ODSC West 2018

Preliminary Daily Session Schedule

Schedule Guide for Pass Holders

The ODSC Talks/Workshop schedule includes Friday November 2nd and Saturday November 3rd. It is available is to Silver, Gold, Platinum, Platinum Business, and VIP Pass holders

The Training/Workshop schedule includes Wednesday October 31st, and Thursday November 1st. It is available to Training,  Gold ( Thurs Nov 1st only), Platinum, and VIP pass holders

The Accelerate AI schedule is for Wednesday October 31st, and Thursday November 1st September 19th. It is available to Accelerate AI, Platinum Business, and VIP pass holders

Speaker and speaker schedule times are subject to change. More sessions added weekly

West 2018 Preliminary Schedule

We are delighted to announce our West 2018 Preliminary Schedule which lists 70% of our sessions.  Session times will be added in the coming week in addition to more talks, workshop, and training sessions. 
-Accelerate AI West
Wednesday, October 31st
Thursday, November 1st
Wednesday, October 31st
Thursday, November 1st
Accelerate AI Registration
Accelerate AI Registration
Accelerate AI Keynote 1 (Day 2)
Accelerate AI Keynote 2 (Day2)
Expo Hall Demos
Startup Showcase
Morning Coffee Break
Morning Coffee Break
How to Embrace The Opportunities Of The Algorithmic Economy

Innovation | Cross Industry | Talk


Look at your washing machine! It’s the dawn of the algorithmic revolution. You don’t care about temperature, number of rinces and spin speed. You just put your dirty clothes and it works! Our entire world will tomorrow run like your washing machine.
During the industrial revolution, we automatized manual work. We replaced craftsmen by assembly lines; and today, robots are helping or replacing humans in many factories. Algorithms are the robots of the information era. Accountants, lawyers, traders, doctors, salesmen, information workers and even perhaps programmers will be helped, and perhaps later replaced by algorithms. After the Data Driven Economy, Internet of Things, and Industry 4.0 will generate more and more data. Connected objets will generate data, and algorithms will be the key to create value from these Geopbytes of data.
The future will not be management by data scientists, but by algorithms scientists, who will transform data into previsions and actions. How to transform your organization and these opportunities into dollars, that is the question. The Algorithmic Economy will bring new business models. If you are managing a traditional company, you need to embrace this evolution. If you are a startup, you have great opportunities in front of you, if you know how the business works. Let’s talk together!

Philippe Nieuwbourg
Analyst, Author | Decido
Role and placement of data science in the organization

Innovation | Cross Industry | Talks


Companies employ various means of differentiation in order to gain a competitive advantage in the market. Traditional differentiators include network economies, branding, economies of scale, and so on. But the availability of data and compute resources, combined with the emergence of new business models, have enabled data science to become a strategic differentiator for some companies.
Eric Colson explores what it means to differentiate by data science and explains why companies must now think very differently about the role and placement of data science in the organization. If data science is going to be part of your competitive strategy, it warrants rethinking how the company is organized, how it defines its roles, and how it attracts and retains top talent.
Topics include:
* When and why the Data Science team should report to the CEO.
* Why data science is different from other departments like engineering, finance, or marketing
* How to create compelling roles for your data science team
* How to foster innovation without structured programs
* Considerations for measuring data science talent

Eric Colson
Chief Algorithmic Officer | StichFix
Experiment Driven Culture: AI and Machine Learning in E-commerce

Innovation | Cross Industry | Talks


Machine learning and artificial intelligence play a crucial role in modern day e-commerce platforms. In this talk, Kamelia will walk through practical examples of machine learning and artificial intelligence in e-commerce including learning to rank and click through rate prediction using deep learning techniques. She will also explain the requirements of building a culture to enable full integration of machine learning and artificial intelligence with business through experimentation and share lessons learned along the way.

Kamelia Aryafar, Ph.D
Vice President, Head of Data Science | Overstock.com
An Ethical Foundation for the AI-driven Future

Innovation | Cross Industry | Talks


Do we have a moral role as data scientists? How do you balance your responsibility to do what’s best for the company with your moral responsibility as a member of society?

As data becomes more accessible to everyone, and as AI and ML technologies become part of our everyday life, the data team takes on a very important moral role as the conscience of the corporation. Beyond just governing AI/ML processes to ensure they are accurate, it is now the data teams’ job to use their skills to be the moral compass of their organization and prevent unchallenged biases from influencing the world behind their walls. Are you taking charge to ensure data is used properly?

In this session, Harry Glaser, CEO & Founder of Periscope Data, will look at examples of cases where using machine learning and/or AI for classification, prediction, ranking and more run a strong risk of delivering immoral outcomes if unchallenged. He will explain how companies are taking charge to ensure their data is used properly, how to add human intelligence to artificial intelligence, and how to equip your data team with the right tools like NLP, SQL, Python, and R in a unified platform to perform their jobs while upholding their ethical duties.

Lastly, Harry will make the case that CDOs and their teams will increasingly have the role of “Chief Conscience” at the company, giving them a stronger seat at the executive table to make sure data and algorithms are used the right way.

Harry Glasser
Co-Founder & CEO | Periscope Data
AI-Beyond Cats and Dogs

Business | Cross Industry | Talks


Demystifying AI: What really is AI? Is it new, just like gravity was new when Newton explained it!
Applications : Will we ever go beyond distinguishing between Cats and Dogs
Pitfalls: Good morning I know AI won’t work.
Success: How to setup for it

Rohit Tandon
SVP Business Leader and Chief Analytics Officer | Genepact
Lunch Break
Lunch Break
Reality Check: Beyond the Hype. Real Companies Doing Real Business Getting Real Value with AI

Business | Cross Industry | Talks


AI – everyone is talking about it but who is actually doing it (and generating business results). This session takes an industry by industry perspective on true AI adoption disambiguating the hype from the reality, the theoretical from the practical and the research labs from ROI.
In Alyssa’s presentation, she will:
Showcase companies getting actual real value from leveraging artificial intelligence and discuss ideas around how any company, from SMB to enterprise, can use artificial intelligence within their own business and industry.
See examples of AI working in the real world featuring the following industries:
– eCommerce/Retail
– Robotics and IoT
– Agriculture Technology
– Education

Alyssa Rochwerger
VP of Product | Figure Eight
Deep Learning in Finance: An experiment and A reflection

Innovation | Finance | Talks


In this talk we investigate the protability of a quantitative trading strategy based on Deep Learning methods. Specically we focus on a variant of the Recurrent Neural Network (RNN), the Long Short Term Memory Network (LSTM) and show its predictive power on stock price data. We use LSTM networks for selecting stocks using historical price. The reason why RNNs are good for regression or classification of time series or data where time ordering matters is that RNNs capture the variation through time, thanks to its internal state dynamics. We made two studies, the rest focuses on predicting stock returns using one stock at a time. The hit-ratio in this experiment lies in the range 0.47 and 0.60 for the worst respectively best performing stock on unseen \live”” data. The second experiment looks at the whole universe of stocks simultaneously. In this experiment our model achieves a hit-ratio between 0.50 and 0.71 on unseen \live”” data. From this experiment two portfolios were constructed, a long portfolio and a long-short portfolio with a Sharpe ratio of 8 respectively 10 for each of the portfolios. Our stock universe in both studies is composed of 50 stocks from the S&P 500.

Dr. Miquel Noguer Alonso
Adjunct Professor | Columbia University
Even Data Science Needs Sidekicks

Business | Talks


Analytics, Data and Technology have unparalleled capacity to transform the way we work, but their real-world implementation needs more than just data science PhDs and Hadoop clusters. Many organization are creating or expanding special teams to bring analytics into the fold, but the journey is not without its share of significant hurdles. We often find ourselves stranded in legacy systems, separated by siloes and curtailed by the fear of change. A mindset shift required at the operative level, with a mix of EQ and IQ based approach. In this session, we will discuss the companions and sidekicks to Data Science that act as streamliners, accelerators, and safety nets in the race to materiality.

Neeraj Arora
Global Head of Decision Science and Data Automation Personal Insurance | AIG
Qunatitative Factor Investing Using Alternative Data And Machine Learning

Business | Finance | Talks


To gain an edge in the markets quantitative hedge fund managers require automated processing to quickly extract actionable information from unstructured and increasingly non-traditional sources of data. The nature of these “alternative data” sources presents challenges that are comfortably addressed through machine learning techniques. We illustrate use of AI and ML techniques that help extract derived signals that have significant alpha or risk premium and lead to profitable trading strategies.
This session will cover the following topics:
The broad application of machine learning in finance
Extracting sentiment from textual data such as news stories and social media content using machine learning algorithms
Construction of scoring models and factors for complex data sets such as supply chain graph, options (implied volatility skew, term structure)and ESG (Environmental, Social and Governance)
Robust portfolio construction using multi-factor models by blending in factors derived from alternative data with the traditional factors such as fama-french five factor model.

Arun Verma, PhD
Head-Quant Research Solutions | Bloomberg
Agile Experimentation – from ideas to deployment

Innovation | Cross Industry | Talks


As investment in data science continued to grow at CCC, the technology department adapted the software development lifecycle. From idea generation that collaborations between technology, strategy and the business, to quick experiments that test those ideas against the baseline, to an enhanced QA process that measures accuracy not just functionality, to deployment on a hyper-scale platform and finally to the support processes that make sure the predictions are within spec and a way to re-train and improve the models, this new software development lifecycle is at the forefront of process innovation.

John Haller
Chief Data Scientist and VP | CCC Informaiton Services
Best Practices for Deploying Machine Learning in the Enterprise

Innovation | Cross Industry | Talks


The hype around machine learning has reached epic levels, and many executives are eager to reap the benefits for their company. But how should they get started – and what can they learn from others’ experiences?

There is little information to help managers and business executives understand and deploy machine learning in the enterprise. This is understandable for two reasons: 1) machine learning has only recently reached a point where there were enough practical applications to merit attention by managers, and 2) (in part due to 1) there have been relatively few people that have significant experience deploying machine learning in the enterprise.

Attendees will learn how to:

“” Recognize patterns of success and failure with ML projects.
“” Find ML talent in a time of high demand.
“” Evaluate potential ML projects, build a project plan, and interpret results.
“” Avoid common ML project pitfalls.
“” Build systems to retain the value of an ML project after implementation.

The talk will give managers and executives concrete strategies for putting machine learning into practice. Although Robbie’s lessons are shaped by his technical experience, attendees won’t need a programming or machine learning background.”

Robbie Allen
CEO | Infinia ML
10 Things I Learned Deploying AI into Human Environments

Innovation | Cross Industry | Talks


Anyone who has worked as a practicing data scientist knows that sometimes the best model doesn’t win: integrating new systems and processes that come with AI often present challenges that require creative work around to ensure long-term success. Often the solution to this problem comes through inclusion of key stakeholders, and understanding the power balance of the organization. In this session The Data Guild’s Cameron Turner will share some of the triumphs and pitfalls in building/deploying AI solutions for Fortune 100 companies and strategies for making solutions stick. 

Cameron Turner
Co-founder | The Data Guild
AI as a service and platforms

Business | Cross Industry | Talks


Abstract coming soon!

Aarthi Srinivasan
Director of Product Management/Machine Learning/Block Chain | Target
Just How Much Data Is Required to Make Autonomous Vehicles Truly Road-Ready?

AI models guiding autonomous vehicle systems must be trained to anticipate and react correctly (i.e. safely) when faced with any of the myriad road scenarios they could encounter. Industry leaders are harnessing ever-greater volumes of data in order to perform this training, exposing AI to vast image datasets of known road factors and collected experiences that AI can call upon when interpreting given scenarios. This exposure to new data is essential to the pursuit of Level 3 (L3) autonomous driving, in which vehicles are able to take responsibility for all safety-critical functions under certain conditions (but where the human driver must take over when the AI encounters an unfamiliar situation).

AI is already able to solve many discrete driving issues, such as visually recognizing traffic light colors and obeying those rules. However, the tougher challenge is that, while image dataset volumes are exponentially increasing, this growth leads to diminishing returns as far as how much that data actually improves an AI model. And while L3 autonomous driving means that AI can navigate safely in all but less common “edge cases,” the reality is that driving in the real world means encountering such edge cases fairly frequently. Because of this, autonomous vehicle companies face significant questions as to how to know when their AI has reached the threshold of being safe enough for the road – and how much data that is going to take. This question is exacerbated by ongoing consumer expectations around (and apprehension about) self-driving cars, where even achieving accident rates that are twice or three times safer than human drivers may still not be enough to win their trust. The presentation will address the question of how much data will be required to succeed in reaching L3, and possible paths to get there and beyond.

Alexandr Wang
CEO| Scale
Afternoon Coffee Break
Afternoon Coffee Break
Accelerate AI Networking Reception
Reverse Startup Pitch
Dinner with Data Scientists
Dinner with Data Scientists
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See our Summary for an Event Overview

Schedule Summary
Open Data Science Conference