Behzad Golshan

Grouping students in educational settings

Given a class of large number of students, each exhibiting a different ability level, how can we group them into sections so that the overall gain for students is maximized? This question has been a topic of central concern and debate amongst social scientists and policy makers for a long time. We propose a framework for rigorously studying this question, taking a computational perspective. We present a formal definition of the grouping problem and investigate some of its variants. Such variants are determined by the desired number of groups as well as the definition of the gain for each student in the group. We focus on two natural instantiations of the gain function and we show that for both of them the problem of identifying a single group of students that maximizes the gain among its members can be solved in polynomial time. The corresponding partitioning problem, where the goal is to partition the students into non-overlapping groups appear to be much harder. However, the algorithms for the single-group version can be leveraged for solving the more complex partitioning problem. Our experiments with generated data coming from different distributions demonstrate that our algorithm is significantly better than the current strategies in vogue for dividing students in a class into sections.

Profit-maximizing cluster hires

Team formation has been long recognized as a natural way to acquire a diverse pool of useful skills, by combining experts with complementary talents. This allows organizations to effectively complete beneficial projects from different domains, while also helping individual experts position themselves and succeed in highly competitive job markets. Here, we assume a collection of projects ensuremath{P}, where each project requires a certain set of skills, and yields a different benefit upon completion. We are further presented with a pool of experts ensuremath{X}, where each expert has his own skillset and compensation demands. Then, we study the problem of hiring a cluster of experts T ⊆ X, so that the overall compensation (cost) does not exceed a given budget B, and the total benefit of the projects that this team can collectively cover is maximized. We refer to this as the ClusterHire problem. Our work presents a detailed analysis of the computational complexity and hardness of approximation of the problem, as well as heuristic, yet effective, algorithms for solving it in practice. We demonstrate the efficacy of our approaches through experiments on real datasets of experts, and demonstrate their advantage over intuitive baselines. We also explore additional variants of the fundamental problem formulation, in order to account for constraints and considerations that emerge in realistic cluster-hiring scenarios. All variants considered in this paper have immediate applications in the cluster hiring process, as it emerges in the context of different organizational settings.

Whither Social Networks for Web Search

Access to diverse perspectives nurtures an informed citizenry. Google and Bing have emerged as the duopoly that largely arbitrates which English language documents are seen by web searchers. A recent study shows that there is now a large overlap in the top organic search results produced by them. Thus, citizens may no longer be able to gain different perspectives by using different search engines. We present the results of our empirical study that indicates that by mining Twitter data one can obtain search results that are quite distinct from those produced by Google and Bing. Additionally, our user study found that these results were quite informative. The gauntlet is now on search engines to test whether our findings hold in their infrastructure for different social networks and whether enabling diversity has sufficient business imperative for them.

Forming beneficial teams of students in massive online classes

Given a class of large number of students, each exhibiting a different ability level, how can we form teams of students so that the expected performance of team members improves due to team participation? We take a computational perspective and formally define two versions of such team-formation problem: the MAXTEAM and the MAXPARTITION problems. The first asks for the identification of a single team of students that improves the performance of most of the participating team members. The second asks for a partitioning of students into non-overlapping teams that also maximizes the benefit of the participating students. We show that the first problem can be solved optimally in polynomial time, while the second is NP-complete. For the MAXPARTITION problem, we also design an efficient approximate algorithm for solving it. Our experiments with generated data coming from different distributions demonstrate that our algorithm is significantly better than any of the popular strategies for dividing students in a class into sections.

A Study of Distinctiveness in Web Results of Two Search Engines

Google and Bing have emerged as the diarchy that arbitrates what documents are seen by Web searchers, particularly those desiring English language documents. We seek to study how distinctive are the top results presented to the users by the two search engines. A recent eye-tracking has shown that the web searchers decide whether to look at a document primarily based on the snippet and secondarily on the title of the document on the web search result page, and rarely based on the URL of the document. Given that the snippet and title generated by different search engines for the same document are often syntactically different, we first develop tools appropriate for conducting this study. Our empirical evaluation using these tools shows a surprising agreement in the results produced by the two engines for a wide variety of queries used in our study. Thus, this study raises the open question whether it is feasible to design a search engine that would produce results distinct from those produced by Google and Bing that the users will find helpful.

SOFIA SEARCH: a tool for automating related-work search

When working on a new project, researchers need to devote a significant amount of time and effort to surveying the relevant literature. This is required in order to gain expertise, evaluate the significance of their work and gain useful insights about a particular scientific domain. While necessary, relevant-work search is also a time-consuming and arduous process, requiring the continuous participation of the user. In this work, we introduce Sofia Search, a tool that fully automates the search and retrieval of the literature related to a topic. Given a seed of papers submitted by the user, Sofia Search searches the Web for candidate related papers, evaluates their relevance to the seed and downloads them for the user. The tool also provides modules for the evaluation and ranking of authors and papers, in the context of the retrieved papers. In the demo, we will demonstrate the functionality of our tool, by allowing users to use it via a simple and intuitive interface.

Overlap in the Web Search Results of Google and Bing

Google and Bing have emerged as the diarchy that arbitrates what documents are seen by Web searchers, particularly those desiring English language documents. We seek to study how distinctive are the top results presented to the users by the two search engines. A recent eye-tracking has shown that the web searchers decide whether to look at a document primarily based on the snippet and secondarily on the title of the document on the web search result page, and rarely based on the URL of the document. Given that the snippet and title generated by different search engines for the same document are often syntactically different, we first develop tools appropriate for conducting this study. Our empirical evaluation using these tools shows a surprising agreement in the results produced by the two engines for a wide variety of queries used in our study. Thus, this study raises the open question whether it is feasible to design a search engine that would produce results distinct from those produced by Google and Bing that the users will find helpful.

A Team-Formation Algorithm for Faultline Minimization

Abstract In recent years, the proliferation of online resumes and the need to evaluate large populations of candidates for on-site and virtual teams have led to a growing interest in automated team-formation. Given a large pool of candidates, the general problem requires the selection of a team of experts to complete a given task. Surprisingly, while ongoing research has studied numerous variations with different constraints, it has overlooked a factor with a well-documented impact on team cohesion and performance: team faultlines. Addressing this gap is challenging, as the available measures for faultlines in existing teams cannot be efficiently applied to faultline optimization. In this work, we meet this challenge with a new measure that can be efficiently used for both faultline measurement and minimization. We then use the measure to solve the problem of automatically partitioning a large population into low-faultline teams. By introducing faultlines to the team-formation literature, our work creates exciting opportunities for algorithmic work on faultline optimization, as well as on work that combines and studies the connection of faultlines with other influential team characteristics.

Homogeneity in Web Search Results: Diagnosis and Mitigation

Access to diverse perspectives nurtures an informed citizenry. Google and Bing have emerged as the duopoly that largely arbitrates which English-language documents are seen by web searchers. We present our empirical study over the search results produced by Google and Bing that shows a large overlap. Thus, citizens may not gain different perspectives by simultaneously probing them for the same query. Fortunately, our study also shows that by mining Twitter data, one can obtain search results that are quite distinct from those produced by Google, Bing, and Bing News. Additionally, the users found those results to be quite informative. We also present two novel tools we designed for this study. One uses tensor analysis to derive low-dimensional compact representation of search results and study their behavior over time. The other uses machine learning and quantifies the similarity of results between two search engines by framing it as a prediction problem. Although these tools have different underpinnings, the analytical results obtained using them corroborate each other, which reinforces the confidence one can place in them for finding meaningful insights from big data.

Overlap Between Google and Bing Web Search Results!: Twitter to the Rescue?

Access to diverse perspectives nurtures an informed citizenry. Google and Bing have emerged as the duopoly that largely arbitrates which English language documents are seen by web searchers. We present our empirical study over the search results produced by Google and Bing that shows a large overlap. Thus, citizens may not gain different perspectives by simultaneously probing them for the same query. Fortunately, our study also shows that by mining Twitter data one can obtain search results that are quite distinct from those produced by Google and Bing. Additionally, the users found those results to be quite informative.