IVADO

13th Annual Conference on Neural Information Processing Systems (NIPS) 2016
2016-12-05 08:00 - 2016-12-10 17:00 (America/New_York) | Centre Convencions Internacional Barcelona, Plaça de Willy Brandt, 11-14, Barcelona, 08019, Espagne
The Thirtieth Annual Conference on Neural Information Processing Systems (NIPS) is a multi-track machine learning and computational neuroscience conference that includes invited talks, demonstrations, symposia and oral and poster presentations of refereed papers. Following the conference, there are workshops which provide a less formal setting.

IEEE International Conference on Data Science and Advanced Analytics
2016-10-17 - 2016-10-19 | Montreal Marriott Chateau Champlain, 1050 de la Gauchetiere West, Montreal, QC, H3B 4C9, Canada
Following the first successful edition DSAA’2014 held in 2014 in Shanghai, then the second successful edition DSAA’2015 held in Paris, the 2016 IEEE International Conference on Data Science and Advanced Analytics (IEEE DSAA’2016), sponsored by the IEEE Computational Intelligence Society, aims to provide a premier forum that brings together researchers, industry practitioners, as well as potential users of big data, for discussion and exchange of ideas on the latest theoretical developments in Data Science as well as on the best practices for a wide range of applications.
DSAA is also technically sponsored by ACM through SIGKDD.
Research and Applications
IEEE DSAA’2016 will consist of two main Tracks: Research and Application; the Research Track is aimed at collecting contributions related to theoretical foundations of Data Science and Data Analytics. The Application Track is aimed at collecting contributions related to applications of Data Science and Data Analytics in real life scenarios. DSAA solicits then both theoretical and practical works on data science and advanced analytics.
Prof. Yoshua Bengio, Prof. Juliana Freire and Prof. David Donoho will be keynote speakers.

Droit ouvert pour données ouvertes
2016-09-20 | Plaza Montréal, 420 Sherbrooke Ouest, Montréal, Québec, Canada
Les données ouvertes sont à la mode… Les potentialités économiques sont considérables. Les données sont la «monnaie» du numérique et les appétits se manifestent de tous bords tous côtés. Qu’elles soient publiques, privées, anonymisées, hébergées dans les «nuages» ou non, les enjeux juridiques se multiplient que ce soit en terme de vie privée, de sécurité, de contrat, de responsabilité, etc.
Face à cette réalité, il importe de considérer le droit comme un outil susceptible d’encadrer les meilleures pratiques en la matière. Néanmoins, ce droit, tout comme le domaine qu’il tente d’encadrer doit être ouvert afin d’être véritablement efficace. Un droit ouvert pour les données ouvertes; un droit qui puisse véritablement assurer une protection des usagers sans profiter seulement aux intérêts commerciaux.

Impact of online tracking on vehicle routing with dynamic travel
2016-08-04 | Université de Montréal, Pavillon André-Aisenstadt, Pavillon André-Aisenstadt, Montréal, QC, H3T 1J4, Canada
With an application in the pick-up of blood samples
This talk analyzes the impact of vehicle tracking devices, such as global positioning systems, on a vehicle routing problem with time windows in the presence of dynamic customer requests and dynamic travel times. It is empirically demonstrated that substantial improvements are achieved over a previously reported model which does not assume such tracking devices. We also analyze how the system handles dynamic perturbations to the travel times that lead to earliness or lateness in the planned schedule. The benefit of the approach is illustrated for the pick-up of blood samples and the delivery of medical items, for which unusual time windows are considered.
Speaker: Nicolas Zufferey, full professor of operations management at the University of Geneva in Switzerland.

Deep Learning Summer School
2016-08-01 - 2016-08-07 | Lieu sans nom, Montreal, Canada
Deep neural networks that learn to represent data in multiple layers of increasing abstraction have dramatically improved the state-of-the-art for speech recognition, object recognition, object detection, predicting the activity of drug molecules, and many other tasks. Deep learning discovers intricate structure in large datasets by building distributed representations, either via supervised, unsupervised or reinforcement learning.
This summer schools is aimed at graduate students and industrial engineers and researchers who already have some basic knowledge of machine learning (and possibly but not necessarily of deep learning) and wish to learn more about this rapidly growing field of research.
This year’s edition of the summer school is organized by Aaron Courville and Yoshua Bengio.

Analyse des réseaux avec l'ESPUM!
2016-07-04 - 2016-07-08 | École de santé publique de l’Université de Montréal, 7101, avenue du Parc, 3ième étage, bureau 3014-8, Montréal, QC, H3N 1X9, Canada
École d’été – Maîtriser l’analyse des réseaux de relations sociales
L’École de santé publique de l’Université de Montréal organise cinq journées complètes pour appliquer l’analyse de réseaux de relations sociales dans les disciplines des sciences sociales et de la santé à des fins de recherche, d’évaluation, d’intervention et de gestion.

Contact MTL présente Yoshua Bengio!
2016-06-28 17:00 - 19:00 | Maison Notman, 51 Rue Sherbrooke Ouest, Montréal, QC, H2X 1X2, Canada
Le professeur Bengio aura l’occasion de nous entretenir sur l’état actuel des développements technologiques et scientifiques en intelligence artificielle, ainsi que de l’importance de la délibération collective à propos des décisions à prendre ainsi que des impacts présents et futurs de cette technologie dans notre société.

Big data dans le domaine de la santé
2016-05-10 17:30 - 21:00 | Amphithéâtre Justine Lacoste – CHU Sainte-Justine, 3175 Chemin de la Côte-Sainte-Catherine, Montréal, QC, H3T 1C4, Canada
Les opportunités et les défis du big data dans le domaine de la santé
Pour cette édition du Hacking Health Café, nous avons réuni des professionnels de l’industrie de la santé et des chercheurs universitaires pour discuter de l’innovation dans le domaine de santé possible avec le big data, les données ouvertes et l’intelligence artificielle.
Ne manquez pas cette opportunité d’apprendre davantage de l’expérience de plusieurs professionnels de la recherche et de l’industrie. Vous aurez également la chance de leur poser des questions durant le panel de discussion!
Comment tirer avantage des données existantes dans votre organisation? Participez au Health Hacking Cafe le 10 mai pour découvrir toutes les opportunités et les défis du big data.
Hacking Health est fier de s’associer à L’institut de valorisation des données (IVADO), le regroupement le plus performant en publications au monde sur la recherche opérationnelle et le deep learning.
Les présentations du HH Café auront lieu à l’amphithéâtre Justine Lacoste du CHU Sainte-Justine.

Conférenciers
Jonathan Vallée, Directeur Données Scientifiques, AlayaCare – www.alayacare.com
Louis-Martin Rousseau, Professeur, Polytechnique Montréal
Joseph Tcherkezian, Chercheur Associé, Cartagene – www.cartagene.qc.ca
Sébastien Lemieux, chercheur, IRIC (Institut de recherche en immunologie) – www.iric.ca
Nicolas Chapados, conseiller scientifique en chef, Imagia – www.imagia.com

Optimization days and PROMÉTHÉE days 2016
2016-05-02 08:00 - 2016-05-04 17:00 | HEC Montréal, 3000, chemin de la Côte-Sainte-Catherine, Montréal , québec, H3T 2A7, Canada
The Optimization days are an annual event held conjointly with the Inter-university research centre on enterprise networks, logistics and transportation (CIRRELT) and the Group for research in decision analysis (GERAD).
The themes of these days are focusing on theories, numerical methods and optimization applications. They give researchers interested in these fields the opportunity to meet with new individuals and new institutions from across the globe in orde to generate exchanges and potential collaborations

DIRO's Women in computer sciences day
2016-03-16 09:00 - 17:00 | Université de Montréal, Pavillon André-Aisenstadt, Pavillon André-Aisenstadt, Montréal, QC, H3T 1J4, Canada
Conference and networking:
Interested in a career in computer sciences? Join us for the DIRO Women’s day on March 16th, 2016 to meet passionate and inspirational women, that are currently working in the field of computer sciences and information technology as researchers or employees of prestigious companies such as Google and Ubisoft.
When : Wednesday 16th of March, from 9AM to 5PM
Where : Université de Montréal, Pavillon André-Aisenstadt (Montréal)
Price : Free ! Lunch included.
For more information on the event:
Official page (in French)

A 40-year journey in data science and technology, from academia to industry
2016-03-11 10:45 - 12:00 | Université de Montréal, Pavillon André-Aisenstadt, Pavillon André-Aisenstadt, Montréal, QC, H3T 1J4, Canada
Presentation of Philippe Collard – Président, Le Link Inc., Canada
Over the course of the past 40 years, I have been witness to and an actor of an amazing transformation in what we called “technology”. My first exposure to this world was when I wrote my first program for an an IBM 360 on punch cards. I was later involved in medical imaging (where we were first exposed to “big data”). Then went on to work for NASA on climate science in the 90s. And I have been involved in more technology ventures since then from the industry side.
I have now more power in my pocket in my iPhone 6 than I had at my disposal when I tried to analyse cloud images on Cray computers 20 years ago. What has been this journey? What did I learn from it? and how should we look at technology today? What about artificial intelligence? what about big data? what about virtual reality? how do we make sense of it all, sorting through what is real, what could become real, what probably will never be and what is hype? Follow me on a time travel trip: from the past to the future.
SÉMINAIRE DU GERAD
March 11th, 2016 10:45AM – 12:00PM (Noon)
Room 4488
Pavillon André-Aisenstadt
Campus de l’Université de Montréal
2920, chemin de la Tour Montréal QC H3T 1J4 Canada

Opportunities and frontiers in machine learning for applied sciences
2016-02-26 14:00 - 15:00 | Université de Montréal, Pavillon André-Aisenstadt, Pavillon André-Aisenstadt, Montréal, QC, H3T 1J4, Canada
Presentation of Mohammad Attarian Shandiz – Université McGill, Canada
Modern methods in machine learning have provided many opportunities for solving complex problems in applied science. Hence, for a data scientist is essential to be familiar with the most important and current fields of research in machine learning and data mining. In this talk, the most significant fields of research in machine learning and data mining are introduced based on the survey in the database of scientific journals. Subsequently, various applications of machine learning for some challenging problems in medicine, finance and engineering are discussed. Lastly, the results of optimized classifiers for a classification problem in the field of lithium-ion batteries are presented. Ensemble methods including random forests and extremely randomized trees provided the highest accuracy of prediction among other methods for the classification based on the Monte Carlo cross validation tests.
GERAD seminar
February 26t,h 2016 2:00PM – 3:00PM
Room 4488
Pavillon André-Aisenstadt
Campus de l’Université de Montréal
2920, chemin de la Tour Montréal QC H3T 1J4 Canada

Optimizing Google's Cloud infrastructure
2016-02-19 10:45 - 12:00 | Université de Montréal, Pavillon André-Aisenstadt, Pavillon André-Aisenstadt, Montréal, QC, H3T 1J4, Canada
Conference of Brian Eck – Google, États-Unis
Google’s Cloud infrastructure consists of the servers, network, datacenters and software that run all of Google’s services like Search, Gmail and Youtube as well as it’s fast growing external business, Google Cloud Platform. We’ll discuss three case studies in optimizing this infrastructure, covering challenges not only in solving the analytical problems but also in solving the business problems to reach real implementation and impact. Case studies will include some subset of:

Optimizing build frequency for adding capacity to the network,
Analyzing scenarios of metro network topology to inform strategy, including cost implications of adjusting forecasts,
Building ‘forecast tolerant’ networks that buffer for fault-tolerance and forecast variation,
Low-cost methods of physical grooming on a city-level fiber infrastructure,
Reducing stranded compute resources through simulation,
Increasing resource utilization through statistical oversubscription.

GERAD seminar
February 19th, 2016 at 10:45AM – 12:00PM (Noon)
Room 4488
Pavillon André-Aisenstadt
Campus de l’Université de Montréal
2920, chemin de la Tour Montréal QC H3T 1J4 Canada

Big data & mixed-integer (nonlinear) programming
2016-01-22 16:00 - 17:00 | UQÀM, Pavillon Président-Kennedy, Salle PK 5115, 201, avenue du Président-Kennedy, Montreal, québec, Canada
Andrea Lodi – Full professor, Canada Excellence Research Chair in Data Science for Real-Time Decision-Making, Polytechnique Montréal.
This conference is in english.
“In this talk, I review a couple of applications on Big Data that I personally like and I try to explain my point of view as a Mathematical Optimizer — especially concerned with discrete (integer) decisions — on the subject. I advocate a tight integration of Data Mining, Machine Learning and Mathematical Optimization (among others) to deal with the challenges of decision-making in Data Science. Those challenges are the core of the mission of the Canada Excellence Research Chair in “Data Science for Real-time Decision Making” that I hold.” Prof. Andrea Lodi.

GERAD: Reverse Stackelberg games: Theory and applications in traffic control
2016-01-18 10:00 - 12:00 | Université de Montréal, Pavillon André-Aisenstadt, Pavillon André-Aisenstadt, Montréal, QC, H3T 1J4, Canada

Yoshua Bengio: Artificial Intelligence and Deep-Learning
2015-09-10 | Université de Montréal, Pavillon André-Aisenstadt, Pavillon André-Aisenstadt, Montréal, QC, H3T 1J4, Canada
Conference of Yoshua Bengio, full professor in Artificial Intelligence at the department of Computer Science and Operations Research of Université de Montréal
