PLEASE NOTE: The dates for FTC 2019 have changed to 24-25 October, 2019. The change was necessitated due to venue availability.

Speakers

Fernando Koch

Fernando Koch

Senior Technical Solutions Lead, IBM

Dr. Fernando Koch is a hands-on innovator, business developer, and technology enthusiast. He is a Senior Technical Solutions Lead with IBM Global Services, Eisenhower Fellow, and ACM Distinguished Speaker. He has more than 30 years of IT Industry experience and deep understanding about the practical utilization of disruptive technologies by academia and industry. Recently, he was the Director of Research with Samsung Research Institute and Invited Professor at Korea University. He received his Ph.D. in Computer Sciences from Utrecht University, The Netherlands (2009). He co-edited 6 books, more than 30 patents and 60 papers on Artificial Intelligence, Social Computing, Ubiquitous Computing, Distributed Computing, and others.

Keynote Title: Disruptive Technologies and the Future of Society

Abstract: The new generation of technology development -- including Computational intelligence, Cognitive Computing, Internet of Things, Social Computing and Virtual Reality, and others – will disrupt the economic and social model of every human endeavor. Advances in these domains are inevitable, irreversible, and their impact is immeasurable. The questions are: how to promote strategies to embrace, commercialize, and monetize these new technologies? How to prepare business and society to this new technology revolution? And, how to position current business to be part of this evolution reaping the benefits of disruptive technologies?

A.J. Brush

A.J. Brush

Principal Program Manager, Microsoft

A.J. Bernheim Brush's area of expertise is Human-Computer Interaction with a focus on Ubiquitous Computing. Part of the Microsoft Cortana product group since January 2016, she spent the previous 12 years in Microsoft Research. In Cortana, her team works on connecting Cortana with other assistants like Alexa and improving the speech interaction experience. A.J. is most well known for her research on technologies for families and her expertise conducting field studies of technology. She has built and deployed numerous sensing systems into homes. She received an alumni achievement award in 2017 from UW’s Paul G. Allen School of Computer Science, a Borg Early Career Award in 2010, and has over 20 patents. Her research has received a 10-year impact award, 2 best paper awards, and several best paper nominations. A.J. is a member of the UbiComp Steering Committee, on the Advisory Board of the ACM Proceedings on Interactive, Mobile, Wearable, and Ubiquitous Technology (IMWUT), an editor for Pervasive Computing Magazine, and a Senior Member of the ACM. To encourage diversity in computing, she serves on the CRA-W board and was co-chair from 2014 - 2016.

Keynote Title: Personal Digital Assistants: Are we realizing the vision?

Abstract: With the rapid adoption of digital assistants across many hardware form factors built by different companies, this is an exciting time to be working on natural language interactions and digital assistance. Drawing inspiration from past research and visions of personal digital assistants, we will examine where today's digital assistants excel and where they fall short. We'll celebrate the breakthroughs, discuss the challenges and concerns, and explore remaining opportunities.

Robinson Piramuthu

Robinson Piramuthu

Chief Scientist for Computer Vision, eBay

As Chief Scientist for Computer Vision, Robinson drives eBay’s computer vision science strategy. With over 20 years of experience in computer vision, his expertise includes large scale visual search, coarse and fine-grained visual recognition, object detection, computer vision for fashion, 3D cues from 2D images, figure-ground segmentation and deep learning for vision, among other topics. Before joining eBay in 2011, he received his PhD in Electrical Engineering and Computer Science from the University of Michigan in 2000 specializing in information theory and statistical image processing. He also has a MS in control theory from the University of Florida, specializing in robust and nonlinear control systems.

Keynote Title: Refining the eCommerce Experience: A Neural Approach to Image Processing

Abstract: When shopping in-store, an iconic moniker or distinctive pattern helps a customer recognize their favorite brands. But how does this translate when shopping online – how does an eCommerce platform recognize your favorite brand? While computer vision and image processing can help kick start the process, what’s needed is an approach that dives deeper into the way infrastructures identify and mark images – one that is neural and constantly processing information. In this session, eBay’s Chief Scientist for Computer Vision, Robinson Piramuthu, will discuss how eCommerce players can leverage deep neuron networks to understand how and why infrastructures identify images and refine recommendations, all through the lens of eBay’s work to train its AI to look beyond logos and patterns to recognize brands using computer vision.

Robinson Piramuthu

Shashank Prasanna

Sr. Technical Evangelist, AI/ML, Amazon Web Services

Shashank Prasanna is an AI & Machine Learning Technical Evangelist at Amazon Web Services (AWS) where he focuses on helping engineers, developers and data scientists solve challenging problems with machine learning. Prior to joining AWS, he worked at NVIDIA, MathWorks (makers of MATLAB & Simulink) and Oracle in product marketing, product management, and software development roles. Shashank holds an M.S. in electrical engineering from Arizona State University.

Keynote Title: How to scale machine learning experiments in the cloud?

Abstract: Machine learning involves a lot of experimentation. Data scientists spend several days, weeks or months performing algorithm search, model architecture search, hyperparameter search etc. In this session, we'll discuss the importance of experimentation in the scientific method applied to machine learning. We'll take a look at designing and running experiments to study effect of various factors on accuracy, complexity, robustness and other responses. Through examples, we'll then see how you can easily run large-scale machine learning experiments in the cloud using container technologies such as Amazon Sagemaker and Kubernetes.