Data Science Seminar Series

The Data Science Seminar Series bring high profile researchers to Université Grenoble Alpes to show the increasing role of data science in modern research. It is open to all researchers of Université Grenoble Alpes. Lectures will be followed by a coffee break. Students from the M2 programs MSIAM Data Science, MoSIG Data Science and SIGMA and enroled in the data science seminar course have to attend to the seminars (marked by a *).
The seminars will be on Thurdsay afternoon between 15:30 and 17:30 and will be followed by a coffee break. 

Seminars 2017-2018 (preliminary titles) :

October 12 (Amphi 018, RDC bâtiment UFR IM2AG)*: audio-visual analysis for human-robot interaction by Radu Horaud (INRIA Grenoble)

Robots have gradually moved from factory floors to populated spaces. Therefore, there is a crucial need to endow robots with communicative skills. One of the prerequisites of human-robot communication (or more generally, interaction) is the ability of robots to perceive their environment, to detect people, to track them over time, and to identify communicative cues, such as “who looks at whom” and “who speaks to whom”. Therefore we are interested in analysing situations in which several people are present, understand their activities, estimate who speaks and who doesn’t, etc. For that purpose we combine computer vision, audio signal processing and machine learning methods. We will briefly present the research that we carried out within this topic, and stress the importance of learning from sensory data.

Related articles
- Audio-Visual Speaker Diarization Based on Spatiotemporal Bayesian Fusion
- Tracking a Varying Number of People with a Visually-Controlled Robotic Head


October 26 (Auditorium Bâtiment Imag)*: Toward Interactive User Data Analytics by Sihem Amer-Yahia (CNRS, LIG, Grenoble)

User data can be acquired from various domains. This data is characterized by a combination of demographics such as age and occupation and user actions such as rating a movie, reviewing a restaurant or buying groceries. User data is appealing to analysts in their role as data scientists who seek to conduct large-scale population studies, and gain insights on various population segments. It is also appealing to novice users in their role as information consumers who use the social Web for routine tasks such as finding a book club or choosing a restaurant.  User data exploration has been formulated as identifying group-level behavior such as Asian women who publish regularly in databases. Group level exploration enables new findings and addresses issues raised by the peculiarities of user data such as noise and sparsity. I will review our work on one-shot and interactive user data exploration. I will then describe the challenges of developing a visual analytics tool for finding and connecting users and groups.

Related article
- Toward Interactive User Data Analytics

November 16 (salle 320 bâtiment UFR IM2AG): Attribution modeling increases efficiency of bidding in display advertising by Eustache Diemert (Criteo Research, Paris)

Predicting click and conversion probabilities when bidding on ad exchanges is at the core of the programmatic advertising industry. Two separated lines of previous works respectively address i) the prediction of user conversion probability and ii) the attribution of these conversions to advertising events (such as clicks) after the fact. We argue that attribution modeling improves the efficiency of the bidding policy in the context of performance advertising. Firstly we explain the inefficiency of the standard bidding policy with respect to attribution. Secondly we learn and utilize an attribution model in the bidder itself and show how it modifies the average bid after a click. Finally we produce evidence of the effectiveness of the proposed method on both offline and online experiments with data spanning several weeks of real traffic from Criteo, a leader in performance advertising.

November 30 (date to confirm) (salle 320 bâtiment UFR IM2AG): Title will come soon by Pierre Borgnat

Related articles
- Community detection algorithms: A comparative analysis
- Graph Wavelets for Multiscale Community Mining

December 14 : Neural networks and Random Matrix Theory by Romain Couillet (Ecole Centrale Supélec, Univerty Paris-Saclay)

Related articles
Application de la théorie des grandes matrices aléatoires à l'apprentissage pour les mégadonnées
- Une Analyse des Méthodes de Projections Aléatoires par la Théorie des Matrices Aléatoires

January 18 :
Big data for telecom by Merouane Debbah (Ecole Centrale Supélec, Univerty Paris-Saclay, Vice President  R&D Center Huawei France, Director of the Mathematical and Algorithmic Sciences Lab)

Mobile cellular networks are becoming increasingly complex to manage while classical deployment/optimization techniques are cost-ineffective and thus seen as stopgaps. This is all the more difficult considering the extreme constraints of 5G networks in terms of data rate (more than 10 Gb/s), massive connectivity (more than 1000000 devices per km2), latency (under 1ms) and energy efficiency (a reduction by a factor of 100 with respect to 4G network). Unfortunately, the development of adequate solutions is severely limited by the scarcity of the actual ressources (energy, bandwidth and space). Recently, the community has turned to a new ressource known as Artificial Intelligence at all layers of the network to exploit the increasing computing power afforded by the improvement in Moore’s law in combination with the availability of huge data in 5G networks. This is an important paradigm shift which considers the increasing data flood/huge number of nodes as an opportunity rather than a curse. In this talk, we will discuss through various examples how the recent advances in big data algorithms can provide an efficient framework for the design of 5G Intelligent Networks.
Related articles
Wireless AI: Challenges and Opportunities
- Machine Learning for Wireless Networks with Artificial Intelligence: A Tutorial on Neural Networks

Published on November 8, 2017