Enjoy a full day conference this September and appreciate the tribute to the southern reputation of charm and elegance, that The Academy offers. During coffee breaks and meals, take in the old-world elegance and style of days gone by, in the heart of bustling midtown Atlanta.
Aran Khanna, Software Engineer, Amazon Web Services
Aran Khanna is a software engineer in the deep learning research team at Amazon Web Services, led by Professor Alex Smola. Aran is the technical lead for the development of the Apache MXNet framework for Mobile, IoT and Edge devices, working to allow for deployment and management of efficient deep network models across a broad set of devices outside of the data center, from Raspberry Pis to smartphones to NVIDIA Jetsons. Aran recently graduated from Harvard’s Computer Science department before joining the AWS team.
High Performance Deep Learning on Edge Devices With Apache MXNet:
Deep network based models are marked by an asymmetry between the large amount of compute power needed to train a model, and the relatively small amount of compute power needed to deploy a trained model for inference. This is particularly true in computer vision tasks such as object detection or image classification, where millions of labeled images and large numbers of GPUs are needed to produce an accurate model that can be deployed for inference on low powered devices with a single CPU. The challenge when deploying vision models on these low powered devices though, is getting inference to run efficiently enough to allow for near real time processing of a video stream. Fortunately Apache MXNet provides the tools to solve this issues, allowing users to create highly performant models with tools like separable convolutions, quantized weights and sparsity exploitation as well as providing custom hardware kernels to ensure inference calculations are accelerated to the maximum amount allowed by the hardware the model is being deployed on. This is demonstrated though a state of the art MXNet based vision network running in near real time on a low powered Raspberry Pi device. We finally discuss how running inference at the edge as well as leveraging MXNet’s efficient modeling tools can be used to massively drive down compute costs for deploying deep networks in a production system at scale.
Le Song, Assistant Professor, College of Computing, Georgia Institute of Technology
Le Song is an assistant professor in the College of Computing, Georgia Institute of Technology. He received his Ph.D. in Machine Learning from University of Sydney and NICTA in 2008, and then conducted his post-doctoral research in the Department of Machine Learning, Carnegie Mellon University, between 2008 and 2011. Before he joined Georgia Institute of Technology, he was a research scientist at Google. His principal research direction is machine learning, especially nonlinear methods and probabilistic graphical models for large scale and complex problems, arising from artificial intelligence, social network analysis, healthcare analytics, and other interdisciplinary domains. He is the recipient of the NSF CAREER Award’14, AISTATS’16 Best Student Paper Award, IPDPS’15 Best Paper Award, NIPS’13 Outstanding Paper Award, and ICML’10 Best Paper Award. He has also served as the area chair for leading machine learning conferences such as ICML, NIPS and AISTATS, and action editor for JMLR.
Understanding Deep Learning for Big Data: The complexity and scale of big data impose tremendous challenges for their analysis. Yet, big data also offer us great opportunities. Some nonlinear phenomena, features or relations, which are not clear or cannot be inferred reliably from small and medium data, now become clear and can be learned robustly from big data. Typically, the form of the nonlinearity is unknown to us, and needs to be learned from data as well. Being able to harness the nonlinear structures from big data could allow us to tackle problems which are impossible before or obtain results which are far better than previous state-of-the-arts.
Nowadays, deep neural networks are the methods of choice when it comes to large scale nonlinear learning problems. What makes deep neural networks work? Is there any general principle for tackling high dimensional nonlinear problems which we can learn from deep neural works? Can we design competitive or better alternatives based on such knowledge? To make progress in these questions, my machine learning group performed both theoretical and experimental analysis on existing and new deep learning architectures, and investigate three crucial aspects on the usefulness of the fully connected layers, the advantage of the feature learning process, and the importance of the compositional structures. Our results point to some promising directions for future research, and provide guideline for building new deep learning models.
Jacob Eisenstein, Assistant Professor, School of Interactive Computing, Georgia Institute of Technology
Jacob Eisenstein is an Assistant Professor in the School of Interactive Computing at Georgia Tech. He works on statistical natural language processing, focusing on computational sociolinguistics, social media analysis, discourse, and machine learning. He is a recipient of the NSF CAREER Award, a member of the Air Force Office of Scientific Research (AFOSR) Young Investigator Program, and was a SICSA Distinguished Visiting Fellow at the University of Edinburgh. His work has also been supported by the National Institutes for Health, the National Endowment for the Humanities, and Google. Jacob was a Postdoctoral researcher at Carnegie Mellon and the University of Illinois. He completed his Ph.D. at MIT in 2008, winning the George M. Sprowls dissertation award. Jacob’s research has been featured in the New York Times, National Public Radio, and the BBC. Thanks to his brief appearance in If These Knishes Could Talk, Jacob has a Bacon number of 2.
Jennifer Marsman, Principal Software Development Engineer, Microsoft
Jennifer Marsman is a Principal Software Development Engineer in Microsoft’s Developer and Platform Evangelism group, where she educates developers on Microsoft’s new technologies. In this role, Jennifer is a frequent speaker at software development conferences around the world. In 2016, Jennifer was recognized as one of the “top 100 most influential individuals in artificial intelligence and machine learning” by Onalytica. She has been featured in Bloomberg for her work using EEG and machine learning to perform lie detection. In 2009, Jennifer was chosen as “Techie whose innovation will have the biggest impact” by X-OLOGY for her work with GiveCamps, a weekend-long event where developers code for charity. She has also received many honors from Microsoft, including the “Best in Role” award for Technical Evangelism, Central Region Top Contributor Award, Heartland District Top Contributor Award, DPE Community Evangelist Award, CPE Champion Award, MSUS Diversity & Inclusion Award, Gold Club, and Platinum Club. Prior to becoming a Developer Evangelist, Jennifer was a software developer in Microsoft’s Natural Interactive Services division. In this role, she earned two patents for her work in search and data mining algorithms. Jennifer has also held positions with Ford Motor Company, National Instruments, and Soar Technology. Jennifer holds a Bachelor’s Degree in Computer Engineering and Master’s Degree in Computer Science and Engineering from the University of Michigan in Ann Arbor. Her graduate work specialized in artificial intelligence and computational theory. Jennifer blogs at http://blogs.msdn.com/jennifer and tweets at http://twitter.com/jennifermarsman.
Qiaoling Liu, Lead Data Scientist, CareerBuilder
Qiaoling Liu is a lead data scientist in CareerBuilder’s Information Extraction and Retrieval team under Data Science R&D group. Her team owns the projects of Company Name Normalization, School Name Normalization, Skill Identification and Normalization, and Recruitment Edge Signals at CareerBuilder. Her research interests include information retrieval, text mining, and semantic web. She received a Ph.D. in Computer Science and Informatics from Emory University, and a B.S. in Computer Science and Technology from Shanghai Jiao Tong University in China. During her PhD studies, she was a student recipient of the 2011, 2012, 2013 Yahoo! Faculty Research and Engagement Program (FREP) Award.
Tim Chartier, Chief Academic Officer, Tresata
Chief Researcher for Tresata and Professor of Mathematics and Computer Science at Davidson College Dr. Tim Chartier specializes in sports analytics. He frequently consults on data analytics questions, including projects with ESPN Magazine, ESPN’s Sport Science program, NASCAR teams, the NBA, and fantasy sports sites. In 2014, Tim was named the inaugural Math Ambassador for the Mathematical Association of America, which also recognized Dr. Chartier’s ability to communicate math with a national teaching award. His research and scholarship were recognized with the prestigious Alfred P. Sloan Research Fellowship. Published by Princeton University Press, Tim authored Math Bytes: Google Bombs, Chocolate-Covered Pi, and Other Cool Bits in Computing. Through the Teaching Company, he taught a 24-lecture series entitled Big Data: How Data Analytics Is Transforming the World. In K-12 education, Tim has also worked with Google and Pixar on their educational initiatives. Dr. Chartier has served as a resource for a variety of media inquiries, including appearances with Bloomberg TV, NPR, the CBS Evening News, USA Today, and The New York Times.
Alexandra Johnson, Software Engineer, SigOpt
Alexandra works on everything from infrastructure to product features to blog posts. Previously, she worked on growth, APIs, and recommender systems at Polyvore (acquired by Yahoo). She majored in computer science at Carnegie Mellon University with a minor in discrete mathematics and logic, and during the summers she A/B tested recommendations at internships with Facebook and Rent the Runway.
Robert Morris, CTO and Co-Founder, Predikto, Inc.
Robert Morris, Ph.D. is Co-founder and CTO of Predikto, Inc. He is also an award winning academic (formerly Associate Professor of Criminology (with tenure) at the University of Texas at Dallas). At UTD, he taught a variety of courses covering advanced data analytics and machine learning for the social sciences and for operations research. He has published over 50 peer-reviewed journal articles across many disciplines in outlets such as PLOS One, Journal of Quantitative Criminology, Justice Quarterly, Intelligence, etc.
Robert’s expertise lies in machine learning approaches for longitudinal processes to predict and explain human (criminal) behavior. However, he now applies this philosophy into Predikto’s patent pending automated machine learning platform, which has been successful predicting unplanned events across a range of different equipment classes within the IoT space, including: freight locomotives (electric and diesel), high-speed commuter trains, quay cranes, rail cars, commercial aircraft, datacenter HVAC, and more.
Venkatesh Ramanathan, Data Scientist, PayPal
Venkatesh is a senior data scientist at PayPal where he is working on building state-of-the-art tools for payment fraud detection. He has over 20+ years experience in designing, developing and leading teams to build scalable server side software. In addition to being an expert in big-data technologies, Venkatesh holds a Ph.D. degree in Computer Science with specialization in Machine Learning and Natural Language Processing (NLP) and had worked on various problems in the areas of Anti-Spam, Phishing Detection, and Face Recognition.
Large Scale Graph Processing & Machine Learning Algorithms for Payment Fraud Prevention:
PayPal is at the forefront of applying large scale graph processing and machine learning algorithms to keep fraudsters at bay. In this talk, I’ll present how advanced graph processing and machine learning algorithms such as Deep Learning and Gradient Boosting are applied at PayPal for fraud prevention. I’ll elaborate on specific challenges in applying large scale graph processing & machine technique to payment fraud prevention. I’ll explain how we employ sophisticated machine learning tools – open source and in-house developed.
I will also present results from experiments conducted on a very large graph data set containing millions of edges and vertices.
Talha Obaid, Email Security, Symantec
Talha Obaid is an AntiSpam engineer for Email Security.cloud at Symantec, where he joined from MIT’s CENSAM research centre. In his current role, he is utilizing Data Science to fight Spam and Malware. He loves democratizing Machine Learning, whilst recently speaking at ‘Google ML Experts Day’ in 2017, and at ‘Google GDG DevFest’ in 2016. While onsite, Talha delivered several sessions about Machine Learning at Symantec. He was acknowledged scores of times at Symantec, procuring ‘Symantec Innovator’ title twice, winning ‘Symantec STAR Innovation Day’, and clinching numerous ‘Symantec Applause Awards’. Prior to Symantec Talha worked at MIT’s CENSAM research centre; while working on Hydraulic Modelling and Simulation, he transitioned into a founding member of a spinoff; Visenti, which was acquired by Xylem. Earlier Talha also held Technical Leadership position at Mentor Graphics. During his career, his contributions landed him four spinoffs, five patents, a trade-secret and few publications. Talha holds a Bachelor’s degree in Computer Science with Honors, and a Masters’ degree in Information Systems from National University of Singapore, where he specialized in Business Analytics and Cluster computing in his dissertation. Besides work, Talha actively contributes to Data Science community; as a lead co-organizer for PyDataSG – 2k+ member strong group, holding regular monthly meet ups. Additionally, Talha conducts TeachEdison workshops too. He is a certified First-Aider as well. @ObaidTal
A Machine Learning approach for detecting a Malware:
The project is to improve the way we detect script based malware using Machine Learning. Malware has become one of the most active channel to deliver threats like Banking Trojans and Ransomware. The talk is aimed at finding a new and effective way to detect the malware. We started with acquiring both malicious and clean samples. Later we performed feature identification, while building on top of existing knowledge base of malware. Then we performed automated feature extraction. After certain feature set is obtained, we teased-out feature which are categorical, interdependent or composite. We applied varying machine learning models, producing both binary and categorical outcomes. We cross validated our results and re-tuned our feature set and our model, until we obtained satisfying results, with least false-positives. We concluded that not all the extracted features are significant, in fact some features are detrimental on the model performance. Once such features are factored-out, it results not only in better match, but also provides a significant gain in performance.