You can browse through some of my slides on Issuu.

- 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 queres

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 - 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