Applying AI & Machine Learning To Finance and Technology

MIAMI, FEBRUARY 8 & 9 2018

The Data Science Salon is a destination conference which brings together specialists face-to-face to educate each other, illuminate best practices, and innovate new solutions in a casual atmosphere with food, drinks, and entertainment.

Data Science Salon unites the brightest leaders in finance and technology in Miami data science fields. We gather to educate each other, illuminate best practices, and innovate new solutions. Data Science Salon | MIA is a two day conference with workshops for executives, data scientists, developers, and business development professionals alike. We’ve collected extensive data to figure out just the right mix of people, content, and entertainment to make our conferences as seamless, informative, and fun as possible.

Raise your profile and establish thought leadership for your business. Join the most influential leaders in data science and share your knowledge about the latest machine learning techniques and interesting case studies. Data Science Salon will bring together twenty speakers per event. With a full day of diverse content there’s plenty of opportunities to share your knowledge, attract new hires and make lifelong business connections.

SPEAKERS

DAYS

WORKSHOPS

ATTENDEES

Featured speakers

Meet our speakers

Marc Fridson

Principal Data Scientist at Carnival Cruise Line

Catalina Arango

Founder and CEO, Exteractions

Douglas Pestana

Senior Data Scientist at Life Extension

Chelsea Douglas

Senior Data Scientist at Plotly

Schedule

10:45AM - 11:00AM
Coffee Break
12:15PM - 1:15PM
Lunch

ABSTRACT:
There are so many open sourced and other AI frameworks available. Tensorflow, Caffe, Torch, PyTorch, Caffe2, MXNet, Spark MLlib, CNTK, H2O, DMTK, Theano, Scikit, etc. Each of these frameworks have certain strengths. Some of tem work with certain languages and some do not. In addition, some of these frameworks have been used by popular authors of tech papers and have open sourced the code. Some of these are free and some are not. Some frameworks are better to use for image processing use cases and some are not. Organizations and individuals are confused on which framework is best for what type of use case and which framework is better. I would like to discuss and compare various frameworks. Also I would like to pick a few use cases that will be implemented as a hands-on exercise using a few frameworks.

AUDIENCE:
This is for those executives that are looking to decide on what framework to use. This workshop is also for those implementerswho want to implement a couple of use cases on a couple of
different frameworks

AGENDA:
1. Discuss a lot of frameworks: their use, peculiarities, capabilities, when to use and when not to use, which one is
better than the other, which language works better with which framework, which framework works better with GPUs / large data, etc. Bring any questions you have and we could discuss.

2. Implement a couple of use cases in number crunching, NLP and Image processing areas and get a feel of how the
framework responds.

SK Reddy, Chief Product Officer AI & ML at Digitalist Group
3:15PM - 3:45PM
Coffee Break

Join us for some beer, wine and yummy bites on the 6th Floor at Venture Cafe.

Expect a surprise guest!

8:00am - 8:55am
Coffee & Registration

Come early and get a book of your choice from O’Reilly Media (first come first serve)

Anna Anisin Founder and CEO at Formulatedby
Andrea Lubell Co Founder at Innergy Meditation
Brian Macdonald, Director of Hockey Analytics at Florida Panthers

Carnival is the world’s largest travel leisure company, with a combined fleet of over 100 vessels across 10 cruise line brands and growing. We analyze social channels (Facebook, Twitter, Instagram), web analytics and booking data to predict customer behavior and develop marketing strategies. This session will discuss the challenges of mining all of this data and some of the Machine Learning techniques we use to segment our customers (e.g. Clustering) and predicting the value of a customer (e.g. Regression).

ManChon (Kevin) U | Senior Director, Head of Marketing Analytics & Data Science at Carnival Cruise Line & Marc Fridson | Principal Data Scientist at Carnival Cruise Line
11:00am - 11:30am
Coffee Break

This talk is going to examine differences between doing Machine Leaning and Statistics. We assume that these two cultures tackle a large set of the same or similar problems hence a comparison is justified.

The differences between the two cultures can be observed by comparing
1) what is considered important while participating in them, and
2) the methodologies employed for solving “real life”, practical problems.

Obviously, the cultural differences stem from practitioners backgrounds and schooling and we are going to point major differences of educational and experience backgrounds. (Also, jobs.)

General observations will be stated and an overview of concrete examples is going to be presented. The concrete examples are from the (sub-)fields of data analysis, prediction, recommendations, time series, gambling mathematics.

Several examples are going to be discussed in more detail. We are going to consider how to look for or come up with the new examples showing the cultural differences. (Some of) the major approaches and tricks of each culture are going to be identified and listed.

Anton Antonov, Applied Mathematics Consultant
Catalina Arango, Founder and CEO, Extrations

A short introduction to cancer immunotherapy followed by several machine learning problems which arise from designing personalized cancer vaccines.

Alex Rubinsteyn, Research Scientist at Icahn School of Medicine at Mount Sinai
1:00pm - 1:45pm
Picnics (Lunch), Music & Meet & Greet Your Peers

Irma Becerra-Fernandez, Ph.D. Provost & Chief Academic Officer at St Thomas University sits down with the experts themselves:
Joshua Patterson, Director of Applied Solutions Engeneering at NVIDIA
Mauro Damo, Sr Data Scientist at Dell Technologies
Emily Riederer, Senior Data Scientist at Capital One

Irma Becerra-Fernandez sits down with Joshua Patterson, Mauro Damo and Emily Riederer

The increasing need for clustering in several scientific domains has inevitably driven the creation of innovative algorithms, each designed to perform more efficiently in certain applications. More specifically, in many applications the data entities involved can be portrayed effectively by a graph as a collection of nodes and edges. One of the most established algorithms for graph clustering problems is the Markov Cluster Algorithm (MCL).

When dealing with large and complex datasets, the underlying graphs can easily reach proportions that independent computing systems are inadequate to deal with. Additionally, the graphs encountered are typically sparse: the number of edges is far smaller than might be possible in a fully-connected graph. Consequently, there is a concrete need for algorithms that are designed to handle sparse graph clustering utilizing distributed computing resources.

Our motivation was the development of a distributed architecture, able to accommodate large and sparse graphs, to actualize the MCL and R-MCL algorithm. The Apache Spark framework was chosen due to its ability to utilize distributed resources and its proven track record. Although Spark is a framework capable of handling massive datasets, it currently does not provide rich support for computation with sparse matrices and sparse graphs. Hence, methods have been implemented to enable the exploitation of sparse adjacency matrices in distributed sparse matrix multiplication, a critical component of MCL. The proposed solution can handle arbitrarily large inputs, provide almost linear speed-up with the addition of computational resources and output results directly comparable to the non-distributed reference MCL implementation.

Athanassios Kintsakis, Senior Machine Learning Engineer at Capital One Financial
David Frigeri, Practice Area Lead | Advanced Analytics and Visualization at Slalom Consulting
3:30pm - 3:45pm
15 min Coffee + Snack Break + Comedian

Deep learning has found great success in computer vision, translation, and speech recognition. However, its applications in medicine are only starting to emerge. In this talk, I plan to discuss some of the applications and inner workings of different types of deep learning (CNNs, RNNs, and DNNs) for medicine.

Anabetsy Rivero, Cofounder and CEO at Metastatic AI

For most data scientist building models is hard work, but deploying them into production and impacting business processes can be even harder. In fact, research shows that only about 10% of data science models get deployed into production, and those that do can take between 6 to 9 months to be deployed. This session will highlight the challenges that data scientist and organizations alike face when trying to deploy machine learning models and how to overcome these challenges. It will examine several use cases where models built in R and Python have been able to deliver impactful results across several industries.

Director of Data Science at Alteryx

Big Data is in big trouble. Many executives don’t even know what their data scientists are doing.

Despite that, the Big Data hype machine is in full throttle. Data Scientist has been called the “sexiest job of the 21st century.” And “Data is the New Oil” is the marketing slogan data professionals use to smooth-talk Corporate America that Big Data is a necessity in their organization.

But when the C-Suite asks their data scientists what is the ROI on data science? Crickets. Followed by a vague response like, “we are leveraging data to deliver insights and drive growth.”

In this talk, I provide specific case studies on how data science provided no-bullsh**, measurable impact to the bottom line. And I give a blueprint to data scientists on how to measure money from their models and data products using a “Data Science Scoreboard.”

Douglas Pestana, Senior Data Scientist at Life Extension

The evolution of data consumption across many industries has inspired many interactive user models that challenge traditional workflows. Fixed-Income Portfolio Managers and Credit Analysts who manage risk and look for relative value opportunities demand innovative tools to interpret ever-expanding sets of data whether traditional financial data or non-traditional/behavioral data sets. Within reproducible research, interactive data visualization allow users to adjust parameters and interact with the information in an intuitive manner. Users can explore new and existing credit data sets, models, and charts with rich and engaging visual cues, dig into areas of relevance, and quickly see links across multi-dimensional data sets.

Moody Hadi, Senior Director - Innovation and Product Research at S&P Global
Mollie Pettit, Data Scientist at Metis

BE A SPEAKER

Event tickets

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WHAT PEOPLE ARE SAYING

The Data Science Salon series is the most important new conversation happening in the industry right now.
Eduardo Arino de la RubiaDomino Data Lab
What I appreciate about the Data Science Salon is that it’s a conference I actually want to go to – it’s fun, you learn some great things, you meet some great people, and at the end of the day you feel energized rather than drained. It’s taken me out of my silo and put me in the community. I think it’s great.
Jack PalmerPlotly
Their conferences are smaller, more intimate, with lots of opportunities for workshops and networking, which helps fill that need in the data science community to get together from time to time.
Roger MagoulasO'Reilly Media

sponsors

INFO

  • CIC Miami, 1951 NW 7th Ave Suite 600, Miami, FL 33136
  • info@formulated.by
  • (415) 322-0760
  • February 8 & 9, 2018
  • 8:00 AM – 8:00 PM