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HIIT Presents Research on Privacy Management and Live Tweeting at ACM CSCW 2012
HIIT is contributing to the annual ACM CSCW conference by presenting initial results from research on privacy management and live tweeting. Both projects are conducted in collaboration with UC Berkeley’s School of Information.
Airi Lampinen and Kai Huotari, together with associate professor Coye Cheshire from UC Berkeley, present a position paper titled Privacy Management Strategies and Online Photo Sharing: A Pilot Survey in the CSCW 2012 Workshop on Reconciling Privacy with Social Media. The results from the pilot survey are the first public reporting of an ongoing survey project. Airi Lampinen and professor Cheshire also serve on the program committee of the workshop.
Kai Huotari also presents a poster titled Glitter: A Mixed-Methods Study of Twitter Use During Glee Broadcasts together with UC Berkeley researchers Kimra McPherson, F. Yo-Shang Cheng, David Humphrey, Andrew L. Brooks and professor Cheshire. The study is a part of a larger project investigating live tweeting during TV watching.
CSCW 2012 takes place in Seattle, Washington, USA, on February 11-15. This year, over 600 attendees have registered for the conference – more than for any prior CSCW.
HIIT Postdoc Call - dl. 29.2.2012
Helsinki Institute for Information Technology HIIT conducts basic and strategic research on information technology. It is a joint research institute of the Aalto University and the University of Helsinki. HIIT is a leading IT institute with research ranging from fundamental methods and technologies to novel applications and their impact on other sciences, people and society. HIIT's key competences are in analysis of large sets of data, probabilistic modeling of complex phenomena, Internet architecture and technologies, mobile and human-centric computing, and user-created media.
HIIT now calls for applications for postdoctoral researchers. Excellent applicants in the research fields of HIIT are welcome (contact persons: research group leaders and HIIT research programme directors: http://www.hiit.fi/programmes). The positions will be filled for three years maximum, starting 2 May 2012, or as agreed.
The postdoctoral researchers will undertake research work, teaching and student supervision in some of HIIT's research areas, but preferably providing an extension to the competences presently available in HIIT. Each postdoc will belong to one or more HIIT research groups, and be mentored by one or more group leaders / PI's. See www.hiit.fi/research
The applications should include:
- A research statement, including preferences on HIIT groups and topics, and preliminary research plan (at most 3 pages)
- Curriculum vitae
- List of publications with links to pdf-versions of the three most relevant publications
- Degree certificate of the PhD degree. In case the doctoral degree is still pending, an up-to-date study transcript and a plan for completion of the degree should be provided. The doctoral degree should be completed during 2012.
- Two letters of reference or email addresses of two senior academics willing to provide more information.
The applications should be submitted using a web form at https://elomake.helsinki.fi/lomakkeet/32660/lomake.html. The research statement, CV, list of publications and degree information should be attached in pdf.
The salary for fresh postdocs is usually about 3000-3600 euro / month and will increase with experience. The contract includes occupational health benefits and Finland has a comprehensive social security system.
The applicants will be informed about the decisions as soon as possible, at the latest during April 2012.
Deadline:
February 29, 2012
Employing university:
Aalto University or
University of Helsinki
Contact persons in HIIT for practical information:
HR Coordinator Stefan Ehrstedt, stefan.ehrstedt at hiit.fi,
Planning secretary Päivi Kuuppelomäki, kuuppelo at cs.helsinki.fi
(please use both addresses).
Contact persons in HIIT for scientific information:
The respective research group leaders and HIIT research programme directors, http://www.hiit.fi/programmes
Change of name of prof. Veli Mäkinen's group
The research group led by professor Veli Mäkinen at Department of Computer Science, University of Helsinki is re-named as Genome-Scale Algorithms (previously Succinct data structures).
The web page of the group is http://www.cs.helsinki.fi/gsa/
We develop algorithms and data structures for the analysis of genome-scale data. Such data is abundant due to modern molecular biology measurement techniques like high-throughput sequencing. We are especially interested in applications of compressed data structures, that make it possible to analyse the often highly redundant data within the space of their information content. We also study other scalability aspects like distributed computation/storage around genome-scale data.
Tenure track or tenured position in machine learning, probabilistic modelling or data mining
Tenure track or tenured position in machine learning, probabilistic modelling or data mining
The position is located at Aalto University, Department of Information and Computer Science (http://ics.tkk.fi/en/), Helsinki, Finland, and is open to outstanding individuals who hold a doctorate and have excellent potential for a successful scientific career. Candidates in the areas of machine learning, probabilistic modelling and data mining are especially welcome. Detailed information about the position, as well as the application process and its requirements, is provided at http://dept.ics.tkk.fi/calls/tenuretrack2012/.
The closing date of the call is 16 January 2012.
Visitors at HIIT: Bernardo Huberman, Jorma Rissanen, Peter Auer
Well known visitors at HIIT:
Bernardo Huberman at ICS Forum / Physics Colloquium on Thu 6 Oct 2011, giving a talk "Social Media and Attention" and visiting HIIT Innopoli on Fri 7 Oct 2011.
Jorma Rissanen at ALT/DS 2011 conference on Fri 7 Oct 2011, giving a talk "Optimal Estimation"
Peter Auer (University of Leoben, Austria) at ALT/DS 2011 conference on Fri 7 Oct 2011, giving a talk "Models for Autonomously Motivated Exploration in Reinforcement Learning"
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