M.Tech., Computer Science, Indian Institute of Technology, Kanpur, India, 2006.


I like to work on machine learning problems motivated from important real-world applications.  A sample of my current and recent research projects include:
Three-dimensional (3D) integration is a breakthrough technology to achieve ``More Moore and More Than Moore,'' and provides numerous benefits (better performance, lower power consumption, and higher bandwidth) by utilizing vertical interconnects and 3D stacking. In this project, we are developing scalable machine learning based methodologies for design, control, and test to convert the potential of 3D integration into reality for Big Data computing. We are exploring both Through-Silicon-Via (TSV) and Monolithic 3D (M3D) technologies for integration.

Collaborators: Partha Pande @ WSU and Krish Chakrabarty @ DukeHow can we build AI technology to enable efficient human-computer communication for collaboratively solving complex problems? We are working towards developing this technology for communication via natural language. [Funded by DARPA as part of the Communicating with Computers (CwC) program]
How can we learn to predict structured outputs (e.g., sequences, trees, and graphs)? Structured prediction tasks arise in a variety of domains including natural language processing (e.g., POS tagging, dependency parsing, coreference resolution) and computer vision (e.g., object detection, semantic segmentation).
How can we build intelligent computer systems that can achieve deep language understanding? In the Deep Reading and Learning project, we are trying to learn a high-level representation called event graphs (a form of Abstract Meaning Representation) from raw text. Towards this goal, we are working on several sub-problems: 1) Entity co-reference resolution within a document; 2) Joint entity and event co-reference resolution across documents; 3) Joint models for entity linking and discovery; and 4) Learning general scripts of events. See our AAAI2014 paper on script learning, EMNLP2014 paper on co-reference resolution, and AAAI2015 paper on learning for Easy-first framework. [Funded by DARPA as part of the DEFT program] How can we learn relational world knowledge rules (e.g., Horn clauses) from natural texts to support textual inference? Natural texts are radically incomplete (writers don't mention redundant information) and systematically biased (writers mention exceptions to avoid the readers from making incorrect inferences), which makes the rule learning very hard. We solve this problem by modeling the pragmatic relationship between what rules exist and what things will be mentioned (e.g., Gricean maxims).  We worked with BBN and other researchers from CMU, University of Washington and ISI. See our NIPS2011 and ACML2011 papers for details. [Funded by DARPA as part of the Machine Reading program] How can we integrate information from multiple sources to learn better ? In the past, we worked on DARPA's Integrated learning project, where the goal was to learn a complex problem solving task from a single demonstration of the expert. We learned the cost function that the expert is minimizing while producing the demonstration by formulating it as an inverse optimization problem. Our component's name was DTLR (Decision Theoretic Learner and Reasoner). We worked with other researchers from Lockheed-Martin, ASU, RPI, UMD, UMASS, UIUC and Georgia Tech. See our TIST2012 paper for details. [Funded by DARPA]


  •  Learning Algorithms for Link Prediction based on Chance-Constraints
  • Janardhan Rao Doppa, Jun Yu, Prasad Tadepalli, and Lise Getoor
  • Proceedings of European Conference on Machine Learning (ECML-2010)
  • PDF
  •  Towards Learning Rules from Natural Texts
  • Janardhan Rao Doppa, Mohammad Nasresfahani, Mohammad S. Sorower, Thomas G. Dietterich, Xiaoli Fern, and Prasad Tadepalli
  • Proceedings of NAACL 2010 Workshop on Formalisms and Methodologies in Learning by Reading.
  • PDF
  • Chance-Constrained Programs for Link Prediction
  • Janardhan Rao Doppa, Jun Yu, Prasad Tadepalli, and Lise Getoor
  • Proceedings of NIPS 2009 Workshop on Analyzing Networks and Learning with Graphs.
  • PDF
  • An Ensemble Learning and Problem Solving Architecture for Airspace Management
  • Xiaoqin Zhang, Sung Wook Yoon, Phillip DiBona, Darren Scott Appling, Li Ding, Janardhan Rao Doppa, Derek T. Green, Jinhong K. Guo, Ugur Kuter, Geoffrey Levine, Reid MacTavish, Daniel McFarlane, James Michaelis, Hala Mostafa, Santiago Ontanon, Charles Parker, Jainarayan Radhakrishnan, Antons Rebguns, Bhavesh Shrestha, Zhexuan Song, Ethan Trewhitt, Huzaifa Zafar, Chongjie Zhang, Daniel D. Corkill, Gerald DeJong, Thomas G. Dietterich, Subbarao Kambhampati, Victor R. Lesser, Deborah L. McGuinness, Ashwin Ram, Diana F. Spears, Prasad Tadepalli, Elizabeth T. Whitaker, Weng-Keen Wong, James A. Hendler, Martin O. Hofmann, and Kenneth R. Whitebread
  • Proceedings of AAAI Conference on Innovative Applications of Artificial Intelligence (IAAI-2009)
  • PDF

  • Teaching

    Courses at WSU:

    In the past, I was Instructor for the following courses:

    Current Students

    Former Students

    Professional Service

        Workshop Organization:
        Journal Editorial Service:
        Senior Program Committee Member:
        Program Committee Member:

    Machine Learning Reading Group (MLRG)

    At WSU, I often run focused reading groups on topics related to my current projects.
    At OSU, I organized and led several reading groups on a wide variety of topics (2009-2014). Some of them include:

    Awards and Honors


    I'm passionate about cricket. Playing cricket helps me remain sane amidst the hectic research life. I try to play in the nearby cricket leagues during the summers. I played for OSU Cricket club in 2007, 2008 and 2009. Our team Chak De Oregon won the 2009 NWCL cricket championship.  In 2010, I played for Chak De Oregon in NWCL (Div I) and for Portland club in OCL. We won the 2010 OCL T20 championship. In 2011, I played for only Portland club in OCL as part of the budget cut on cricket. After 2011 season I became very busy and could not justify my time spent on cricket, so I stopped playing. I used to maintain my cricket scores here

    I like to cook, but I don't like to spend too much time on it. So I follow an engineering methodology for cooking, which provides a good trade off between  preparation time and quality of the food! Does this remind you of my research work on trading off computation time and quality of the predictions? :)