Keynote Speakers:

 

Angela Bassa, Director of Data Science at iRobot link64

Bassa

“On Keeping Things Simple” 

Angela leads the newly-formed team through development of machine learning algorithms, sentiment analysis, and anomaly detection processes. Angela is also a technical advisor for Mirah, a startup focused on making behavioral healthcare more objective and data-driven. Her previous projects earned accolades such as INFORMS’ Edelman award for Achievement in Operations Research and the Management Sciences; and the Massachusetts Innovation & Technology Exchange award for Big Data and Analytics Innovations. She discovered data science while studying math at MIT, only back then it wasn’t called that yet. Over the past two decades she has learned to lead data teams in academic, commercial, and industrial applications.

 

 

Drew Conway, Founder and CEO of Alluvium link64

conway

“Industrial Machine Intelligence: The Golden Braid of Data Streams, AI, and Human Expertise”

Drew is a world-renowned data scientist, entrepreneur, author, and speaker. He’s also the founder and CEO of Alluvium.  Along with his experience building companies, Drew has advised and consulted for companies across many industries, ranging from fledgling start-ups to Fortune 100 companies, as well as academic institutions and government agencies at all levels.  As a visionary in data science and large-scale computational – Drew will share his views on the emerging stack of big data technologies and how intelligent software systems will support normal business operations. He will focus on the benefits of AI to industrial businesses.

 

 

Rachel Slaybaugh, Assistant Professor of Nuclear Engineering at UC Berkeley linkedin32

Slaybaugh

“Nuclear Energy: What Can Analytics Do for Economics and Safety?”

Abstract: The nuclear energy industry is at a crossroads: existing nuclear reactors are struggling to operate economically in some tough markets, and construction of new designs in the U.S. is slow and over budget. At the same time, interest in and development of the next generation of nuclear reactors is growing at an unprecedented rate, and some other nations are building new reactors efficiently. Can the current fleet reduce costs? Will the next generation of designs be “walkaway safe” and cost-competitive? What about safeguards and recycling of nuclear fuel? Data Analytics and Machine Learning can be impactful in answering these questions. This talk will frame some of the big challenges in nuclear energy and how Data Analytics are starting to be used. We’ll also look to the future in terms of where the biggest impacts are likely to be and what we can do to move quickly.

 

 

Raluca Ada Popa, Assistant Professor of Computer Science at UC Berkeley link64

Raluca

“Securing Machine Learning Systems”

Raluca Ada Popa is a co-director of the RISELab at UC Berkeley, as well as a co-founder and CTO of a cybersecurity startup called PreVeil. Raluca developed practical systems that protect data confidentiality by computing over encrypted data, as well as designed new encryption schemes that underlie these systems. Raluca has received her PhD in computer science as well as her Masters and two BS degrees, in computer science and in mathematics, from MIT. She is the recipient of an Intel Early Career Faculty Honor award and George M. Sprowls Award for best MIT CS doctoral thesis.

 

 

Xaiver Amatrain, Co-founder and CTO of Curai linkedin32

Armatrain

“ML to cure the world”

Abstract: The practice of medicine involves diagnosis, treatment, and prevention of diseases. Recent technological breakthroughs have made little dent to the centuries-old system of practicing medicine: complex diagnostic decisions are still mostly dependent on “educated” work-ups of the doctors, and rely on somewhat outdated tools and incomplete data. However, with a growing research community as well as tech companies working on machine learning advances to medicine, the hope for healthcare renaissance is definitely not lost. In this talk we will explore how to apply machine learning such as recommender systems to important aspects of medicine such as patient diagnosis and treatment. After reviewing some lessons learned from years of designing ML  systems in general and recommender systems in particular, I will dive into recent uses of AI and ML methods in medicine. I will highlight the importance of personalized medicine that takes into account not only the user, but also the context, and other metadata. I will also discuss challenges in designing ML-based medical systems that are accurate, but at the same time engaging and trustworthy for the user.

 

 

Special Guests:

 

Bill Ruh, CEO of GE Digital link64

Bill_Ruh

 

 

Patrick Franklin, VP – Predix Platform, GE Digital

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Sponsors:

 

Jeff Erhardt, VP of Intelligent Systems at wise.io, GE Digital linkedin32

team-jeff

 

 

Josh Bloom, VP of Data & Analytics at wise.io, GE Digital linkedin32

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Lunch Sponsor:

 

Mike Dolbec, Venture Capital Managing Director, GE Digital link64

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Invited Speakers:

 

Siyu Wu, Staff Data Scientist at GE Digital linkedin32
Siyu_Wu

“Prediction of fatigue related cumulative damage and crack growth”

 

 

Sarah Lukens, Data Scientist at Meridium (GE Digital) linkedin32

sarah_lukens

“Data-driven analytics for Asset Performance Management (APM) software development”

 

 

Steve Piche, Software Director at GE Power Digital Solutions linkedin32

Steve Piche

“Machine Learning and Optimization for Reducing Emissions and Improving Efficiency in Coal-Fired Power Generation Units”

 

 

Ruth Bergman, Head of Health Analytics Lab at GE Research Center, Israel linkedin32

Ruth_Bergman

“Data Aggregation and Sharing – a necessity for Digital Twins in Healthcare”

 

 

Omer Barkol, Senior Researcher at GRC Israel linkedin32

Omer_Barkol

“Unreasonable Effectiveness of Data For Healthcare Imaging”

 

 

Chayan Chakrabarti, Staff Data Scientist at GE Digital linkedin32

Chakrabarti

“Navigating a Rocky Marriage: Data Science and Physics Models, Challenges and Solutions”

 

 

Sameer Vittal, Director of Data & Analytics for GE Power – Global Fleet Services linkedin32

sameer_vittal

“Machine Learning Use Case Study for Remote Monitoring and Diagnostic Global Services”

 

 

Zhiqiang Ma, Data Scientist at GE Power linkedin32

zhiqiang_ma

“Machine Learning for Enriching Industrial Asset Data”

 

 

Shyam Boriah, Principal Data Scientist, GE Ventures linkedin32

shyam_boriah

“Machine learning in the Industrial Inspections Domain”

 

 

Doron Shaked, Senior Researcher at GRC Israel linkedin32

doron_shaked

“A Novel Recurrent CNN Architecture for Continuously Improving Segmentation from Video; An Ultra-Sound Nerve Segmentation Application”

 

 

June Andrews, Principal Data Scientist at wise.io, GE Digital linkedin32

june-team

Aviation Innovations in the Industrial Internet of Things

 

 

Damian Eads, Principal Data Engineer & co-founder at wise.io, GE Digital linkedin32

damian-tux-full

“Guiding Principles for Machine Learning in Production”

 

 

Dave Cronin, VP of User Experience at GE Digital linkedin32

dave_cronin

“Principles ofUI/UX for ML”

 

Invited Poster Presenters:

 

Renato Keshet, Senior Researcher at GRC Israel linkedin32

renato keshet

“Ultrasound Doppler Exam Automation by Machine Learning”

 

 

Emad Elsebakhi, Senior Data Scientist at Baker Hughes (GE) linkedin32

emad elsebakhi

“Digital Solutions and Big Data Analytics in Oil and Gas: Enhance Oil Recovery and Intelligence Oil Field”

 

 

Saurabh Thapliyal, Sr. Staff Software Architect, GE Digital linkedin32

saurabh_thapliyal

“An architecture for online update and monitoring of ML models in Production environments”

 

 

Shaddy Abado, Staff Data Scientist GE Digital linkedin32

Abado_Shaddy_2014

“Machine learning challenges in industrial internet”