Prof. Steven Dow on Advancing Collective Innovation

Lecturer : 
Prof. Steven Dow (UC San Diego)
Event type: 
Guest lecture
Event time: 
2017-06-19 15:00 to 16:00
Place: 
Unioninkatu 34, Auditorium IV, 2nd floor (Accessibility: Fabianinkatu 33)
Description: 
Advancing Collective Innovation
Society's most daunting problems call for new strategies that engage many diverse stakeholders in a design process in order to solve bigger and messier problems. While the Internet makes it easy to find and coordinate people, we need to advance fundamental knowledge and technologies for "collective innovation", where groups collectively explore and refine solutions for big problem areas. To explore this, my research group contributes novel interactive systems to better understand 1) how to productively select and build on the most promising and creative ideas; 2) how to effectively engage in large-scale participatory design by gathering feedback from communities of stakeholders; and 3) how to engage citizens in decision-making processes related to civic issues. To guide and motivate the design of these systems, this research builds on theories of design thinking and collective intelligence.
 
Steven Dow is an Assistant Professor in the department of Cognitive Science at UC San Diego where he researches human-computer interaction, social computing, and creativity. Steven received the National Science Foundation CAREER Award in 2015 and was co-PI on three other NSF grants, a Google Faculty Grant, and the Hasso Plattner Design Thinking Research Grant. Before UCSD, Steven was an Assistant Professor of Human-Computer Interaction at Carnegie Mellon University and a postdoc in Computer Science at Stanford University where he won the Postdoctoral Research Award. He received an MS and PhD in Human-Centered Computing from the Georgia Institute of Technology, and a BS in Industrial Engineering from University of Iowa.

Machine Learning Coffee seminar "Statistical Ecology with Gaussian Processes"

Lecturer : 
Jarno Vanhatalo
Event type: 
HIIT seminar
Doctoral dissertation
Respondent: 
Opponent: 
Custos: 
Event time: 
2017-06-05 09:15 to 10:00
Place: 
Exactum D123, Kumpula
Description: 

Jarno Vanhatalo, Assistant Professor in Statistics, University of Helsinki

On Priors and Bayesian Variable Selection in Large p, Small n Regression

Abstract: Ecology studies the distribution and abundance of species, and their interactions with other species and the environment. Key questions in ecology include what are the environmental factors and interspecies dependencies that drive species distributions, how these processes together affect species community structures and how environmental changes, such as climate change, affect species distribution and species communities. These questions are essentially about variable selection and causal and predictive inference. Hence, statistics has a central role in answering them. The species distribution models (SDMs) used for these analyses are traditionally based on generalized linear and additive models. In this talk I will present how Gaussian processes (GPs) can be used in SDMs and what benefits and challenges this provides. I will present recent results on GP based species distribution modeling in the Baltic Sea and Great Barrier Reef, Australia. I will discuss the potential future development and current challenges related to computation and model building.

Machine Learning Coffee seminars are weekly seminars held jointly by the Aalto University and the University of Helsinki. The seminars aim to gather people from different fields of science with interest in machine learning. Talks will begin at 9:15 am and porridge and coffee will be served from 9:00 am.

Next talks:

June 12, Otaniemi: Zhirong Yang: Learning Data Representation by Large-Scale Neighbor Embedding
--after June 12, we'll have a summer break and continue on September 4, 2017--

Welcome!

Polarization and social media in Turkey

Lecturer : 
Onur Mat
Event type: 
Guest lecture
Doctoral dissertation
Respondent: 
Opponent: 
Custos: 
Event time: 
2017-05-30 10:00 to 11:30
Place: 
Otaniemi, T-building, A211
Description: 

I am a software engineer, data analyst and a researcher working mostly on social media data. I am primarily developing tools and methodologies for democratisation of collection and distribution of news. 

 

I am also concerned about the polarization in societies. I am developing and contributing to existing tools and researches that help break echo chambers and improve communication and understanding between ideologically isolated groups. Recently I have collaborated with MIT Media Lab's Social Machines Team to adopt FlipFeed to Turkey (flipfeed-tr.media.mit.edu). 

 

Last year I have co-authored and presented a paper at IAMCR media conference in UK titled "Do media really set the political agenda? Applying the network agenda-setting model to Twitter". I am currently working on a follow-up paper which will be titled "Who sets the agenda? An analysis on Turkey’s political Twittersphere".

 

My blog: blog.genelizleyici.com (sorry, only in Turkish for now)

Exploring Hand-Based Haptic Interfaces for Mobile Interaction Design

Event type: 
Doctoral dissertation
Doctoral dissertation
Respondent: 
Yi-Ta Hsieh
Opponent: 
Professor Kaisa Väänänen (Tampere University of Technology, Finland)
Custos: 
Professor Giulio Jaccuci (University of Helsinki)
Event time: 
2017-06-08 12:00 to 15:00
Place: 
University of Helsinki Exactum Building, Auditorium CK112 (Gustaf Hällströmin katu 2b)
Description: 

M.Sc. Yi-Ta Hsieh will defend his doctoral thesis Exploring Hand-Based Haptic Interfaces for Mobile Interaction Design on Thursday the 8th of June 2017 at 12 o'clock noon in the University of Helsinki Exactum Building, Auditorium CK112 (Gustaf Hällströmin katu 2b). His opponent is Professor Kaisa Väänänen (Tampere University of Technology, Finland), and custos Professor Giulio Jaccuci (University of Helsinki). The defence will be held in English.

Exploring Hand-Based Haptic Interfaces for Mobile Interaction Design

Visual attention is crucial in mobile environments, not only for staying aware of dynamic situations, but also for safety reasons. However, current mobile interaction design forces the user to focus on the visual interface of the handheld device, thus limiting the user's ability to process visual information from their environment. In response to these issues, a common solution is to encode information with on-device vibrotactile feedback. However, the vibration is transitory and is often difficult to perceive when mobile. Another approach is to make visual interfaces even more dominant with smart glasses, which enable head-up interaction on their see-through interface. Yet, their input methods raise many concerns regarding social acceptability, preventing them from being widely adopted. There is a need to derive feasible interaction techniques for mobile use while maintaining the user's situational awareness, and this thesis argues that solutions could be derived through the exploration of hand-based haptic interfaces.

The objective of this research is to provide multimodal interaction for users to better interact with information while maintaining proper attention to the environment in mobile scenarios. Three research areas were identified. The first is developing expressive haptic stimuli, in which the research investigates how static haptic stimuli could be derived. The second is designing mobile spatial interaction with the user's surroundings as content, which manifests situations in which visual attention to the environment is most needed. The last is interacting with the always-on visual interface on smart glasses, the seemingly ideal solution for mobile applications. The three areas extend along the axis of the demand of visual attention on the interface, from non-visual to always-on visual interfaces.

Interactive prototypes were constructed and deployed in studies for each research area, including two shape-changing mechanisms feasible for augmenting mobile devices and a spatial-sensing haptic glove featuring mid-air hand-gestural interaction with haptic support. The findings across the three research areas highlight the immediate benefits of incorporating hand-based haptic interfaces into applications. First, shape-changing interfaces can provide static and continuous haptic stimuli for mobile communication. Secondly, enabling direct interaction with real-world landmarks through a haptic glove and leaving visual attention on the surroundings could result in a higher level of immersed experience. Lastly, the users of smart glasses can benefit from the unobtrusive hand-gestural interaction enabled by the isolated tracking technique of a haptic glove. 

Overall, this work calls for mobile interaction design to consider haptic stimuli beyond on-device vibration, and mobile hardware solutions beyond the handheld form factor. It also invites designers to consider how to confront the competition of cognitive resources among multiple tasks from an interaction design perspective.

Machine Learning Coffee Seminar: "Learning Data Representation by Large-Scale Neighbor Embedding"

Lecturer : 
Zhirong Yang
Event type: 
HIIT seminar
Event time: 
2017-06-12 09:15 to 10:00
Place: 
Lecture room T2, CS building, Konemiehentie 2, Otaniemi
Description: 

Zhirong Yang, Professor of Computer Science, Aalto University

Learning Data Representation by Large-Scale Neighbor Embedding

Abstract: Machine learning, the state-of-the-art data science, has been increasingly influencing our life. Encoding data in a suitable vector space is the fundamental starting point for machine learning. A good vector coding should respect the relations among the data items. However, conventional methods that preserve pairwise or higher order relationship are very slow and consequently they can handle only small-scale data sets. We have been developing a family of unsupervised methods called large-scale Neighbor Embedding (NE) which substantially accelerate the vector coding. Our method can thus learn low-dimensional vector representation for mega-scale data according to their neighborhoods in the original space. With our efficient algorithms and a wealth of neighborhood information, Neighbor Embedding significantly outperforms small-scale NE and many other existing approaches for learning data representation. Besides generic feature extraction, our work also delivers two important tools as special cases of Neighbor Embedding for data visualization and cluster analysis, which scales up these applications by an order of magnitude and enables the current-sized visualization and clustering for interactive use. Because neighborhood information is naturally and massively available in many areas, our method has wide applications as a critical component in scientific research, next-generation DNA sequence analysis, natural language processing, educational cloud, financial data analysis, market studies, etc.

Machine Learning Coffee seminars are weekly seminars held jointly by the Aalto University and the University of Helsinki. The seminars aim to gather people from different fields of science with interest in machine learning. Talks will begin at 9:15 am and porridge and coffee will be served from 9:00 am.

Next talks:

* we'll have a summer break and continue on September 4, 2017 *

Welcome!

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