Helsinki ICT Research Events

TITLE:
De-novo motif discovery in DNA sequences using parsimonious Markov models

ABSTRACT:
Computational methods for the prediction of transcription factor binding sites in DNA sequences are necessary for deepening our understanding of gene regulation and thus are of increasing interest in bioinformatics and computational genomics.

We discuss de-novo motif discovery approaches based on...

Scalable Overlay Design for Topic-Based Publish/Subscribe Systems and Fast Overlay Construction Algorithms
Prof. Roman Vitenberg
University of Oslo

ABSTRACT

Pub/sub is a paradigm for asynchronous communication that is commonly used in a great variety of industrial applications, such as news tickers, delivery of financial data, Military applications, and many others. While client-server...

Sampling Massive Online Graphs: Challenges, Techniques, and Applications to Facebook

Maciej Kurant
University of California, Irvine

Abstract

Online Social Networks (OSNs) have recently emerged as a new killer application, and are of interest to a number of communities, ranging from computer science and engineering to social sciences. Because of their sheer size (Facebook alone has more than 800...

Syksyn Startup, 9.11. klo 17-20, Linus Torvalds -auditoriossa

Helsingin yliopiston tietojenkäsittelytieteen laitoksen alumniyhdistys TKT-alumni ry järjestää perinteisen Syksyn Startup -tilaisuuden. Tilaisuudessa laitoksellamme opiskelleet ja sieltä valmistuneet kertovat mitä nykyään touhuavat. Tyypillisesti kyseessä itse perustettu kasvuyritys.

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The University of Helsinki (together with Hecse and HIIT) will organize an international autumn school on computational creativity. We cordially invite PhD and MSc students and researchers who are interested in being introduced to various aspects of computational creativity by experts in the field. More information and registration at http://www.cs....

Niko Välimäki will give a talk:

Title: Efficient Path Kernels for Reaction Function Prediction

Abstract: Kernels for structured data are rapidly becoming an essential part of the machine learning toolbox. Graph kernels provide similarity measures for complex relational objects, such as molecules and enzymes. Graph kernels based on walks are popular due their fast computation but their predictive...