Big Data Visualization & AI Summit 2017 will be held in San Jose on September 27th. This Conference will bring together industry professionals and thought leaders from the field of Big Data Science & Analytics. It will help in understanding and implementing data driven strategies in your business.This will also provide an excellent opportunity to interact and network with some of the top minds in Big Data & Analytics.



                                                                                           7 Speakers. 7 Topics. 100 Tickets

Wednesday, 27 September, 2017
Archana Akhaury, CTO and Founder, 1.21GWS

Do you trust data you visualize ? Why do you trust? What is the basis? Can you explain it and stand by it? Big data is the reason why we are faced with plethora of data sources and varieties that make gathering trusted data source and visualize very difficult. To correctly dereference your visualization, you have to go back to source and transformation system pipelines at first. Thanks to ETL, organizations have achieved it to some extent. But, what about trust? Data you analyze for analytics based on machine learning and AI has to be trusted both for test and training at first. Learning cannot happen on untrusted data.

One way to achieve this goal is to find your sensitive data records and protect sensitive data elements thus binding trust to record, data source and user. You need to have enterprise policy to protect sensitive data elements and promote these bindings. Once achieved, data you source is protected at source. AL and ML that drive core mainstream analytics now-a-days stand to benefit from policy driven data protection and record binding.

Sunil Sabat,
Director of Product Management (Big Data),

With increasing connectivity and software components, today’s car resembles a digital device or at least it is the rapidly changing perception of the consumer. Recent hacking events and discovery of software vulnerabilities have forced Automakers to prioritize introduction of seamless software update mechanism and in-vehicle data agents, which will also pave the way for ‘Connected Car’ and eventually 'Self-driving Car' eras. Meanwhile, big data and analytics use cases in the traditional IoT domain have been crippled by the absence of robust business cases and monetizing opportunities. Heavy regulations and penalties force automakers to adopt to advance analytics technologies when introducing connected features, thus, creating a solid business case and abundant opportunities for large enterprizes to start-ups. Recent acquisitions in the automotive domains are proof of modernization of the 100 years old auto industry and ecosystem. In this presentation, the speaker will provide insides of these winds of change from a perspective of a Silicon Valley entrepreneur. The speaker will explain challenges and opportunities with the vehicle data and analytics while also explaining how Movimento is solving some of these challenges by working closely with the automakers.

Dr. Pratik Desai,
Director of Engineering, Movimento

With digital transformation springing everywhere, companies are collecting and analyzing unprecedented amount of data -- on their products, processes, customers, employees to name a few. The data along with AI is being used to drive decision making process throughout an organization - from hiring new employees, scheduling work, to detecting abnormal access to key resources. The pervasiveness of such use is quickly making ethics a front-and-center issue. This talk will focus on three pillars of responsible AI: the right for explanation, uphold algorithmic accountability, and eliminate algorithmic bias.

Edy Liongosari
Chief Research Scientist, Accenture

Dr. May Wang Dr. May Wang is chief technology officer (CTO) and Co-founder of ZingBox, securing connected devices. May was previously the Head of Asia Pac Research and a Principal Architect in the Cisco CTO office, leading the Internet of Things (IoT) innovation. She was responsible for driving new technology initiatives. The security algorithm developed in her Ph.D. dissertation is deployed in Cisco’s bestselling network switches. May’s work focuses on real-time large data analysis, cybersecurity, and next generation network architecture. May also serves as a Venture Partner at SAIF, a $4B Private Equity firm. She is on advisory board of several VC firms and tech companies, as well as a Stanford Angels & Entrepreneurs member. May has authored a book of biographies titled “Women Executives in Silicon Valley” and was the 10th President of NACSA, a high-tech professional organization with over 4,000 members. May has been the recipient of numerous awards including Silicon Valley Women of Influence. She received her Ph.D. from Stanford University in Electrical Engineering.

Dr May Wang,
Co-founder & CTO, ZingBox

Gartner suggested that by 2020, fully 40 percent of all data science tasks will be automated. Data science automation will put data science-like technologies and methods in the hands of nontraditional data scientists -- "citizen data scientists." This presentation starts by explaining data science pipeline in great details and then explores key steps which can be automated. In this presentation, audience will learn key technical details related to automated feature engineering, stacked or grid based model building using prominent machine learning engines or libraries and model interpretation methods. Model interpretability helps understanding model production pipeline results and effectively explain results to target audiences.

Avkash Chauhan,
Vice President, H2O.ai

Joey Fitts, Founder & CEO, Outelligence
Dr. Jeannice Fairrer Samani, Senior Manager, Anita Borg Institute for Women in Technology




Super Early Bird USD 300 Registration ends on 27 July, 2017 SOLD OUT
Early Bird USD 400 Registration ends on 27 August, 2017 SOLD OUT
Standard Price USD 500 Registration ends on - 27 September,2017
Attractive Group registration tickets available. For details, please click here..
*Registration pass is inclusive of full day access to all conference venues, along with refreshments and executive lunch.

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