Presentations

You can browse through some of my slides on Issuu.

  1. Predicting Positive and Negative Links with Noisy Queries: Theory & Practice
    Allerton 2018, Graph theory and machine learning, October 2018, Urbana-Champaign
  2. Multifaceted Large-Scale Graph Mining
    Hariri Institute, Boston MA, September 2018 
  3. Graph Clustering with Faulty Oracles and Motifs
    MIT CSAIL, Cambridge MA, September 2018 
  4. Mining Graphs for Faster Deep Learning
    Schloss Dagstuhl – Leibniz-Zentrum für Informatik , June 2018
  5. Mining tools for large-scale networks
    Open Data Science Conference (ODSC), Boston MA, May 2018
  6. Minimizing Polarization and Disagreement in Social Networks
    Slides
    WWW 2018, April 2018, Lyon France
  7. Mining tools for large scale networks
    Foundation for Research and Technology – Hellas (FORTH), April 2018, Heraklion
  8. Predicting Positive and Negative Links with Noisy Queries: Theory & Practice
    University of Cyprus, March 2018
  9. Predicting Positive and Negative Links with Noisy Queries: Theory & Practice
    Aalto University, March 2018 
  10. Clustering graphs using motifs
    U. of Helsinki, March 2018
  11. Predicting Positive and Negative Links with Noisy Queries: Theory & Practice
    Aarhus University, March 2018 
  12. Predicting Positive and Negative Links with Noisy Queries: Theory & Practice
    BARC, Copenhagen, March 2018 
  13. Predicting Positive and Negative Links with Noisy Queries: Theory & Practice
    ECE Seminar, Northeastern University, November 2017
  14. Predicting Positive and Negative Links with Noisy Queries: Theory & Practice
    Theory Seminar, Boston University, September 2017
  15. Predicting Positive and Negative Links with Noisy Queries: Theory & Practice
    Data Science Seminar, Boston University, September 2017
  16. Graph Clustering Problems
    Legendary Entertainment,  Boston August 2017
  17. Motif-aware graph mining
    SIAM Annual Meeting, Pittsburgh, July 2017
  18. Motif-aware graph mining
    GraphEx Symposium, MIT, May 2017
  19. Motif-aware graph mining
    Allerton Conference, September 2016
  20. Mining Tools for Large-Scale Networks
    Aarhus University, April 2016
  21. Mining Tools for Large-Scale Networks
    IT Copenhagen University, April 2016
  22. Mining Tools for Large-Scale Networks
    UC Santa Cruz University, March 2016
  23. Mining Tools for Large-Scale Networks
    ETH Zurich, March 2016
  24. Mining Tools for Large-Scale Networks
    University of Illinois Urbana-Champaign, February 2016
  25. Mining Tools for Large-Scale Networks
    Boston University, February 2016
  26. Mining Tools for Large-Scale Networks
    University Colorado-Boulder, February 2016
  27. Mining Tools for Large-Scale Networks
    Northeastern University, January 2016
  28.  Scalable Large Near-Clique Detection in Large-Scale Networks via Sampling
    University of Maryland
  29. Scalable Large Near-Clique Detection in Large-Scale Networks via Sampling
    Ohio State University
  30. Dense subgraph discovery
    Google Research
  31. Large-Scale Graph Mining
    IBM T.J. Watson Research Center
  32. Streaming Graph Partitioning in the Planted Partition Model
    ACM Conference on Online Social Networks (COSN’15)
  33. Scalable Large Near-Clique Detection in Large-Scale Networks via Sampling
    Stanford University
  34. Scalable Large Near-Clique Detection in Large-Scale Networks
    Signals, Inference, and Networks (SINE) Seminar
    University of Illinois Urbana-Champaign
  35. Dense subgraph discovery
    Data-driven Algorithmics
  36. Scalable Large Near-Clique Detection in Large-Scale Networks via Sampling
    21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2015)
  37. Dense subgraph discovery
    Tutorial at 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2015)
  38. Scalable dense subgraph discovery
    Random Structures and Algorithms (RSA), July ’15
  39. Scalable dense subgraph discovery
    International Symposium on Optimization (ISMP), July ’15
  40. Space- and Time-Efficient Algorithms for Maintaining Dense Subgraphs on One-Pass Dynamic Streams
    STOC 2015, June ’15
  41. Scalable dense subgraph discovery
    University of Cyprus, June ’15
  42. Provably Fast Inference of Latent Features from Networks
    24th International World Wide Web Conference (WWW 2015), May’15
  43. The k-clique Densest Subgraph Problem
    24th International World Wide Web Conference (WWW 2015), May’15
  44. Algorithmic Analysis of Large Datasets
    Universitat Pompeu Fabra, May ’15
  45. Modern Data Mining Algorithms
    Draper Laboratory, December ’14
  46. Algorithmic Analysis of Large Datasets
    SEAS Harvard University, November ’14
    Youtube video
  47. Algorithmic Analysis of Large Datasets (pptx)
    Brown University, May ’14
    Host: Philip Klein
  48. Large-Scale Graph Mining
    Imperial College London, May ’14
    Host: Moez Draief
  49. Algorithmic Analysis of Large Datasets
    Google NYC, April ’14
    Host: Vahab Mirrokni
  50. Mathematical Techniques for Modeling and Analyzing Large Graphs (pdf)
    Aalto Science Institute, January ’14, Helsinki
  51. Modeling Intratumor Gene Copy Number Heterogeneity using Fluorescence in Situ Hybridization data (pptx, pdf)
    WABI ’13, September ’13, Nice
  52. Fennel: Streaming Graph Partitioning for Massive Scale Graphs (pdf)
    MASSIVE ’13, September ’13, Nice
  53. Denser than the densest subgraph: extracting optimal quasi-cliques with quality guarantees (pptx, pdf)
    KDD ’13, August ’13, Chicago
  54. Mathematical and Algorithmic Analysis of Network and Biological data
    Thesis Defense, Carnegie Mellon University, May 2013
  55. Processing, Analyzing and Mining Big Graph Data
    Machine learning lunch seminar, Carnegie Mellon University, April 2013
    Video
  56. Random Graphs and Complex Networks
    Guest lecture, TELCOM2125: Network Science and Analysis
    Host: Konstantinos Pelechrinis
    April 2013
  57. Mathematical and Algorithmic Analysis of Biological data and Networks
    Abstract
    Brown University, April 2013
    Host: Eli Upfal
  58. Fennel: Streaming Graph Partitioning for Massive Scale Graphs
    Microsoft Research, Cambridge UK, November 2012
    Host: Milan Vojnovic
  59. On Certain Topics on Networks and Optimization: Theorems, Algorithms and Applications
    Yahoo! Research Barcelona, Barcelona, August 2012
    Host: Aris Gionis
  60. On Certain Properties of Random Apollonian Networks (pptx, pdf)
    WAW 2012, June 2012, Halifax
  61. Triangle Counting and Vertex Similarity
    Canadian Mathematical Society, December 2011, Toronto
    Invited Speaker
  62. High Degree Vertices, Eigenvalues and Diameter of Random Apollonian Networks
    15th International Conference on Random Structures and Algorithms RSA 2011, Atlanta
  63. Counting Triangles in Real-World Networks
    SIAM Conference on Computational Science and Engineering (CSE11), Reno Invited Speaker
  64. Approximate Dynamic Programming using Halfspace Queries and Multiscale Monge Analysis (pptx)
    SODA 2011, San Francisco
  65. Approximate Dynamic Programming and Denoising aCGH data (pptx)
    Machine Learning Seminar 2011, Carnegie Mellon University
    Video
  66. Efficient Triangle Counting via Degree-based Partitioning (pptx)
    Machine Learning Seminar 2011, Carnegie Mellon University
    Video
  67. Approximate Dynamic Programming and Denoising aCGH data (pptx)
    ACO Seminar 2011, Carnegie Mellon University
  68. Efficient Triangle Counting via Degree-based Partitioning (pptx)
    ACO Seminar 2011, Carnegie Mellon University
  69. Efficient Triangle Counting via Degree-based Partitioning (pptx)
    WAW 2010, Stanford University
  70. The Determinant of Random Bernoulli Matrices
    Discrete Math 21701, Carnegie Mellon University
  71. Approximate Dynamic Programming
    Carnegie Mellon University
  72. Unmixing of Tumor States in aCGH data
    Carnegie Mellon University
  73. Data Mining with MapReduce: Graph and Tensor Algorithms with Applications
    Master Thesis, Carnegie Mellon University
  74. MACH: Fast Randomized Tensor Decompositions
    SIAM Data Mining 2010, Columbus OH
  75. Algorithms for Denoising aCGH Data
    MLD Speaking Skills, Carnegie Mellon University
  76. Welcome Talk (MLD Open House) (welcome.ppt)
    MLD Open House, Carnegie Mellon University
  77. Spectral Counting of Triangles in Power-Law Networks via Element-Wise Sparsification
    ASONAM 2009, Athens
  78. DOULION: Counting Triangles in Massive Graphs with a Coin
    KDD 2009, Paris
  79. Approximate Triangle Counting
    Poster Presentation, Machine Learning Summer School 2009, Chicago
  80. Basics of Spectral Graph Theory
    Carnegie Mellon University, Paris
  81. On Polygonal Numbers and Fermat’s Conjecture
    Additive Number Theory, Carnegie Mellon University
  82. Graph Mining Guest Lecture 15-826 Multimedia Databases and Data Mining (CMU)
  83. Fast Counting of Triangles in Large Real Networks without counting: Algorithms and Laws (pps)
    IEEE Data Mining (ICDM), 2008 Italy
  84. Fast Counting of triangles in large networks: Algorithms and laws (ppt)
    Theory Seminar of Rensselaer Polytechnic Institute
    Host: Petros Drineas
    Invited Talk
  85. 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
Some talks of mine have been recorded.


Denser than the Densest Subgraph: Extracting Optimal Quasi-Cliques with Quality Guarantees


Doulion: Counting Triangles in Massive Graphs with a Coin

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