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Marco Tavora
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  • RIo de Janeiro
  • Brazil
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Latest Activity

Mariuz Niewczas liked Marco Tavora's blog post Connections between Neural Networks and Pure Mathematics
May 25
Prabhakar Gupta Garla commented on Marco Tavora's blog post How the Mathematics of Fractals Can Help Predict Stock Markets Shifts
"How can I print this paper without edges trimmed off?"
May 7
Prabhakar Gupta Garla liked Marco Tavora's blog post How the Mathematics of Fractals Can Help Predict Stock Markets Shifts
May 7
Marco Tavora's blog post was featured

Neural Quantum States

Picture by By Tatiana Shepeleva/shutterstock.comOne of the most challenging problems in modern theoretical physics is the so-called many-body problem. Typical many-body systems are composed of a large number of strongly interacting particles. Few such systems are amenable to exact mathematical treatment and numerical techniques are needed to make progress. However, since the resources required to specify a…See More
Jan 9
Tim Matteson liked Marco Tavora's blog post Connections between Neural Networks and Pure Mathematics
Jan 6
Marco Tavora's blog post was featured

Connections between Neural Networks and Pure Mathematics

Nowadays, artificial intelligence is present in almost every part of our lives. Smartphones, social media feeds, recommendation engines, online ad networks, and navigation tools are examples of AI-based applications that affect us on a daily basis.Deep learning has been systematically improving the state of the art in areas such as speech recognition, autonomous driving, machine translation, and visual object recognition. However, the reasons why deep learning works so spectacularly well are…See More
Jan 5
John Morrissey commented on Marco Tavora's blog post How the Mathematics of Fractals Can Help Predict Stock Markets Shifts
"Instead of lags, you could work in the spectral domain. The power spectral density (Fourier transform of the autocorrelation) decays with exponent -(1+2H)."
Sep 22, 2019
Zunqiu Chen liked Marco Tavora's blog post How the Mathematics of Fractals Can Help Predict Stock Markets Shifts
Aug 2, 2019
Cesar Mendoza commented on Marco Tavora's blog post How the Mathematics of Fractals Can Help Predict Stock Markets Shifts
"only half of it"
Jul 17, 2019
Marco Tavora and Jane Brewer are now friends
Jul 17, 2019
Norberto J. Sanchez liked Marco Tavora's blog post How the Mathematics of Fractals Can Help Predict Stock Markets Shifts
Jul 17, 2019
William Stowe liked Marco Tavora's blog post How the Mathematics of Fractals Can Help Predict Stock Markets Shifts
Jul 9, 2019
Norberto J. Sanchez liked Marco Tavora's blog post How the Mathematics of Fractals Can Help Predict Stock Markets Shifts
Jul 7, 2019
Riccardo Cannaviello liked Marco Tavora's blog post How the Mathematics of Fractals Can Help Predict Stock Markets Shifts
Jul 7, 2019
Marco Tavora posted a blog post

How the Mathematics of Fractals Can Help Predict Stock Markets Shifts

In financial markets, two of the most common trading strategies used by investors are the momentum and mean reversion strategies. If a stock exhibits momentum (or trending behavior as shown in the figure below), its price on the current period is more likely to increase (decrease) if it has…See More
Jul 4, 2019
Marco Tavora's blog post was featured

How the Mathematics of Fractals Can Help Predict Stock Markets Shifts

In financial markets, two of the most common trading strategies used by investors are the momentum and mean reversion strategies. If a stock exhibits momentum (or trending behavior as shown in the figure below), its price on the current period is more likely to increase (decrease) if it has…See More
Jul 4, 2019

Profile Information

Company:
Prior Solutions
Job Title:
Co-Founder and CTO
Seniority:
Executive
Job Function:
Data Science, Machine Learning, Deep Learning
Industry:
Consulting
Short Bio:
I am a theoretical physicist and data scientist. During my Ph.D. at New York University, my research was on quantum many-body systems out of equilibrium, one of today's hottest research fields in theoretical physics. In 2015 I earned a Ph.D. for my thesis "Prethermalization, universal scaling at macroscopic short times, and thermalization following a quantum quench." Subsequently, I spent two years as a postdoctoral researcher focusing on applications of computational physics methods to areas such as quantum chaos, non-equilibrium quantum dynamics, and thermalization viability. I've published several articles in the most prestigious peer-reviewed journals in the world.

I have 15 years of experience building mathematical/statistical models spanning a wide range of disciplines, including: non-equilibrium quantum systems, marketing attribution for digital and offline channels (using machine learning, game theory, stochastic processes and hierarchical Bayesian models), influencer marketing, marketing mix modeling, record linkage/data matching/entity recognition, conjoint analysis (using support vector machines), bidding optimization for digital media (using original functional optimization techniques) and many others.

I was a speaker at the American Physical Society (APS) March Meeting, the largest physics conference in the United States, in 2013, 2015 and 2016 (for more details and also information about other conferences see my personal website www.marcotavora.me)

In 2017 I co-founded Prior Solutions, a data-driven consulting company with clients in more than ten countries around the world.
LinkedIn Profile:
http://marcotavora.me
Interests:
Contributing, Networking

Marco Tavora's Blog

Connections between Neural Networks and Pure Mathematics

Posted on January 5, 2020 at 5:30am 0 Comments

Nowadays, artificial intelligence is present in almost every part of our lives. Smartphones, social media feeds, recommendation engines, online ad networks, and navigation tools are examples of AI-based applications that affect us on a daily basis.

Deep learning has been systematically improving the state of the art in areas such as speech recognition, autonomous driving, machine translation,…

Continue

Neural Quantum States

Posted on January 5, 2020 at 4:30am 0 Comments



Picture by By Tatiana Shepeleva/shutterstock.com

One of the most challenging problems in modern theoretical physics is the so-called many-body problem. Typical many-body systems are composed of a large number of strongly interacting particles. Few such systems are amenable to exact…

Continue

How the Mathematics of Fractals Can Help Predict Stock Markets Shifts

Posted on July 4, 2019 at 12:30am 3 Comments

In financial markets, two of the most common trading strategies used by investors are the momentum and mean reversion strategies. If a stock exhibits momentum…

Continue

Deep Learning Explainability: Hints from Physics

Posted on May 20, 2019 at 11:46am 0 Comments


Nowadays, artificial intelligence is present in almost every part of our lives. Smartphones, social media feeds, recommendation engines, online ad networks, and navigation tools are some…

Continue

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