Big Data & AI Conference Speakers

Arthur Holland Michel

Arthur Holland Michel

Co-Director, The CSD, Bard College

Arthur Holland Michel is the Founder and Co-director of the Center for the Study of the Drone, an interdisciplinary research and art collective at Bard College. He’s also a journalist, researcher and has written for a wide variety of media establishments.

He is a co-author of The Drone Primer: A Compendium of Key Issues, and Drone Sightings and Close Encounters in the National Airspace. Most recently, he’s the author of a new book titled Eyes in the Sky: The Secret Rise of The Gorgon Stare and How It Will Watch Us All.

Gorgon Stare is the name of an aerial surveillance system built by the Pentagon that has been used successfully in combat zones and, secretly, over some American cities. Michel will give a presentation and participate in the Ethics in AI/Facial Recognition discussion panel.

Attendees who register for the conference will receive a free copy of Eyes in the Sky.

Keynote Title: Intelligent Eyes in the Sky: The Past, Promise, and Perils of Automated Surveillance.

Keynote Abstract: This presentation will examine some of the remarkable and startling things that happen when a powerful camera is paired with computer vision and flown thousands of feet in the air over your average city. Revealing the many ongoing efforts to automate the age old art of airborne spycraft, Arthur will describe the potential risks of pairing vast surveillant power with an intelligent brain, and offer a few thoughts on how we can unlock this technology’s formidable potential while avoiding its urgent perils.

Veena Somareddy

Veena Somareddy

Co-Founder & President, NeuroRehabVR

Veena Somareddy is the Co-founder and CTO at NeuroRehabVR, a VR healthcare start-up aimed at building virtual/augmented reality training exercises with the use of machine learning for patients with neurological disorders.
 

She is also the Chapter President for VR/AR Association Dallas and co-organizer of the Women in VR – Dallas chapter, focusing on mentor-ship, skill development and building a community of talented individuals who are enthusiastic about being a part of this emerging industry.

Tom Edwards

Tom Edwards

Chief Digital Officer - Agency, Epsilon

Tom was recognized in 2019 by OnCon as a Marketing Trailblazer and a Marketing Contributor for thought leadership. Tom was also recently named a 2017 Marketing Technology Trailblazer by Advertising Age. For the past 19 years, Tom has focused on the intersection of emerging technology and its impact on consumer behavior and how to bring “Innovation to Reality”™.

Tom regularly provides thought leadership and industry commentary via business and advertising publications that have garnered millions of views. He speaks regularly (domestic & international) as a futurist on topics such as data design, artificial intelligence, Gen Z and the evolution of consumer experience.

Anna Sidorova

Anna Sidorova

Associate Professor, University of North Texas

Anna Sidorova is an Associate Professor at the University of North Texas where she conducts research on organizational impact of digital technologies and teaches a variety of graduate and undergraduate courses, including a graduate course on Artificial Intelligence in Business.

Dr. Sidorova has over 20 journal publications, as well as numerous conference publications related to strategic impact of IT, AI governance, business intelligence and analytics, business process management, enterprise architecture and open-source software development. At UNT, she has been recognized for teaching (College of Business Teaching Innovation Award, 2016) as well as research (Junior Researcher Award, 2009).

Besides her academic work, Anna Sidorova is the founder and CEO of Arcturus Intelligence Solutions, LLC, which offers training and consulting services related to AI strategy and governance. Prior to joining UNT, Dr. Sidorova was with PwC performance improvement practice in Moscow, Russia, where she advised clients on business process improvement and strategy implementation.

Presentation’s Title: Enabling AI governance through AI business Description Standard.

Presentation’s Title: What questions should business people ask before investing in AI? Given the inherent complexity and continuous evolution of AI technologies, descriptions of AI-enabled products either avoid discussing the specific nature of AI capabilities, or overwhelm the reader with a barrage of technical terms. In such situation, there is a need for an open standard for describing AI capabilities in terms that would enable an effective evaluation of such capabilities from the business perspective.

The purpose of this presentation is to propose an AI business description framework, a set of principles for developing an open AI business description standard. The standard is proposed to include a set of guiding questions that business decision makers should ask when evaluating AI-enabled products, as well as a set of standards for evaluating answers to such questions. The presentation will also propose a set of processes for engaging AI and business communities in the development and maintenance of the standard.

Randall Hunt

Randall Hunt

Sr Software Engineer & Tech Evangelist, AWS

Randall Hunt is a Senior Technical Evangelist and Software Engineer at Amazon Web Services in Los Angeles. Randall spends most of his time building demos and writing about new services and launches. Python is his favorite programming language but he can sometimes be found struggling with C++.

Prior to working at AWS, Randall launched rockets at NASA and SpaceX but he found his programming passion at MongoDB. Randall has worked on a variety of technical and business challenges in many different verticals with over a decade of cloud experience at NASA, SpaceX, MongoDB, and AWS. He is a total space nerd.

Presentation’s Title: Building WhereML an AI powered Twitter Bot for guessing locations of pictures with MXNet

Presentation’s Abstract: Learn how we built and deployed the @WhereML Twitter bot that can identify where in the world a picture was taken using only the pixels in the image. We’ll dive deep on artificial intelligence and with the MXNet framework and also talk about working with the Twitter Account Activity API.

In this session attendees will learn how we designed, built, and deployed the @WhereML Twitter bot which can identify where in the world a picture was taken using only the pixels in the image. We’ll dive deep on artificial intelligence and deep learning with the MXNet framework and explore the Twitter Account Activity API.

Ashlesha Nesarikar

Ashlesha Nesarikar

Founder & President, Plano Intelligence, Inc.

Ashlesha Nesarikar is Founder and President of Plano Intelligence Inc, a startup based in Plano, Texas. Plano Intelligence has won significant support from US Ignite, NSF, cities, and universities. They were invited to present iNotify to Smart Cities at their national level meeting in Denver, CO. This generated interest from cities and universities to deploy iNotify in their communities. Currently, they are closely working with two cities and a school district to integrate an iNotify pilot into their existing security infrastructure.

Ashlesha is a computer science undergraduate student at the University of Texas at Dallas.

Presentation’s Title: iNotify: AI to Prevent Gun Violence

Presentation’s Abstract: iNotify is a state-of-the art AI technology solution that elevates gun public safety to the next level. iNotify prevents fatalities, improves first responder response times, and quickly identifies victims and their needs in shooting situations. Large scale pilots are investigated and planned.

iNotify is a versatile multipurpose cloud-based AI platform capable of real-time generation and delivery of intelligence from video, audio, and text. This is coupled with an application for smart devices that notifies users in real-time.

This presentation will introduce iNotify, the reason for its existence, and current of development and deployment.

Chintan Shah

Chintan Shah

Vice President, HYLA Mobile

Chintan Shah is a senior Information management executive with over 15 years of experience building and leading organizations centered on Technology, Analytics, Machine Learning, Digital transformation, Business Intelligence, Data warehousing and Big Data.

For many years, Chintan has been working with C-level execs and helping companies convert data into actionable intelligence creating competitive advantage. Chintan has not only developed data and analytical solutions for internal consumption but has also productized data/analytics solutions to monetize as “Analytics as a product”.

He has a BS degree from the University of Baroda (India), an MS in Computer Information Systems from the Southern Illinois University Edwardsville, and a Graduate Certificate in Business Data Mining from Oklahoma State University.

Presentation’s Title: Artificial Intelligence and Machine Learning in Mobile Device Reverse Logistics and InsurTech

Presentation’s Abstract: Mobile device trade-in is a $27B industry that is projected to grow to $39B by 2025. Installment plans, Device leases, and upgrade plans have spurred the growth of mobile device buybacks and trade-ins. AI and ML are playing a significant role in not only supporting the growth but also modernizing the industry. AI/ML is assisting with pricing analytics, computer vision-based triaging solutions, robotic processing, etc. to name a few use cases.

With the increasing cost of new mobile devices, mobile device insurance has never been more important. AI and ML are playing a big role to fundamentally change how mobile device insurance is offered, priced and serviced.

Tiffany Ricks

Tiffany Ricks

Founder & Chief Hacker, Hacware, Inc.

Tiffany Ricks is the founder and CEO of Hacware, Inc., an AI product that prevents enterprise email hacking by learning employee behaviors, hacking the employee, and automates training recommendations on email cybersecurity awareness. She’s also the founder and COO of Female Founders of Dallas.

Tiffany has over 15 years of experience in the software development industry and 9 years as a White Hat Hacker. She has spent her career building software applications for national brands including L3 Technologies and the US Air Force.

Tiffany has a Bachelors of Science degree in Computer Science from the Texas Christian University and a Master of Science in Management from Texas A&M University at Commerce. She will be a panelist on the Robots and Automation discussion panel.

Corey Clark

Corey Clark

CTO, Balanced Media | Technology

Dr. Clark serves as CTO of BALANCED Media | Technology as well as the Deputy Director of Research at SMU Guildhall and Assistant Professor of Computer Science at SMU. He has over 15 years of experience with a background in finding solutions to large-scale problems by combining several areas of study, such as gaming, systems biology, distributed computing, artificial intelligence and Machine Learning.

He has raised almost $20MM in research, seed, and series funding from government and private entities, and has served as CTO of a gaming technology startup that was acquired.

He has led multiple Advanced DoD R&D projects from concept to commercialization and most recently led a team to a $2.5MM XPRIZE Grand Prize win. Dr. Clark’s innovations in human-in-the-loop machine learning techniques have led to numerous publications, patents.

Presentation’s Title: Constructivist Augmented Machine Learning: Using video games and human-in-the-loop techniques to directly transfer human knowledge into ML models

Presentation’s Abstract: ML has made many strides over the past several years, but in most cases the overall training methodology has remained consistent. The current ML training process mimics Kolb’s Experiential Learning paradigm found in classrooms. This model drives students to learn from personal experimentation, often without any outside instruction. This technique can provide rapid understanding, but also has the drawback of not being able to take advantage of knowledge and experience provided by expert guidance.

Constructivism is a broader learning theory which incorporates the addition of knowledge gained from past experiences as well as social interaction and collaboration with an expert. This allows for students to learn from an instructor’s past experience and knowledge as a supplement to the experimentation process.

BALANCED’s HEWMEN platform utilizes Constructivist Augmented Machine Learning (CAML) methodology that allows for humans to interact with ML algorithms and techniques. CAML is a human-in-the-loop methodology scaled by using human computation video game techniques. This process allows algorithm guidance by augmenting inputs as well as directly modifying hidden layers, weight and connections throughout the training process. Humans are capable of identifying patterns and optimization opportunities during training and can subsequently modify the ML model to take advantage of the human’s intuition. In short, CAML allows for the direct transference of human knowledge into ML models.

Adding CAML to an existing ML pipeline can improve model accuracy, compress model size, or allow model improvement in absence of large data sets. This talk will show examples of CAML being used to guide ML model when analyzing medical and satellite imagery as well as knowledge transfer directly into the Leela Zero deep learning model (Open Source version AlphaGo Zero). The process is compatible with HEWMENs distributed ML techniques, such as federate learning, which allows for scaling of CAML on both human and machine components.
 
Key Takeaways Points:
1. See examples of how human-in-the-loop techniques can dramatically accelerate ML training as well as techniques to extract knowledge from trained ML models.
2. Demonstration of BALANCED’s HEWMEN platform, which combines video games, human intuition and distributed computing into a single cloud environment.

Robert M. Atkins

Robert M. Atkins

CEO, Balanced Media | Technology

As CEO of BALANCED Media | Technology, Robert is focused on company management and product design which will inspire a culture of Collaborative Innovation, Immersive Learning, and Interactive Machine Learning. As a founding member of 5 interactive entertainment companies, Robert has contributed heavily to over 50+ titles, with some noted as the industry’s highest acclaimed franchises including:  Quake: SOA, Duke Nukem 3D, SiN, Heavy Metal, Tomb Raider, 007, SiN Episodes, Star Trek, Metal of Honor and Counter Strike.

During his 23-year career, Robert has been highly involved in Product Design, Creative Direction, Company Management, Brand Building, Education and Marketing. Robert’s companies and products have won dozens of industry awards and worldwide acclaim. Many of the products and technology have laid the groundwork for emerging interactive technologies and eSports. In 2018, Robert received the Industry Giant’s Community Icon Award  for his career and work at BALANCED.

Robert will deliver an introduction to Dr. Corey Clark’s presentation.

Riccardo Biasini

Riccardo Biasini

Chief System Architect, comma.ai

Riccardo is the CEO of comma.ai. He received a BS and an MS in Automotive Engineering from the University of Pisa, Italy. He joined Tesla in 2011, where he worked on simulation and controls, leading the development of the Tesla’s Traffic Aware Cruise Control.

In 2016, he joined comma.ai where he was among the main contributors of the in-house self driving technology. The project then became open source in 2017, with the name of openpilot. Currently, openpilot has thousands of users around the world.

Presentation’s Title: Data Collection, Use And Cost For Self-Driving Cars.

Presentation’s Summary: Self driving cars make extensive use of machine learning models to perceive their surroundings and take driving actions that are both safe and human-like. Large data sets are required to train models, but data isn’t free. Collection capability and storage cost aren’t trivial problems for companies that develop self driving technology. In my presentation, I’ll lay out comma‘s approach to data collection, use and cost for self driving cars.

Dr. Edward Peters

Edward Peters, PhD

Founder & CEO, Data Discovery Sciences

Dr. Peters leads Data Discovery Sciences, a boutique consulting firm specializing in digital transformation and has recently developed the Zero-Point Solution, a set of techniques that guide the identification and profitable implementation of RPA and cognitive technology projects. Prior to Data Discovery Sciences, he was CEO of OpenConnect Systems, an early leader in automated business process discovery and RPA.

He has been the recipient of numerous awards, including the Ernst and Young Entrepreneur of the Year, Regional Finalist in 2003, 2004 and 2008. He was also named Maryland Tech Council Entrepreneur of the Year in 2004.

He holds a Ph.D. in Applied Economics (process mining) from the University of Amsterdam, The Netherlands, an M.S. in Industrial Engineering and a B.A. in Government, both from Lehigh University, Bethlehem, PA. He has also attended programs at the Stanford University Graduate School of Business and MIT’s Sloan School of Management. He has held faculty positions in applied economics at Katholieke Universetiet (KU Leuven) in Leuven, Belgium and in mathematics at Lebanon Valley College in Annville, PA. His writings have been published in scientific peer reviewed journals as well as the popular press (Forbes, The Financial Times, The Hill).

Presentation’s Title: Intelligent Automation for Fun and Profit!

Presentation’s Abstract: The term “intelligent automation” has been used to describe everything from the application of AI/RPA to Python coding when discussing how to increase business process productivity; but what exactly is it? What’s needed is a common sense definition and an approach to understanding how to use it effectively. This requires understanding the strengths of the various technology components (e.g., machine learning, deep learning, RPA, “smart RPA, computer vision, etc.) as well as how they can be effectively applied to achieve cost effective productivity growth within an organizations unique set of processes and tasks.

This session presents an immediately-useful framework for guiding the application of “intelligent automation” technologies coupled with a quantitative method based on “Value Leak Analysis TM” to establish ROI targets.

Karl Weinmeister

Karl Weinmeister

Cloud AI Advocacy Manager, Google

Karl is a Manager of Cloud AI Advocacy at Google, based in Austin, TX. He leads a global team of data science and engineering experts who engage with users at events, build impactful content, and influence Google product strategy. Previously, he was Director of Engineering at SparkCognition, where he led cross-functional teams that included building an ML-based malware detection.

Karl holds a BS in Computer Science from Duke University, and an MBA from the University of Texas – Austin.

Presentation’s Title: Real-World Machine Learning with TensorFlow and Cloud ML

Presentation’s Abstract: In this session, attendees will receive a brief introduction to machine learning and neural networks. Then, we will discuss various ways to use machine learning, from pre-trained model APIs for vision and language, to custom model development tools. We’ll also discuss how to deploy models into production. We will see how these technologies are applied in real-world customer scenarios.

Karl will also be the co-instructor, with Fatih Nar, of the End-to-End Machine Learning with TensorFlow on Google Cloud Platform workshop. Visit this link for more info and to register.

KC Tung

K.C. Tung

AI Architect, Microsoft

KC holds a PhD in Molecular Biophysics from The University of Texas Southwestern Medical Center in Dallas, TX.

Presentation’s Title: AI/ML Model Characterization for Performance, Interpretability, Fairness and Reliability

Presentation’s Abstract: Artificial intelligence (AI) has been transformed by machine learning (ML) methodologies due to ML’s advantage in scalability in cloud and vast choices of open-source as well as commercial, off-the-shelf (COTS) solutions. However, the complexity of ML puts AI at the center of discussion regarding decision fairness or transparency, ethics, as well as reliability. As these concerns impact feasibility and adoptions of AI in enterprises, there is a need for generalized guidelines or approaches to evaluate ML model performance in the context of these concerns

This talk will provide a framework that can help determine the performance, fairness and transparency through ML model characterization. Methods and approaches to model characterization will be proposed. After this talk, audiences will have a better understanding in: 1. How to evaluate and understand the limit of ML model built by data science team; 2. Functional knowledge in relevant model key performance metrics (KPI) that helps decision makers in model validation, adoption or deployment. 3. For ML practitioners and engineers, common practices in fields for dealing with data class imbalance or sampling bias.

Anais Dotis-Georgiou

Anais Dotis-Georgiou

Developer Advocate, InfluxData

Anais is a Developer Advocate for InfluxData with a passion for making data beautiful with the use of Data Analytics, AI, and Machine Learning. She takes the data that she collects, does a mix of research, exploration, and engineering to translate the data into something of function, value, and beauty. When she is not behind a screen, you can find her outside drawing, stretching, boarding, or chasing after a soccer ball.

Presentation’s Title: When Holt-Winters is better than Machine Learning

Presentation’s Abstract: Machine Learning (ML) gets a lot of hype, but its Classical predecessors are still immensely powerful, especially in the time series space. Error, Trend, Seasonality Forecast (ETS), Autoregressive Integrated Moving Average (ARIMA), and Holt-Winters are three Classical methods that are not only incredibly popular but also excellent time series predictors. In fact, these Classical Methods outperform several other ML methods including Long Short Term Memory (LTSM) and Recurrent Neural Networks (RNN) in One-Step Forecasting.

In this talk, I’ll show you how the Holt-Winters forecasting algorithm works. Then we’ll use the HOLT_WINTERS() function with InfluxData to make our own time series forecast.

Bob Dutcher

Bob Dutcher

VP, Product Marketing, Oracle

Bob Dutcher is Vice President of Product Marketing for Oracle and is responsible for global customer adoption of Oracle Analytics and Big Data solutions. He has over 20 years of experience working with data, analytics, and finance teams across the globe to transform organizations with the power of data.

Before joining Oracle, Bob led domestic and global product management and marketing teams at various companies, such as Moody’s Analytics, SPSS, Cartesis, and Business Objects. He is passionate about machine learning and AI and presents at conferences around the world to impart Oracles’ vision about the power of data, machine learning, and analytics.

Presentation’s Title: Adopt AI Across The Business

Presentation’s Abstract: Eager to adopt AI in your company? There is a seismic shift in the business landscape to harness transformational technologies to improve customer experiences, drive more significant revenues, and lower operational costs. AI is no longer an experiment or project in a lab. Learn how real AI solutions are driving innovative advancements across the business, including Sales, HR, and Finance departments.

Fatih Nar

Fatih Nar

Customer Engineer, Google

Fatih is a Cloud Customer Engineer at Google where he has been guiding his customers in their virtualization, cloud on-boarding and big data journey. Fatih held senior engineering positions previously in Verizon Wireless as Distinguished Engineer, in Canonical/Ubuntu as Senior Solution Architect and in Ericsson as Senior Solution Architect across different parts of the world with technology areas including NFV/SDN, Cloud, IP-TV, Connected/Smart Home and Vehicles.

Fatih holds an MSc in Information Technologies and a BSc in Electronics Engineering.

Fatih will be the co-instructor, with Karl Weinmeister, of the End-to-End Machine Learning with TensorFlow on Google Cloud Platform workshop. Visit this link for more info and to register.

Gabriel Bianconi

Gabriel Bianconi

Founder, Scalar Research

Gabriel is a Machine Learning scientist with experience in applying cutting-edge academic research to solving real-world problems.

At Google and Facebook, he worked on backend infrastructure for enterprise tools responsible for billions of dollars in revenue. He’s also created an advertising supply-side platform that handled millions of ad requests per day, built an algorithmic trading platform and quantitative strategies for cryptoasset markets that handled over US$10M in volume. He’s held positions at startups and investment firms.

Gabriel has a B.S. & M.S. in computer science from Stanford University, where he received multiple academic distinctions, including the President’s Award for Academic Excellence. His B.S. thesis investigated quantum deep learning algorithms using NASA’s D-Wave quantum computer, and was selected for a presentation at the AQC 2017 Conference in Tokyo, Japan.

His M.S. research, conducted at the Stanford Partnership in AI-Assisted Care, focused on improving clinical care and reducing monitoring costs in hospitals using Machine Learning and computer vision, and resulted in a first-author manuscript selected as Top 10 Research Paper at the NIPS Machine Learning for Health 2017 Workshop.

Presentation’s Title: Introduction to Face Processing with Computer Vision

Presentation’s Abstract: Ever wonder how Facebook’s facial recognition or Snapchat’s filters work? Faces are a fundamental piece of photography, and building applications around them has never been easier with open-source libraries and pre-trained models.

In this talk, we’ll help you understand some of the computer vision and machine learning techniques behind these applications. Then, we’ll use this knowledge to develop our own prototypes to tackle tasks such as face detection (e.g. digital cameras), recognition (e.g. Facebook Photos), classification (e.g. identifying emotions), manipulation (e.g. Snapchat filters), and more.

Ramin Keene

Ramin Keene

CEO & Founder, Fuzzbox.io

Ramin is the founder of fuzzbox.io, which brings together Machine Learning, A/B testing, progressive delivery, and chaos engineering to help companies explore the unknowns of their applications, uncover risk, and manage complexity safely.

He has spent the last decade helping large companies put machine learning into production and scale their data infrastructure. He is based on Seattle.

Presentation’s Title: Fast, Reliable, Yet Catastrophically Failing!?! Safely Avoiding Incidents When Putting Machine Learning Into Production

Presentation’s Abstract: Safely releasing machine learning based services into production presents a host of challenges that even the most experienced SRE may not expect. Severe incidents with stable infrastructure, invisible errors rates, IMPROVING response times, but the business failing catastrophically losing millions of dollars? Absolutely!

As an operator of production systems now being increasingly asked to release and manage machine learning based systems, Welcome to ML in production, where everything you know about running, deploying, and monitoring systems is harder and riskier.

We’ll outline some severe outages seen in the wild, their causes, and detail how emergent cutting edge techniques from the DevOps and SRE world around “testing in prod”, progressive delivery, and deterministic simulation are the PERFECT solution for increasing safety, resilience, and confidence for SREs operating and managing ML based services at scale.

 

Jon Bratseth

Jon Bratseth

Distinguished Architect, Verizon

Jon Bratseth is a Distinguished Architect in Verizon and the architect and one of the main contributors to Vespa.ai, the open source Big Data serving engine.
Jon has 20 years experience as architect and programmer on large distributed systems.

He has a master’s degree in Computer Science from the Norwegian University of Science and Technology.

Presentation’s Title: Big Data Serving: The Last Frontier. Processing and Inference at Scale in Real Time

Presentation’s Abstract: Offline and stream processing of big data sets can be done with tools such as Hadoop, Spark and Storm, but what if you need to process big data at the time a user is making a request? This talk introduces Vespa.ai, an open source big data serving engine which targets the serving use cases of big data by providing response times in the tens of milliseconds at high request rates.

Daniel Vollmer

Daniel Vollmer

Computational Linguist, Lymba

Daniel Vollmer is a Computational Linguist with Lymba, a Dallas-based AI firm specializing in Natural Language Processing. At Lymba, Daniel works on multiple NLP projects, including LSTMS for Named Entity Recognition and Document Classification. His current research work leverages existing knowledge bases as input features for deep learning techniques. Daniel holds a BA from UC San Diego in Linguistics and a MSc in Speech and Language Processing from The University of Edinburgh in Scotland.

Presentation’s Title: Applying NLP: Use Cases of Natural Language Processing in Commercial Applications

Presentation’s Abstract: My presentation will look at how companies are applying NLP, the impact of deep learning on performance and methodology, then a deep dive on an automotive use case leveraging chat bot transcripts for regulatory reporting.

Steve Ardire

Steve Ardire

AI Start-up Advisor 'Force Multiplier' & Merchant of Light

Steve is AI startup advisor ‘force multiplier’ (currently advising 8 AI startups) who shapes serendipity to connect and illuminate the dots that matter (aka The Merchant of Light) leveraging significant relationship capital and personal branding. He is active speaker at events whose favorite topic is #AugmentedIntelligence for #futureofwork. For more follow @sardire and connect http://www.linkedin.com/in/sardire

Presentation’s Title: Augmented Intelligence will become the future of work

Presentation’s Abstract: There’s lots of anxiety about automation of jobs because machines are learning how humans do things – they’re also learning how to do them better. But this automation is mostly for procedural work that humans don’t really like doing. Augmented Intelligence, where machines and humans work together, allows us to better understand and improve operations too complex for the human mind to manage with faster time to insights. It will redefine management and place greater emphasis on soft skills, that is, critical thinking, cognitive flexibility, emotional intelligence, imagination, and creativity, with a greater emphasis on lifelong learning.

This presentation will show why augmented Intelligence will become the future of work and competitive imperative to change the way industries operate and businesses compete.

Babar Bhatti

Babar Bhatti

Co-Founder, Dallas AI

Babar is a business and technology leader with 18+ years of expertise in Analytics, Product Management, Cloud Computing, Digital Platforms,
Machine Learning and Enterprise Software. He co-founded, grew and sold MutualMind, a SaaS analytics product company. MutualMind served the world’s top brands, including IBM North America/EU/Japan, American Airlines, Kraft, Nestle, P&G, Walgreens and Walmart.

He led technology and product management and spear-headed partnerships with IBM (AI/Watson partner, UBX partner and client for marketing and demand generation), Twitter, Facebook and more.

He’s the winner of 5 innovation awards at Verizon and MutualMind, and a member of the Association for the Advancement of Artificial Intelligence. He holds a master’s degree in Civil and Environmental Engineering and Technology and
Policy from MIT.

Presentation’s Title: Mitigating Bias and Fairness in AI

Presentation’s Abstract: Introduction to the concept of Bias in AI, why it matters and how to mitigate the effects of bias in data and algorithms for AI.

Ron Dagdag

Ron Dagdag

Sr Software Engineer, Crestron Electronics

During the day, Ron Dagdag is a Senior Software Engineer at Crestron Electronics where he supports developers with their IoT, Cloud and Voice AI assistant development. On the side, Ron is an active participant in the community as a Microsoft MVP, speaker, maker and blogger. He is passionate about Augmented Intelligence, studying the convergence of Augmented Reality/Virtual Reality, Machine Learning and the Internet of Things.

Presentation’s Title: Engineering + Analytics = Product

Presentation’s Abstract: This presentation is a skit based on the graphic novels by Andrew Ng. In it, our heroes are caught between the worlds of analytics and engineering as they battle doing machine learning in the most inept way possible (i.e. – a business setting). Will our intrepid heroes actually pull it off? Find out and learn from our mistakes!

Key Takeaway Points: 1. Overcoming the language barrier; 2. Testing business value; 3. Knowing your teams strengths and weaknesses; 4. Arrogance = Ego + Ignorance; 5. Iteration helps

Troy Mann

Troy Mann

Data Engineer, Toyota Fin. Services Securities USA Corp.

Troy Mann is a combination cloud architect, data engineer, and developer. He works very closely with data scientists and analysts to test and bring their models to production. He has participated in the development of multiple IoT and machine learning products. Troy regularly participates in Kaggle contests, seeking to constantly improve. His passion is making models of the world in his computer.

Presentation’s Title: Engineering + Analytics = Product (Troy will co-present this skit with Ron Dagdag)

Presentation’s Abstract: This presentation is a skit based on the graphic novels by Andrew Ng. In it, our heroes are caught between the worlds of analytics and engineering as they battle doing machine learning in the most inept way possible (i.e. – a business setting). Will our intrepid heroes actually pull it off? Find out and learn from our mistakes!

Key Takeaway Points: 1. Overcoming the language barrier; 2. Testing business value; 3. Knowing your teams strengths and weaknesses; 4. Arrogance = Ego + Ignorance; 5. Iteration helps

Saurabh Shah

Saurabh Shah

Partner, IBM Cognitive Sciences

Saurabh is a Partner in the Cognitive & Analytics practice in IBM Services,
and has established and led multiple practice areas within IBM’s Cognitive
and Artificial Intelligence Services. As a trusted advisor to the C-Suite, he has
helped multiple clients design, operationalize and execute their cognitive and
digitization strategies. He currently leads the Global Practice on Cognitive
Knowledge Worker. Recent leadership roles included establishing and leading
the IBM Garage and Cognitive Customer Care solutions.

In IBM Garage, he established a methodology and platform within client organizations to use AI and Machine Learning to innovate at a rapid pace while delivering business value. As the leader of CognitiveCustomer Care, he worked with multiple clients and inspired them to use AI, Automation and Cognitive solutions as a catalyst to deliver millions of dollars in savings by transforming and disrupting their customer strategy.

With 20+ years of cross industry experience, Saurabh has worked with several
large clients across a variety of industries.

Presentation’s Title: Improve Customer Care and Increase Your Bottom Line with AI

Presentation’s Abstract: What if you had the idea that could save the company millions and help your firm create a competitive advantage. What if you could take this idea and in a matter of a few weeks test your hypothesis with real data and develop the proof you need to present to your C-Suite. What if you could then execute and deliver the idea within a matter of a few months. What if you could have the agility of a startup while operating within the constraints of a large organization.

This presentation will help you answer those questions and become your company’s hero.

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Big Data & AI Conference

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Ethics in AI/Facial recognition

Most American adults are in a face-recognition database accessible to law enforcement. Our panel of experts will tackle this and other issues related to this technology.

Driverless cars

Hype or ready for prime time? You won't want to miss this discussion panel featuring a few of the best minds working on driverless vehicles technology.

Robots & automation

Are robots and process automation going to cost us our jobs? Our panel of experts will examine that and other issues related to robots and automation.

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What We do

https://bigdataaiconference.com is an event website with a focus on conferences related to Big Data and Artificial Intelligence. As such information we provide on the website pertains to our past and upcoming events. We don't sell you anything directly. We do, however, sell you tickets using a third party ticketing platform called Eventbrite, and monitor website traffic using Google Analytics.

Information we collect

Information we collect directly is your email address, when you subscribe to our newsletter. And to help us better understand the nature of traffic that flows through the website, we use Google Analytics, which uses cookies inserted into your browser to track your activities on the website. Other third party services we use, like Eventbrite, also use cookies to make the service it provides function properly.

Our Privacy Policy

Our privacy policy is very simple; we do not sell any personal data that we collect from you either directly when you subscribe to our newsletter or via a third party when you, for example, purchase a ticket or tickets to an event that we organize.

How we use the information we collect

When you subscribe to our newsletter, we use your email address to send you updates about our events. The personal information we have access to via Eventbrite we use to help us understand where our attendees come from.

How we use cookies

Cookies inserted into your Web browser via Google Analytics are used to compile aggregate data about your activities while you on our website so that we can offer better site experiences and content in the future. You can configure your browser to not store cookies, but that will severaly impact your user experience while on our website. We recommend that you whitelist our website if you don't want every website you visit to set cookies on your browser.

How to contact us

Code of Conduct

Code of Conduct

All attendees, speakers, sponsors and volunteers at our conference are required to agree with the following code of conduct. Organisers will enforce this code throughout the event. We expect cooperation from all participants to help ensure a safe environment for everybody.

Our conference is dedicated to providing a harassment-free conference experience for everyone.

Harassment includes offensive verbal comments related to gender, gender identity and expression, age, sexual orientation, disability, physical appearance, body size, race, ethnicity, religion, technology choices, sexual images in public spaces, deliberate intimidation, stalking, following, harassing photography or recording, sustained disruption of talks or other events, inappropriate physical contact, and unwelcome sexual attention.

Participants asked to stop any harassing behavior are expected to comply immediately.

Sponsors are also subject to the anti-harassment policy. In particular, sponsors should not use sexualised images, activities, or other material. Booth staff (including volunteers) should not use sexualised clothing/uniforms/costumes, or otherwise create a sexualised environment.

If a participant engages in harassing behavior, the conference organisers may take any action they deem appropriate, including warning the offender or expulsion from the conference with no refund.

If you are being harassed, notice that someone else is being harassed, or have any other concerns, please contact a member of conference staff immediately. Conference staff can be identified as they'll be wearing branded clothing and/or badges.

Conference staff will be happy to help participants contact hotel/venue security or local law enforcement, provide escorts, or otherwise assist those experiencing harassment to feel safe for the duration of the conference. We value your attendance.

We expect participants to follow these rules at conference and workshop venues and conference-related social events.

© Big Data AI Conference 2019. All rights reserved