REGISTRATION & LIGHT BREAKFAST


Zahra Timsah - Co-Founder & COO, Founder and CEO - Generation Z, Ward Medication Management
WELCOME
Zahra Timsah - Generation Z, Ward Medication Management
With 13 years of experience in healthcare and healthcare technology, Dr. Timsah, and through her AI-consultancy group, specialises in the development of Artificial Intelligence powered-platforms for healthcare entities. Dr. Timsah received her PhD from UT Health/MD Anderson in biochemistry and cancer biology with multiple high impact publications; and was a faculty member in the UK. Dr. Timsah holds an MBA from the University of Leeds Business School with multiple ongoing leadership and advisory roles at pharmaceutical and biotechnology companies such as Protox Therapeutics, Kiromic, Bristol Myers and Johnson& Johnson. She holds an executive degree from MIT in Artificial Intelligence and has successfully developed AI platforms for multiple medical and drug-development purposes. Her most recent projects, developed through her consultancy company, have been the development of 1) state-of-the-art cognitive AI platform for the discovery and validation of cancer immunotherapy which cuts down on the cost of drug discovery and manufacturing; 2) a trend building AI-platform to detect the success vs. failure probabilities of clinical trials allowing for the proper design of the trials and ease of selection by patients; 3) and a medication management platform that identifies the optimal medication regimen and drug combinations to maximise adherence and minimise the harmful side effects of medications while promoting cost reduction. The latter (co-developed and exclusively marketed through Ward Medication Management, Inc) has been valuable for patients, caregivers and payers looking to enhance the process of medication managements."

AI TRANSFORMATION IN BUSINESS


Vivek Thakral - Director, Artificial Intelligence - General Electric
Intelligent Process Automation
Vivek Thakral - General Electric
Digital Technology leadership program and MBA graduate with over 16 years of experience. Driving productivity benefits across Finance, Supply Chain, Commercial Operations, and Human Resource Operations by executing strategic programs, improving processes, and deploying artificial intelligence technologies. Leveraging analysis and collaborative leadership to drive organizational improvements and enhance user experience.
Expertise in robotic process automation, artificial intelligence, cybersecurity, technology risk and compliance. Leading globally distributed teams and have lived/worked in USA, India, Italy, China.



Lynn Calvo - AVP of Emerging Data Technology - GM Financial
Driving Innovation with Machine Learning in the Enterprise
Lynn Calvo - GM Financial
Driving AI Innovation with Machine Learning in the Enterprise
GM Financial, the wholly-owned captive finance subsidiary of General Motors, will discuss their AI journey with Machine Learning, Deep Learning, and Natural Language Processing. In this session, GM Financial will discuss their challenges, technology choices, and initial successes:
· Addressing a wide range of Machine Learning use cases, from credit risk analysis to improving customer experience
· Implementing multiple different tools (including TensorFlow™, Apache Spark™, Apache Kafka®, and Cloudera®) for different business needs
· Deploying a multi-tenant hybrid cloud environment with containers, automation, and GPU-enabled infrastructure
Gain insights from this enterprise case study, and get perspective on Kubernetes® and other game-changing technology developments.
Lynn Calvo is the AVP of Emerging Data Technology at GM Financial where he introduced big data analytics and machine learning capability into the lines of business and continues work with enabling its application for multiple AI use cases. Lynn is a recognized inventor on three US patents. He holds a master’s in Computer Science and is a former IT consultant servicing Fortune 50 clients with big data, data center, and security next-generation research. Lynn’s forward thinking and depth of knowledge allow him to skillfully leverage his 30-years of results-oriented IT experience into a winning model for GM Financial.



Biao 'Bill' Chang - Senior Data Scientist - eBay
Interactive Improvement Between AI Algorithms and Business Insights
Biao 'Bill' Chang - eBay
Interactive Improvement Between AI Algorithms and Business Insights
The adoption of Artificial Intelligence (AI) technologies among businesses is growing faster than ever before in recent years. With this remarkable growth, there are also emerging pain points with algorithms behaving as black-boxes and concerns that traditional human intelligence might become less relevant. This talk will touch on cases where human business insights help to improve AI models and where business insights are gained from AI models. Experience will be shared on developing AI solutions in an enterprise environment through this business-in-the-loop approach.
Biao "Bill" Chang is a Senior Data Scientist with eBay's Risk Management team. His work centers around developing scalable AI solutions for risk management and fraud detection on eBay's marketplace platform, spanning from problem definition, modeling development to model deployment and monitoring. Sample projects include building and productionizing real-time fraud detection model covering a portfolio of over 500 million listings monthly. Bill obtained his Ph.D. in engineering from the Georgia Institute of Technology and previously worked with Capital One on data analytics and data product.



COFFEE
AI FOR BUSINESS VALUE
Adam McMurchie - Barclays
Adam McMurchie is leader in Devops and an A.I expert working in the banks SAO platform on the forefront of technology development in finance. With a broad exposure to a range of technologies, Adam drives an ethos of simplification, cloud agnosticism and specialises in spotting the next trends in fin tech. Additionally, Adam also has a background in science with a physics degree specialising in NeuroComputing and is a polyglot linguist & seasoned translator. Adam has pooled these skills to deliver full stack novel solutions from tensor flow driven mobile apps, to personalized banking chatbots. Adam also develops apps designed around the ethos of Social Utility, including Flood/Storm reporting, EV Vehicle bay monitoring and preservation of endangered languages.




Sunanda Parthasarathy - Associate Director Data Science - Wayfair
Superior Customer Service with AI: Personalization at Wayfair
Sunanda Parthasarathy - Wayfair
Superior Customer Service with AI: Personalization at Wayfair
With an ever-expanding catalog of products that are inherently harder to buy with just an online description, Wayfair has a unique challenge in personalizing a customer’s journey to find their one special piece of home furnishing among zillion things home. In this talk I will go over multiple case studies where we have successfully demonstrated the benefit of applying ML and AI techniques to personalize a customer’s touchpoint, both onsite and off it. Starting from using reinforcement learning techniques that help send the right content to customers via emails, to applying computer vision and neural network methods to help curate the right kind of products onsite, the talk will aim to share the challenges we faced en-route to creating one of a kind online furniture shopping experience. Attendees can hope to take away key insights on how best to translate cutting-edge data science technology into real business decisions and typical challenges faced in scaling these technologies.
Sunanda KP, is an Assoc. Dir. of Data Science at Wayfair Inc where she leads a team of data scientists and engineers to build ML solutions that lead to a better shopping experience for all Wayfair customers. Prior to this, she was leading innovation R&D projects at the AdTech startup DataXu, that spanned the spectrum of Multi-Touch Attribution to developing new analytical product solutions that helped clients achieve optimum Marketing ROI. Through her career in data science she has enjoyed focusing her quantitative expertise in mathematical modeling to business questions that has led to multiple high-revenue analytics products. Her current passion is to develop an intelligent system that recommends the optimal product/message exposure to customers to make their online shopping experience very personal and enjoyable. Before entering the data science world, she was a Princeton Postdoctoral fellow, working on the forefront of the next generation quantum materials that will replace silicon in a computer chip. Prior to that she received her PhD in physics from Purdue University working on solving open problems in the field of transport physics. Her research accomplishments have been recognized in the form of many awards including the H.Y.Fan Award for excellence in physics research. She is an active member in the local start-up, tech and data science community and is the co-organizer of one of the oldest meetup groups in Boston- The Data Scientist. She enjoys bringing together leading minds in the field of data science and AI, to engage in thought leadership in this nascent field.


OPPORTUNITIES OF AI IN ENTERPRISE


Hao Yi Ong - Research Scientist - Lyft
Operationalizing Machine Learning for Lyft's Business Platforms
Hao Yi Ong - Lyft
Architecting a Real-Time Optimization Platform for Driver Positioning Products
At Lyft, we think a lot about trading off between the immediacy and quality of the response in automated decision-making. On one end, machine learning dominates products that require near-instantaneous feedback such as in fraud and customer support. On the other end, we have complicated workflows that crawl through user graphs to derive weekly macro-level business insights. This talk focuses on the “in-betweens” such as driver positioning and rider-driver matching that require time to aggregate market-level signals before any useful decision can be made. We explore the design principles that we have come to recognize in developing scalable infrastructure that enable fast, iterative, Science-heavy model and product development of real-time optimization workflows.
Key Takeaways:
- Science DevOps is every bit as important as building the Science models
- It's important for Research/Data Scientists to develop models with an understanding of the Eng infra and affect its development
- Similarly, it's important for Eng to work closely with Science to understand the infra needs and not over-index on a specific business application.
Hao Yi is a Research Scientist at Lyft. On the Driver Positioning team, Hao Yi leads the development of the optimization framework and models that power the Personal Power Zones and Hot Spots products that replaces the Driver Prime Time dynamic pricing experience. Previously, Hao Yi combated transaction and driver fraud on the Integrity team. There, he championed new approaches and led ML improvements that helped Lyft achieve best-in-class status in fraud rate within the ridesharing industry. On Support Experience, Hao Yi developed deep learning models for support ticket classification and routing that led to massive reductions in false positive tickets and manual ticket re-routing. Before Lyft, Hao Yi worked on drone traffic management with NASA Ames as a graduate at Stanford.




Dancy Li - Data Science Manager - Facebook
AI for Diverse Avenues/Applications: E-Commerce
Dancy Li - Facebook
AI for Diverse Avenues/Applications: E-Commerce
I am a data science manager at Facebook. I work on Marketplace, where we enable people to connect via commerce on Facebook, with a focus on using applied data and analytics to build eCommerce behavior. Prior to coming to Facebook, I worked as a statistician for Capital One where I built risk and revenue models for the credit card business.

IMPLEMENTING AI AT SCALE


Prakash Mall - Senior Director - Robotics Process Automation and Chatbots - Target
AI in Enterprises with Specific Relevance to Retail
Prakash Mall - Target
AI in Enterprises with Specific Relevance to Retail
Artificial intelligence is changing the business landscape across wide variety of industries, Retail being one of them heavily influenced and transformed by the recent advancement of AI. This talk with delve deep into • Opportunity in large-scale retail enterprise • Ai brings competitive advantage - Use of AI in retail from traditional ML (personalization, forecasting, optimization, etc) to Deep learning (behavioral analysis, chatbots, surveillance, knowledge graph, etc – dealing in voice, text and video). Talk of specific use case with relevant examples • Challenges in large scale AI implementation – data, computing, talent • AI in Target Corp – where, how • Where are we heading? Where does different technological evolutions converge – IoT, AI, blockchain, etc
Prakash Mall is a senior director of intelligent digital robotics and AI products including intelligent RPA, enterprise chatbots and video management and video analytics for surveillance. Prakash leads engineering for many product groups dealing with large scale distributed computing to AI with deep learning into text (NLP) and video. Prakash has over 20 years of experience building enterprise scale products in the Retail, Insurance and Healthcare sectors. His expertise is in building scalable open source systems and driving business value using emerging technologies. He has a proven track record of building and managing large global teams of 150+. Prior to joining Target, Prakash spent over 12 years at IBM including 8 years at the company’s US office.



LUNCH
INTERPRETING MODELS


Vinod Bakthavachalam - Senior Data Scientist - Coursera
Scaling AI in Education: The Importance of Feedback Loops and Interpretable Models in Driving Adoption
Vinod Bakthavachalam - Coursera
CourseMatch: Using Word Embeddings for Scaling Access to Online Education during COVID
At Coursera our mission is to provide everyone, anywhere access to high quality education to transform their lives. As the COVID crisis forces teaching and learning to shift online, there is an acute need to support both universities and students at this time. To enable universities and students to take advantage of the large number of high quality, online courses on Coursera, we have utilized word embeddings to map on-campus university catalogs to their online Coursera equivalents, ensuring students can continue their studies and universities can support their programs. During this presentation we will review the theory behind world embeddings and discuss this novel application in greater detail.
Vinod Bakthavachalam is a Data Scientist working with the Content Strategy and Enterprise teams, focusing on using Coursera's data to understand what are the most valuable skills across roles, industries, and geographies. Prior to Coursera, he worked in quantitative finance and studied Economics, Statistics, and Molecular & Cellular Biology at UC Berkeley.

AI & PEOPLE


Himani Agrawal - Machine Learning Engineer - AT&T
Enriching Creativity in Applied AI with Interdisciplinary Backgrounds and Diverse Perspectives
Himani Agrawal - AT&T
Enriching Creativity in Applied AI with Interdisciplinary Backgrounds and Diverse Perspectives
Today AI is being created by a very homogeneous set of people. Not only it is homogeneous in terms of race and color but also in terms of academic backgrounds. I am a very unlikely researcher to become an AI engineer but I believe it is crucial so that AI created by heterogenous set of people is more fair and inclusive. I will be sharing my personal journey into AI coming from engineering, applied mathematics, biophysics and music background. I will also be sharing my machine learning project at AT&T: how I am utilizing machine learning to detect network outage and prevent unnecessary dispatch in an event of network outage.
Himani Agrawal, PhD is a Machine Learning Engineer at AT&T Chief Data Office where she is applying machine learning in the telecommunications industry to proactively predict network outage and avoid customer dispatch. She has a very interdisciplinary research background encompassing fields such as applied mathematics, biophysics and data science. Apart from work, she is passionate about promoting women in technology and actively participates with the Anita Borg Institute, Women in Machine Learning and Data Science, Women in Machine Learning and Society of Women Engineers. She is actively involved in the machine learning and data science communities at AT&T: Data Night Live and Data Powered Insights. She has had speaking engagements at international conferences like Grace Hopper Celebration of Women in Computing, Society of Women Engineers Annual Conference, American Society of Mechanical Engineers and Society of Engineering Science Annual Technical Meeting. She is passionate about Italian Classical singing and currently trains with University of North Texas Voice Professor Dr. Lauren McNeese.




Kyle Tate - Senior Data Science Lead - Shopify
Data Scientists Should Build Dashboards (and write ETL too)
Kyle Tate - Shopify
Data Scientists Should Build Dashboards (and write ETL too)
Bringing machine learning (ML) powered products to a large production environment requires a diverse skillset: ML experts who can identify which approach/algorithm will best fit the problem at hand, engineers who can take the prototypes from the ML experts and productionize them, and analysts who can measure how successful the solution has been. It is common to see companies organize themselves into teams that are responsible for each of these tasks: data scientists, data engineers and data analysts respectively. At Shopify we believe this is sub-optimal, as understanding of the actual problem being solved leaks at each interface. Instead we build data science teams that are responsible for all three of these tasks. This talk will outline how we organize our data science teams to meet these challenges and bring ML powered products to production.
Kyle Tate is a senior data science lead at Shopify. He leads the teams responsible for building the data products that power Shopify Capital, Fraud Protect for Shopify Payments and Shopify's internal risk management. Kyle is interested in taking the ideas and techniques from the pages of machine learning textbooks and papers and bringing them to life in the real world.



COFFEE
OBSTACLES IN AI IMPLMENTATION


Margaret Mayer - Vice President of Software Engineering - Capital One
Growing Pains of AI Adoption: Advice & Lessons Learnt
Margaret Mayer - Capital One
Creating Applications That Converse, Understand and Become More Intelligent
Advances in natural language processing (NLP) are increasingly eroding the barriers between frictionless human-machine interaction, creating opportunities to develop nuanced and natural communication techniques that more accurately convey person-to-person dialogue. In this talk, I'll discuss how convolutional neural networks and long short-term memory networks are enabling more sophisticated NLP systems and building the necessary components to lay the foundation for advanced dialogue.
Margaret is a Vice President of Software Engineering for Conversational AI Platforms at Capital One. She sets the technology strategy for Eno, Capital One’s banking chatbot, as well as enterprise messaging capabilities for email, push and SMS. Margaret is also an advocate for closing the gap in women in technology as a member of Capital One’s Women in Technology working group. Margaret has been with Capital One for 20 years, in roles of increasing responsibility within Technology and Operations. She holds a BS in Operations Research & Industrial Engineering from Cornell University and her MS and PhD from Lehigh University in the same field. Prior to joining Capital One, she spent three years as an Assistant Professor at the University of Virginia, in Systems Engineering. Her research interests have been varied, but always along the common theme of using technology to solve complex business problems. Margaret is a board member of CodeVA.org and the Computer Science Industrial Advisory Board at Virginia Commonwealth University. Margaret lives in Richmond, VA with her husband and two teenage daughters.




Sandeep Golkonda - Professional Data Scientist - AT&T
Let the Models Speak: Model Interpreters
Sandeep Golkonda - AT&T
Let the Models Speak: Model Interpreters
Curiosity kills. We have advanced to building machine learning algorithms for real-time predictions that can help us predict almost accurately every time. It’s like we have built a magic box that will answer all our questions. The more curios question is why did the magic box predict what it did. During the session I would like to share why you should/shouldn’t trust your model but you should explain the reasoning of your predictions that help Business users to understand your Magic box. I would like to share about how at AT&T we are using Model Interpreters to help business users identify the elements of the predictions that can give intuitive reasoning behind each prediction.
Sandeep David Golkonda is a Data Scientist at AT&T Chief Data Office he actively researches and applies Machine Learning and AI solutions for real-time analytics problems. His, current work is focused on building AI framework to optimize network events. He is an active member of School of AI and Teaching Assistant for machine learning and deep learning. He holds M.S in computer science and M.S in business analytics.


THE FUTURE OF AI IN ENTERPRISE

PANEL: A Look To The Future: Security, Risk & Other Big Obstacles for AI in Enterprise
Priyanka Kharat - PCCI
Growing up during some of the fastest economic growth times in India, Priyanka has always been as much curious in understanding how modern inventions advancing humankind by leaps and bounds affected the societal sanctities as much as she has been in figuring out the inner workings of sophisticated systems built around telecommunication, software, databases and now machine learning and AI analytics. After finishing her bachelors from College of Engineering in Pune(India), Priyanka came to the US to pursue her masters from USC and spent almost a decade working for companies like Qualcomm and Intel to define some of the then new experiences on smartphones and internet-of-things using audio/video and camera integration through bare-bone software to begin with, followed by computer vision analytics to bring connectivity and context to the solutions. After a long sprint in the Silicon Valley, Priyanka decided to explore the other side of her passion through technology and machine learning by moving to Dallas and working for Parkland Center of Clinical Innovation (PCCI) an advanced not-for-profit clinical research organization which aims at pioneering ways to create healthier communities. As a Director of ML/AI at PCCI, Priyanka and her team are now streamlining clinical/healthcare data model development workflows from data engineering, training/re-training, deployment to statistical model evaluation fielding unique challenges like high dimensionality nature of healthcare data, flexibility of tuning parameters during prospective testing under clinician supervision and upholding compliance to HIPAA standards at all times to name a few. Trained under some of most prudent software and system architects of the world, Priyanka wows for nothing less than superior software engineering practices including test-driven development, agile methodologies, DevOps and loves to be plugged into discussions of revolving around bringing these time-tested practices into mainstream ML/AI development cycles while keeping enough room to usher in a new phase of "Responsible AI"


Babar Bhatti - Dallas AI
Babar Bhatti is a technology entrepreneur, digital executive and expert in the areas of analytics and AI. He is co-founder of Dallas AI, a meetup of about 1600 members focused on AI and Deep Learning. He is a frequent speaker and presenter on the topic of AI, Machine Learning and Digital Technologies. He has recently spoken at Harvard Business School Club of DFW, Financial Executives International, UTD School of Business and many other conferences. Babar is currently a Principal at CoreLogic. Formerly, Babar was the CEO and Co-founder of MutualMind, a social media analytics company.
He holds dual MS from MIT in Engineering and Technology and Policy.
Dean Teffer - JASK
Dean Teffer is Principal Scientist for Machine Learning at JASK, which delivers the first AI-powered SOC platform that enables autonomous security operations. At JASK he is working with a highly talented team of developers, analysts and scientists to create a new type of network security product. Dean designs and develops model-driven machine learning systems for streaming and distributed production environments, with his specialties including anomaly detection, probabilistic modeling and clustering. Dean has worked for the past 9 years in network security, and his teams have made significant contributions in early detection for counter intelligence applications, among other achievements.
Dean previously served in program management roles at the UT Austin Applied Research Labs, Siemens, Precision Traffic Systems and Compuware. He has a PhD in Computer Engineering and a Masters in Physics from the University of Texas at Austin.


Clint Wheelock - Tractica
Clint Wheelock is the founder and managing director of Tractica. He leads all research operations at the firm, including management of its analyst team as well as client interactions and consulting engagements. His personal research focuses on artificial intelligence and user interface technologies.
Wheelock has an extensive background in market intelligence focused on emerging technologies. Most recently, he was founder and president of Pike Research, a leading market intelligence firm focused on the global clean technology industry, which was acquired by Navigant Consulting, after which Wheelock led the rebranded Navigant Research business as its managing director. In this role, he managed all aspects of company operations, including research, sales, marketing, finance, and operations. Prior to forming Pike Research, Wheelock was chief research officer at ABI Research, vice president at the NPD Group, and research director at In-Stat. Previous positions also include senior product management and strategic marketing roles at Qwest Communications and Verizon Communications, as well as prior experience in management consulting and private investment banking. Wheelock holds an MBA from the University of Dallas and a BA from Washington & Lee University.



CONVERSATION & DRINKS

DOORS OPEN


Lisa Vaughn - Master of Data Science Program Manager - University of Houston
WELCOME
Lisa Vaughn - University of Houston
Lisa Vaughn is the program manager for the Master of Science in Statistics & Data Science (“MSDS”) program at the University of Houston. She is also a Department of Mathematics faculty member and teaches undergraduate statistics courses. As MSDS program manager, she oversees, advises, and manages approx. 30 students in the program (which began in the fall of 2017). She serves as a liaison between the university and local Houston corporations – building, growing, and maintaining relationships with potential future employers of MSDS graduates. She is in charge of helping current students secure summer-2019 data science internships in the Houston area. Lisa holds a B.S. in mathematics from the University of Houston as well as a Master of Statistics degree from Rice University.


STARTUP SESSION


Stephen Odaibo - CEO & Founder - RETINA-AI
The Intersection of AI, Mobile Devices, and Healthcare
Stephen Odaibo - RETINA-AI
Using Artificial Intelligence to Solve Diabetes and Diabetic Retinopathy
Diabetes is a devastating chronic disease that affects up to 35 million Americans, and up to 500 million people worldwide. The disease resulted in up to 1.6 million deaths worldwide in 2016. Early detection is the key to preventing the many morbid complications of diabetes, and key to decreasing the rate of mortality. There are too few physicians in the world to address the problem of diabetes, and furthermore the cost of relying on humans is significantly prohibitive. Artificial Intelligence (AI) presents a compelling and necessary approach to curb the effects of diabetes and thereby save millions of lives annually; while also improving the quality of life of half a billion people suffering with the disease. In this talk, I will discuss the progress RETINA-AI Health Inc is making in developing and deploying Artificial Intelligence systems to screen people around the world for diabetic retinopathy.
Dr. Stephen G. Odaibo is Founder, CEO, and Chief Software Architect of RETINA-AI, a company using Artificial Intelligence to improve Healthcare. He is a Retina specialist, Mathematician, Computer Scientist, and Full-Stack AI Engineer. Dr. Odaibo is the only Ophthalmologist in the world with advanced degrees in both Mathematics and Computer Science. In 2017 UAB College of Arts and Sciences awarded Dr. Odaibo its highest honor, the Distinguished Alumni Achievement Award. In 2005 he won the Barrie Hurwitz Award for Excellence in Clinical Neurology at Duke Univ. School of Medicine where he topped the class in Neurology and in Pediatrics. In 2016 Dr. Odaibo delivered the Opening Keynote address at the Global Ophthalmologists Meeting in Osaka Japan. And he delivered the inaugural Special Guest Lecture in Ophthalmology at the University of Ilorin, Nigeria. In 2018, Dr. Odaibo delivered the keynote address at the National Medical Association's New Innovations in Ophthalmology Session. And he delivered a Plenary Keynote address on AI in Healthcare at AI Expo Africa in Cape town, South Africa. He is author of the book "Quantum Mechanics and the MRI Machine'' (2012), and of the book "The Form of Finite Groups: A Course on Finite Group Theory" (2016).




Chris Fregly - Founder - PipelineAI
End-to-End, Multi-Cloud, Continuous Machine Learning in Production with PipelineAI, TensorFlow, and Kafka
Chris Fregly - PipelineAI
End-to-End Continuous Machine Learning in Production with PipelineAI, Spark ML, TensorFlow AI, PyTorch, Kafka, TPUs, and GPUs
Traditional machine learning pipelines end with life-less models sitting on disk in the research lab. These traditional models are typically trained on stale, offline, historical batch data. Static models and stale data are not sufficient to power today's modern, AI-first Enterprises that require continuous model training, continuous model optimizations, and lightning-fast model experiments directly in production. Through a series of open source, hands-on demos and exercises, we will use PipelineAI to breathe life into these models using 4 new techniques that we’ve pioneered: * Continuous Validation (V) * Continuous Optimizing (O) * Continuous Training (T) * Continuous Explainability (E).
The Continuous "VOTE" techniques has proven to maximize pipeline efficiency, minimize pipeline costs, and increase pipeline insight at every stage from continuous model training (offline) to live model serving (online.) Attendees will learn to create continuous machine learning pipelines in production with PipelineAI, TensorFlow, and Kafka.
Chris Fregly is Founder at PipelineAI, a Real-Time Machine Learning and Artificial Intelligence Startup based in San Francisco. He is also an Apache Spark Contributor, a Netflix Open Source Committer, founder of the Global Advanced Spark and TensorFlow Meetup, author of the O’Reilly Training and Video Series titled, "High Performance TensorFlow in Production with Kubernetes and GPUs." Previously, Chris was a Distributed Systems Engineer at Netflix, a Data Solutions Engineer at Databricks, and a Founding Member and Principal Engineer at the IBM Spark Technology Center in San Francisco.




Eunice Chendjou - Founder - DataGig
DataGig: The Digital Apprenticeship Marketplace for the nextGen Data Professionals
Eunice Chendjou - DataGig
DataGig: The Digital Apprenticeship Marketplace for the nextGen Data Professionals
DataGig is a digital apprenticeship marketplace that helps connect aspiring data scientists with potential employers for their big data and analytics projects on-demand. We connect trained students looking for work experience with business owners looking for data analytics and machine learning work. A student work with a business for free as part of their training for 10 hours a week and up to 3 months. Once students gain experience, they can then earn money as freelance data professionals all through our platform. DataGig apprentice develops professional skills in data analytics, data science, and data engineering. For small businesses, we help them grow by leveraging the labor of our students.
Eunice is the founder at DataGig, a digital apprenticeship marketplace that helps connect aspiring data scientists with potential employers for their big data and analytics projects on-demand. Prior to founding DataGig, Eunice was a Product Consultant and Analyst for the Canadian Business Unit at Apple. She is also a Founder Institute graduate, a program that teaches all facets of start-up development and growth from concept, to fund-raising, to validation and expansion, all the way to exit. Eunice strongly believes in building the next generation of women and minorities in the tech industries. She volunteers by mentoring students at Code2College, a non-profit whose mission is to dramatically increase the number of girls, underrepresented students of color and low-income students who enter STEM degree programs and fields.


Mayur Saxena - Droice Labs
Strategies for AI Adoption in Hospitals
At Droice, we use artificial intelligence to help clinicians make better decisions for individual patients. What treatment should a patient be given? Are there any potential complications? What tests need to be done? All of these (and many more) are examples of difficult, data-intensive questions that doctors must answer every day. By answering these questions, AI has the power to fundamentally transform healthcare but has been hampered by slow adoption into common clinical practice. This presentation will describe Droice Labs technology and address strategies for translating AI into deployable hospital solutions.



Tasha Nagamine - Founder and Chief of AI - Droice Labs
Strategies for AI Adoption in Hospitals

COFFEE
ALGORITHMS FOR INDUSTRY


Tian Su - Director of Machine Learning - Walmart
The Evolution of Recommendation Engine Algorithms: From Association Rule Mining To Deep Learning
AI APPLICATIONS IN INDUSTRY


Daniel Ellis - Tech Lead - Reddit
NEWS & MEDIA: Machine Learning for Better User Experience: A Reddit Case Study
Daniel Ellis - Reddit
Machine Learning for Better User Experience: A Reddit Case Study
Reddit is a network of communities where individuals can find experiences built around their interests, hobbies and passions. With more than 330 million monthly active users, Reddit is where people go to discover the conversations they care about the most. In this session, Reddit's Relevance Tech Lead Daniel Ellis will reflect on the history of how Reddit's feed has evolved over the years. He'll then share the company's journey through using both heuristics and machine learning to ultimately create a better experience for its users.
Daniel Ellis is the Tech Lead for the Relevance team at Reddit. In this role, he is responsible for production-side machine learning engineering, and scaling of the feeds and recommendation systems. Ellis brings an expertise in scalable backend development from previous roles at Sauce Labs and 829 Studios.




Sath Rao - Director, Digital Solutions for Manufacturing - Hitachi Vantara
MANUFACTURING: Roadmap to Accelerating Value from AI for Manufacturing
Sath Rao - Hitachi Vantara
Roadmap to Accelerating Value from AI for Manufacturing
Analytics and AI are driving insights-based manufacturing transformations. However, a methodical transformation journey roadmap with clear progressive goals is important to reap the full benefits and maximizing return on data. The session will address the following themes: How are manufacturing paradigms changing with the progressive use of AI based insights? What are lessons for other industries? How has Hitachi leveraged AI for it’s manufacturing transformations? How is Hitachi Vantara helping bring new solutions to help accelerate industry transformation?
Sath Rao has led industry level capability development, go-to-market strategy, and delivered on the digital transformation agenda including business model innovation.
In his current role as Director, Digital Solutions for Manufacturing, Product Marketing at Hitachi Vantara, he is responsible for delivering new product innovations, strategic marketing, and global go-to-market for solutions in collaboration with product management. In his earlier roles, he led pioneering work on the Factory of the Future Vision 2030 initiative and was also a member of the Board of Governors of the Manufacturing Leadership Council (now part of National Association of Manufacturers). He has extensive manufacturing, consulting, software development experience for Industrial IOT, analytics, AI, IT and OT experience and while working for companies like Fujitsu, Wipro, Schneider Electric (Invensys), Frost & Sullivan and Bechtel & Pacific Corp JV-EnergyWorks.




Joseph Michiels - Senior Product Manager Shipping Data Science - eBay
E-COMMERCE: How eBay Shipping uses ML to Improve Experiences
Joseph Michiels - eBay
Using ML to Improve Customer Delivery Experiences at eBay
eBay shipping data science teams solve a number of problems for our buyers and sellers using various machine learning models. Joe will walk through a few examples including how we make better buyer and seller experiences by estimating delivery speed, shipping costs, and tracking information.
Joe Michiels is the Senior Product Owner of the eBay Shipping Science team. Shipping Data Science uses big data and machine learning techniques to make predictions such as how soon a package will arrive, recommendations for shipping services, and the logic driving key eBay features such as eBay Guaranteed Delivery. Joe has been with eBay for over 5 years and before shipping science was product owner of eBay’s Global Shipping Program and the affiliate marketing channel eBay Partner Network. Joe has a MBA from UW Foster School of Business and a B.S. in Cognitive Science from U.C. Berkeley.




Julia Badger - Project Manager for Robotics and Intelligence for Human Spacecraft - NASA
SPACE: Robotics and AI: The Future of People in Space
Julia Badger - NASA
Human-Machine Interaction in Deep Space
This talk will discuss plans for human exploration to the moon and beyond, and how human-machine teams will be essential to our success. As communication latency and bandwidth constraints increase, astronauts will rely more and more on technology and augmented intelligence to solve problems and conduct their missions. Pre-deployments of spacecraft and habitats will necessitate robotic caretakers to assist ground controllers in maintaining these assets in deep space. The teaming of crew and ground support with these machines present interesting operational and mission challenges that are being explored today.
Dr. Julia Badger is the Project Manager for the Robotics and Intelligence for Human Spacecraft team at NASA-Johnson Space Center. She is responsible for the research and development of humanoid robotic (Robonaut) and autonomous system capabilities, on the Earth, the International Space Station, and for future exploration, that include dexterous manipulation, autonomous spacecraft control and caretaking, and human-robot interfaces. Julia has a BS from Purdue University, and an MS and PhD from the California Institute of Technology, all in Mechanical Engineering. Her work has been honored with several awards, including NASA Software of the Year, Early Career, and Director’s Commendation Awards.



LUNCH
AI FOR GOOD


Zuby Onwuta - Founder - Think and Zoom
Combining EEG & AI to Overcome Learning Disabilities
Zuby Onwuta - Think and Zoom
Combining EEG & AI To Overcome Learning Disabilities
This talk aims to introduce the idea of using EEG to handle reading controls on a digital device, and as a result, improve accessibility and augment learning, especially for People with Disabilities. This becomes very useful for an estimated 629 million people globally who are visually impaired, according to Overseas Development Institute, London, and usually end up with a 90% illiteracy rate. I am visually impaired and legally blind, which inspired me to invent "Think and Zoom: Brian control for Blind Assistive Tech", to enable people like me to use our thoughts to control zooming or text-to-speech functions.
Zuby Onwuta is the inventor of "Think and Zoom: Brain control for Blind Assistive Tech", and Founder of Think and Zoom, a startup that is "creating a world where visual impairment no longer steals dreams or kills careers". Hardships from losing his medical, US Army and engineering dreams to visual impairment, inspired him to invent "Think and Zoom", a solution that reads and responds to human brain waves, to provide vision augmentation and reading assistance. Previous work experience include software engineering roles at Lucent, Goldman Sachs & Co and IBM. Accomplishments include speaking engagements at TEDx, SU Exponential Medicine, American Academy of Ophthalmology, UN and Disability Advocacy at the US Congress. He holds a Bachelors in Computer Engineering from the University of Illinois, Chicago, and certificates in innovation and healthcare from MIT, Harvard Business and Harvard Medical. More about him is available at ZubyOnwuta.com.




Ahmed Mahgoub - Chief Innovation Engineer - Agrisource Data
Seed to Shelf: Harnessing AI to Eliminate Waste in the Food Supply Chain
Ahmed Mahgoub - Agrisource Data
Seed to Shelf: Harnessing AI to Eliminate Waste in the Food Supply Chain
More than one-third of all food produced on the planet is lost due to waste. Much of this loss occurs between the point of harvest and retail shelves because of the great level of uncertainty around the quantity and timing of supply. Agrisource Data uses multi-layer data to feed our proprietary artificial intelligence engine giving stakeholders throughout the food supply chain unprecedented insight into what’s coming off the field and when so that labor, logistics, processing and contracts can be executed more efficiently and with greater confidence, reducing the loss and getting more food into consumers’ hands.

FUTURISTIC BREAKTHROUGHS & AI APPLICATIONS


Laurence Perreault Levasseur - Flatiron Research Fellow, Cosmology, Data Science - Flatiron Institute
AI to Explore The Universe: Neural Networks & Gravitational Lenses
Laurence Perreault Levasseur - Flatiron Institute
AI to Explore The Universe: Neural Networks & Gravitational Lenses
Machine learning methods have seen a rapid expansion in the recent years. In this talk, I will discuss our results on using deep convolutional neural networks to estimate the parameters of strong gravitational lenses from telescope data. Estimating these parameters with traditional maximum-likelihood modeling methods is a time and resource consuming procedure, involving several data preparation steps and a difficult optimization process. With deep convolutional neural networks we are able to estimate these parameters in a fully automated way 10 million times faster than traditional modeling methods and with a similar accuracy. I will also discuss how to robustly quantify the uncertainties of these networks. This allows them to be a fast alternative to MCMC sampling. With the advent of large volumes of data from upcoming ground and space surveys and the remarkable speed offered by these networks, deep learning promises to become an indispensable tool for the analysis of large survey data.
Laurence joined the Flatiron Institute in September 2018 as member of the CCA. Prior to this, she was a KIPAC postdoctoral fellow at Stanford University, where she conducted research in applications of machine learning methods to cosmology. Laurence completed her PhD degree at the University of Cambridge in DAMTP, where she worked on applications of open effective field theory methods to the formalism of inflation. She received her B.Sc. and M.Sc. degrees from McGill University.

PANEL: Predictions for The Next Decade of AI Applications
Seshu Yalamanchili - Visa
Seshu is the Director for Artificial Intelligence Application Strategy at Visa. He is responsible for developing long term vision, roadmap and execution of AI to transform the business. Prior to Visa, Seshu was responsible for AI application strategy at General Motors’ Autonomous Vehicle Development organization. Seshu has over twenty years experience in developing and executing large scale, Global transformational initiatives in Aerospace, Automotive and Financial Industries, leveraging the emerging technologies such as Mobile, IoT, Cloud, AI, etc. Seshu has MBA from Northwestern University’s Kellogg School Management and Master’s in Engineering from Virgnia Tech.


Amy Gross - VineSleuth
She is the Founder and CEO of VineSleuth, which uses sensory science, rock solid data and Artificial Intelligence to help people discover wines, foods and other beverages that they like, personally. Amy’s work with VineSleuth in personalization, flavor data and Artificial/ Augmented Intelligence has been noted in the New York Times, Fortune, CNN, and Better Homes and Gardens. She has keynoted for live audiences as large as 12,000, and presented on AI and wine at trade conferences in the US, Italy and Spain.


Sandeep Golkonda - AT&T
Let the Models Speak: Model Interpreters
Curiosity kills. We have advanced to building machine learning algorithms for real-time predictions that can help us predict almost accurately every time. It’s like we have built a magic box that will answer all our questions. The more curios question is why did the magic box predict what it did. During the session I would like to share why you should/shouldn’t trust your model but you should explain the reasoning of your predictions that help Business users to understand your Magic box. I would like to share about how at AT&T we are using Model Interpreters to help business users identify the elements of the predictions that can give intuitive reasoning behind each prediction.
Sandeep David Golkonda is a Data Scientist at AT&T Chief Data Office he actively researches and applies Machine Learning and AI solutions for real-time analytics problems. His, current work is focused on building AI framework to optimize network events. He is an active member of School of AI and Teaching Assistant for machine learning and deep learning. He holds M.S in computer science and M.S in business analytics.


Brandon Ellett - Hypergiant
Brandon Ellett's 18-year career in technology, finance and data-driven businesses gives him a unique perspective on forecasting methodologies and artificial intelligence. Ellett has rolled up his sleeves in an executive capacity for the fastest-growing retail electricity provider in the United States, served on the launch team of a national integrated technology consolidator, led the development of field technologies for an energy services company, and founded a specialty investment fund and PAIR prediction platform for the retail power marketplace. Ellett recently joined HyperGiant as Director to parlay his insights and experiences to a machine revolution driven by human intelligence.


END OF SUMMIT

Ask the Experts During the Coffee Break - NETWORKING
Networking & Ask the Experts During the Coffee Break

Infrastructure Slowing Your AI Projects? Nuts & Bolts of AI-Ready Infrastructure - WORKSHOP
Workshop with Dave Logan, Pure Storage

Predicting & Preventing Outages with Machine Learning Forecasting - DEEP DIVE
Deep Dive Workshop with Myra Haubrich and Sharath M, Adobe

Education Corner: Exploring STEM & Data Science in Houston - NETWORKING
Networking Break with Rising Starts & Local Tech Programs

Tips & Tricks from Leading Investors in AI with Q&A - NETWORKING
Get Your Questions Answered by Leading VC's

Designing Ethical AI Solutions - WORKSHOP
Workshop with Greg Adams, Accenture