We are pleased to announce our next event “Causal Inference – Part I” on November 25th, 19h at Movistar Centre. Doors will open at 18:45.
Machine learning models or A/B testing are useful methods to make business decisions. But sometimes they are not feasible or present some limitations. Moreover, in many cases, we need to address questions such as: what would have happened if instead of doing X we had done Y? Can we have an estimate of the effect of one variable into another? In these cases, causal inference becomes the best option. And unsurprisingly, companies such as Uber are using causal inference as part of their data science efforts.
We are organizing two events to properly discuss the potential of causal inference and when it makes sense to apply it. In this first session, Bartek Skorulski and Aleix Ruiz de Villa will show the whole spectrum of causality, from AB tests to causal inference. You can register here
Aleix Ruiz de Vila holds a Ph.D. in mathematics. He has been head of data science at LaVanguardia, SCRM – Lidl, and Onna. He’s a co-organizer of Barcelona Data Science and Machine Learning Meetup and board member of Societat Catalana de Matemàtiques. He currently teaches at BData and Uoc and he is a data science consultant specialized in causal inference. Check https://medium.com/tag/casual-causal-inference/latest for an introduction to causal inference
Bartek Skorulski, Data Scientist and Ph.D. in Dynamical System. He works as a Senior Data Scientist, Recommender System Lead in Telefonica Innovation Alpha. Previously, he was working as Staff Insight Analyst in Schibsted, Data Science Team Lead in Lidl-SCRM and Data Scientist in King. Moreover, he has many years of experience as an academic researcher and teacher. Now he collaborates with the Polytechnic University of Catalonia, University of Barcelona and Kschool teaching Machine Learning, Deep Learning, and Data Management courses. He is also co-organizer of Barcelona Data Science and Machine Learning Meetup.
La Societat Catalana de Matemàtiques conjuntament amb la Societat Catalana d’Estadística organitza una jornada dedicada a promoure l’activitat de les matemàtiques i l’estadística en el món empresarial. El títol d’aquesta jornada és La Funció de les Dades. Les places són limitades i cal enregistrar-se prèviament en aquest enllaç.
La primera part de la jornada (matí) estarà formada per tallers d’una hora que tenen l’objectiu de donar eines, algunes més conegudes i d’altres de menys, que ajudin a les empreses a treballar amb les seves dades.
En la segona part (tarda) tindrem xerrades amb gent del món empresarial que ens explicarà la seva visió i experiència al voltant de l’ús de les matemàtiques i l’estadística en el món empresarial.
La Jornada tindrà lloc el 14 de novembre a la sala Prat de la Riba, IEC. Carrer del Carme, 47.
In the last months we had two great events and now you can watch online both of them.
The first event was held on January 21st and we discussed if Barcelona can become a European hub for Advanced Analytics and Big Data. As speakers we had Josep Maria Martorell (Associate Director at the Barcelona Supercomputing Center) and Òscar Sala (mVentures Director at the Mobile World Capital Barcelona organization). You can watch it in this link below.
The second event was held on February 14th (Valentine Day!!) and we reviewed how Analytics can play a role in Sports (are we close to a Money Ball world?) As speakers we had Sergi Oliva (Senior Director, Analytics & Strategy at Philadelphia 76ers) and Javier Fernandez (Head of Sports Analytics at FC Barcelona). You can watch in this link below.
We are pleased to announce our next event “Sports Analytics” on February 14th, 19h at Movistar Centre. Doors will open at 18:45. You can register here.
In this session we will focus on how Analytics can play a role in Sports. Are we close to a Money Ball world? As speakers we will have Sergi Oliva (Senior Director, Analytics & Strategy at Philadelphia 76ers) and Javier Fernandez (Head of Sports Analytics at FC Barcelona).
This event could not be possible without the collaboration of Movistar Centre.
In the last months we have seen that Ethics has emerged as an extremely sensitive topic for Data and Analytics community. Most likely, one of the main drivers of this wave of concern was Facebook scandal: Mark Zuckerberg (founder and CEO of Facebook) had to testify in front of US Congress about how his company handles its users’ data and how this could have influenced results in recent elections in several countries. But Facebook is not the only company whose practices are under scrutiny. Tones of questions have also been raised regarding how much personal data Google collects and how this is being used: according to Guillaume Chaslot (an ex-Google engineer), the Youtube algorithm “does not appear to be optimising for what is truthful, or balanced, or healthy for democracy”.
In other words, we are talking not only about privacy but also on how data could even threaten our political system. As Cathy O’Neil writes in her must-read book Weapons of math destruction, “the math-powered applications powering the data economy were based on choices made by fallible human beings. Some of these choices were no doubt made with the best intentions. Nevertheless, many of the models encoded human prejudice, misunderstanding and bias into the software systems that increasingly managed our lives. Like gods, these mathematical models are opaque (…) Their verdicts, even wrong or harmful, were beyond dispute or appeal. And they tended to punish the poor and the oppressed in our society, while making the rich richer”.
As Data-Driven professionals we cannot ignore this inconvenient truth and must address it. This is one of the reasons we at BcnAnalytics organised a session to discuss about Data & Ethics. As speakers we had Carlos Castillo (Distinguished Research Professor at Universitat Pompeu Fabra) and Gemma Galdon (Founder at Eticas Research & Consulting and Researcher at Universitat de Barcelona).
Carlos focused his talk on algorithmic discrimination. He initially reviewed the concept of discrimination from a philosophical perspective and then explained the concept of group discrimination, which means “disadvantageous treatment to an individual because he or she belongs to a specific socially salient group”. According to Carlos a further step is statistical discrimination which can be observed “when group discrimination happens because of some statistical belief, which means that someone has certain data, has looked at this data and based on statistics extracted from this data has decided to treat someone worse than another person”. After reviewing these concepts, Carlos raised the key issue: machine learning algorithms can discriminate.
Why is that? Machine learning systems take data and extract statistical beliefs from this data and therefore they are enabled to discriminate some individuals, regardless of intention and animosity. The key aspect is the consequences of this algorithm in terms of treating worst a person because he or she belongs to a group. Carlos emphasized that to avoid this discrimination, models need to optimize not only accuracy but also need to look at “the risk of two different populations of not getting the same outcome”. Carlos also highlighted how important is that systems are transparent: “if you get a negative outcome, you have to have a way to challenge this decision in a way that is effective… If I am denied a loan or parole, I need to have a way of effectively challenge the decision to say the systems was wrong in my case”.
Gemma started her talk quoting “The Fall of Public Man” from Richard Sennett. “In a city full of sensors and cameras and surveillance everywhere, where would Romeo and Juliet fall in love?”. From Gemma’s perspective, technology is changing our lives and we really need to ask ourselves: Why we are investing in technology? What kind of societies are these technologies creating or promoting? Are we building the cities that we want to build? Do we want to live in a world where everything is remembered? Do we want to live in a world where we can never forget? As she mentioned: “for the first time in history, forgetting is more expensive than remembering. Everything we do is recorded by a camera or a sensor”. Gemma, then, started to review real cases on non-expected outcomes of certain technologies. For instance, smart borders based on biometrics. They were not part of the legislative debate because they were seen “as technical amendments”, but currently biometrics have become our IDs, and certain individuals self-mutilate when they want to hide their identities. In other words, their bodies became their enemies.
Gemma asked herself: “How can we hide behind a technical amendment? And what about false positives? There is no redress mechanism”. According to her the most burning issue is we, as society, did not think technology could fail. But it fails. And this triggers the key issue: the way we do technology is very irresponsible and no one is facing the consequences of their actions, the consequences of their false positives…which might be human rights. Gemma ended her speech highlighting the fact we need to start thinking how technology is impacting our civilization: “we have the responsibility to decide how we build a social-technical infrastructure that is responsible and desirable for our generation and the next generations”.
A few years ago, when we created Bcn Analytics our vision was Barcelona can become a European analytics hub. Our ambition was to foster that different members of community (business, academia, data professionals) could meet and share experiences and knowledge. Now, 3 years after, we feel proud of we accomplished. We have organised 10 meet-ups where fantastic speakers from great organisations have shared their expertise: we had guests from Google, New York University, King.com, La Caixa, Telefonica, Schibsted, Social Point, BBVA, IPSOS or Vistaprint, among others. We also had the chance to organise two Datathons with Social Point so data scientists could compete to win some prizes while having fun with data.
The Datathon is going to be part of the exhibition “After the End of the World” which is being organised by CCCBB. Participants of the datathon will have to build a prediction model on Barcelona pollution levels. We have more than 3.000€ in prizes thanks to our sponsors Social Point, Holaluz and Gauss & Neumann. We also have the support of Mobile World Congress.
Are you coming to this year’s PyConEs? Don’t lose the chance to participate in the PythonHack that is organising Kernel Analytics!
We offer three different challenges:
Accuracy Contest: if you want to prove that your models can beat the rest, this is your contest.
Web App solution: for back-end and front-end developers that can give an operative and fancy solution to an open question
Happy Hour challenge: in-person machine learning challenge. Participants will have free drinks during the challenge!
In order for you to assist to all conferences, Kernel is releasing the datasets of the Accuracy Contest and Web App solution one week before the PyConEs begins.
Happy Hour will also take place after Saturday’s conferences end.
Choose which contest you want to participate in and register as soon as you can because there are limited spots.
Check out the whole video of our last event with NYU professor Joan Bruna, assistant professor of the Courant Institute (NYU). Joan shows us some important applications of Deep Learning and which are the next challenges of this hot field.
We are pleased to anounce our next event and the first in our series: Machine Learning Series II. It is a great honour to have in our panel Joan Bruna, Assistant Professor at Courant Institute, NYU, in the Department of Computer Science.
Joan Bruna will share the learnings and insights of applying various machine learning techniques to a number of different use cases throughout his career, such as image or real-time video recognition, among others. The conference will combine an initial master class and a debate with the audience.
Wednesday, July 19th, 2017 at 19:00 PM (18:45 registration opens)
WHERE Aula Capella, ground floor, at Historical Building of Universitat de Barcelona, at Plaça Universitat, at Gran Via de les Corts Catalanes, 585
Bio. Joan Bruna is an Assistant Professor at Courant Institute, NYU, in the Department of Computer Science, Department of Mathematics (affiliated) and the Center for Data Science, since Fall 2016. He is currently on leave from UC Berkeley (Statistics Department).
His research interests touch several areas of Machine Learning, Signal Processing and High-Dimensional Statistics. In particular, in the past few years he has been working on Deep Convolutional Networks, studying some of its theoretical properties and applications to several Computer Vision tasks.
Before that, he worked at FAIR (Facebook AI Research) in New York, working on Unsupervised Learning. Prior to that, he was a postdoctoral researcher at Courant Institute, NYU, under the supervision of Prof. Yann Lecun.
Joan completed his PhD in 2013 at Ecole Polytechnique, France, under the supervision of Prof. Stephane Mallat. Before his PhD he was a Research Engineer at a semi-conductor company, developing real-time video processing algorithms. Even before that, he did a MSc at Ecole Normale Superieure de Cachan in Applied Mathematics (MVA) and his undergrad at UPC (Universitat Politècnica de Catalunya, Barcelona) in both Mathematics and Telecommunication Engineering.
We are pleased to anounce our next event and the first in our series: Machine Learning Series I. In this talk we will know from 2 industry leaders how they are applying machine learning techniques to produce business results.
It is a great honour to have in our panel Alexandros Kartzoglou, Scientific Director at Telefonica Research, Jose A. Rodriguez-Serrano Data Science Team Lead at BBVA Data & Analytics
Alexandros Katzoglou is Scientific Director of Telefonica Research in Barcelona, Spain working on Machine Learning, Deep Learning, Recommender Systems, Information Retrieval. His team includes researchers in the areas of HCI, Networks and Systems. We are creating Machine Learning algorithms for customer data and data generated by Network operations. He received his PhD from the Vienna University of Technology and was a visiting fellow at the Statistical Machine Learning group at NICTA/ANU in Canberra, Australia. He frequently teaches Machine Learning courses at the UPF/GSE with some R, Python, Statistics, Recommender Systems and Deep Learning.
Jose A. Rodriguez-Serrano is currently a Data Science Team Lead at BBVA Data & Analytics since 2015. Formerly, he was Area Manager of the Machine Learning for Services group at Xerox Research Centre Europe, after being a permanent research scientist since 2010 at the Computer Vision Group. Previously he had been a postdoctoral fellow at the University of Leeds and Loughborough University, UK. He obtained his PhD in 2009 from the Universitat Autonoma de Barcelona and a degree in Physics in 2003 from the University of Barcelona. His main research topics have been image retrieval and learning to represent objects through embeddings of generative models and discriminative embeddings. He has published papers in IEEE Trans. PAMI, IJCV, CVPR or ICCV, among others and has 25 patents and patent applications. His main interest has been to apply state-of-the-art machine learning research to solve problems of industrial interest, and is fascinated by the challenge of making research outcomes simple to use for non-experts