For more recent and complete information, see my Curriculum Vitae.

About Me

I'm currently at Palo Alto Research Center (PARC). My research lies in the field of machine learning and data mining; and spans theory, algorithms, and applications of large complex relational data (i.e., complex graph/network data such as protein interaction networks, social networks, the web graph, and many others). I received my Ph.D. from the Department of Computer Science at Purdue University. My Ph.D. research largely focused on machine learning and data mining algorithms for large dynamic networks.

I am also a recipient of the National Science Foundation Graduate Research Fellowship (NSF GRFP), National Defense Science and Engineering Graduate Fellowship (NDSEG), the Purdue Frederick N. Andrews Fellowship, and Bilsland Dissertation Fellowship Awarded to Outstanding Ph.D. candidates.

Publications (Peer-reviewed)

Nesreen K. Ahmed, Jennifer Neville, Ryan A. Rossi, Nick Duffield, Theodore L. Willke: Graphlet Decomposition: Framework, Algorithms, and Applications, Knowledge and Information Systems (KAIS), 2016.
[ ]

@article{ahmed2016kais,
  author = {Nesreen K. Ahmed and Jennifer Neville and Ryan A. Rossi and Nick Duffield and Theodore L. Willke},
  title = {Graphlet Decomposition: Framework, Algorithms, and Applications},
  journal = {Knowledge and Information Systems (KAIS)},
  pages = {1--32},
  url = {http://github.com/nkahmed/pgd},
  year = {2016}
}
Ryan A. Rossi, Rong Zhou: Toward Interactive Relational Learning, AAAI Conference on Artificial Intelligence, 2016.
[ ]

@inproceedings{rossi2016aaai,
  author = {Ryan Rossi and Rong Zhou},
  title = {Toward Interactive Relational Learning},
  booktitle = {AAAI Conference on Artificial Intelligence},
  pages = {4383--4384},
  year = {2016},
}
This paper introduces the Interactive Relational Machine Learning (iRML) paradigm in which users interactively design relational models by specifying the various components, constraints, and relational data representation, as well as perform evaluation, analyze errors, and make adjustments and refinements in a closed-loop. iRML requires fast real-time learning and inference methods capable of interactive rates. Methods are investigated that enable direct manipulation of the various components of the RML method. Visual representation and interaction techniques are also developed for exploring the space of relational models and the trade-offs of the various components and design choices.

Ryan A. Rossi, Nesreen K. Ahmed: An Interactive Data Repository with Visual Analytics, SIGKDD Explor., pages 37-41, 2015.
[ ]

  @article{nr-sigkdd16,
     title = {An Interactive Data Repository with Visual Analytics},
     author = {Ryan A. Rossi and Nesreen K. Ahmed},
     journal = {SIGKDD Explor.},
     volume = {17},
     number = {2},
     month = {Feb},
     year = {2016},
     issn = {1931-0145},
     pages = {37--41},
     url={http://networkrepository.com},
     doi = {10.1145/2897350.2897355},
     publisher = {ACM}
  }
Nesreen K. Ahmed, Jennifer Neville, Ryan A. Rossi, Nick Duffield: Efficient Graphlet Counting for Large Networks, ICDM, 2015.
[ ]

@inproceedings{ahmed2015icdm,
    title={Efficient Graphlet Counting for Large Networks},
    author={Nesreen K. Ahmed and Jennifer Neville and Ryan A. Rossi 
      and Nick Duffield},
    booktitle={ICDM},
    pages={1--10},
    year={2015}
}
From social science to biology, numerous applications often rely on motifs for intuitive and meaningful characterization of networks at both the global macro-level as well as the local micro-level. While motifs have witnessed a tremendous success and impact in a variety of domains, there has yet to be a fast and efficient approach for computing the frequencies of these subgraph patterns. However, existing methods are not scalable to large networks with millions of nodes and edges, which impedes the application of motifs to new problems that require large-scale network analysis. To address these problems, we propose a fast, efficient, and parallel algorithm for counting motifs of size k = {3, 4}-nodes that take only a fraction of the time to compute when compared with the current methods used. The proposed motif counting algorithms leverages a number of proven combinatorial arguments for different motifs. For each edge, we count a few motifs, and with these counts along with the combinatorial arguments, we obtain the exact counts of others in constant time. On a large collection of 300+ networks from a variety of domains, our motif counting strategies are on average 460x faster than current methods. This brings new opportunities to investigate the use of motifs on much larger networks and newer applications as we show in our experiments. To the best of our knowledge, this paper provides the largest motif computations to date as well as the largest systematic investigation on over 300+ networks from a variety of domains.

Ryan A. Rossi, Rong Zhou: Scalable Relational Learning for Large Heterogeneous Networks, IEEE International Conference on Data Science and Advanced Analytics (DSAA), pages 1-10 2015.
[ ]

@INPROCEEDINGS{rossi2015dsaa-pcmf, 
  author={R. A. Rossi and R. Zhou}, 
  booktitle={IEEE International Conference on Data Science and 
    Advanced Analytics (DSAA)}, 
  title={Scalable Relational Learning for 
    Large Heterogeneous Networks}, 
  year={2015}, 
  pages={1-10}
}
Relational models for heterogeneous network data are becoming increasingly important for many real-world applications. However, existing relational learning approaches are not parallel, have scalability issues, and thus unable to handle large heterogeneous network data. In this paper, we propose Parallel Collective Matrix Factorization (PCMF) that serves as a fast and flexible framework for joint modeling of large heterogeneous networks. The PCMF learning algorithm solves for a single parameter given the others, leading to a parallel scheme that is fast, flexible, and general for a variety of relational learning tasks and heterogeneous data types. The proposed approach is carefully designed to be (a) efficient for large heterogeneous networks (linear in the total number of observations from the set of input matrices), (b) flexible as many components are interchangeable and easily adaptable, and (c) effective for a variety of applications as well as for different types of data. The experiments demonstrate the scalability, flexibility, and effectiveness of PCMF. For instance, we show that PCMF outperforms a recent state-of-the-art parallel approach in runtime, scalability, and prediction quality. Finally, the effectiveness of PCMF is shown on a number of relational learning tasks such as serving predictions in a realtime streaming fashion.

Nesreen K. Ahmed and Ryan A. Rossi: Interactive Visual Graph Analytics on the Web, Proceedings of the 9th International AAAI Conference on Web and Social Media (ICWSM), pages 566-569, 2015.
[ ]

@inproceedings{ahmed-icwsm15,
    title = {Interactive Visual Graph Analytics on the Web},
    author = {Nesreen K. Ahmed and Ryan A. Rossi},
    booktitle = {ICWSM},
    pages={566--569},
    year={2015}
}
Ryan Rossi and Nesreen K. Ahmed: The Network Data Repository with Interactive Graph Analytics and Visualization, Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI) DT, pages 4292-4293, 2015.
[ ]

@inproceedings{nr-aaai14,
    title = {The Network Data Repository with Interactive Graph Analytics 
      and Visualization},
    author={Ryan A. Rossi and Nesreen K. Ahmed},
    booktitle = {Proceedings of the 29th AAAI Conference on 
      Artificial Intelligence},
    pages = {4292--4293},
    url={http://networkrepository.com},
    year={2015}
}
Ryan A. Rossi, David F. Gleich, Assefaw H. Gebremedhin, Parallel Maximum Clique Algorithms with Applications to Network Analysis, SIAM Journal on Scientific Computing (SISC), 37(5), C589–C616 (28 pages), 2015.
[ ]

@article{rossi2015pmc-sisc,
    title={Parallel Maximum Clique Algorithms with Applications 
      to Network Analysis},
    author={Rossi, Ryan A and Gleich, David F and Gebremedhin, Assefaw H},
    journal={SIAM Journal on Scientific Computing (SISC)},
    volume={37},
    number={5},
    pages={28},
    year={2015},
    publisher={Society for Industrial and Applied Mathematics}
}
We present a fast, parallel maximum clique algorithm for large sparse graphs that is designed to exploit characteristics of social and information networks. The method exhibits a roughly linear runtime scaling over real-world networks ranging from a thousand to a hundred million nodes. In a test on a social network with 1.8 billion edges, the algorithm finds the largest clique in about 20 minutes. At its heart the algorithm employs a branch-and-bound strategy with novel and aggressive pruning techniques. The pruning techniques include the combined use of core numbers of vertices along with a good initial heuristic solution to remove the vast majority of the search space. In addition, the exploration of the search tree is parallelized. During the search, processes immediately communicate changes to upper and lower bounds on the size of maximum clique. This exchange of information occasionally results in a super-linear speedup because tasks with large search spaces can be pruned by other processes. We demonstrate the impact of the algorithm on applications using two different network analysis problems: computation of temporal strong components in dynamic networks and determination of compression-friendly ordering of nodes of massive networks.

Ryan Rossi and Nesreen K. Ahmed: Role Discovery in Networks, IEEE Transactions on Knowledge and Data Engineering, pages 1112-1131, 2015.
[ ]

@article{rossi2015roles,
  title={{Role Discovery in Networks}},
  author={Ryan A. Rossi and Nesreen K. Ahmed},
  journal={IEEE Transactions on Knowledge and Data Engineering},
  volume={27},
  number={4},
  pages={1112--1131},
  year={2015},
  publisher={IEEE}
}
Ryan Rossi and Nesreen K. Ahmed: Coloring Large Complex Networks, Social Network Analysis and Mining, Vol. 4, No. 1-228, Springer, pp. 1–37, 2014.
[ ]

@article{rossi2014coloring,
  title={Coloring Large Complex Networks},
  author={Ryan A. Rossi and Nesreen K. Ahmed},
  journal={Social Network Analysis and Mining},
  eid={228},
  volume={4},
  number={1},
  pages={37},
  year={2014}
}
Ryan Rossi: Fast Triangle Core Decomposition for Mining Large Graphs , Advances in Knowledge Discovery and Data Mining, pages 310-322, 2014.
[ ]

@article{rossi2014tcore,
  title={Fast Triangle Core Decomposition for Mining Large Graphs},
  author={Ryan A. Rossi},
  booktitle={Advances in Knowledge Discovery and Data Mining},
  pages={310--322},
  year={2014}
}
Ryan Rossi, Sonia Fahmy, and Nilothpal Talukder: A Multi-Level Approach for Evaluating Internet Topology Generators, IFIP Networking, pages 1-9, 2013.
[ ]

@inproceedings{rossi2013topology,
  title={A Multi-Level Approach for Evaluating Internet Topology Generators},
  author={Ryan A. Rossi and Sonia Fahmy and Nilothpal Talukder},
  booktitle={IFIP Networking},
  pages={1--9},
  year={2013}
}
Ryan A. Rossi, David F. Gleich, Assefaw H. Gebremedhin, Md. Mostofa Ali Patwary, Fast Maximum Clique Algorithms for Large Graphs, Proceedings of the 23rd International Conference on World Wide Web (WWW), 2014.
[ ]

@inproceedings{rossi2014pmc-www,
  title={Fast Maximum Clique Algorithms for Large Graphs},
  author={Ryan A. Rossi and David F. Gleich and Assefaw H. Gebremedhin and 
    Mostofa A. Patwary},
  booktitle={Proceedings of the 23rd International Conference on World 
    Wide Web (WWW)},
  year={2014}
}
Ryan A. Rossi, David F. Gleich, Assefaw H. Gebremedhin, Triangle Core Decomposition and Maximum Cliques, SIAM Workshop on Network Science, 1-2, 2013.   [ ]

@inproceedings{rossi2013trianglecores,
  title={Triangle Core Decomposition and Maximum Cliques},
  author={Ryan A. Rossi and David F. Gleich and Assefaw H. Gebremedhin},
  booktitle={SIAM Workshop on Network Science},
  pages={1--2},
  year={2013}
}
David F. Gleich, Ryan A. Rossi, A Dynamical System for PageRank with Time-Dependent Teleportation, Internet Mathematics, 10:1-2, 188-217, 2014. [ ]

@article{rossi2012dynamical,
  title={A Dynamical System for PageRank with Time-Dependent Teleportation},
  author={David F. Gleich and Ryan A. Rossi},
  journal={Internet Mathematics},
  pages={188--217},
  year={2014}
}
Ryan Rossi, Brian Gallagher, Jennifer Neville, Keith Henderson: Modeling Dynamic Behavior in Large Evolving Graphs, In Proceedings of the Sixth ACM International Conference on Web Search and Data Mining (WSDM), pages 667-676, 2013.
[ ]

@inproceedings{rossi2013modeling,
  title={Modeling Dynamic Behavior in Large Evolving Graphs},
  author={Ryan A. Rossi and Brian Gallagher and Jennifer Neville and 
    Keith Henderson},
  booktitle={WSDM},
  pages={667--676},
  year={2013}
}
Ryan Rossi, Luke K. McDowell, David W. Aha, Jennifer Neville: Transforming Graph Data for Statistical Relational Learning, Journal of Artificial Intelligence Research (JAIR), pages 363-441. AAAI Press, 2012. [ ]

@article{rossi2012transforming,
  title={Transforming Graph Data for Statistical Relational Learning},
  author={Ryan A. Rossi and Luke K. McDowell and David W. Aha and 
    Jennifer Neville},
  journal={Journal of Artificial Intelligence Research (JAIR)},
  volume={45},
  pages={363--441},
  year={2012},
  publisher={AAAI Press}
}
Relational data representations have become an increasingly important topic due to the recent proliferation of network datasets (e.g., social, biological, information networks) and a corresponding increase in the application of Statistical Relational Learning (SRL) algorithms to these domains. In this article, we examine and categorize techniques for transforming graph-based relational data to improve SRL algorithms. In particular, appropriate transformations of the nodes, links, and/or features of the data can dramatically affect the capabilities and results of SRL algorithms. %Thus, we survey approaches and opportunities for relational representation transformation designed to improve the performance of these algorithms. We introduce an intuitive taxonomy for data representation transformations in relational domains that incorporates link transformation and node transformation as symmetric representation tasks. More specifically, the transformation tasks for both nodes and links include (i) predicting their existence, (ii) predicting their label or type, (iii) estimating their weight or importance, and (iv) systematically constructing their relevant features. We motivate our taxonomy through detailed examples and use it to survey competing approaches for each of these tasks. We also discuss general conditions for transforming links, nodes, and features. Finally, we highlight challenges that remain to be addressed.
Ryan Rossi and David Gleich: Dynamic PageRank using Evolving Teleportation, Algorithms and Models for the Web Graph, volume 7323 of Lecture Notes in Computer Science, pages 126-137. Springer, 2012.   [ ]

@article{rossi2012dynamic,
  author = {Ryan A. Rossi and David F. Gleich},
  title = {Dynamic {PageRank} using Evolving Teleportation},
  booktitle = {Algorithms and Models for the Web Graph},
  year = {2012},
  editor = {Anthony Bonato and Jeannette Janssen},
  volume = {7323},
  series = {Lecture Notes in Computer Science},
  pages = {126--137},
  publisher = {Springer}
}
Ryan Rossi, Brian Gallagher, Jennifer Neville, Keith Henderson: Role-Dynamics: Fast Mining of Large Dynamic Networks, Proceedings of the 21st ACM International Conference Companion on World Wide Web (WWW), pages 997-1005, 2012.
[ ]

@inproceedings{rossi2012role,
  title={Role-Dynamics: Fast Mining of Large Dynamic Networks},
  author={Ryan Rossi and Brian Gallagher and Jennifer Neville and 
    Keith Henderson},
  booktitle={Proceedings of the 21st International Conference Companion 
    on World Wide Web (WWW)},
  pages={997--1006},
  year={2012},
  organization={ACM}
}
Ryan Rossi and Jennifer Neville: Time-Evolving Relational Classification and Ensemble Methods, In Proceedings of the Pacific-Asia International Conference on Knowledge Discovery and Data Mining (PAKDD), LNCS 7301, pages 1-13. Springer, 2012.   [ ]

@inproceedings{rossi2012dynamic-srl,
  title={Time-evolving Relational Classification and Ensemble Methods},
  author={Ryan Rossi and Jennifer Neville},
  booktitle={PAKDD},
  pages={1--13},
  year={2012},
  publisher={Springer}
}
Ryan Rossi and Jennifer Neville: Modeling the Evolution of Discussion Topics and Communication to Improve Relational Classification, In Proc. of the 1st SOMA Workshop, 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 89-97, 2010.   [ ]

@inproceedings{rossi2010modeling,
  title={Modeling the Evolution of Discussion Topics and Communication
     to Improve Relational Classification},
  author={Ryan Rossi and Jennifer Neville},
  booktitle={SIGKDD SOMA},
  pages={89--97},
  year={2010}
}
Jean-Louis Lassez, Ryan A. Rossi, Axel E. Bernal: Crick's Hypothesis Revisited: The Existence of a Universal Coding Frame, IEEE International Conference on Bioinformatics and Life Science Computing, AINAW/BLSC, 745-751, 2007. Presented in the US, Russia, Japan, Thailand and Canada at various conferences and keynotes.
[ ]

@inproceedings{rossi2007crick,
  title={Cricks Hypothesis Revisited: The Existence of a Universal
    Coding Frame},
  author={Jean-Louis Lassez and Ryan A. Rossi and Axel E. Bernal},
  booktitle={AINAW},
  volume={1},
  pages={745--751},
  year={2007}
}
Jean-Louis Lassez, Ryan Rossi, Kumar Jeev: Ranking Links on the Web: Search and Surf Engines, New Frontiers in Applied Artificial Intelligence (IEA/AIE), volume 5027 of Lecture Notes of Artificial Intelligence, pages 199-208. Springer, 2008.
[ ]

@article{lassez2008ranking,
  title={Ranking Links on the Web: Search and Surf Engines},
  author={Jean-Louis Lassez and Ryan Rossi and Kumar Jeev},
  journal={New Frontiers in Applied Artificial Intelligence (IEA/AIE)},
  pages={199--208},
  year={2008},
  publisher={Springer}
}
Jean-Louis Lassez, Ryan Rossi, Stephen Sheel, Srinivas Mukkamala: Signature Based Intrusion Detection using Latent Semantic Analysis, IEEE International Joint Conference of Neural Networks, IJCNN, 1068-1074, 2008.   [ ]

@inproceedings{lassez2008signature,
  title={Signature based Intrusion Detection using Latent Semantic Analysis},
  author={Jean-Louis Lassez and Ryan Rossi and Stephen Sheel and 
    Srinivas Mukkamala},
  booktitle={IJCNN},
  pages={1068--1074},
  year={2008}
}
John Stamey, Jean-Louis Lassez, Ryan Rossi, Daniel Boorn: Client-Side Dynamic Metadata in Web 2.0, Proceedings of the 25th ACM International Conference on Design of Communication, 155-161, 2007.   [ ]

@inproceedings{stamey2007dynamic,
  title={Client-side Dynamic Metadata in Web 2.0},
  author={John Stamey and Jean-Louis Lassez and Daniel Boorn and Ryan Rossi},
  booktitle={Proceedings of the 25th annual ACM International Conference on 
    Design of Communication},
  pages={155--161},
  year={2007}
}
Ryan Rossi: Latent Semantic Analysis of the Languages of Life, ISICA, CCIS 51:128-137, 2009.   [ ]

@article{rossi2009latent,
  title={Latent Semantic Analysis of the Languages of Life},
  author={Ryan A. Rossi},
  journal={Computational Intelligence and Intelligent Systems},
  pages={128--137},
  year={2009},
  publisher={Springer}
}
Mark W. Powell, Ryan A. Rossi, and Khawaja S. Shams: A Scalable Image Processing Framework for Gigapixel Mars and Other Celestial Body Images, IEEE Aerospace, 1-11, 2009.   [ ]

@inproceedings{powell2010scalable,
  title={A Scalable Image Processing Framework for Gigapixel Mars and 
    Other Celestial Body Images},
  author={Mark W. Powell and Ryan A. Rossi and Khawaja S. Shams},
  booktitle={IEEE Aerospace},
  pages={1--11},
  year={2010}
}
Khawaja S. Shams, Mark W. Powell, Tom M. Crockett, Jeffrey S. Norris, Ryan Rossi, Tom Soderstrom: Polyphony: A Workflow Orchestration Framework for Cloud Computing, 10th IEEE/ACM Inter. Conf. on Cluster, Cloud and Grid Computing, CCGrid, 606-611, 2010. Also (Amazon AWS Case Study: NASA JPL’s Desert Research and Training [txt])   [ ]

@inproceedings{shams2010polyphony,
  title={Polyphony: A Workflow Orchestration Framework for Cloud Computing},
  author={Khawaja S. Shams and Mark W. Powell and Tom M. Crockett and 
    Jeffrey S. Norris and Ryan Rossi and Tom Soderstrom},
  booktitle={10th IEEE/ACM International Conference on Cluster, Cloud and 
    Grid Computing (CCGrid)},
  pages={606--611},
  year={2010}
}
John Stamey, Ryan Rossi: Automatically Identifying Relations in Privacy Policies, Proceedings of the 27th ACM International Conference on Design of Communication, 233-238, 2009.   [ ]

@inproceedings{stamey2009automatically,
  title={Automatically Identifying Relations in Privacy Policies},
  author={John W. Stamey and Ryan A. Rossi},
  booktitle={Proceedings of the 27th ACM International Conference on Design 
    of Communication},
  pages={233--238},
  year={2009}
}

Technical Reports

Nesreen K. Ahmed, Jennifer Neville, Ryan A. Rossi, Nick Duffield: Fast Parallel Graphlet Counting for Large Networks, Preprint arXiv:1506.04322 (In sub.), 2015.
[ ]

@inproceedings{ahmed15-graphlet-preprint,
    title = {Fast Parallel Graphlet Counting for Large Networks},
    author = {Nesreen K. Ahmed and Jennifer Neville and Ryan A. Rossi and Nick Duffield},
    booktitle = {preprint arXiv:1506.04322},
    pages = {1--25},
    year={2015}
}
From social science to biology, numerous applications often rely on motifs for intuitive and meaningful characterization of networks at both the global macro-level as well as the local micro-level. While motifs have witnessed a tremendous success and impact in a variety of domains, there has yet to be a fast and efficient approach for computing the frequencies of these subgraph patterns. However, existing methods are not scalable to large networks with millions of nodes and edges, which impedes the application of motifs to new problems that require large-scale network analysis. To address these problems, we propose a fast, efficient, and parallel algorithm for counting motifs of size k = {3, 4}-nodes that take only a fraction of the time to compute when compared with the current methods used. The proposed motif counting algorithms leverages a number of proven combinatorial arguments for different motifs. For each edge, we count a few motifs, and with these counts along with the combinatorial arguments, we obtain the exact counts of others in constant time. On a large collection of 300+ networks from a variety of domains, our motif counting strategies are on average 460x faster than current methods. This brings new opportunities to investigate the use of motifs on much larger networks and newer applications as we show in our experiments. To the best of our knowledge, this paper provides the largest motif computations to date as well as the largest systematic investigation on over 300+ networks from a variety of domains.

Ryan Rossi and Nesreen K. Ahmed: NetworkRepository: A Graph Data Repository with Visual Interactive Analytics, pp. 1-6, 2014.
[ ]

@inproceedings{network-repository-2014,
    title = {NetworkRepository: A Graph Data Repository with 
      Visual Interactive Analytics},
    author={Ryan A. Rossi and Nesreen K. Ahmed},
    booktitle = {arXiv CS preprints},
    url={http://networkrepository.com},
    pages={6},
    year={2014}
}
Ryan A. Rossi, Sonia Fahmy, and Nilothpal Talukder, Modeling the Evolution of the Internet Topology: A Multi-Level Evaluation Framework, pp. 1-10, 2013.   [ ]

@inproceedings{rossi2013modeling-evol,
  title={Modeling the Evolution of the Internet Topology: A Multi-Level Evaluation Framework},
  author={Ryan A. Rossi and Sonia Fahmy and Nilothpal Talukder},
  booktitle={Tech Report Purdue CS},  
  pages={1--10},
  year={2013}
}
Ryan A. Rossi, David F. Gleich, Assefaw H. Gebremedhin, Md. Mostofa Ali Patwary, A Fast Parallel Maximum Clique Algorithm for Large Sparse Graphs and Temporal Strong Components, arXiv preprint arXiv:1302.6256, 2013.   [ ]

@article{rossi2013parallel-cliques,
  title={A Fast Parallel Maximum Clique Algorithm for Large Sparse Graphs 
    and Temporal Strong Components},
  author={Ryan A. Rossi and David F. Gleich and Assefaw H. Gebremedhin and 
    Mostofa A. Patwary},
  journal={arXiv preprint arXiv:1302.6256},
  pages={1--9},
  year={2013}
}
Ryan A. Rossi, David F. Gleich, Assefaw H. Gebremedhin, Md. Mostofa Ali Patwary, What if CLIQUE were fast? Maximum Cliques in Information Networks and Strong Components in Temporal Networks, arXiv preprint arXiv:1210.5802, 2013.   [ ]

@article{rossi2012fastclique,
  title={What if CLIQUE were fast? Maximum Cliques in Information Networks 
    and Strong Components in Temporal Networks},
  author={Ryan A. Rossi and David F. Gleich and Assefaw H. Gebremedhin and 
    Mostofa A. Patwary},
  journal={arXiv preprint arXiv:1210.5802},
  pages={1--11},
  year={2012}
}
Ryan Rossi, Brian Gallagher, Jennifer Neville, and Keith Henderson: Modeling Temporal Behavior in Large Networks: A Dynamic Mixed-Membership Model, DOE Scientific and Technical Information, LLNL-TR-514271, 2011.   [ ]

@inproceedings{rossi2011modeling,
  title={Modeling Temporal Behavior in Large Networks: A Dynamic 
    Mixed-Membership Model},
  author={Ryan Rossi and Brian Gallagher and Jennifer Neville and 
    Keith Henderson},
  booktitle={LLNL-TR-514271},
  year={2011},
  pages={1--10}
}
Ryan Rossi and Jennifer Neville: Representations and Ensemble Methods for Dynamic Relational Classification, CoRR abs/1111.5312, 2011.   [ ]

@article{rossi2011representations,
  title={Representations and Ensemble Methods for Dynamic Relational 
    Classification},
  author={Ryan A. Rossi and Jennifer Neville},
  journal={arXiv preprint arXiv:1111.5312},
  pages={1--11},
  year={2011}
}
Ryan Rossi: Discovering Latent Graphs with Positive and Negative Links to Eliminate Spam in Adversarial Information Retrieval, NASA JPL 2009.
[ ]

@inproceedings{rossi2009discovering,
  title={Discovering Latent Graphs with Positive and Negative Links 
    to Eliminate Spam},
  author={Ryan A. Rossi},
  booktitle={JPL Tech Report},
  year={2009},
  pages={1--9}
}

Patents

Ryan Rossi, Rong Zhou: Parallel Collective Matrix Factorization Framework for Big Data, pages 1-22. Patent filed, 2013.

Posters

Ryan Rossi, Rong Zhou: Parallel Collective Matrix Factorization Framework for Big Data, Palo Alto Research Center, Summer Research Colloquium, 2013.

Ryan Rossi, Nesreen K. Ahmed: The Network Data Repository with Interactive Graph Analytics and Visualization, AAAI, 2015.

Ryan Rossi, A Family of Parallel Maximum Clique Algorithms for Sparse Graphs, Purdue CS 50th Anniversary, 2013.

Ryan Rossi, Brian Gallagher, Jennifer Neville, and Keith Henderson, Modeling Dynamic Behavior in Large Evolving Graphs, WSDM, 2013.

Ryan Rossi, Brian Gallagher, Jennifer Neville, and Keith Henderson, Modeling Temporal Behavior in Large Networks: From Predictive Modeling to Anomaly Detection, ISCR Annual Research Symposium at LLNL, 2011.

Ryan Rossi and Jennifer Neville, Temporally-Evolving Network Classification, SIGKDD SOMA, 2010.


Past Research Activities

I spent the summer of 2013 performing research at Palo Alto Research Center (PARC) with Rong Zhou and the rest of the High Performance Analytics (HPA) team. My research focused on fast parallel state-of-the-art matrix factorization methods for massive data. We then designed a fast real-time (online) recommendation system capable of leveraging massive amounts of streaming data. Patents and a publication are forthcoming. In the summers of 2011 and 2012 I was at Lawrence Livermore National Laboratory (LLNL) working with Brian Gallagher on problems related to detecting anomalies and mining large temporal networks. I was supported through the LLNL Scholar (Cyber Defenders Program) part of the Computation Directorate. I presented a version of our Temporal Behavioral Model at LLNL and Purdue. [Poster][Presentation]

I previously visited the Naval Research Laboratory in Washington DC and worked with Dr. David Aha in the Navy Center for Applied Research in Artificial Intelligence and Dr. Luke McDowell of the United States Naval Academy. We focused on surveying approaches and opportunities for relational representation discovery. This lead us to introduce an intuitive taxonomy for relational representation discovery that formulates link discovery and node discovery as symmetric representation tasks (predicting their existence, predicting their label or type, estimating their weight or importance, and systematically discovering their relevant features). During my visit, I was supported by the NREIP Fellowship awarded by the Office of Naval Research (ONR).

The majority of my undergraduate studies were spent working with Dr. Jean-Louis Lassez (Retired IBM T.J. Watson Researcher) on many problems from machine learning, information retrieval, bioinformatics, security, and search engines.

Before attending Purdue, I was a research fellow (USRP, SURF, and Space Grant) at NASA Jet Propulsion Laboratory and California Institute of Technology working with Dr. Mark W. Powell on a Scalable Image Processing Framework for Gigapixel Mars Images. I also had the opportunity to work on extending this framework for Cloud Computing with Khawaja Shams (Amazon AWS Case Study: NASA JPL Desert Research and Training [txt]) and other members of the Planning Software Systems Group (within the Planning and Exploration Systems Mission Directorate).

During the summer of 2008, I worked with Dr. David Jensen and Brian Taylor at the University of Massachusetts Amherst in the Knowledge Discovery Laboratory (supported by the NSF REU Fellowship). The research investigated peer production and collaborative sensing systems in order to discover causal knowledge from these systems through sophisticated simulation techniques.

I also had the chance to work with Dr. Srinivas Mukkamala and jointly with Dr. Jean-Louis Lassez on problems of dimensionality reduction and real-time intrusion detection systems using a technique we designed based on Singular Value Decomposition and a simpler more robust version of Support Vector Machines (Summer 2007).



Research Experience

Research Assistant, Palo Alto Research Center (PARC HPA)
Advisor: Rong Zhou

Research Assistant, Purdue University (2009-2012)
Advisor: Jennifer Neville, Research: Machine Learning, Statistical Relational Learning

Research Assistant, Lawrence Livermore National Laboratory (ISCR)
Advisor: Brian Gallagher, LLNL Scholar: Cyber Defenders Program (Summer 2011)

Research Assistant, Naval Research Laboratory, AI Research Center
Advisor: David Aha, Co-advisor: Luke McDowell, ONR NREIP Fellowship
Relational Representation Discovery in Statistical Relational Learning, (Summer 2010)

Research Assistant, Coastal Carolina University (2005-2009)
Advisor: Jean-Louis Lassez, Retired IBM T.J. Watson Research Center

Research Assistant, NASA Jet Propulsion Laboratory, (Summer 2009)
California Institute of Technology, Space Grant/USRP Fellowship
(Returned to continue my research).

Research Assistant, NASA Jet Propulsion Laboratory, (Spring 2009)
California Institute of Technology, USRP NASA Fellowship
Advisor: Mark Powell (Scalable Image Processing) and Khawaja Shams (Cloud Computing)

Research Assistant, University of Massachusetts at Amherst, KDL, (Summer 2008)
Advisor: David Jensen, Graduate Advisor: Brian Taylor, REU NSF Fellowship

Research Assistant, New Mexico Tech, Institute for Complex Additive Systems
Advisor: Srinivas Mukkamala, Senior Research Scientist, ICASA (Summer 2007)


Teaching Experience

Search Engine Theory, Instructor, Spring 2008
This course was taught from a machine learning perspective using a variety of resources and recent papers along with a series of homeworks and projects implementing the significant parts of a search engine.

Algorithms in Bioinformatics, Teaching Assistant, Fall 2007
Numerical Methods, Teaching Assistant, Spring 2007
Introduction to Bioinformatics, Teaching Assistant, Fa 2008, Fa/Spr 2007, Spr 2006
Introduction to Algorithm Design II, Teaching Assistant, Spring 2006
Introduction to Algorithm Design I, Teaching Assistant, Spring 2006

As a teaching assistant I gave lectures and review sessions; developed homeworks, labs, and programs, held office hours, and maintained course website.


Books / Lecture Notes

Bioinformatics is the application of computational techniques and tools to analyze and manage biological data. This book provides an Introduction to Bioinformatics through the use of Action Labs. These labs allow students to get experience using real data and tools to solve difficult problems. The book comes with supplementary powerpoints, papers, and tools. The labs use data from Breast Cancer, Liver Disease, Diabetes, SARS, HIV, Extinct Organisms, and many others. The book has been written for first or second year computer science, mathematics, and biology students. The book is published by the Digital University Press. [pdf version] (6.2 MB)