Rana Forsati

Postdoctoral Researcher
Department of Computer Science and Engineering
Michigan State University

I am a post-doc researcher at Computer Science and Engineering department, Michigan State University. I obtained my PhD from Shahid Beheshti University (formerly known as The National University of Iran) in 2014. I also spent a wonderful year as a visting research scholar at University of Minnesota working with Professor Mohamed F. Mokbel from March 2013 to March 2014.

Research Interests

I am broadly interested in data mining, machine learning, and optimization with applications in recommender systems, social graph analysis, text mining, and natural language processing. My PhD thesis, titled A Framework for Semantic and Social Recommender Systems, introduces a unified framewrok for recommendation with multiple sources of side information. In particular, it introduces matrix factiorization with trsut and distrust relations between users, distance metric learning from constriant graphs, semantic Levenshtein distance, weighted association rules, binary data clustering and few other algorithms for recommender systems.

Machine Learning for Recommendation

Recommender systems have become ubiquitous in recent years, and are applied in a variety of applications such as Netflix, Amazon, etc. But there are few challenges such as data sparsity and cold-start users/items problems that need to be addressed. We are interested in utilizing and developing machine learning methods such as matrix factorization, ranking, and clustering to resolve these issues.

Document Clustering, Semantic Analysis, and Feature Selection

Document clustering is the application of cluster analysis to textual documents with numerous applications in automatic document organization, topic extraction, recommender systems and fast information retrieval or filtering. In last coupl of years, we have been working on developing efficient algorithms for document clustering. Also, due to computational burden of high-dimensional textual data, selecting informative feature, a problem known as feature selection, has been a focus of our reserch in last couple of years. Extracting semantic of documents, ontlogy mapping, and part-of-speech tagging are other related interesting problems we are interested to explore.

Convex and Non-convex Optimization

Many problems in data mining, machine learning, communication can be cast as optimization problem. Although, in some cases these problems are convex and can be solved efficiently by off-the-shelf convex optimization methods, but in general most of these problems are non-convex or combinatorial. One of our main line of research is developing effieicnt meta-heuristic methods to tackle these hard problems. In particular, developing efficient optimization methods with balanced exploration-exploitation in searching the solution space has been our main research focus.

Metric Learning

Many machine learning approaches rely on some similarity metric including: unsupervised learning such as clustering, information retrieval for learning to rank, in face verification or face identification, and in recommendation systems. Also, aggregation and utilizing of different rich sources of information, or covariates, has been a challenging problem in many machine learning applications. This problem is of great importance, especially when a single view of the data is sparse or incomplete. Despite the recent developments in hybrid methods, the general problem of integrating and aggregating data from various sources due to the diversities still remains. We are interested in learning similarity metric by aggregating multiple sources of side information into a single distance metric that can be used in different applications.

Social Graph Analysis and Colloborative Ranking

In recent years there has been an upsurge of interest in understanding and exploiting social information such as trust and distrust relations among users along with rating data to improve the performance of recommender systems and resolve sparsity and cold-start problems. We research to design novel algorithms to exploit social relations bwetween users, and better understand problems such as propogation of trust/distrust relations, link prediction, influence diffisuion in social networks.


Preprints/In Preparation

  • Rana Forsati, Iman Barjesteh, Abdol-Hossein Esfahanian, and Hayder Radha
    Collaborative social ranking for recommendation with trust and distrust Relations
    in preparation, 2016
  • Iman Barjasteh, Rana Forsati, Abdol-Hossein Esfahanian, and Hayder Radha
    Semi-supervised collaborative ranking with push at top
    arXiv, 2015
  • Rana Forsati, and Abdol-Hossein Esfahanian
    Learning metric from constraint graphs for effective recommendation with multiple sources
    submitted, 2015

Journal Articles

  • Rana Forsati, Iman Barjesteh, Dennis Ross, Abdol-Hossein Esfahanian, and Hayder Radha
    Network completion by leveraging similarity of nodes
    Social Network Analysis and Mining, Springer, 2016.
  • Iman Barjasteh, Rana Forsati, Dennis Ross, Abdol-Hossein Esfahanian, and Hayder Radha
    Cold-start recommendation with provable guarantees: a decoupled approach
    IEEE Transactions on Knowledge and Data Engineering (TKDE), IEEE, 2016
  • Rana Forsati and Mehrnoush Shamsfard
    Symbiosis of combinatorial and evolutionary ontology mapping approaches
    Information Sciences, Elsevier, 2016
  • Rana Forsati, Alireza Moayedikia, Mehrnoush Shamsfard
    An effective Web page recommender using binary data clustering
    Information Retrieval Journal, Springer, 2015
  • Alireza Moayedikia, Richard Jensen, Uffe Kock Wiil, Rana Forsati
    Weighted bee colony algorithm for discrete optimization problems with application to feature selection
    Engineering Applications of Artificial Intelligence, Elsevier, 2015
  • Rana Forsati, Andisheh Keikha, Mehrnoush Shamsfard
    An improved bee colony optimization algorithm with an application to document clustering
    Neurocomputing, Elsevier, 2015
  • Rana Forsati, Mehrdad Mahdavi, Mehrnoush Shamsfard, Mohamed Sarwat
    Matrix factorization with explicit trust and distrust side information for improved social recommendation
    ACM Transactions on Information Systems (TOIS), ACM, 2014
  • Rana Forsati, Mehrnoush Shamsfard
    Novel harmony search-based algorithms for part-of-speech tagging
    Knowledge and Information Systems, Springer, 2014
  • Rana Forsati, Alireza Moayedikia, Richard Jensen, Mehrnoush Shamsfard, Mohammad Reza Meybodi
    Enriched ant colony optimization and its application in feature selection
    Neurocomputing, Elsevier, 2014
  • Rana Forsati, Mehrnoush Shamsfard
    Hybrid PoS-tagging: A cooperation of evolutionary and statistical approaches
    Applied Mathematical Modelling, Elsevier, 2014
  • Rana Forsati, Mehrdad Mahdavi, Mehrnoush Shamsfard, Mohammad Reza Meybodi
    Efficient stochastic algorithms for document clustering
    Information Sciences, Elsevier, 2013
  • Rana Forsati, Hanieh Mohammadi Doustdar, Mehrnoush Shamsfard, Andisheh Keikha, Mohammad Reza Meybodi
    A fuzzy co-clustering approach for hybrid recommender systems
    International Journal of Hybrid Intelligent Systems, 2013
  • Rana Forsati, Sara Valipour Ebrahimi, Keivan Navi, Ezeddin Mohajerani, Hossein Jashnsaz
    Implementation of all-optical reversible logic gate based on holographic laser induced grating using azo-dye doped polymers
    Optics & Laser Technology, Elsevier, 2013
  • Mohsen Mirkhani, Rana Forsati, Alireza Mohammad Shahri, Alireza Moayedikia
    A novel efficient algorithm for mobile robot localization
    Robotics and Autonomous Systems, Elsevier, 2013
  • Rana Forsati, Hanieh Mohammadi Doustdar, Mehrnoush Shamsfard, Andisheh Keikha, Mohammad Reza Meybodi
    A fuzzy co-clustering approach for hybrid recommender systems
    International Journal of Hybrid Intelligent Systems, 2013
  • Rana Forsati, Alireza Moayedikia, Andisheh Keikha
    A novel approach for feature selection based on the bee colony optimization
    International Journal of Computer Applications, 2012
  • Rana Forsati, Mohammad Reza Meybodi
    Effective page recommendation algorithms based on distributed learning automata and weighted association rules
    Expert Systems with Applications, Elsevier, 2010
  • Mehrdad Mahdavi, M Haghir Chehreghani, Hassan Abolhassani, Rana Forsati
    Novel meta-heuristic algorithms for clustering web documents
    Applied Mathematics and Computation, Elsevier, 2008
  • Rana Forsati, AT Haghighat, Mehrdad Mahdavi
    Harmony search based algorithms for bandwidth-delay-constrained least-cost multicast routing
    Computer Communications, Elsevier, 2008

Conference Proceedings


  • Michigan State University

  • Karaj Azad University [2008-2014]

    • Formal Languages and Automata Theory
    • Software Engineering I and II
    • System Analysis
  • Qazvin Azad University [2005-2006]

    • Formal Languages and Automata Theory
  • Zarandieh Institute of Higher Education (ZIHE) [2009-2010]

    • Formal Languages and Automata Theory
    • Software Engineering

Contact Me

Department of Computer Sicnece and Engineering
Michigan State University
Email: last-name@cse.msu.edu