Research and Coursework

Research:

    Collaborative Systems & Crowdsourcing :

• • Task Assignment Optimization in Collaborative Editing: We investigate task assignment optimization in collaborative editing on crowdsourcing platforms. Many popular applications, such as collaborative document editing, sentence translation, or citizen journalism resort to this special form of human-based computing, where, crowd workers with complementary skills and expertise are required to form worker groups to perform editing tasks. Central to any collaborative editing is the aspect of successful collaboration among workers, which, for the first time, is formalized and then optimized in this work.

  • Paper Link - Habibur Rahman, Senjuti Basu Roy, Saravanan Thirumuruganathan, Sihem Amer-Yahia, Gautam Das: Task Assignment Optimization in Collaborative Crowdsourcing. To appear in ICDM 2015 http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7373417

  • Skill Estimation in Team based System: Many emerging applications such as collaborative editing, multi-player games, or fan-subbing require to form a team of experts to accomplish task together. Existing research has investigated how to assign workers to such team-based tasks to ensure the best outcome assuming the skills of individual workers to be known. In this work, we investigate how to estimate individual worker's skill based on the outcome of the team-based tasks they have undertaken.

  • Paper Link - Habibur Rahman, Saravanan Thirumuruganathan, Senjuti Basu Roy, Sihem Amer-Yahia, and Gautam Das: Worker Skill Estimation in Team-Based Tasks. In PVLDB 2015. http://www.vldb.org/pvldb/vol8/p1142-rahman.pdf

• Task Recommendation in Crowdsourcing: Existing research in crowdsourcing has investigated how to recommend tasks to workers based on which task the workers have already completed, referred to as implicit feedback. We, on the other hand, investigate the task recommendation problem, where we leverage both implicit feedback and explicit features of the task.

  • Paper Link -

• Probabilistic Distance Estimation using Crowdsourcing Framework: Estimating all pairs of distances among a set of objects has wide applicability in various computational problems in databases, machine learning, and statistics. This work presents a probabilistic framework for estimating all pair distances through crowdsourcing, where the human workers are

• involved to provide pairwise comparisons between some ob-

• ject pairs in an ordinal scale. 

      Social Analytics:

• • Ranking Item Features by Mining Online User -Item Interaction:  We assume a database of items in which each item is described by a set of attributes, some of which could be multi-valued. We refer to each of the distinct attribute values as a feature. We also assume that we have information about the interactions (such as visits or likes) between a set of users and those items. We would like to rank the features of an item using user-item interactions.

    • Paper Link  - Sofiane Abbar, Habibur Rahman, Saravanan T., Carlos Castillo, and Gautam Das. Ranking Item Features by Mining Online User-Item Interactions. In Proc. of ICDE 2014 http://doi.ieeecomputersociety.org/10.1109/ICDE.2014.6816673

  • Beyond Itemset Mining: We assume a dataset of transactions generated by a set of users over structured items where each item could be described through a set of features. In this paper, we are interested in identifying the frequent featuresets (set of features) by mining item transactions. For example, in a news website, items correspond to news articles, the features are the named-entities/topics in the articles and an item transaction would be the set of news articles read by a user within the same session.

  • Paper Link - Saravanan Thirumuruganathan, Habibur Rahman, Sofiane Abbar, and Gautam Das: Beyond Itemsets: Mining Frequent Featuresets over Structured Items. In PVLDB 2015http://www.vldb.org/pvldb/vol8/p257-thirumuruganathan.pdf

  •  Generating Informative Snippet to Maximize Item Visibility: We exploit the availability of user feedback in collaborative content sites in the form of tags to identify the most important item attributes that must be highlighted in an item snippet. We investigate the problem of finding the top-k best snippets for an item that are likely to maximize the probability that the user preference (available in the form of search query) is satisfied

    • Paper Link -  Mahashweta Das, Habibur Rahman, Vagelis Hristidis, and Gautam Das: Generating Informative Snippet to Maximize Item Visibility. Short paper, CIKM 2013 http://dl.acm.org/citation.cfm?id=2505606

Teaching:

• Design and Analysis of Algorithms(CSE 5311) [Fall 2014]

• Theory of Computer Science

Coursework: