Presentations

You can browse through some of my slides on Issuu.

  1. Algorithmic Primitives for Finding Dense Structures in Rich Graph Data
    Slides available here
    SEA 2026
  2. Wiser than the Wisest of Crowds
    Slides available here
    ECML-PKDD 2024
  3. Social Media and Misinformation (Panel organizer)
    Slides available here
    DSAA 2023
  4. Unveiling the Power and Limitations of Unsupervised Node Embeddings
    AUEB (June 2023)
  5. Unveiling the Power and Limitations of Unsupervised Node Embeddings
    UCY colloquium (June 2023)
  6. Antibenford subgraphs: unsupervised anomaly detection in financial networks
    RelationalAI (May 2023) 
  7. Theory Seminar – Near-Optimal Learning of Joint Alignments and Clusters with a Faulty Oracle
    UMass Amherst 
  8. Recent Advances in Dense Subgraph Discovery
    Brown University (March 2021)
  9. Recent Advances in Dense Subgraph Discovery
    NCSR Demokritos (Feb 2021)
  10. Optimal learning of joint alignments
    2020 IEEE International Symposium on Information Theory (ISIT 2020)
  11. Optimal Learning of Joint Alignments with a Faulty Oracle
    The WebConference 2020  (WWW 2020)
  12. Flowless: Extracting Densest Subgraphs Without Flow Computations
    The WebConference 2020  (WWW 2020)
  13. Optimal learning of joint alignments
    2020 Information Theory and Applications Workshop, San Diego, February 2020
  14. Optimal learning of joint alignments
    University of Cyprus, December 2019
  15. Graph clustering with noisy queries
    Sapienza Universita di Roma, December 2019
  16. Predicting Positive and Negative Links with Noisy Queries: Theory & Practice
    Allerton 2018, Graph theory and machine learning, October 2018, Urbana-Champaign
  17. Learning Networks from Random Walk-Based Node Similarities
    Poster
    NIPS 2018
  18. Multifaceted Large-Scale Graph Mining
    Hariri Institute, Boston MA, September 2018 
  19. Graph Clustering with Faulty Oracles and Motifs
    MIT CSAIL, Cambridge MA, September 2018 
  20. Mining Graphs for Faster Deep Learning
    Schloss Dagstuhl – Leibniz-Zentrum für Informatik , June 2018
  21. Mining tools for large-scale networks
    Open Data Science Conference (ODSC), Boston MA, May 2018
  22. Minimizing Polarization and Disagreement in Social Networks
    Slides
    WWW 2018, April 2018, Lyon France
  23. Mining tools for large scale networks
    Foundation for Research and Technology – Hellas (FORTH), April 2018, Heraklion
  24. Predicting Positive and Negative Links with Noisy Queries: Theory & Practice
    University of Cyprus, March 2018
  25. Predicting Positive and Negative Links with Noisy Queries: Theory & Practice
    Aalto University, March 2018 
  26. Clustering graphs using motifs
    U. of Helsinki, March 2018
  27. Predicting Positive and Negative Links with Noisy Queries: Theory & Practice
    Aarhus University, March 2018 
  28. Predicting Positive and Negative Links with Noisy Queries: Theory & Practice
    BARC, Copenhagen, March 2018 
  29. Predicting Positive and Negative Links with Noisy Queries: Theory & Practice
    ECE Seminar, Northeastern University, November 2017
  30. Predicting Positive and Negative Links with Noisy Queries: Theory & Practice
    Theory Seminar, Boston University, September 2017
  31. Predicting Positive and Negative Links with Noisy Queries: Theory & Practice
    Data Science Seminar, Boston University, September 2017
  32. Graph Clustering Problems
    Legendary Entertainment,  Boston August 2017
  33. Motif-aware graph mining
    SIAM Annual Meeting, Pittsburgh, July 2017
  34. Motif-aware graph mining
    GraphEx Symposium, MIT, May 2017
  35. Motif-aware graph mining
    Allerton Conference, September 2016
  36. Mining Tools for Large-Scale Networks
    Aarhus University, April 2016
  37. Mining Tools for Large-Scale Networks
    IT Copenhagen University, April 2016
  38. Mining Tools for Large-Scale Networks
    UC Santa Cruz University, March 2016
  39. Mining Tools for Large-Scale Networks
    ETH Zurich, March 2016
  40. Mining Tools for Large-Scale Networks
    University of Illinois Urbana-Champaign, February 2016
  41. Mining Tools for Large-Scale Networks
    Boston University, February 2016
  42. Mining Tools for Large-Scale Networks
    University Colorado-Boulder, February 2016
  43. Mining Tools for Large-Scale Networks
    Northeastern University, January 2016
  44.  Scalable Large Near-Clique Detection in Large-Scale Networks via Sampling
    University of Maryland
  45. Scalable Large Near-Clique Detection in Large-Scale Networks via Sampling
    Ohio State University
  46. Dense subgraph discovery
    Google Research
  47. Large-Scale Graph Mining
    IBM T.J. Watson Research Center
  48. Streaming Graph Partitioning in the Planted Partition Model
    ACM Conference on Online Social Networks (COSN’15)
  49. Scalable Large Near-Clique Detection in Large-Scale Networks via Sampling
    Stanford University
  50. Scalable Large Near-Clique Detection in Large-Scale Networks
    Signals, Inference, and Networks (SINE) Seminar
    University of Illinois Urbana-Champaign
  51. Dense subgraph discovery
    Data-driven Algorithmics
  52. Scalable Large Near-Clique Detection in Large-Scale Networks via Sampling
    21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2015)
  53. Dense subgraph discovery
    Tutorial at 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2015)
  54. Scalable dense subgraph discovery
    Random Structures and Algorithms (RSA), July ’15
  55. Scalable dense subgraph discovery
    International Symposium on Optimization (ISMP), July ’15
  56. Space- and Time-Efficient Algorithms for Maintaining Dense Subgraphs on One-Pass Dynamic Streams
    STOC 2015, June ’15
  57. Scalable dense subgraph discovery
    University of Cyprus, June ’15
  58. Provably Fast Inference of Latent Features from Networks
    24th International World Wide Web Conference (WWW 2015), May’15
  59. The k-clique Densest Subgraph Problem
    24th International World Wide Web Conference (WWW 2015), May’15
  60. Algorithmic Analysis of Large Datasets
    Universitat Pompeu Fabra, May ’15
  61. Modern Data Mining Algorithms
    Draper Laboratory, December ’14
  62. Algorithmic Analysis of Large Datasets
    SEAS Harvard University, November ’14
    Youtube video
  63. Algorithmic Analysis of Large Datasets (pptx)
    Brown University, May ’14
    Host: Philip Klein
  64. Large-Scale Graph Mining
    Imperial College London, May ’14
    Host: Moez Draief
  65. Algorithmic Analysis of Large Datasets
    Google NYC, April ’14
    Host: Vahab Mirrokni
  66. Mathematical Techniques for Modeling and Analyzing Large Graphs (pdf)
    Aalto Science Institute, January ’14, Helsinki
  67. Modeling Intratumor Gene Copy Number Heterogeneity using Fluorescence in Situ Hybridization data (pptx, pdf)
    WABI ’13, September ’13, Nice
  68. Fennel: Streaming Graph Partitioning for Massive Scale Graphs (pdf)
    MASSIVE ’13, September ’13, Nice
  69. Denser than the densest subgraph: extracting optimal quasi-cliques with quality guarantees (pptx, pdf)
    KDD ’13, August ’13, Chicago
  70. Mathematical and Algorithmic Analysis of Network and Biological data
    Thesis Defense, Carnegie Mellon University, May 2013
  71. Processing, Analyzing and Mining Big Graph Data
    Machine learning lunch seminar, Carnegie Mellon University, April 2013
    Video
  72. Random Graphs and Complex Networks
    Guest lecture, TELCOM2125: Network Science and Analysis
    Host: Konstantinos Pelechrinis
    April 2013
  73. Mathematical and Algorithmic Analysis of Biological data and Networks
    Abstract
    Brown University, April 2013
    Host: Eli Upfal
  74. Fennel: Streaming Graph Partitioning for Massive Scale Graphs
    Microsoft Research, Cambridge UK, November 2012
    Host: Milan Vojnovic
  75. On Certain Topics on Networks and Optimization: Theorems, Algorithms and Applications
    Yahoo! Research Barcelona, Barcelona, August 2012
    Host: Aris Gionis
  76. On Certain Properties of Random Apollonian Networks (pptx, pdf)
    WAW 2012, June 2012, Halifax
  77. Triangle Counting and Vertex Similarity
    Canadian Mathematical Society, December 2011, Toronto
    Invited Speaker
  78. High Degree Vertices, Eigenvalues and Diameter of Random Apollonian Networks
    15th International Conference on Random Structures and Algorithms RSA 2011, Atlanta
  79. Counting Triangles in Real-World Networks
    SIAM Conference on Computational Science and Engineering (CSE11), Reno Invited Speaker
  80. Approximate Dynamic Programming using Halfspace Queries and Multiscale Monge Analysis (pptx)
    SODA 2011, San Francisco
  81. Approximate Dynamic Programming and Denoising aCGH data (pptx)
    Machine Learning Seminar 2011, Carnegie Mellon University
    Video
  82. Efficient Triangle Counting via Degree-based Partitioning (pptx)
    Machine Learning Seminar 2011, Carnegie Mellon University
    Video
  83. Approximate Dynamic Programming and Denoising aCGH data (pptx)
    ACO Seminar 2011, Carnegie Mellon University
  84. Efficient Triangle Counting via Degree-based Partitioning (pptx)
    ACO Seminar 2011, Carnegie Mellon University
  85. Efficient Triangle Counting via Degree-based Partitioning (pptx)
    WAW 2010, Stanford University
  86. The Determinant of Random Bernoulli Matrices
    Discrete Math 21701, Carnegie Mellon University
  87. Approximate Dynamic Programming
    Carnegie Mellon University
  88. Unmixing of Tumor States in aCGH data
    Carnegie Mellon University
  89. Data Mining with MapReduce: Graph and Tensor Algorithms with Applications
    Master Thesis, Carnegie Mellon University
  90. MACH: Fast Randomized Tensor Decompositions
    SIAM Data Mining 2010, Columbus OH
  91. Algorithms for Denoising aCGH Data
    MLD Speaking Skills, Carnegie Mellon University
  92. Welcome Talk (MLD Open House) (welcome.ppt)
    MLD Open House, Carnegie Mellon University
  93. Spectral Counting of Triangles in Power-Law Networks via Element-Wise Sparsification
    ASONAM 2009, Athens
  94. DOULION: Counting Triangles in Massive Graphs with a Coin
    KDD 2009, Paris
  95. Approximate Triangle Counting
    Poster Presentation, Machine Learning Summer School 2009, Chicago
  96. Basics of Spectral Graph Theory
    Carnegie Mellon University, Paris
  97. On Polygonal Numbers and Fermat’s Conjecture
    Additive Number Theory, Carnegie Mellon University
  98. Graph Mining Guest Lecture 15-826 Multimedia Databases and Data Mining (CMU)
  99. Fast Counting of Triangles in Large Real Networks without counting: Algorithms and Laws (pps)
    IEEE Data Mining (ICDM), 2008 Italy
  100. Fast Counting of triangles in large networks: Algorithms and laws (ppt)
    Theory Seminar of Rensselaer Polytechnic Institute
    Host: Petros Drineas
    Invited Talk
  101. 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 recorded talks