Homepage of Armando Vieira

Data Scientist, Entrepreneur and Speaker

I am an experienced Data Scientist, Speaker, Entrepreneur and AI Innovation Advocate.

I founded Lidinwise, a consultant company, to help companies take advantage of the opportunities of Machine Learning to leverage their data and create competitive data driven business. I work with big organizations, and Startups as well, as a strategic advisor and hands-on consultant.

I obtained a PhD in Physics and pioneered research in Deep Neural Networks back in 2000, with several publications in peer-review journals. I have extensive experience in machine learning and AI in international teams as lead data scientist and head of data science. 

More recently, I focus my efforts in helping organizations leverage the capabilities of machine learning and AI to become data driven and efficient. 

In my last book, Business Applications of Deep Learning, I discuss how AI will disrupt almost every business, from Banking to Medicine and the future of data driven organisations. 

I’m also interested in societal aspects and implications of AI and ethical consequences of decisions taken automatically by cognitive algorithms.
Pioneer in Deep Neural Networks with this publication in 2003 (cited in the recent review on Deep Learning by Jurgen Schmidhuber) and a more recent one on Applicationbs of Deep Learning in Biomedicine and Prediction of online user behaviour on an ecommerce site.

My book “Deep Neural Networks: Overview and Business Applications“. A summary in a format of book chapter is available here.

Screenshot 2018-03-04 07.55.32

My TEDx talk 13 March (minute 11):

My new book chapter “Digital Markets Unleashed“, By Springer, August 2017 – on applications of Deep Learning and AI in Banks.

More on my Linkedin profile or Github account.

Armando Vieira

Check my latest work based on conditional Generative Adversarial Neural networks (cGAN) applied to facade generation.

My video presentation on “AI, from a long Winter to a blossoming Spring“.

My presentation at EARL conference, London 2015:

Portfolio of Customers


I’ll be a speaker @PyData London 2015

The topic will be “Information Retrieval from Knowledge Graphs and non-Structured data sources”. The full schedule of the meeting is here: http://london.pydata.org/schedule/

Talk on Conference of R users in London (REAL)

My talk on Earl Conference, London 2015 will be “Building interactive visualizations with Shiny to explore data from social and health care in UK”. Social and Health care in UK produce annulay immense data aggregated at several levels. To add to the challenge, data is...

Social Care and Health in UK using Google charts

This is my newest visualization work using UK data. http://armandoanalytics.blogspot.pt/2015/03/health-social-care-uk.html

Are Data Scientists magicians?

Sitting atop a mountainous treasure trove of data, most all businesses are thirsty for people who can take a massive set of data and turn it into something meaningful. Whether it’s pinpointing new sources of revenue or predicting the next, best product feature, businesses are...

What’s wrong with AI approach?

There have been tremendous advances in AI and lots of buzz around the possibility of the singularity coming soon acclaimed by scientists like Kurzweil. Inspired by the success of Deep learning, the new kid in town that took the field by storm winning almost all machine learning...

Is Deep Learning capable to solve the AI puzzle?

Deep Learning is the buzz word of the moment. But can it revolutionize artificial intelligence as some suggests? There is good reason to be excited about deep learning, a sophisticated “machine learning” algorithm that far exceeds many of its predecessors in its abilities to...

Machine Learning for business is much more than number crunching

“We have build a marvelous model using Deep Learning!”. Sure, but what does it means when heading for production?  Does the data is as clean at it should? Is it available? At what cost? What are legal and privacy implications? How will customers react? Will decisions...

Early Steps of deep learning

I’m very thankful to Jurgen Schimdhuber for having cited my work on HLVQ on its excellent review as one of the predecessors of Deep Learning. It was published in 2003.

The Business side of Deep Learning

I’m very happy that any area of machine learning (aka, statistical inference and decision-making) is beginning to make impact on real-world problems. I’m also overall happy with the rebranding associated with the usage of the term “deep learning” instead...