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Data Science [clear filter]
Saturday, October 20
 

10:00am

Is the Best Predictor Actually the Best?
It is common that to build a predictive model the data analyst tries to select the best subset of predictors. In this presentation we answer the question, does this subset include the single best predictor, always? We show examples where the best predictor is not always in the best subset, and the worst one actually is included in the best subset. We discuss both numeric and categorical predictors, and show simple extreme examples on how not to set up the data set before building the model. Otherwise the results may be misleading. We use data visualization and measures to explain the differences, such as going from 0.05 to 0.95 r-squared or from a negative adjusted r-squared to a model with that measure close to one.
We conclude with suggestions on how to (or how not to) build models in high-dimensional space, where graphical displays may not be as helpful as desired.

Speakers
avatar for Cesar Acosta

Cesar Acosta

Professor, University of Southern California
Dr. Acosta is a Data Science and Data Analytics professional with many years of experience analyzing highly complex data, building advanced data mining models to predict market outcomes useful to improve decision making in Marketing Analytics, Financial investing, and business operations... Read More →


Saturday October 20, 2018 10:00am - 10:30am
Ballroom # 403B

10:00am

When Classical Statistics Meet Modern Data Science
Modern applications have generated many data sets of complex nature, such as high dimensionality, heterogeneity and unknown structure of interest. In this talk, I will discuss a few ideas on extending traditional statistical methods such as the principal component analysis, expectation maximization, and mixture model, to accommodate challenges in those applications. Theoretical properties, numerical results, and applications in biomedical studies will be discussed.

Speakers
avatar for Weining Shen

Weining Shen

Professor, UC Irvine
Weining Shen is assistant professor of Statistics at UC Irvine. He received his PhD from North Carolina State University in 2013, and his thesis won the Leonard J. Savage Dissertation Award. In 2013-2015, he was a postdoctoral fellow in Department of Biostatistics, M.D. Anderson Cancer... Read More →


Saturday October 20, 2018 10:00am - 10:30am
Ballroom # 409

10:30am

How to Use Data Analytics to “Win the Race for Eyeballs” in the Eye Candy Economy
Today we live in an “Eye Candy Economy”, everyone is craving for delightful content to satisfy their Eyes and Brain. The Digital Media Companies think that “Content is King” and are in a multi-billion dollar race to create more content. But Consumer is the “King Kong” who decides which content is viral and which content is dud. This has created a complex business problem of identifying topics and themes which will be viral and need to produce viral content. Advance Analytics and AI can be used to solve this complex problem by utilizing the Content Data from social media and Social Interaction and other data of the users and content to predict the resonating content that will have a large audience. This talk will help Digital Media Companies to “Win the race for Eyeballs and Revenue”.

Speakers
avatar for Arif Ansari

Arif Ansari

Professor, University of Southern California
Arif Ansari is a Professor in the Data Sciences and Operations Department at Marshall School of Business at University of Southern California, Los Angeles. He is the CEO of Intuition Intelligence Inc. developing Creative Analytics Solution for the Digital Media. He is an expert in... Read More →


Saturday October 20, 2018 10:30am - 11:00am
Ballroom # 409

10:30am

The New Media Ecosystem – Disruption or Dissolution?
We’ll explore the ways data at scale, machine learning, crypto currencies and distributed databases are remaking what we think of as media. We’ll do this from two perspectives:


Disruption - Are established media distribution, content creation, and audience engagement platforms doomed by design? As the media industry is remade by new entrants and new business models, the distinction between media and technology has effectively disappeared. Strategy and differentiation for new media entities is as much about flexible engagement platforms, integrated operations, and partner ecosystems as it is about new ideas and new content. While audiences fragment and migrate, new audience currencies are emerging distinguished by speed, scale, and flexibility. We'll explore the evolution of media engagement models, understand these in the context of current market trends, and explore some of the ways new entrants are using emerging technologies to enable groundbreaking customer experiences.


Dissolution - We’ll also explore the underside of new media platforms, specifically the ways personal data syndication, programmatic content, simplistic learning algorithms, and automated decisioning can be manipulated to perpetuate bias, raise rancor and undermine good faith. Do media and technology companies have obligations that extend beyond the confines of customer relationships? What are the implications of captive ecosystems that are becoming increasingly enmeshed in more areas of our lives?

Speakers
avatar for Gerald Parham

Gerald Parham

Fan Experience and Omnichannel Customer Engagement Leader, IBM
Mr. Parham is a senior product & service innovation leader with 20 years’ experience in media and marketing, technology and creative strategy. Prior to joining IBM, he founded a social discovery platform (yuni vrs) and authored a patent for enabling immersive interaction of user-generated... Read More →


Saturday October 20, 2018 10:30am - 11:00am
Ballroom # 403B

11:00am

(Cancelled) Why Data Governance Matters
The adage of "garbage in, garbage out" could not be truer for data analytics. While the least visible (and not the flashiest), data governance is the most impactful factor to any data driven organization. A good data governance system will allow organizations to lay down a strong foundation to build their data strategies on with consistent and reliable data. This presentation will give an overview of why every organization needs data governance and a framework to build theirs on.

Saturday October 20, 2018 11:00am - 11:30am
Ballroom # 403B

11:00am

Conversational AI for Business Intelligence: Ask Interesting Questions
It often takes a long time for business users in enterprises to get the right data and insights, even if the data is well organized and structured. This is because they may not know where the data is located, how it is modeled, and how to frame the right queries for their needs. As a result, traditionally, access to analytics content from BI and analytics and data science platforms has mostly been limited to power users, business analytics and specialist data scientists with varying degrees of analytical and technical skills. This is where conversational analytics comes in. Conversational chatbots for analytics allows any user to ask voice or text questions of their data and receive back a natural language and potentially, a visual analysis of the most statistically relevant and actionable insight for that user. In this talk I shall describe some of the key AI technologies needed to deploy conversational analytics including natural-language processing (NLP), natural-language generation/narration, chatbots and AI/augmented analytics. I shall also describe some case studies where expanding access to insights from analytics to all employees helped driving strong business impact.

Speakers
avatar for Anand Ranganathan

Anand Ranganathan

Chief AI Officer, Unscrambi
Anand Ranganathan is a co-founder and Chief AI Officer at Unscrambl, Inc. He is a data scientist, AI developer, Big Data developer, architect and researcher rolled into one person. He is leading Unscrambl’s product development in several cutting-edge areas, including context-aware... Read More →


Saturday October 20, 2018 11:00am - 11:30am
Ballroom # 409

11:30am

Application of Random Forests for Educational Video Games
Prediction and interpretation with trees and random forests for learning behavior in educational video games. How extracting these insights can help improve adaptative learning systems.

Speakers
avatar for Marie H Roy

Marie H Roy

Data Scientist, Age of Learning
I am a data scientist, soon to be PhD, specialized in predictive modeling and ensemble methods. I work in a learning sciences team in an EdTech company with a goal to leverage machine learning for educational technology.


Saturday October 20, 2018 11:30am - 12:00pm
Ballroom # 409

11:30am

The Fashionable Misunderstandings of Data Science in the Industry
Along the years, I have realized that many business leaders do not understand what Data Science is or why they would need experts of the field in their organization. Data Scientists even get too often confused with Data Analysts, Business Intelligence Analysts or Data Engineers.


But Data Science, Big Data, Machine Learning or AI are fashionable concepts so companies need to hire Data Scientist, right? So how do we recruit those specialists? Sometime the job descriptions list a set of requirements that demand 3 PhDs to achieve, sometimes you just need to know Excel to apply or sometime the job description is actually describing a Data Engineer or BI analyst. Few years ago, every Data Scientists had to know Hadoop or NoSQL for some reason. Few years later, it was about knowing Spark. Today it is about knowing everything in Deep Learning or/and being a Kaggle master.


When the companies finally get hold of a new Data Science recruit with the 3 PhDs, the question remains: what should they do with them? Too many times you will find Data Scientists ending up doing dashboard reporting instead of looking for ML driven business solutions. Too many times the companies simply do not have the engineering infrastructure around the data to leverage any data science solution. Too many times, the Data Scientists are actually discouraged to explore new ideas because of the upper management’s fear around the statistical uncertainty intrinsic to the field. Let’s discuss about those misunderstandings surrounding Data Science

Speakers
avatar for Damien Benveniste

Damien Benveniste

Lead Data Scientist, Rackspace
Damien is currently a Lead Data Scientist at Rackspace, one of the leading companies in managed cloud computing. He has worked in diverse industries such as a market place for virtual items (Opskins), e-commerce (Bluestem Brands), healthcare (Transfuse Solutions) or data storage (EMC... Read More →


Saturday October 20, 2018 11:30am - 12:00pm
Ballroom # 403B

1:00pm

Data Science in Academia vs. Indusry. What is Right for Me?
During the last few years, data science has brought about many changes to how we live and work. Students interested in careers in data science often ask "do I need a graduate degree to work as a data scientist?" Obviously, while there is no one-size-fits-all answer to that question, there are some important questions to consider when making career choices in data science. I compare and contrast academia vs industry as career options after bachelors degree or masters degree based on my personal experience in research and industry.

Speakers
avatar for Sang-Yun Oh

Sang-Yun Oh

Professor, UC Santa Barbara
I worked for a number of years between my bachelor’s degree in physics at UC Berkeley, and my Ph.D. in computational and mathematical engineering at Stanford. My work experience span research and industry as medical imaging research engineer at Lawrence Berkeley National Lab, director... Read More →


Saturday October 20, 2018 1:00pm - 1:30pm
Ballroom # 403B

1:00pm

Latent Dirichlet Allocation on Player Session Behavior in Gaming
As data scientists at Riot Games, our goal is to focus on providing the best player experience possible. Understanding how players play individual sessions and how their styles evolve over time is key to evaluating our products and helps make decisions for future designs. In this talk, I will present how we use Latent Dirichlet Allocation (LDA), a three-level hierarchical Bayesian model, to identify major session topics in League of Legends and how we construct a player segmentation with session clusters.

Speakers
avatar for Ran Cao

Ran Cao

Data Scientist, Riot Games
Ran Cao is a Data Scientist for League of Legends at Riot Games. League of Legends is one of the most-played games in the world, with frequent updates to expand the possibilities for players. Her data science background has focused on Bayesian modeling across genetics, public policy... Read More →


Saturday October 20, 2018 1:00pm - 1:30pm
Ballroom # 409

1:30pm

Deep Learning for NLP (Natural Language Processing)
As my main focus is on applying Deep Learning techniques in NLP (Natural Language Processing) field, I would love to speak about couple of my research projects which I did with New York University. First one was on Reading Comprehension task, where we tried to beat the state of the art models with experiments on attention based RNN (Recurrent Neural Network) models. We performed our task on two Daily Mail reading comprehension data set. Second one was with Prof. Kyunghyun Cho, where our main goal was to develop a system capable of summarizing content for user query. Here as well we tried to solve the problem with variety of convolution and recurrent neural networks with attention.


I would like to start my session by introducing the development of Deep Learning since last few years in NLP domain. I can control the technical level of my speech based on the level of audience.


More information on these research projects is available on these links below:
Summarization : https://github.com/up276/NLP2K16/blob/master/Two_Layer_Attention_Reading_Comprehension/NLP_Project_Paper_up276_vec241.pdf


Reading Comprehension: https://github.com/up276/QueryBasedSummarizationNLP/blob/master/IndependentStudyReport_vec241_up276.pdf


Other projects: https://cims.nyu.edu/~up276/info.html

Speakers
avatar for Urjit Patel

Urjit Patel

Data Scientist, Praedicat Inc | New York University
I hold a Master’s degree in Data Science from New York University. I am an active researcher in Deep Learning and NLP domain. I was part of Prof. Kyunghyun Cho's (CIFAR Azrieli Global Scholar) NLP research group at NYU. I was a Teaching Assistant for Prof. Yann LeCun (Director of... Read More →


Saturday October 20, 2018 1:30pm - 2:00pm
Ballroom # 403B

1:30pm

Marketing Like a Musician
I saw that the IDEAS conference is coming to L.A. in October. Since L.A. is my home, I would love to come and speak. I do talks about how to think like musicians when it comes to problem solving, marketing, engaging audiences, etc. I’d also be happy to share how I grew a Facebook audience for my business, PianoCub, to over 44K followers.
Here’s some footage of a recent talk of mine: https://www.youtube.com/watch?v=p7NiUDs9gx0

Speakers
avatar for David Brown

David Brown

Entrepreneur/Musician, PianoCub
David Asher Brown is a rare voice among young, American composers. The Metropolitan Opera’s William Lewis praised him as, “one of the most brilliant music makers it has been my pleasure to serve”. His composition craft innovatively draws on the sounds and techniques of everything... Read More →


Saturday October 20, 2018 1:30pm - 2:00pm
Ballroom # 409

2:00pm

Analytics for Industrial Cyber Control Security
Modern industrial control systems are highly automated and often controlled by computers and connected devices. In addition to physical failures, cyber attacks present an increasing threat to normal system operations. We propose an unsupervised learning approach for automated detection of adverse cyber attacks. In particular, we will devise the unsupervised learning based approaches for anomaly detection using only datasets collected under normal system conditions. Anomaly detection techniques are extremely useful for real-life system security management because cyber attacks rarely happen in practice even though their impact could be disastrous. We conduct detailed studies on a water treatment plant in which water processes are all connected by computer control systems. We demonstrate our analytics tools can effectively detect new types of cyber attacks and, moreover, can locate potential attacking points and compromised control units or devises

Speakers
avatar for Honggang Wang

Honggang Wang

Assistant Professor, University of La Verne
Honggang Wang is an assistant professor in Analytics in College of Business and Public Management at University of La Verne. He received his Bachelor of Science degree in Power Engineering from Shanghai Jiao Tong University, Shanghai, China, in 1996, Master of Science in Manufacturing... Read More →


Saturday October 20, 2018 2:00pm - 2:30pm
Ballroom # 409

2:00pm

Data Science for Good – A Case Study in Wisconsin Higher Ed
Speakers
avatar for Fletcher Riehl

Fletcher Riehl

Data Scientist, System1
I am a Data Scientist at System1. System1 is the world’s largest independent marketplace for keyword pay-per-click advertising. I work on optimization and forecasting projects for the SEM and native advertising lines of business. Prior to System1 I was a software engineer at Maker... Read More →


Saturday October 20, 2018 2:00pm - 2:30pm
Ballroom # 403B

2:30pm

Beyond Chat Bots: Towards Truly Intelligent Conversational Agents
Alexa and other chat bots have taken the world by storm. However, the current reality is that natural language applications have severe limitations, failing on tasks that even a child could easily handle. They typically have no memory of what was said earlier, don't reason about their task, and have no common sense. Moreover, they are usually engineered to perform just one specific task -- i.e. they do not have any general intelligence.


Originally (the first wave), conversational agents were programmed manually using logic flow. Everyone is well aware of limitations in scaling and maintaining such system. More recently (in the second wave), big data and machine learning have automated task classification (intent identification) and some key parameter extraction. However, we now also understand the difficulty in obtaining massive amounts of manually tagged training data, and the problems with lack of transparency, reasoning, and real-time adaptability in these systems.


This talk will explain how a cognitive architecture approach can overcome many of these limitations and provide a much more intelligent platform for conversation agent. A practical example of this 'third wave' technology will be demonstrated.

Speakers
avatar for Peter Voss

Peter Voss

CEO/Chief Scientist, Aigo.ai Inc
Peter Voss’ careers include being an entrepreneur, engineer, and scientist. His experience includes growing a computer solutions company from zero to a 400-person IPO. For the past 15 years, his focus has been on developing AGI (artificial general intelligence). In 2009 Peter founded... Read More →


Saturday October 20, 2018 2:30pm - 3:00pm
Ballroom # 409

2:30pm

Deploying Data Science Engines to Production: Comparing Options + Code Examples
Reducing the gap between R&D and production is still a challenge for data science/ machine learning engineering groups in many companies. Typically, data scientists develop the data-driven models in a research-oriented programming environment (such as R and python). Next, the data/machine learning engineers rewrite the code (typically in another programming language) in a way that is easy to integrate with production services. This process has some disadvantages: 1) It is time consuming; 2) slows the impact of data science team on business; 3) code rewriting is prone to errors. A possible solution to overcome the aforementioned disadvantages would be to implement a deployment strategy that easily embeds/transforms the model created by data scientists. Packages and products such as jPMML, MLeap, PFA, Amazon SageMaker, and PMML among others are developed for this purpose. In this talk we review some of the mentioned packages along with a coding exercise, motivated by a real world project at Meredith Corp. The project involves development of a near real-time recommender system, which includes a predictor engine, paired with a set of business rules.

Speakers
avatar for Mostafa Majidpour

Mostafa Majidpour

Senior Data Scientist, Meredith
Mostafa Majidpour is a Senior Data Scientist with Meredith (previously Time Inc) working on harvesting the power of machine learning for various user experience related recommendation engines. Previously at ZEFR, he has been involved in building/improving their forecasting engine... Read More →


Saturday October 20, 2018 2:30pm - 3:00pm
Ballroom # 403B

3:00pm

Location Intelligence Goes Big in Digital Marketing
The dawn of digital marketing began with search ads, then proceeded to social ads and now has evolved to include geospatial ads. Location Intelligence is the discipline that analyzes the petabytes of location data generated daily by mobile devices to extract meaningful and actionable consumer insights.


Predicting customer intent is an age-old problem. With more than 80% of sales still occurring in brick and mortar stores, it is important to understand the customer intent. Currently, many marketers such as Facebook and Snapchat are using location data to calculate ROI and lift of in-store visits for their ads. In this session, I will discuss ways to advance our understanding of store visitors using location intelligence: who they are and what they do in the physical world.

Speakers
avatar for Annie Flippo

Annie Flippo

Director of Data Science, inMarket
Annie Flippo works as the Head of Data Science & Analytics at Thinknear by Telenav, a GPS navigation software, connected car and mobile advertising company. She is a data science expert focusing on the application of machine learning techniques to extract insights in the areas of... Read More →


Saturday October 20, 2018 3:00pm - 3:30pm
Ballroom # 409

3:00pm

Machine Learning on Social Networks
Social networks are extremely common in our modern-day world. These networks are composed of individuals and connections, where connections here represent social interactions such as friendship, peer or work relationships, and communication. Social network data can originate from many sources including online social networks, mobile phones, email, financial transactions or a variety of digital communications channels over which individuals interact. Social networks are typically very large and heterogeneous, and while the construction of features from this data can be quite complex it can often justify itself by providing great signal for machine learning prediction tasks.


In this presentation I will describe the social networks that I work with as a data scientist in Tala, how we construct these networks from our various data sources and how we use these networks for machine learning tasks including credit scoring and fraud prediction. I will talk about the complexities and nuances of using this data, and considerations and challenges that arise when building live scoring machine learning models based on this data. I will describe local network features (based on individuals' direct connections) and global network features (based on an individual’s position or centrality within the network), and approximation methods for global metrics which allow for their rapid implementation in live models. I will discuss the effectiveness of models based on network features and conclude with other use cases of networks in data science.

Speakers
avatar for Peter Fennell

Peter Fennell

Data Scientist, Tala
peter fennell is a Senior Data Scientist at Tala, where he uses a suite of tools including EDA, statistics, machine learning and engineering to build credit and fraud models for our global markets.Previous to Tala peter fennell was a postdoctoral researcher in statistics, networks... Read More →


Saturday October 20, 2018 3:00pm - 3:30pm
Ballroom # 403B

3:30pm

Labeling Foot Traffic in Dense Locations
Mobile sensor data is enabling us to better understand user behavior. It is now possible to accurately and persistently model foot traffic to brick-and-mortar retail stores and other points of interest in near real-time. However, this capability comes with increasing state-of-the-art data processing and machine learning challenges.
Locations of stores in dense areas, such as shopping malls, are often indistinguishable using lat/long or street address data because of their close geographical proximity. This creates a complex problem for accurate foot traffic estimation to these stores, especially in the absence of accurate, large-scale ground truth data. The problem is further aggravated by the diverse nature of user behavior and visit frequency to stores of different categories.
In this talk, I describe our approach at Sense360 to solve this. In particular, I will talk about framing this issue as a probabilistic learning problem, engineering features from point-of-interest data, and using regularization. The developed approach is currently being used at Sense360 to further boost the accuracy of our market research insights

Speakers
avatar for Om Patri

Om Patri

Data Scientist, Sense360
Om is a Data Scientist at Sense360, a market research and insights firm in Culver City, CA, which enables some of the world’s largest restaurant and retail companies to continuously measure and optimize their business in real-time. He focuses on using AI and machine learning approaches... Read More →


Saturday October 20, 2018 3:30pm - 4:00pm
Ballroom # 403B

3:30pm

Understanding Chatbots and Building Chatbot using Rasa Tech Stack
Understanding Chatbots and Building Chatbot using Rasa Tech Stack
Brief Description: Chatbots are one of the most emerging topic in the space of Artificial Intelligence. We have already started seeing a lot of chat-bots around us wherein we can literally do conversation with those virtual assistants without even realizing its a bot not a real human.
All the big giants of information technology are coming up with their own frameworks to build chat-bots. Industry have already started putting a lot of focus towards building Machine Learning/Deep Learning based chatbots. Considering it is one of the most important and hot topic across industry, this session is going to explain how Natural Language Processing can be used to develop state of the art chat bots or virtual assistant.
This session will explain all the nuts and bolts of a chatbot, explaining how Natural Language Understanding(NLU) plays a vital role in developing a chatbot. And how can we create our own NLU system using NLP or Text Analytics. We will also showcase a chatbot demo that can search restaurants given any natural language query by the user. Also we will help audience to learn building a chatbot using one of the open source platform. And finally how we can integrate our chatbot with one of public channels such as Facebook, Skype or Slack.

Speakers
avatar for anuj saini

anuj saini

Manager Technology, Publicis Sapient
Anuj Saini is a Subject Matter Expert in the area of Natural Language Processing, Search Technologies, Statistics, Analytics, Modelling, Data Science, Data Mining and Machine Learning. He is currently working with Publicis.Sapient.He has more than 9 years’ of industry experience... Read More →


Saturday October 20, 2018 3:30pm - 4:00pm
Ballroom # 409

4:00pm

Becoming Best Buds: Modernizing How Government Treats Business
Government is not known for being business friendly, taxes suck, and bureaucracies move slow. All these things are true - so how can government switch things up enough so that it actually helps entrepreneurs and business owners?
Learn how the Los Angeles Office of Finance is answering this question through use cases that look at technology, people, and process working in unison towards goals and impact.
During our time together we will learn protips and strategies in change management through product development, data management, and human relations.
We'll explore basic strategy, use of tools, visualization techniques, lessons learned from wins and failures, and actionable steps to implement lessons in your own work.
This presentation is not limited to government employees - everyone interacts with government, either as an individual or a professional, so come learn how the second largest city in the country is improving the relationships between government and its local business community.

Speakers
avatar for Juan Vasquez

Juan Vasquez

Data Programs Manager, City of Los Angeles Office of Finance
Juan Vasquez is a leader in modernizing and optimizing how a 400-person organization collects, reports, manages, and uses data, and more importantly the actionable insights derived from it. Some of his projects include a mapped time series analysis to identify top-performing census... Read More →


Saturday October 20, 2018 4:00pm - 4:30pm
Ballroom # 409

4:00pm

Data Science That Drives Business Outcomes: A Case in the Practice Growth Space
As a discipline, Data Science continues to experience a boom in growth. Not too long ago, use of data and models were mostly confined to Finance departments, but they've now become critical in optimizing across all company functions, with ever-growing use cases in Product, Marketing, Operations, and a multitude of others. With such growth, many companies have gotten caught up in the hype, doing data science just for the sake of doing it, and without a clear plan on how it will have an impact on the business and on the bottom line. To get value, Data Science teams must work with business stakeholders to build data products that are embedded into company functions and business goals. Learn how PatientPop, the leader in the Practice Growth platform space, is undertaking this challenge in a way that serves Doctors, improves experience of their patients, and amplifies our business. In this talk, I'll share our vision for a combined data science and business strategy, and show how our business drives our data science efforts, which in turn improve existing functions and enable entirely new ones.

Speakers
avatar for Charalampos Papadimitriou

Charalampos Papadimitriou

Lead Data Scientist, PatientPop
Charalampos founded and is the Lead Data Scientist of the data science team at PatientPop, where he works on data science projects, programs, and process. Previously, he worked at DataScience.com where he lead data science consulting engagements with client companies big and small... Read More →


Saturday October 20, 2018 4:00pm - 4:30pm
Ballroom # 403B

4:30pm

Choosing the Right Cloud, a Machine Learning Approach
As cloud services have grown in maturity and offerings, the differentiations between them has shrunk. For example, many CSPs now provide capabilities to host and run Jupyter Notebooks for Data Science workloads, but what makes one different than the other? As a data scientist, I wanted to see if it would be possible to classify respective clouds by using machine learning. An effect dataset and model could provide insights to the differences in userbases among clouds, and could help those new to developing in cloud environments a resource to choose a cloud in which other users having similar coding styles, interests, and needs

Speakers
avatar for Nick Acosta

Nick Acosta

Developer Advocate, IBM
Before becoming a Developer Advocate at IBM, Nick studied computer science at Purdue University and the University of Southern California, and was a high performance computing consultant for Hewlett-Packard in Grenoble, France. He now specializes in machine learning and interacting... Read More →


Saturday October 20, 2018 4:30pm - 5:00pm
Ballroom # 403B

4:30pm

Start Your Career in Data Science
Speakers
avatar for Jason Geng

Jason Geng

Chair of the Board, IDEAS
As committee chair of International Data Engineering and Science Association (IDEAS), Jason organized series of AI and data science conference at Dallas, Chicago and Los Angeles. Jason is adjunct professor at University of Southern California. When worked at Symantec, Jason designed... Read More →


Saturday October 20, 2018 4:30pm - 5:00pm
Ballroom # 409