Adventures in building Emotional Intelligence Technologies

Lecturer : 
Rosalind Picard
Event type: 
Guest lecture
Event time: 
2017-02-03 15:15 to 16:30
Place: 
Small Hall, Main Building, University of Helsinki, Fabianinkatu 33, Helsinki
Description: 

The next lecture in the Helsinki Distinguished Lecture Series on Future Information Technology will be given by Professor Rosalind Picard from the MIT Media Lab.

The lecture is free of charge and open to everyone interested in the latest research in information technology. The lecture will be followed by an informal cocktail event.

Registration is now closed (but we still have some space remaining).

 

Adventures in building Emotional Intelligence Technologies

Abstract

Years ago, I set out to create technology with emotional intelligence, demonstrating the ability to sense, recognize, and respond intelligently to human emotion. At MIT, we designed studies and developed signal processing and machine learning techniques to see what affective insights could be reliably obtained. In this talk I will highlight the most surprising findings during this adventure. These include new insights about the "true smile of happiness," discovering new ways cameras (and your smartphone, even in your handbag) can compute your bio-signals, finding electrical signals on the wrist that reveal insight into deep brain activity, and learning surprising implications of wearable sensing for autism, anxiety, sleep, memory, epilepsy, and more. What is the grand challenge we aim to solve next?

About the Speaker

Rosalind Picard, ScD, FIEEE is founder and director of the Affective Computing Research Group at the MIT Media Laboratory, co-founder of Affectiva, providing emotional intelligence technology used by 1/3 of the Global Fortune 100, and co-founder and Chief Scientist of Empatica, improving lives with clinical-quality wearable sensors and analytics. Picard is the author of over 250 articles in computer vision, pattern recognition, machine learning, signal processing, affective computing, and human-computer interaction. She is known internationally for her book, Affective Computing, which helped launch the field by that name. Picard holds bachelors in Electrical Engineering (EE) from Georgia Tech and Masters and Doctorate degrees in EE and CS from MIT. Picard’s inventions have been twice named to "top ten" lists, including the New York Times Magazine's Best Ideas of 2006 for the Social Cue Reader, and 2011's Popular Science Top Ten Inventions for a Mirror that Monitors Vital Signs.

 

 

Machine Learning Coffee Seminar: "Likelihood-free inference and predictions for computational epidemiology"

Lecturer : 
Jukka Corander
Event type: 
Event
Event time: 
2017-01-30 09:15 to 10:00
Place: 
Exactum D123, Kumpula
Description: 

Helsinki region machine learning researchers will start our week by an exciting machine learning talk and discussion over coffee. The talks will start 9:15, with coffee served from 9:00. This week's talk is:

Likelihood-free inference and predictions for computational epidemiology
 
Jukka Corander
Professor, University of Helsinki and University of Oslo
 
Abstract:
Simulator-based models often allow inference and predictions under more realistic assumptions than those employed in standard statistical models. For example, the observation model for an underlying stochastic process can be more freely chosen to reflect the characteristics of the data gathering procedure. A major obstacle for such models is the intractability of the likelihood, which has to a large extent hampered their practical applicability. I will discuss recent advances in likelihood-free inference that greatly accelerate the model fitting process by exploiting a combination of machine learning techniques. Applications to several novel models in infectious disease epidemiology are used to illustrate the potential offered by this approach.

Machine Learning Coffee Seminar: "Metabolite identification through machine learning"

Lecturer : 
Juho Rousu
Event type: 
Event
Event time: 
2017-01-23 09:15 to 10:00
Place: 
Seminar room T5, CS building, Konemiehentie 2, Otaniemi
Description: 

Helsinki region machine learning researchers will start our week by an exciting machine learning talk and discussion over coffee. The talks will start 9:15, with coffee served from 9:00. This week's talk is:

Metabolite identification through machine learning
 
Juho Rousu
Associate Professor, Aalto University
 
Abstract:
Identification of small molecules from biological samples remains a major bottleneck in understanding the inner working of biological cells and their environment. Machine learning on data from large public databases of tandem mass spectrometric data has transformed this field in recent years, witnessing an increase of identification rates by 150%. In this presentation, I will outline the key machine learning methods behind this development: kernel-based learning of molecular fingerprints, multiple kernel learning, structured prediction as well as some recent advances.
 
The next talk is:
30.1. at 9:15 in Kumpula Exactum D123: Jukka Corander "Likelihood-free inference and predictions for computational epidemiology”

 

Machine Learning Coffee Seminar: "Probabilistic programming: Bayesian modeling made easy"

Lecturer : 
Arto Klami
Event type: 
Event
Event time: 
2017-01-16 09:15 to 10:00
Place: 
Exactum D123, Kumpula
Description: 

Helsinki region machine learning researchers will start our week by an exciting machine learning talk and discussion over coffee. The talks will start 9:15, with coffee served from 9:00. This week's talk is:

Probabilistic programming: Bayesian modeling made easy
 
Arto Klami
Academy Research Fellow, University of Helsinki
 
Abstract:
Probabilistic models are principled tools for understanding data, but difficulty of inference limits the complexity of models we can actually use. Often we need to develop specific inference algorithms for new models (which might take months), and need to restrict ourselves to tractable model families that might not match our beliefs about the data. Probabilistic programming promises to fix this, by separating the model description from the inference: With probabilistic programming languages we can specify complex models using a high-level programming language, letting a black-box inference engine take care of the tricky details. This talk covers the basic idea of probabilistic programming and discusses how well its promises hold now and in the future.
 
The next talks are:
23.1. at 9:15 in Otaniemi CS building T5: Juho Rousu "Metabolite identification through machine learning"
30.1. at 9:15 in Kumpula Exactum D123: Jukka Corander "Likelihood-free inference and predictions for computational epidemiology”

 

Machine Learning Coffee Seminar: "Unsupervised machine learning for matrix decompositions"

Lecturer : 
Erkki Oja
Event type: 
Event
Event time: 
2017-01-09 09:15 to 10:00
Place: 
Seminar room T6, CS building, Konemiehentie 2, Otaniemi
Description: 
Starting January 9, Helsinki region machine learning researchers will start our week by an exciting machine learning talk and discussion over coffee. The talks will start 9:15, with coffee served from 9:00. The first talk is:
 
Unsupervised Machine Learning for Matrix Decomposition
 
Erkki Oja
Professor Emeritus, Aalto University
 
Abstract:
Unsupervised learning is a classical approach in pattern recognition and data analysis. Its importance is growing today, due to the increasing data volumes and the difficulty of obtaining statistically sufficient amounts of labelled training data. Typical analysis techniques using unsupervised learning are principal component analysis, independent component analysis, and cluster analysis. They can all be presented as decompositions of the data matrix containing the unlabeled  samples. Starting from the classical results, the author reviews some advances in the field up to the present day.
 
The next talks are:
16.1. at 9:15 in Kumpula Exactum D123: Arto Klami "Probabilistic programming: Bayesian modeling made easy"
23.1. at 9:15 in Otaniemi CS building T5: Juho Rousu "Metabolite identification through machine learning"
30.1. at 9:15 in Kumpula Exactum D123: Jukka Corander "Likelihood-free inference and predictions for computational epidemiology”

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