Competitions: Timed Analysis Competition
The Timed Analysis Competition (TAC) is an on-site competition day-of that will be held in Meeting Room A & B. Competition time: 10:10 A.M. - 1:10 P.M. Presentations time: 2:30 P.M. - 4:00 P.M. Because the teams will be hard at work, we ask all attendees that they try to keep excessive noise and distractions away from this area. Teams will present their ďŹ ndings to an open audience beginning at (time). For those whoplan on spectating, we ask that you remain respectful and do not create distractions for the TAC Teams. Winners will receive a cash prize and will be announced during the closing ceremony! 1st Place $200
2nd Place $100
The judges of the TAC are:
3rd Place $50
Competitions: Predictive Modeling Competition
The Predictive Modelling Competition is an o-site competition that teams work on in the 3 weeks that precede the convention. The PMC teams’ task on-site is to present their ideas to judges concisely, right after the opening ceremony, to an open audience in Meeting B. For those who plan on spectating, we ask that you remain respectful and do not create distractions for the PMC Teams. Winners will receive a cash prize and will be announced during the closing ceremony! 1st Place $200
2nd Place $100
The judges of the PMC are:
3rd Place $50
Competitions: Research Exhibition
Our research exhibition will be in front of the ballrooms. As an attendee, you are free to walk through this area to check out the exhibits. As you roam the exhibition, be sure to ask the researchers questions about their research. The representatives will be seated next to their projects ready to engage with you. Winners will receive a cash prize and will be announced during the closing ceremony! 1st Place $200
2nd Place $100
The judges of the Research Exhibition are:
Where: Big Ballroom B+C
Tech Talks: Anup Rao Cisco [ 10:45-11:45pm ] Topic: Big Data in the Supply Chain and Manufacturing
Where: Ballroom A
Anup Rao is a Distinguished IT Engineer at Cisco. He has been at Cisco for 18 years with several roles both in product development and internal facing IT functions. He currently serves as the chief technologist for Supply Chain Management. Over the last several years, Cisco has invested signiďŹ cantly in the area of big data and data science. This includes an enterprise IT supported Hadoop cluster, other in-memory computing platforms, as well as a Data Science program for employees in partnership with academia. There are several areas within Networking, Security and Operations where these are in use. In this talk, we will introduce how Cisco uses big data platforms and data science in the area of Supply Chain Management and Manufacturing. SpeciďŹ cally use cases and examples in the area of product costing and forecasting, backlog management, product testing and digital thread will be discussed.
Ted Xiao, Gautham Kesineni, Will Guss UC Berkeley Machine Learning Group [ 11:50-12:50pm ] Topic: Neural Network and Machine Learning Research Where: Ballroom A The Berkeley Machine Learning Group will be discussing their research with neural networks, deep learning, machine learning, and a programming language classiďŹ cation project with GitHub.
Data Science in the Industry Panel [1:10-2:00pm] Topic: Data Science Panel Where: Big Ballroom Led by: Jimmy Nguyen (LinkedIn), Jasmine Nettiksimmons (Stitch Fix), Cha Li (Lumo Bodytech), Derek Chang (Lumo Bodytech), Joseph Lei (CA Department of Technology, OďŹƒce of Digital Innovation)
Derek Chang, Cha Li Lumo Bodytech [ 2:10-3:00pm ] Topic: Data Science at a Startup Where: Ballroom A Derek received his PhD from Stanford in Electrical Engineering working in the field of nonlinear optics. He formerly worked at Optimizely and now works at Lumo Bodytech as a data scientist. Cha's background is in Computer Science and Machine Learning. Freshly on sabbatical, he was one of two data scientists at Lumo Bodytech as well as one of their first engineers. He's currently enamored with GPUs as a general computing platform and coffee. Lumo Bodytech is a motion science company that combines sensor data with advanced algorithms to optimize human movement for better health, performance, and injury prevention. As a startup, we’ve overcome many challenges around building our data infrastructure and applying our data to practical, as well as novel, problems. In this talk, we will share our experiences with data engineering and talk about the unique analytics and machine learning projects that have had an impact on our product and business.
Vladimir Iglovikov TrueAccord [3:10-4:00] Topic: Competitive Machine Learning as a way to improve your chances to get a Data Science job Where: Ballroom A Dr. Vladimir Iglovikov is a Senior Data Scientist at TrueAccord (2016- current). He received his MS Degree in MS in High Energy Physics at St. Petersburg State University (2001-2010), and received his PhD in Condensed Matter Physics at UC Davis (2010-2015). At UC Davis, he was also a Teaching Assistant and Lecturer. He is also well-regarded in Kaggle competitions. The specifics of Competitive Machine Learning will be discussed and compared to the Machine Learning in industry and academia. Skills obtained through Competitive Machine Learning may benefit your academic performance and/or help you find a good Data Science job. The examples will include several machine competitions hosted at Kaggle.com.
Ken Imwinkelried River City Bank [4:10-5:00] Topic: Aggregation of data in different fields such as agriculture and commercial real estate Where: Ballroom A
VP and Senior Credit Manager: Directly responsible for managing a portfolio of commercial loan commitments, including: Commercial & Industrial (C&I); Commercial Real Estate (CRE); Construction; and Syndicated Credits. My core functionalities consist of financial review/analysis as well as managing client needs through loan structuring, pricing, negotiations and ongoing tracking/compliance. Beyond direct portfolio management, manage and oversee a team of Credit Analysts in performing similar functions as previously detailed.
Andrew Critch MIRI [5:10-6:00] Where: Ballroom A Critch earned his PhD in mathematics at UC Berkeley studying applications of algebraic geometry to machine learning models. His current research interests include logical uncertainty, open source game theory, and avoiding arms race dynamics between nations and companies in AI development. How should algorithms reason about algorithms? In this talk I will present a new algorithm which provably reasons well about other algorithms and mathematical questions more generally, using Brouwer's fixed point theorem and a particular stock-market-like mechanism. This work is part of a growing research effort to prepare for the existence of highly intelligent AI systems in the future, and there remain many open technical problems whose solutions could help ensure the co-existence of human beings with increasingly automated economies. These range from developing learning algorithms that are more amenable to human feedback and context changes, to bargaining mechanisms for ensuring peaceful joint ownership of powerful AI systems, to further theoretical results on how algorithms should safely reason about other algorithms.