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Charalampos E. Tsourakakis
Postdoctoral Fellow
School of Engineering and Applied Sciences, Harvard University
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I am also affiliated with the Theory of Computation Group, the EconCS Group, and Berkman center for Internet & Society.
Here at Harvard University, I work closely with Professor Michael Mitzenmacher and Professor David Parkes.
Before joining Harvard, I was a Postdoctoral Fellow at Brown University (CS and ICERM) where I had the fortune
to work with Professor Eli Upfal.
Research interests My main research interest lies in data-driven algorithmics and applications. Specifically, I am interested in the theoretical foundations of data science, and in applications involving data-driven discovery. Application domains I am interested in include social networks, the World Wide Web and healthcare. Big data analytics Methods, efficient algorithms, theory and implementations to analyze large datasets Single-pass streaming algorithms for real-time analytics Graphs and networks Theory: Graph theory, random graphs and graph algorithms Applications: Mining real-world datasets, including the Web graph, social networks and biological datasets Pattern theory Efficient optimization techniques Machine learning and data mining Bayesian probability theory
论文共 79 篇
TwitterMancer: Predicting Interactions on Twitter Accurately.
Risk-Averse Matchings over Uncertain Graph Databases.
Opinion Dynamics with Varying Susceptibility to Persuasion.
Joint Alignment from Pairwise Differences with a Noisy Oracle.
Scalable motif-aware graph clustering.
Predicting Positive and Negative Links with Noisy Queries: Theory & Practice.
Predicting Positive and Negative Links with Noisy Queries: Theory & Practice.
Node Immunization on Large Graphs: Theory and Algorithms
ADAGIO: Fast Data-aware Near-Isometric Linear Embeddings.
Predicting Signed Edges with $O(n^{1+o(1)} \log{n})$ Queries
Chromatic Correlation Clustering
Rainbow Connection of Random Regular Graphs.
Streaming Graph Partitioning in the Planted Partition Model.
Space- and Time-Efficient Algorithm for Maintaining Dense Subgraphs on One-Pass Dynamic Streams.
Dense Subgraph Discovery: KDD 2015 tutorial
Scalable Large Near-Clique Detection in Large-Scale Networks via Sampling
The K-clique Densest Subgraph Problem
FENNEL: streaming graph partitioning for massive scale graphs
Some Properties of Random Apollonian Networks.
Toward Quantifying Vertex Similarity in Networks.
Streaming Graph Partitioning in the Planted Partition Model.
PEGASUS: A System for Large-Scale Graph Processing.
Algorithmic techniques for modeling and mining large graphs (AMAzING)
Colorful triangle counting and a MapReduce implementation
Rainbow Connectivity of Sparse Random Graphs.
Efficient Triangle Counting in Large Graphs via Degree-Based Vertex Partitioning.
On certain properties of random apollonian networks
Rainbow Connection of Sparse Random Graphs
High Degree Vertices, Eigenvalues and Diameter of Random Apollonian Networks
HADI: Mining Radii of Large Graphs
PEGASUS: mining peta-scale graphs
Counting triangles in real-world networks using projections
Approximation algorithms for speeding up dynamic programming and denoising aCGH data
Approximate dynamic programming using halfspace queries and multiscale Monge decomposition
Large Scale Graph Mining with MapReduce: Counting Triangles in Large Real Networks.
Radius Plots for Mining Tera-byte Scale Graphs: Algorithms, Patterns, and Observations.
Robust unmixing of tumor states in array comparative genomic hybridization data.
Approximate Dynamic Programming for Fast Denoising of aCGH Data
Efficient Triangle Counting in Large Graphs via Degree-based Vertex Partitioning
PEGASUS: A Peta-Scale Graph Mining System
DOULION: counting triangles in massive graphs with a coin
MACH: Fast Randomized Tensor Decompositions
PEGASUS: A Peta-Scale Graph Mining System Implementation and Observations
Two heads better than one: pattern discovery in time-evolving multi-aspect data
Fast Counting of Triangles in Large Real Networks without Counting: Algorithms and Laws
Fast Counting of Triangles in Large Real Networks: Algorithms and Laws