DSS-2018-03: WILLIAM LEE and ANTHONY TOCKAR

Data Science Sydney proudly presents our speakers for MARCH 2018: WILLIAM LEE: "MALICIOUS ANDROID APPLICATION DETECTION USING RECURRENT NEURAL NETWORKS"

ANTHONY TOCKAR: "INTERPRETABLE MACHINE LEARNING"

200 seats available, first come - first served for members on the RSVP-yes list. Please ensure that you keep your RSVP up to date. If you cannot make it, please make you spot available for others as soon as possible.
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Registration opens at 5:30pm and close at 6:15pm, sharp. Food and beverages between 6pm and 6:15pm and late comers cannot be admitted.

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WILLIAM LEE: "MALICIOUS ANDROID APPLICATION DETECTION USING RECURRENT NEURAL NETWORKS"

About the Talk: Millions of malicious Android applications, including ransomware and coin-miners, are generated by cyber attackers every month. Those malware often have randomly generated application IDs and they are signed with randomly generated certificates. Due to the high volume of data, typical machine learning models based on manual feature engineering or signature based detection systems can hardly tackle the cyber security problems.

In this talk, William will introduce a malware classification model based on recurrent neural networks. The model can learn generalization about malicious string patterns from apps’ ID and certificate subject info using character-level embedding and LSTM (Long Short Term Memory) layers.

About the Speaker: William Lee is a senior researcher at a cyber security company, where he focuses on developing deep neural networks. Highlights of his work have included research to malware classification and clustering models. He presented machine learning topics at Virus Bulletin, Caro and AVAR security conferences.

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ANTHONY TOCKAR: "INTERPRETABLE MACHINE LEARNING"

About the Talk: Interpretability matters! Machine learning models have been getting more accurate, but also more complex over the past few years. However in many settings data scientists are often tethered to linear models or decision trees because they are easy (relatively!) to explain. Moreover developments in data ethics and governance are increasing the pressure on us data scientists to explain our models, and to ensure that discrimination or other unwanted outcomes are avoided. In this talk, Anthony will outline the latest and greatest in machine learning interpretability and explain why it is a crucial part of any data scientist's toolkit.

About the Speaker: Anthony Tockar is director and cofounder at Verge Labs, a new type of AI company focused on the applied side of machine learning. A jack-of-all-trades, he has worked on problems across insurance, technology, telecommunications, loyalty, sports betting and even neuroscience. He is a qualified actuary, and completed an MS in Analytics at the prestigious Northwestern University.
After hitting the headlines with his posts on data privacy at Neustar, he returned to Sydney to practice as a data scientist and cofounded the Minerva Collective, a not-for-profit focused on using data for social good, as well as several meetup groups. His key missions are to extend the reach and impact of data science to help people, and to assist Australian businesses to become more data driven.