Presentations

You can browse through some of my slides on Issuu.

  1. Motif-aware graph mining

    Allerton Conference, September 2016

  2. Mining Tools for Large-Scale Networks
    Aarhus University, April 2016

  3. Mining Tools for Large-Scale Networks
    IT Copenhagen University, April 2016

  4. Mining Tools for Large-Scale Networks
    UC Santa Cruz University, March 2016

  5. Mining Tools for Large-Scale Networks
    ETH Zurich, March 2016

  6. Mining Tools for Large-Scale Networks
    University of Illinois Urbana-Champaign, February 2016

  7. Mining Tools for Large-Scale Networks
    Boston University, February 2016

  8. Mining Tools for Large-Scale Networks
    University Colorado-Boulder, February 2016

  9. Mining Tools for Large-Scale Networks
    Northeastern University

  10.  Scalable Large Near-Clique Detection in Large-Scale Networks via Sampling
    University of Maryland

  11. Scalable Large Near-Clique Detection in Large-Scale Networks via Sampling
    Ohio State University
  12. Dense subgraph discovery
    Google Research
  13. Large-Scale Graph Mining
    IBM T.J. Watson Research Center
  14. Streaming Graph Partitioning in the Planted Partition Model
    ACM Conference on Online Social Networks (COSN’15)
  15. Scalable Large Near-Clique Detection in Large-Scale Networks via Sampling
    Stanford University
  16. Scalable Large Near-Clique Detection in Large-Scale Networks
    Signals, Inference, and Networks (SINE) Seminar
    University of Illinois Urbana-Champaign
  17. Dense subgraph discovery
    Data-driven Algorithmics
  18. Scalable Large Near-Clique Detection in Large-Scale Networks via Sampling
    21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2015)
  19. Dense subgraph discovery
    Tutorial at 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2015)
  20. Scalable dense subgraph discovery
    Random Structures and Algorithms (RSA), July ’15
  21. Scalable dense subgraph discovery
    International Symposium on Optimization (ISMP), July ’15
  22. Space- and Time-Efficient Algorithms for Maintaining Dense Subgraphs on One-Pass Dynamic Streams
    STOC 2015, June ’15
  23. Scalable dense subgraph discovery
    University of Cyprus, June ’15
  24. Provably Fast Inference of Latent Features from Networks
    24th International World Wide Web Conference (WWW 2015), May’15
  25. The k-clique Densest Subgraph Problem
    24th International World Wide Web Conference (WWW 2015), May’15
  26. Algorithmic Analysis of Large Datasets
    Universitat Pompeu Fabra, May ’15
  27. Modern Data Mining Algorithms
    Draper Laboratory, December ’14
  28. Algorithmic Analysis of Large Datasets
    SEAS Harvard University, November ’14
    Youtube video
  29. Algorithmic Analysis of Large Datasets (pptx)
    Brown University, May ’14
    Host: Philip Klein
  30. Large-Scale Graph Mining
    Imperial College London, May ’14
    Host: Moez Draief
  31. Algorithmic Analysis of Large Datasets
    Google NYC, April ’14
    Host: Vahab Mirrokni
  32. Mathematical Techniques for Modeling and Analyzing Large Graphs (pdf)
    Aalto Science Institute, January ’14, Helsinki
  33. Modeling Intratumor Gene Copy Number Heterogeneity using Fluorescence in Situ Hybridization data (pptx, pdf)
    WABI ’13, September ’13, Nice
  34. Fennel: Streaming Graph Partitioning for Massive Scale Graphs (pdf)
    MASSIVE ’13, September ’13, Nice
  35. Denser than the densest subgraph: extracting optimal quasi-cliques with quality guarantees (pptx, pdf)
    KDD ’13, August ’13, Chicago
  36. Mathematical and Algorithmic Analysis of Network and Biological data
    Thesis Defense, Carnegie Mellon University, May 2013
  37. Processing, Analyzing and Mining Big Graph Data
    Machine learning lunch seminar, Carnegie Mellon University, April 2013
    Video
  38. Random Graphs and Complex Networks
    Guest lecture, TELCOM2125: Network Science and Analysis
    Host: Konstantinos Pelechrinis
    April 2013
  39. Mathematical and Algorithmic Analysis of Biological data and Networks
    Abstract
    Brown University, April 2013
    Host: Eli Upfal
  40. Fennel: Streaming Graph Partitioning for Massive Scale Graphs
    Microsoft Research, Cambridge UK, November 2012
    Host: Milan Vojnovic
  41. On Certain Topics on Networks and Optimization: Theorems, Algorithms and Applications
    Yahoo! Research Barcelona, Barcelona, August 2012
    Host: Aris Gionis
  42. On Certain Properties of Random Apollonian Networks (pptx, pdf)
    WAW 2012, June 2012, Halifax
  43. Triangle Counting and Vertex Similarity
    Canadian Mathematical Society, December 2011, Toronto
    Invited Speaker
  44. High Degree Vertices, Eigenvalues and Diameter of Random Apollonian Networks
    15th International Conference on Random Structures and Algorithms RSA 2011, Atlanta
  45. Counting Triangles in Real-World Networks
    SIAM Conference on Computational Science and Engineering (CSE11), Reno Invited Speaker
  46. Approximate Dynamic Programming using Halfspace Queries and Multiscale Monge Analysis (pptx)
    SODA 2011, San Francisco
  47. Approximate Dynamic Programming and Denoising aCGH data (pptx)
    Machine Learning Seminar 2011, Carnegie Mellon University
    Video
  48. Efficient Triangle Counting via Degree-based Partitioning (pptx)
    Machine Learning Seminar 2011, Carnegie Mellon University
    Video
  49. Approximate Dynamic Programming and Denoising aCGH data (pptx)
    ACO Seminar 2011, Carnegie Mellon University
  50. Efficient Triangle Counting via Degree-based Partitioning (pptx)
    ACO Seminar 2011, Carnegie Mellon University
  51. Efficient Triangle Counting via Degree-based Partitioning (pptx)
    WAW 2010, Stanford University
  52. The Determinant of Random Bernoulli Matrices
    Discrete Math 21701, Carnegie Mellon University
  53. Approximate Dynamic Programming
    Carnegie Mellon University
  54. Unmixing of Tumor States in aCGH data
    Carnegie Mellon University
  55. Data Mining with MapReduce: Graph and Tensor Algorithms with Applications
    Master Thesis, Carnegie Mellon University
  56. MACH: Fast Randomized Tensor Decompositions
    SIAM Data Mining 2010, Columbus OH
  57. Algorithms for Denoising aCGH Data
    MLD Speaking Skills, Carnegie Mellon University
  58. Welcome Talk (MLD Open House) (welcome.ppt)
    MLD Open House, Carnegie Mellon University
  59. Spectral Counting of Triangles in Power-Law Networks via Element-Wise Sparsification
    ASONAM 2009, Athens
  60. DOULION: Counting Triangles in Massive Graphs with a Coin
    KDD 2009, Paris
  61. Approximate Triangle Counting
    Poster Presentation, Machine Learning Summer School 2009, Chicago
  62. Basics of Spectral Graph Theory
    Carnegie Mellon University, Paris
  63. On Polygonal Numbers and Fermat’s Conjecture
    Additive Number Theory, Carnegie Mellon University
  64. Graph Mining Guest Lecture 15-826 Multimedia Databases and Data Mining (CMU)
  65. Fast Counting of Triangles in Large Real Networks without counting: Algorithms and Laws (pps)
    IEEE Data Mining (ICDM), 2008 Italy
  66. Fast Counting of triangles in large networks: Algorithms and laws (ppt)
    Theory Seminar of Rensselaer Polytechnic Institute
    Host: Petros Drineas
    Invited Talk
  67. Two heads better than one: pattern discovery in time-evolving multi-aspect data (ppt)
    European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases
    (ECML-PKDD 2008), 2008 Belgium
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