User Interfaces and the Environment: Exploiting Human Abilities to Improve Mobile Interaction

Lecturer : 
Antti Oulasvirta
Event type: 
HIIT seminar
Event time: 
2010-11-01 13:15 to 14:00
Place: 
Computer Science building, hall T2
Description: 

Our next speaker for HIIT Otaniemi seminar series is Antti Oulasvirta from the
"Ubiquitous Interaction" group of the Helsinki Institute for Information
Technology HIIT. Before his talk, he will also give a short overview on the
research areas of the group.

All ICS@Aalto researchers are also warmly welcome to attend the seminar!

HIIT seminar Otaniemi, Monday November 1, 13:15
Location: Computer Science building, hall T2


Antti Oulasvirta
Ubiquitous Interaction Group
Helsinki Institute for Information Technology HIIT

Title:
User Interfaces and the Environment: Exploiting Human Abilities to Improve Mobile Interaction

Abstract:
In the field of human-computer interaction (HCI), user interfaces have been analyzed in terms of information exchange  between the human user and the computer. In my work, I have started to investigate mobile interfaces as a special case in  HCI where the environment plays a critical role. This talk first seeks to prove that, if approached within the traditional  frameworks of HCI, mobile interfaces will remain inherently inferior in comparison to their desktop counterparts. I will then  make a case for "embodied interaction"--i.e., leveraging users' knowledge of their environment, their ability to exploit its  structure, and their ability to transform tasks by means of action. Mobile mixed reality interfaces is emerging as a promising  area that puts the ideas of embodied interaction into practice, demonstrating ways in which mobile users can literally sense  and act through digital information. To conclude, I will argue that embodied interaction 1) will eventually go beyond the rates of  information throughput possible for traditional mobile interfaces and 2) may emerge as a key enabler for the next  generation of mobile computers.

Short Bio:
Antti Oulasvirta is a Senior Researcher at the Helsinki Institute for Information Technology HIIT where he directs the  Ubiquitous Interaction group (http://www.hiit.fi/uix). His research focus lies at the intersection of human-computer interaction, mobile and ubiquitous computing, and cognitive psychology. Dr. Oulasvirta received his doctorate in Cognitive Science from the University of Helsinki in 2006, after which he was a Fulbright Scholar at the School of Information in UC Berkeley. During his postgraduate studies, he was an exchange student at UC Berkeley’s Neuropsychology Lab and did an internship at
T-Labs in Berlin. Dr. Oulasvirta is a docent (adjunct faculty) of computer science at the University of Helsinki and a docent of cognitive science at the University of Jyväskylä.

For more information (full CV and publications), please see:
http://www.hiit.fi/u/oulasvir

A Grid-Based Algorithm for On-Device GSM Positioning

Lecturer : 
Petteri Nurmi
Event type: 
HIIT seminar
Event time: 
2010-11-05 10:15 to 11:00
Place: 
Exactum C222
Description: 

ABSTRACT:
The talk introduces a grid-based GSM positioning algorithm that can be deployed entirely on mobile devices. The algorithm uses Gaussian distributions to model signal intensity variations within each grid cell. Position estimates are calculated by combining a probabilistic centroid algorithm with particle filtering. In addition to presenting the positioning algorithm, we describe methods that can be used to create, update and maintain radio maps on a mobile device. We have implemented the positioning algorithm on Nokia S60 and Nokia N900 devices and we evaluate the algorithm using a combination of offline and real world tests. The results indicate that the accuracy of our method is comparable to state-of-the-art methods, while at the same time having significantly smaller storage requirements.

Inverse problems and Bayesian tracking: An application to neurophysiological data

Lecturer : 
Cristina Campi
Event type: 
HIIT seminar
Event time: 
2010-11-12 10:15 to 11:00
Place: 
Exactum C222
Description: 

ABSTRACT:
As a first issue, during this talk I will discuss the framework of inverse problems: what they are, why they are difficult to solve and what are the possible strategies for solving them. Then I will focus on Bayesian tracking as a statistical inversion method for solving dynamical inverse problems. As a final step, I will present some results obtained applying a Bayesian Tracking algorithm to real data recorded using Magnetoencephalography.

BIO:
I am a postdoctoral researcher in the Neuroinformatics research group. I received my PhD in Mathematics and Application from the University of Genova, Italy on April 2010. I spent six months as postdoc at the Department of Mathematics, University of Genova. During my PhD I dealt with the study of the solution of inverse problems for infering information about the brain activity from neurophysiological data.

Computational Methods for Reconstruction and Analysis of Genome-Scale Metabolic Networks

Event type: 
Doctoral dissertation
Doctoral dissertation
Respondent: 
Esa Pitkänen
Opponent: 
professor Jacques van Helden
Custos: 
professor Esko Ukkonen
Event time: 
2010-11-12 12:00 to 14:00
Place: 
Main building, auditorium XII
Description: 

Esa Pitkänen will defend his thesis "Computational Methods for Reconstruction and Analysis of Genome-Scale Metabolic Networks" on 12 Nov 2010 at 12 noon in the Main building, auditorium XII. His opponent is Professor Jacques van Helden (Université Libre de Bruxelles, Belgia) and custos Professor Esko Ukkonen.

Abstract:

Metabolism is the cellular subsystem responsible for generation of energy from nutrients and production of building blocks for larger macromolecules. Computational and statistical modeling of metabolism is vital to many disciplines including bioengineering, the study of diseases, drug target identification, and understanding the evolution of metabolism. In this thesis, we propose efficient computational methods for metabolic modeling. The techniques presented are targeted particularly at the analysis of large metabolic models encompassing the whole metabolism of one or several organisms. We concentrate on three major themes of metabolic modeling: metabolic pathway analysis, metabolic reconstruction and the study of evolution of metabolism.

In the first part of this thesis, we study metabolic pathway analysis. We propose a novel modeling framework called gapless modeling to study biochemically viable metabolic networks and pathways. In addition, we investigate the utilization of atom-level information on metabolism to improve the quality of pathway analyses. We describe efficient algorithms for discovering both gapless and atom-level metabolic pathways, and conduct experiments with large-scale metabolic networks. The presented gapless approach offers a compromise in terms of complexity and feasibility between the previous graph-theoretic and stoichiometric approaches to metabolic modeling. Gapless pathway analysis shows that microbial metabolic networks are not as robust to random damage as suggested by previous studies. Furthermore the amino acid biosynthesis pathways of the fungal species Trichoderma reesei discovered from atom-level data are shown to closely correspond to those of Saccharomyces cerevisiae.

In the second part, we propose computational methods for metabolic reconstruction in the gapless modeling framework. We study the task of reconstructing a metabolic network that does not suffer from connectivity problems. Such problems often limit the usability of reconstructed models, and typically require a significant amount of manual postprocessing. We formulate gapless metabolic reconstruction as an optimization problem and propose an efficient divide-and-conquer strategy to solve it with real-world instances. We also describe computational techniques for solving problems stemming from ambiguities in metabolite naming. These techniques have been implemented in a web-based sofware ReMatch intended for reconstruction of models for 13C metabolic flux analysis.

In the third part, we extend our scope from single to multiple metabolic networks and propose an algorithm for inferring gapless metabolic networks of ancestral species from phylogenetic data. Experimenting with 16 fungal species, we show that the method is able to generate results that are easily interpretable and that provide hypotheses about the evolution of metabolism.

Time-Consistency Problem in the Long Term Decision Making

Lecturer : 
Professor Leon Petrosjan
Event type: 
Guest lecture
Event time: 
2010-11-18 12:00 to 14:00
Place: 
Kumpula (room C222)

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