ISTeC Distinguished Lectures

Fall 2017 Lectures

  • Oct. 16 – Dr. Patrick McDaniel: “Tracing the Arc of Smartphone Application Security” and “The Limitations of Machine Learning in Adversarial Settings”
  • Oct. 30 – Dr. Ashok Srivastava: “Large-Scale Machine Learning and AI: A Cross-Industry Perspective”

Oct. 16 – Dr. Patrick McDaniel

Lecture #1: “Tracing the Arc of Smartphone Application Security”

Mon., 11:00 a.m.-12:00 p.m. at Morgan Library Event Hall (10:30 a.m. reception)
The introduction of smart phones in the mid-2000s forever changed the way users interact with data and computation—and through it prompted a renaissance of digital innovation. Yet, at the same time, the architectures, applications and services that fostered this new reality fundamentally altered the relationship between users and security and privacy. In this talk I map the scientific community’s evolving efforts over the last decade in evaluating smartphone application security and privacy. I consider several key scientific questions and explore the methods and tools used to answer them. Through this exposition, I show how our joint understanding of adversary and industry practices have matured over time, and briefly consider how these results have informed and shaped technical public policy in the United States. I conclude with a discussion of the open problems and opportunities in mobile device security and privacy.

Lecture #2: “The Limitations of Machine Learning in Adversarial Settings”

Mon., 2:00-3:00 p.m. at Computer Science Building 130
Advances in machine learning have enabled to new applications and services to computationally process inputs in previously unthinkably complex environments. Autonomous cars, automated analytics, adaptive communication systems and self-aware software systems are now revolutionizing markets and blurring the lines between computer systems and real intelligence. In this talk, I consider whether the current use of machine learning in security-sensitive contexts is vulnerable to nonobvious and potentially dangerous manipulation. Here, we examine sensitivity in any application whose misuse might lead to harm—for instance, forcing adaptive network in an unstable state, crashing an autonomous vehicle or bypassing an adult content filter. I explore the use of machine learning in this area particularly in light of recent discoveries in the creation of adversarial samples, and posit on future attacks on machine learning. The talk is concluded with a discussion of the unavoidable vulnerabilities of systems built on probabilistic machine learning, and outline areas for defensive research in the future.

Speaker Biography:

Patrick McDanielPatrick McDaniel, Ph.D. is a Distinguished Professor in the School of Electrical Engineering and Computer Science at Pennsylvania State University, Fellow of the IEEE and ACM, and Director of the Institute for Networking and Security Research. Professor McDaniel is also the program manager and lead scientist for the Army Research Laboratory’s Cyber-Security Collaborative Research Alliance. Patrick’s research focuses on a wide range of topics in computer and network security and technical public policy. Prior to joining Penn State in 2004, he was a senior research staff member at AT&T Labs-Research.

 

 

Oct. 30 – Dr. Ashok Srivastava

Lecture: “Large-Scale Machine Learning and AI: A Cross-Industry Perspective”

Mon., 11:00 a.m.-12:00 p.m. at Morgan Library Event Hall (10:30 a.m. reception)
Over the past several years, there has been an increasing interest in building and deploying large-scale machine learning and AI capabilities on massive, streaming data sets. This talk provides an overview of the key issues that arise in building such systems from the algorithmic, technological, and organizational perspective. Key results across a variety of industries will be presented.

Speaker Biography:

Ashok Srivastava Ashok N. Srivastava, Ph.D., recently became the Senior Vice President and Chief Data Officer at Intuit and will be creating a global team focused on large-scale machine learning and artificial intelligence. He was formally a vice president with Verizon where his global team focused on building new revenue-generating products and services powered by big data and AI. He is a Consulting Professor at Stanford in the Electrical Engineering Department and is the Editor-in-Chief of the AIAA Journal of Aerospace Information Systems. Ashok is a Fellow of the IEEE, the American Association for the Advancement of Science (AAAS), and the American Institute of Aeronautics and Astronautics (AIAA).

Formerly he was the Principal Scientist for Data Sciences at NASA Ames Research Center and led the System-Wide Safety and Assurance Technologies project at NASA which has a five-year budget of nearly $150M.

Srivastava has significant experience in the internet, adtech, e-commerce, banking, finance, and securities areas. He has lead major engagements as a private consultant and as Senior Director at Blue Martini Software and Senior Consultant at IBM. He is a thought leader in the area of big data analytics, social media, optimization, machine learning, and data mining and has given presentations worldwide on these subjects. He served as a Venture Advisor focusing on big-data analytics at Trident Capital, and was on the advisory board of several startups.

Srivastava is the author of more than 100 research articles in data mining, machine learning, and text mining, and has edited a book on Text Mining: Classification, Clustering, and Applications. Two additional books were published in 2012: Advances in Machine Learning and Data Mining for Astronomy and Machine Learning for Engineering Systems Health Management. A fourth book was released in 2017: Large-Scale Machine Learning in the Earth Sciences.

He has five patents awarded and more than 30 under file. He has won numerous awards including the IEEE Computer Society Technical Achievement Award for “pioneering contributions to intelligent information systems,” the NASA Exceptional Achievement Medal for contributions to state-of-the-art data mining and analysis, the NASA Honor Award for Outstanding Leadership, the NASA Distinguished Performance Award, several NASA Group Achievement Awards, the Distinguished Engineering Alumni Award from the University of Colorado Boulder, the IBM Golden Circle Award, and the Department of Education Merit Fellowship.