Presentations

You can browse through some of my slides on Issuu.

  1. Optimal learning of joint alignments
    2020 IEEE International Symposium on Information Theory (ISIT 2020)
  2. Optimal Learning of Joint Alignments with a Faulty Oracle
    The WebConference 2020  (WWW 2020)
  3. Flowless: Extracting Densest Subgraphs Without Flow Computations
    The WebConference 2020  (WWW 2020)
  4. Optimal learning of joint alignments
    2020 Information Theory and Applications Workshop, San Diego, February 2020
  5. Optimal learning of joint alignments
    University of Cyprus, December 2019
  6. Graph clustering with noisy queres
    Sapienza Universita di Roma, December 2019
  7. Predicting Positive and Negative Links with Noisy Queries: Theory & Practice
    Allerton 2018, Graph theory and machine learning, October 2018, Urbana-Champaign
  8. Multifaceted Large-Scale Graph Mining
    Hariri Institute, Boston MA, September 2018 
  9. Graph Clustering with Faulty Oracles and Motifs
    MIT CSAIL, Cambridge MA, September 2018 
  10. Mining Graphs for Faster Deep Learning
    Schloss Dagstuhl – Leibniz-Zentrum für Informatik , June 2018
  11. Mining tools for large-scale networks
    Open Data Science Conference (ODSC), Boston MA, May 2018
  12. Minimizing Polarization and Disagreement in Social Networks
    Slides
    WWW 2018, April 2018, Lyon France
  13. Mining tools for large scale networks
    Foundation for Research and Technology – Hellas (FORTH), April 2018, Heraklion
  14. Predicting Positive and Negative Links with Noisy Queries: Theory & Practice
    University of Cyprus, March 2018
  15. Predicting Positive and Negative Links with Noisy Queries: Theory & Practice
    Aalto University, March 2018 
  16. Clustering graphs using motifs
    U. of Helsinki, March 2018
  17. Predicting Positive and Negative Links with Noisy Queries: Theory & Practice
    Aarhus University, March 2018 
  18. Predicting Positive and Negative Links with Noisy Queries: Theory & Practice
    BARC, Copenhagen, March 2018 
  19. Predicting Positive and Negative Links with Noisy Queries: Theory & Practice
    ECE Seminar, Northeastern University, November 2017
  20. Predicting Positive and Negative Links with Noisy Queries: Theory & Practice
    Theory Seminar, Boston University, September 2017
  21. Predicting Positive and Negative Links with Noisy Queries: Theory & Practice
    Data Science Seminar, Boston University, September 2017
  22. Graph Clustering Problems
    Legendary Entertainment,  Boston August 2017
  23. Motif-aware graph mining
    SIAM Annual Meeting, Pittsburgh, July 2017
  24. Motif-aware graph mining
    GraphEx Symposium, MIT, May 2017
  25. Motif-aware graph mining
    Allerton Conference, September 2016
  26. Mining Tools for Large-Scale Networks
    Aarhus University, April 2016
  27. Mining Tools for Large-Scale Networks
    IT Copenhagen University, April 2016
  28. Mining Tools for Large-Scale Networks
    UC Santa Cruz University, March 2016
  29. Mining Tools for Large-Scale Networks
    ETH Zurich, March 2016
  30. Mining Tools for Large-Scale Networks
    University of Illinois Urbana-Champaign, February 2016
  31. Mining Tools for Large-Scale Networks
    Boston University, February 2016
  32. Mining Tools for Large-Scale Networks
    University Colorado-Boulder, February 2016
  33. Mining Tools for Large-Scale Networks
    Northeastern University, January 2016
  34.  Scalable Large Near-Clique Detection in Large-Scale Networks via Sampling
    University of Maryland
  35. Scalable Large Near-Clique Detection in Large-Scale Networks via Sampling
    Ohio State University
  36. Dense subgraph discovery
    Google Research
  37. Large-Scale Graph Mining
    IBM T.J. Watson Research Center
  38. Streaming Graph Partitioning in the Planted Partition Model
    ACM Conference on Online Social Networks (COSN’15)
  39. Scalable Large Near-Clique Detection in Large-Scale Networks via Sampling
    Stanford University
  40. Scalable Large Near-Clique Detection in Large-Scale Networks
    Signals, Inference, and Networks (SINE) Seminar
    University of Illinois Urbana-Champaign
  41. Dense subgraph discovery
    Data-driven Algorithmics
  42. Scalable Large Near-Clique Detection in Large-Scale Networks via Sampling
    21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2015)
  43. Dense subgraph discovery
    Tutorial at 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2015)
  44. Scalable dense subgraph discovery
    Random Structures and Algorithms (RSA), July ’15
  45. Scalable dense subgraph discovery
    International Symposium on Optimization (ISMP), July ’15
  46. Space- and Time-Efficient Algorithms for Maintaining Dense Subgraphs on One-Pass Dynamic Streams
    STOC 2015, June ’15
  47. Scalable dense subgraph discovery
    University of Cyprus, June ’15
  48. Provably Fast Inference of Latent Features from Networks
    24th International World Wide Web Conference (WWW 2015), May’15
  49. The k-clique Densest Subgraph Problem
    24th International World Wide Web Conference (WWW 2015), May’15
  50. Algorithmic Analysis of Large Datasets
    Universitat Pompeu Fabra, May ’15
  51. Modern Data Mining Algorithms
    Draper Laboratory, December ’14
  52. Algorithmic Analysis of Large Datasets
    SEAS Harvard University, November ’14
    Youtube video
  53. Algorithmic Analysis of Large Datasets (pptx)
    Brown University, May ’14
    Host: Philip Klein
  54. Large-Scale Graph Mining
    Imperial College London, May ’14
    Host: Moez Draief
  55. Algorithmic Analysis of Large Datasets
    Google NYC, April ’14
    Host: Vahab Mirrokni
  56. Mathematical Techniques for Modeling and Analyzing Large Graphs (pdf)
    Aalto Science Institute, January ’14, Helsinki
  57. Modeling Intratumor Gene Copy Number Heterogeneity using Fluorescence in Situ Hybridization data (pptx, pdf)
    WABI ’13, September ’13, Nice
  58. Fennel: Streaming Graph Partitioning for Massive Scale Graphs (pdf)
    MASSIVE ’13, September ’13, Nice
  59. Denser than the densest subgraph: extracting optimal quasi-cliques with quality guarantees (pptx, pdf)
    KDD ’13, August ’13, Chicago
  60. Mathematical and Algorithmic Analysis of Network and Biological data
    Thesis Defense, Carnegie Mellon University, May 2013
  61. Processing, Analyzing and Mining Big Graph Data
    Machine learning lunch seminar, Carnegie Mellon University, April 2013
    Video
  62. Random Graphs and Complex Networks
    Guest lecture, TELCOM2125: Network Science and Analysis
    Host: Konstantinos Pelechrinis
    April 2013
  63. Mathematical and Algorithmic Analysis of Biological data and Networks
    Abstract
    Brown University, April 2013
    Host: Eli Upfal
  64. Fennel: Streaming Graph Partitioning for Massive Scale Graphs
    Microsoft Research, Cambridge UK, November 2012
    Host: Milan Vojnovic
  65. On Certain Topics on Networks and Optimization: Theorems, Algorithms and Applications
    Yahoo! Research Barcelona, Barcelona, August 2012
    Host: Aris Gionis
  66. On Certain Properties of Random Apollonian Networks (pptx, pdf)
    WAW 2012, June 2012, Halifax
  67. Triangle Counting and Vertex Similarity
    Canadian Mathematical Society, December 2011, Toronto
    Invited Speaker
  68. High Degree Vertices, Eigenvalues and Diameter of Random Apollonian Networks
    15th International Conference on Random Structures and Algorithms RSA 2011, Atlanta
  69. Counting Triangles in Real-World Networks
    SIAM Conference on Computational Science and Engineering (CSE11), Reno Invited Speaker
  70. Approximate Dynamic Programming using Halfspace Queries and Multiscale Monge Analysis (pptx)
    SODA 2011, San Francisco
  71. Approximate Dynamic Programming and Denoising aCGH data (pptx)
    Machine Learning Seminar 2011, Carnegie Mellon University
    Video
  72. Efficient Triangle Counting via Degree-based Partitioning (pptx)
    Machine Learning Seminar 2011, Carnegie Mellon University
    Video
  73. Approximate Dynamic Programming and Denoising aCGH data (pptx)
    ACO Seminar 2011, Carnegie Mellon University
  74. Efficient Triangle Counting via Degree-based Partitioning (pptx)
    ACO Seminar 2011, Carnegie Mellon University
  75. Efficient Triangle Counting via Degree-based Partitioning (pptx)
    WAW 2010, Stanford University
  76. The Determinant of Random Bernoulli Matrices
    Discrete Math 21701, Carnegie Mellon University
  77. Approximate Dynamic Programming
    Carnegie Mellon University
  78. Unmixing of Tumor States in aCGH data
    Carnegie Mellon University
  79. Data Mining with MapReduce: Graph and Tensor Algorithms with Applications
    Master Thesis, Carnegie Mellon University
  80. MACH: Fast Randomized Tensor Decompositions
    SIAM Data Mining 2010, Columbus OH
  81. Algorithms for Denoising aCGH Data
    MLD Speaking Skills, Carnegie Mellon University
  82. Welcome Talk (MLD Open House) (welcome.ppt)
    MLD Open House, Carnegie Mellon University
  83. Spectral Counting of Triangles in Power-Law Networks via Element-Wise Sparsification
    ASONAM 2009, Athens
  84. DOULION: Counting Triangles in Massive Graphs with a Coin
    KDD 2009, Paris
  85. Approximate Triangle Counting
    Poster Presentation, Machine Learning Summer School 2009, Chicago
  86. Basics of Spectral Graph Theory
    Carnegie Mellon University, Paris
  87. On Polygonal Numbers and Fermat’s Conjecture
    Additive Number Theory, Carnegie Mellon University
  88. Graph Mining Guest Lecture 15-826 Multimedia Databases and Data Mining (CMU)
  89. Fast Counting of Triangles in Large Real Networks without counting: Algorithms and Laws (pps)
    IEEE Data Mining (ICDM), 2008 Italy
  90. Fast Counting of triangles in large networks: Algorithms and laws (ppt)
    Theory Seminar of Rensselaer Polytechnic Institute
    Host: Petros Drineas
    Invited Talk
  91. 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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