If we knew what it was we were doing, it would not be called research, would it?
-- Albert Einstein

Current Projects

Check out the home-page for more information.

Past Projects

  • Abstractions in Machine Learning: We hope to exploit symmetries and other implicit domain abstractions to scale up a variety of machine learning and inference algorithms. We have developed symmetry-aware UCT algorithms in MDPs (Paper 1, Paper 2). We have also devised novel notions of symmetries such as contextual symmetries, variable-value symmetries, and block-value symmetries in probabilistic graphical models for downstream inference via Monte-Carlo sampling. We work on reducing computation in Markov Decision Process (MDP) algorithms such as UCT by aggregating symmetric states and state-action pairs. We also work on exploiting similar properties in the context of probabilistic inference. A recent paper on this work.

  • Machine Learning over Crowdsourced Training Data: Does Machine Learning change when training data is generated over crowdsourcing? Yes. We first study the quality-size tradeoff in building training datasets. We extend active learning to "Re-Active Learning", which allows the same data point to be relabeled by a different worker over the crowdsourced platform. Finally, we devise novel algorithms in scenarios when data has severe class imbalance.

  • Decision-Theoretic Optimization of Crowdsourcing: Crowd-sourcing has taken over the business world by storm in the last few years. Although it is touted as "Artificial Artificial Intelligence", there are huge opportunities for AI to contribute to its success. A vision paper describes our approach to this synergy. We have investigated decision-theoretic techniques to automatically control workflows on a crowd-sourcing platform such as Amazon's Mechanical Turk, and have obtained significant quality improvements for the same price. Recent papers on this work: Paper 1, Paper 2, and Paper 3.

  • Coherent Large-Scale Multi-Document Summarization: How to produce coherent, human readable summaries from a set of 10 related documents? How about 100? How about 1000? What is best summary format when the amount of information that is summarized is huge? We answer these difficult questions through two systems, GFLOW and SUMMA. The first is coherent summarizer for short document collections and the latter produces hierarchical summaries for large collections. The papers on this work: Paper 1 and Paper 2. And SUMMA demo.

  • Applications of Open IE: Open Information Extraction is a domain-independent knowledge representation language that is different from linguistic suggestions such as semantic role labeling or domain-specific ontologies. We work on exploring the various applications that Open IE enables. We recently released OREO, a rapidly retargetable software to map open extractions to a domain ontology. In this recent paper we show that Open IE representation beats dependency parsing, and semantic role labelers in learning useful word vector representations via deep learning. Earlier, in this paper we used Open IE to automatically induce domain-independent event schemas.

  • Commonsense Knowledge Extraction: Automatically creating corpora of commonsense knowledge based on reasoning over extracted information from the Web. We automatically learned selectional preferences and meta-properties of relations present in natural language text. We also built a large repository of relational n-grams -- a semantic analog to the n-grams corpus, which were used to induce event schemas completely automatically. All results from this project are publically available: set of functional relations, selectional preference demo, and relational n-grams corpus.

  • NLP over Microblogs: Micro-blogging sites such as Twitter have exploded in popularity in the recent times. Tweets often represent the most up-to-date information and "buzz" on a vast spectrum of topics, however, their sheer number adds to huge information overload. We recently released a suite of NLP tools for tweets. We are currently designing automated information extraction systems over Twitter. A recent paper and a demo of automatically generated calendar of events.

  • Large-scale Probabilistic Planning: Solving large Markov Decision Processes by combining several optimal as well as approximate techniques. We hope to alleviate the memory bottleneck in solving the large MDPs and scale to large, industry sized probabilistic planning problems. Some significant papers on this work: Paper 1 and Paper 2. Our planner, Glutton, was runners up in 2011 International Probabilistic Planning competition.

  • Half-Open Information Extraction: Open Information Extraction, while a scalable paradigm, suffers from the drawback that it does not normalize its extractions with a domain schema. Our recent work explores middle grounds between completely open and completely closed variants of IE to leverage benefits of both. An article on this work.

  • Formal Inference in Translation Graph: Developing probabilistic inference techniques to formalize inference in translation graphs, a graph that is formed by combining all available dictionaries between all possible languages in the world. An efficient and high quality inference procedure will enable the system to produce good translations from a sense in one language to several languages, even when there is no available dictionary between the exact pair of languages. A journal paper on this work and the AAAI Nectar version.

  • Open Information Extraction over News: A relation-independent question-answering system over thousands of current news articles. We apply Textrunner information extraction technology as well as news-specific heuristics to construct a massive knowledge base of current events. This information can be queried by asking specific questions or by keyword search.

  • Hybridizing Planners: A fast but suboptimal planner may be hybridized with a slow but optimal one to yield a high-quality, anytime planner that solves the problems in intermediate times. We developed HybPlan, a planner that hybridized GPT and MBP for probabilistic planning.

  • Concurrent Probabilistic Temporal Planning: Developing high-quality and efficient techniques to solve MDPs that formulate probabilistic planning problems involving durative and concurrent actions.

  • Publications

    A complete list of publications can be found here.

    Software, Demos and Data

    A complete list of released softwares, demos and data can be found here.

    Service

  • Program Chair: ICAPS'17, CODS'16.
  • Track Chair: IJCAI'16 AI & Web Track.
  • Tutorial Chair: EMNLP'18, AAAI'16, AAAI'15.
  • Area Chair: NAACL'18, AAAI'18, IJCNLP'17, EMNLP'17, ACL'17, WWW'17, COLING'16, ACL'15, EMNLP'13.
  • Associate Editor: JAIR.
  • Senior PC Member: AAAI'18, HCOMP'16, IJCAI'15, ICAPS'13, IJCAI'13, IJCAI'11, AAAI'11, AAAI'10 AI & Web Track.
  • PC Member: ICAPS'18, NAACL'16, WSDM'15, HCOMP'14, AAAI'14, ECAI'14, WWW'14, HCOMP'13, KDD'13, EACL'12, EMNLP'12, ACL'11, ICAPS'11, EMNLP'10, ICAPS'10, ICAPS'09, IJCAI'09, ICAPS'08, AAAI'08, ICAPS'07, AAAI'05.
  • Reviewer: TACL, JAIR, AIJ, JMLR, AAMAS, ACM TIST, IEEE TKDE, CHI, COLING, NAACL, UbiComp.
  • Panel Member: NSF Division of Information and Intelligent Systems (2008, 2010, 2013)
  • Tutorial: MDPs for Probabilistic Planning at AAAI'12, Probabilistic Planning at Planning & Scheduling School'12, Probabilistic Temporal Planning at ICAPS'07.
  • Workshop: UW-MSR Symposium on Crowdsourcing Online Personalized Education, A Reality Check for Planning and Scheduling Under Uncertainty at ICAPS'08.

  • Presentations in the AI group (grad school times)

    1. Introduction to Semantic Web (Fall 2001)
    2. PGRAPHPLAN : A planner for probabilistic domains (Spring 2002)
    3. Nursebot : Robot assistants for the elderly (Spring 2002)
    4. SPUDD : A planner for probabilistic domains (Fall 2002)
    5. A survey of Relational MDP approaches (Fall 2003)
    6. Introduction to Markov Decision Processes (Fall 2003)
    7. A survey of Hierarchical Reinforcement Learning Techniques (Spring 2005)