|10:00-10:10||OPENING PLENARY||SLC102||Int’l Data Engineering and Science Association|
|10:10-10:40||SESSION||SLC102||Practical and Collaborative Method to Jump Start into Machine Learning with Jupyter Notebooks and Google Collab
by Tarek Hoteit, Thomson Reuters
|SLC303||Un-Siloing Data Science Team
by Aravind Chiruvelli, ThoughtWorks
|10:45-11:15||SESSION||SLC102||Knowledge Engineering: How Artificial Intelligence Has Transformed from Magic to Method through Applied Science
by Charlie Burgoyne, Valkyrie Intelligence
|SLC303||Process Mining Meets AI and ML
by Viswanath Puttagunta, DIVERGENCE.ai
|11:20-11:50||SESSION||SLC102||Performing Data Analytics that Scales
by Sergey Maydanov, Intel Corporation
|SLC303||Getting Plugged Into Data Science
by Caitlin Hudon, Web.com
|11:55-12:25||SESSION||SLC102||Predictive Analytics: Digital Market Research & Challenges
by Dr. Kannappan Ramu, Saihasys Inc
|SLC303||Is AI Hype or Will it Transform Your Business?
by Angela Hood, ThisWay Global
|13:30-14:30||PANEL||SLC102||Discussion of Latest Big Data Technologies and How the Technologies Can Be Applied to Certain Use Cases to Solve Big Data Problems
by Jerry Watson, Enterprise Metrics / Sadu Hegde, CorData Solutions / Padmanand Warrier, Independent Investor and Board Advisor
|14:35-15:05||SESSION||SLC102||Leveraging Blockchain to Transform the Digital Supply Chain
by Vipul Tiwari, Reni Analytics
|SLC303||Digital Cracks in Banking and the Rising Impact of FinTechs
by Sidharth Nandi, Digital TaaS
|15:10-15:40||SESSION||SLC102||Combining Machine Learning and Blockchain To Create Greater Trust
by Mark Lynd, Relevant Track
|SLC303||Inspire and Engage Data Science Novices by Reducing the R Learning Curve
by Jesse Mostipak, Teaching Trust
|SLC303||Using Machine Learning to Diagnose Patients with Temporomandibular Disorders (TMD)
by Robert Chong, Clockwork Solutions
|SLC303||Catching Medication Adverse Events Before They Occur
by Boryana Manz, PCCI
|18:00-20:30||Networking Party||British Beverage Company|
Presentation Topic: Practical and collaborative method to jump-start into machine learning with Jupyter Notebooks and Google Collab.
Presentation Topic: Un-siloing Data Science Teams
Presentation Topic: Knowledge Engineering: How Artificial Intelligence has transformed from magic to method through applied science.
While AI hype abounds, there are incredible examples of the dramatic impact AI and machine learning can have on industry. We’ll discuss a few little-known case studies that demonstrate the tangible value AI can provide and the thread that connects them all. That thread is the scientific field of “knowledge engineering” where the structure and relationships of data are optimized for business insights.
Presentation Topic: Process Mining meets AI and ML
Presentation Topic: Performing data analytics that scales
A new approach is required for addressing both scalability and productivity aspects of a data science that combines two distinct worlds, the best of HPC world and the best of database worlds.
Starting with a brief overview of scalability aspects with respect to modern hardware architecture we will characterize what big data problem is, its inherit characteristics and how these map onto a modern data analytics software design choices.
We will also overview some data analytics use cases illustrating that the Big Data is not tied to cluster of machines within a data center.
Finally, we will present a few case studies using industry known data analytics libraries, such as Scikit-learn, Intel® Data Analytics Acceleration Library and others, to illustrate the scalability aspect.
Presentation Topic: Getting Plugged Into Data Science
Field notes from a data scientist on getting plugged into the data science world, including topics like:
+ Career tracks within “data science”
+ Advice for finding your first data science job
+ The data science skills they don’t teach in school
+ A secret for overcoming imposter syndrome
Dr. Kannappan Ramu
Presentation Topic: Predictive Analytics: Digital Market Research & Challenges
Today’s companies want to decrease data storage on their local sites and improve the time-to-value for their analytical data. On-premises big data analytics have significant operational limitations and companies desire to transform the way their real-time data is ingested, processed and delivered to provide meaningful business predictions and facilitate accurate decision making, while also lowering cost. Now and in the future to uncover niche audience/customer needs has become increasingly challenging for market researchers due to the wealth of data and emerging technologies that are readily available.
Presentation Topic: Is AI Hype or Will it Transform Your Business?
Jerry Watson, Sadu Hegde, Padmannand Warrier
Panel Topic: Discussion of latest big data technologies and how the technologies can be applied to certain use cases to solve big data problems
Jerry Watson will spend the first 15-20 minutes presenting slides of the primary big data technologies in use today, such as Hadoop (Pig, Hive, etc.), Elastic Map Reduce, Apache Spark, Amazon Web Services, IBM Watson Analytics, Microsoft Cortana/Azure, Google Analytics. The next 15-20 minutes will be Jerry asking the 2 panel members questions about matching the technologies to use cases to solve big data problems.
Presentation Topic: Leveraging Blockchain to transform the Digital Supply Chain
– Origins of Blockchain
– Current Usage
– Applications & Use Cases of BlockChain in various industries
– Looking Forward
Presentation Topic: Digital cracks in banking and the rising impact of FinTechs
Banking models which have stayed resilient for centuries rooted in their traditional ways of serving customers have started to experience cracks driven by the exposure of their inherent flaws and fintech powered digital disruption with the dawn of 4th industrial revolution. The rise of digital-only banks has started to fundamentally question the premise of a traditional brick-n-mortar outfit. The incumbent financial institutions and fintechs have started to identify ways towards a consumer centric view to reposition their relationship from competition to collaboration. What are the traits of survivors and disruptors? What does it mean to the consumers?
Presentation Topic: Combining Machine Learning and Blockchain To Create Greater Trust
1. Creating intelligent data-driven experiences
2. Examples of smart, immutable, tamper-proof applications
3. Developing offerings with trust as their notable characteristic
2018 will be a huge year for machine learning and blockchain, but it is the ability to combine them that drives differentiation and provide greater competitive advantage.
Presentation Topic: Inspire and engage data science novices by reducing the R learning curve
Dr. Valarie J. Bell
Presentation Topic: Data’s ‘Social DNA’: Decoding the People Behind the Data
Presentation Topic: Using Machine Learning to Diagnose Patients with Temporomandibular Disorders (TMD)
Presentation Topic: Catching medication adverse events before they occur
Within the last 10 years, healthcare providers have transitioned to electronic health records, enabling the development and implementation of data-driven approaches at scale to improve patient care and hospital operations. The challenges are balancing complex outcomes and integrating the solutions within the provider workflow. Over the last five years, PCCI (Parkland Center for Clinical Innovation) has pioneered the use of advanced analytics and artificial intelligence to solve healthcare’s challenges at Parkland and beyond.
PARADE (Patients at Risk for ADEs) is a multifaceted predictive score developed for Parkland Hospital, Dallas, to stratify newly admitted patients. It generates real-time actionable worklists within the electronic health record that trigger timely interventions by care teams. The PARADE model reconciles multivariable logistic regression models for best practices and ADEs. Post-implementation data reveals that consults for high-risk patients have tripled without additional pharmacy resources. PARADE-enhanced workflow has enabled efficient use of limited resources for improved outcomes.