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