Join us online on March 7, 2022, for the Women in Data Science (WiDS) Worldwide conference, a technical conference featuring outstanding women doing exceptional work in data science and related fields, in a wide variety of domains. Everyone is welcome and encouraged to attend. Broadcasted LIVE from Stanford University 8am – 5pm PST.
In this workshop, I would like to share my journey transitioning from an electrical engineer focusing on ultra-low power integrated circuit design to an AI Solution Architect. Through specific examples of how the two fields connect, I will discuss the fundamentals of deep learning and data-driven hardware design. I will start with my experience in the semiconductor industry designing application-specific and data-dependent hardware for IoT systems and then discuss how this experience led to my career in AI specializing in areas including high-performance computing, edge computing, and more recently, federated learning.
I hope the attendees will not only find the technical content informative but also see how a growth mindset truly helped me find my career passion. Having a broad knowledge of the eco-system that supports AI applications – such as the hardware stack, hardware level optimization, and application-specific hardware design – can be very helpful to understanding and choosing the right platform for operational AI. I also hope to use this opportunity to connect with fellow AI/hardware enthusiasts in WiDS.
This workshop was conducted by Chu Lahlou, AI Specialized Cloud Solution Architect at Microsoft.
WiDS 2022 Career Panel
Moderated by Suzanne Weekes, Executive Director, SIAM
Panelists:
– Cecilia Aragon, Professor, Human Centered Design & Engineering, University of Washington
– Sharon Hutchins, VP & Chief of Operations, Intuit AI+Data
– Tamara Kolda, Mathematical Consultant, MathSci.ai
– Maggie Wang, Robotics Software Engineer, Skydio
Maggie Wang, Robotics Software Engineer at Skydio, presents a Technical Vision Talk at the WiDS Worldwide conference.
Skydio is the leading US drone company and the world leader in autonomous flight. Our drones are used for everything from capturing amazing video, to inspecting bridges, to tracking progress on construction sites. Using six 4K navigational cameras, our drones create a 3D model of its surroundings that updates at a rate of over one million data points per second, and runs up to nine deep neural networks to predict into the future.
In this talk, Maggie will discuss how data-driven processes are used in Skydio 3D Scan, a revolutionary adaptive scanning software that enables Skydio drones to autonomously generate 3D models with comprehensive coverage and ultra-high resolution.
In this workshop, we engage beginner and intermediate participants interested in getting started with Deep Learning and the Internet of Things (IoT). We’ll do hands-on exercises where you’ll use a webcam and a neural network to recognize images, aggregate data, and run real-time IoT analytics. Our goal is to get you excited about IoT and Deep Learning, and to set you up for success with various types of projects for work, school, and beyond.
This workshop was conducted by Louvere Walker-Hannon, Shruti Karulkar, & Sarah Mohamed from MathWorks.
Want to learn more about trends like AI, IoT and wearable tech? In less than one hour, we will cut through the hype by building a “smart” fitness tracker using your own mobile device.
We’ll do hands-on exercises: you’ll acquire data from sensors, design a step counter and train a human activity classifier. You will leave motivated and ready to use machine learning and sensors in your own projects!
This workshop was conducted by Louvere Walker-Hannon, Shruti Karulkar, & Sarah Mohamed from MathWorks.
Cindy Orozco Bohorquez, Ph.D. Candidate at Stanford hosts a workshop on ‘Parallel Computing 101: All you need to know about the hardware that powers data science’.
Eileen Martin, Assistant Professor at Virginia Tech hosts a workshop on ‘Why we love arrays for data science’ in which she walks through some of the basics of computer architecture and how it affects the performance of our codes for common data analysis techniques.
Have an opportunity to Meet-the-Speakers from WiDS Worldwide! Speaker Maria Schuld, Senior Researcher at University of KwaZulu-Natal is interviewed by Margot Gerritsen, Professor at Stanford University.
Best of WiDS features Fei-Fei Li on her talk ‘Teaching Computers to See with Big Data’ from Stanford 2015!
Best of WiDS features Sanghamitra Bandhyopdhyay in her ‘Fireside Chat’ from WiDS Bengaluru @ Intuit, 2020!
Andrea Goldsmith, Dean of Engineering and Applied Science at Princeton University discusses why data scientists with diverse perspectives, experiences, and knowledge are needed for the field to thrive and achieve maximum impact. She paints a vision for a diverse and inclusive culture in data science, and propose how to achieve that vision.
Join us in a fireside chat with Noble Prize Winner and Professor of Physics and Astronomy, UCLA, Andrea Ghez.
Panel discussion on ‘The Democratization of Data’
Moderator: Margot Gerritsen, Professor at Stanford University,
Panelists:
-Mary Gray, Senior Principal Researcher at Microsoft Research and Associate Professor, The School of Informatics, Computing, and Engineering at Indiana University
-Zhamak Dehghani, Director of Next Tech Incubation, Thoughtworks
-Amanda Obidike, Executive Director, STEMi Makers Africa
Join us in a fireside chat with Corinne Vigreux, Co-Founder and CMO of TomTom and Founder Codam College!
Maria Schuld, Senior Researcher at Xanadu, and the University of KwaZulu-Natal provides an overview of quantum machine learning research and illustrate that quantum algorithms can be trained like neural nets, but look formally very similar to kernel methods.
Shafi Goldwasser, Director of the Simons Institute for the Theory of Computing, Professor of Electrical Engineering and Computer Science at the University of California Berkeley, Professor of Electrical Engineering and Computer Science at MIT and Professor of Computer Science and Applied Mathematics at the Weizmann Institute of Science Israel, speaks on how cryptographic models and tools can and should play a role in ensuring the trustworthiness of AI and machine learning and address problems such as privacy of training input, model verification and robustness against adversarial examples.
Interview with Ruth Marinshaw, Chief Technology Officer, Research Computing, Stanford University
Mala Anand, EVP, President of Leonardo, Data & Analytics at SAP, presents Healthcare Beyond the Horizon — Going Digital to Improve People’s Lives at the WiDS 2018 Conference held at Stanford University on March 5, 2018.
Never before have there been so many promising breakthrough technologies available – and with it, opportunities to dramatically change the way we live every day. Nowhere is this more evident than in Healthcare, where we see technologies like Analytics, IoT, Machine Learning, Big Data and Blockchain playing significant roles in transforming people’s lives all over the planet. Mala Anand, President of SAP Leonardo, Data & Analytics, will provide some insight on how the healthcare industry is going digital and how far we can possibly go to improve patient outcomes.
It takes nature and evolution more than five hundred million years to develop a powerful visual system in humans. The journey for AI and computer vision is about fifty years. In this talk, I will briefly discuss the key ideas and the cutting edge advances in the quest for visual intelligences in computers. I will particularly focus on the latest work developed in my lab for both image and video understanding, powered by big data and the deep learning (a.k.a. neural network) architecture.
Fei-Fei Li, Chief Scientist, AI/ML, Google Cloud, Professor of Computer Science, Stanford University
Director, Artificial Intelligence Lab
The upstream oil & gas industry (i.e. the exploration for and production of hydrocarbons) needs to reap the benefits of new technology to improve efficiency. Making more effective use of increasing amounts of collected data is on the verge of transforming the business.
Transformation through data analytics is equally relevant on both the operational and financial sides of the business.
On the upstream operational side: for decades now, we have been inventing new and increasingly sophisticated tools (both hardware and software) to generate new data types that extend the boundaries of geoscience knowledge, and allow us to understand our hydrocarbon reservoirs in ever increasing detail. Historically, we have processed only a fraction of the data collected, but that is changing. Now, among the most important criteria governing the efficiency of oil and gas companies are the hugely increased volume of data collected but also the variety, velocity and veracity of information that can be extracted from that data. That’s data science! Data analytics as a discipline is now increasingly integrated within our upstream workflows in drilling, reservoir characterization and the actual production (extraction) of hydrocarbons in the most economically efficient ways possible. To this end, one goal is the development of an analytics platform that will perform a key role in increasing productivity through the simultaneous optimization of drilling planning and execution, the improvement of asset utilization and the overall reduction of non-productive time.
On the financial side: the oil and gas industry has a long history of being secretive and, as a result, judgment of the quality and accuracy of non-technical data has proved very difficult. In general, insufficient attention has been paid to addressing these challenges leading to unnecessary volatility in price movements through inadequate or conflicting data, and this volatility impacts decision-making within companies. In the information age, where markets react instantaneously to a multitude of data sources, it is time to understand better this key driver of our industry. Decision-enabling information is extremely critical to the efficient functioning of an industry that is driven by the signals coming from commercial markets. Understanding the quality and accuracy of that information through data science is a key enabler in filling a major gap currently preventing more effective management of oil and gas company assets.
Digital transformation, implying the transition from desktop to the cloud and mobile devices, easy access to information, new scalable online services and automated industrial workflows, is about to radically change the way we work in any industry (oil and gas, defense, transport, automotive, medicine, telecom, logistics, etc). This is no longer a trend, but a reality clearly demonstrated by the world’s most valuable companies adopting expanded and enhanced data analytics in response to common drivers of operational efficiency, operational safety and accuracy of real-time decision-making. That’s the promise of Big Data, to really understand the systems that make our technological industry. As you begin to understand the interactions of all the constituent components then you can build systems that are better and more effective at addressing the key industry drivers, irrespective of the industry. New technology is increasingly playing a huge new role. Data is the new oil!
Dr. Gottlib-Zeh describes how data science is transforming the oil and gas industry for better planning and efficiency, for both drilling and production.
The vast ocean of data created in today’s digital world offers enormous potential. However, the key to unlocking that potential lies not in the data itself, but in the science that refines it. The well-defined processes and toolsets designed for legacy BI solutions do not meet the needs of today’s big data analytics environments. Diane will share Intel’s investments in both the technology and the ecosystem to enable the next breakthrough insights.
