2018 USENIX Security and AI Networking Conference

May 11, 2018
Atlanta, GA, USA

The 2018 USENIX Security and AI Networking Conference (ScAINet ’18) will take place on Friday, May 11, 2018, at the Georgia Tech Hotel and Conference Center in Atlanta, GA, USA.

Recent developments in AI have created new challenges and opportunities for security. The Security and AI Networking conference (ScAInet) will bring together a diverse cross-section of security and ML, including researchers and engineers from academia and industry. Our goal is to explore the rich frontier at the intersection of artificial intelligence, machine learning, and cybersecurity. This will be a one-day, vendor-neutral event, with a focus on community building, networking, and cross-pollination between industry and academia. Presented topics will include:

  • Advances in AI/ML techniques
  • AI/ML applications to cybersecurity
  • Existing state-of-the-art approaches to security problems

The full program is now available and consists of a single track of invited talks and will conclude with a panel session.

Registration Information

The registration fee for ScAINet '18 is $250.

Questions? Contact conference@usenix.org.

Refunds and Cancellations

The cancellation deadline is Wednesday, May 2, 2018. Please see USENIX Registration Substitution and Cancellation Policy page for more information.

Venue

Georgia Tech Hotel and Conference Center
800 Spring St. NW
Atlanta, GA, 30308
USA
+1 404.347.9440

Hotel Reservation Deadline: Tuesday, April 10, 2018

USENIX has negotiated a special room rate of $189 for conference attendees. Book your room online or call +1 800.706.2899 or +1 404.838.2100 and mention USENIX or ScAINet '18.

Parking

Overnight parking is $15 per night. For unlimited in-and-out access to the garage, an $18 pass is available.

Georgia Tech Hotel and Conference Center

Questions?

Registration: conference@usenix.org or +1 510.528.8649
Membership: office@usenix.org or +1 510.528.8649
Sponsorship: sponsorship@usenix.org or +1 510.528.8649 x17
Student Grants: students@usenix.org or +1 510.528.8649

Conference Organizing Committee

Duen Horng (Polo) Chau
Georgia Institute of Technology

Duen Horng (Polo) Chau is an Assistant Professor at Georgia Tech, in the School of Computational Science and Engineering. He co-directs the MS Analytics program. His research bridges data mining and human-computer interaction (HCI) to create scalable interactive tools for making sense of massive datasets and solving real world problems. His Ph.D. in Machine Learning from Carnegie Mellon University won CMU's Computer Science Dissertation Award, Honorable Mention.

He received faculty awards from Google, Yahoo, LexisNexis; Raytheon Faculty Fellowship; Edenfield Faculty Fellowship; Outstanding Junior Faculty Award; Symantec fellowship (twice); Best Student Paper Awards at SDM’14 and KDD’16 (runner-up). He published over 100 refereed articles.

He is a steering committee member of ACM IUI conference, IUI’15 co-chair, and IUI’19 program co-chair.

His research led to deployed technologies by Facebook, Symantec (protects 120M people from malware), and Atlanta Fire Rescue Department; he holds 3 patents. His security and fraud detection research made headlines.

Andrew Gardner
Symantec

Andrew Gardner founded and leads the Center for Advanced Machine Learning (CAML) at Symantec, where his team conducts research and development in core machine learning, deep learning and other techniques.

His career spans over 20 years across enterprise, clinical and academic settings. Past roles range from software architect to machine learning consultant to chief data scientist. Gardner is an active researcher with more than 20 peer-reviewed papers and a dozen filed patents. He teaches a graduate course in machine learning annually at the Georgia Institute of Technology, and frequently presents technical topics around the country.

Gardner received degrees in electrical engineering from the University of Tennessee–Knoxville (BSEE), and the Georgia Institute of Technology (MSEE and Ph.D.)

Anisha Mazumder
Microsoft

Anisha Mazumder is an Applied Machine Learning Engineer at Microsoft working on developing machine learning models for detection of security threats in Azure. Her interests lie in application of state-of-the-art machine learning solutions powered up to the industry scale to solve the most interesting and pressing problems. She completed her Bachelors' degree from Jadavpur University, India in 2012 and her PhD degree from Arizona State University in 2016. Her dissertation focused on application of algorithms, machine learning and graph theory to solve optimization problems in different types of networks such as social networks, storage networks among others.

Alina Oprea
Northeastern University

Alina Oprea is an Associate Professor at Northeastern University’s College of Computer and Information Science since August 2016. Alina is interested in extracting meaningful intelligence from different data sources for various security applications, designing resilient machine learning techniques for applications in different domains, and understanding the challenges of securing machine learning against advanced attacks. Prior to her position at Northeastern, Alina was a consultant research scientist at RSA Laboratories. Alina received a BS in mathematics and computer science from the University of Bucharest in Romania and M.Sc. and Ph.D. degrees in computer science from Carnegie Mellon University in 2003 and 2007, respectively. She is currently an associate editor for the ACM Transactions on Privacy and Security (TOPS) journal and co-chair of the Network and Distributed System Security (NDSS ) conference in 2018. She is the recipient of the Best Paper Awards at the 2005 NDSS Conference and 2017 AISEC Conference. In 2011 Alina received the Technology Review TR35 award for her research in cloud security.

Aleatha Parker-Wood
Symantec

Aleatha Parker-Wood is a researcher/manager in the Center for Advanced Machine Learning at Symantec, and leads a research team focused on protecting users and their data through advanced machine learning. She received a Ph.D. in Computer Science from University of California, Santa Cruz for her work on scientific data management. Her work currently focuses on differential privacy for ML and using deep learning for code analysis, with previous work in file system search, forensics, and AI for Go. She has authored several books and articles as well as numerous patent filings, and most recently served as research co-chair of MSST 2017.

Nikolaos Vasiloglou
Georgia Institute of Technology

Nikolaos Vasiloglou holds a PhD from the department of Electrical and Computer Engineering at Georgia Institute of Technology. His thesis was focused on scalable machine learning over massive datasets. After graduating from Georgia Tech he founded Analytics1305 LLC and Ismion Inc. He has architected and developed the PaperBoat machine learning library which has been successfully integrated and used in the LogicBlox and HPCCSystems platforms. He has also served as a machine learning consultant for Predictix, Revolution Analytics, Damballa, Tapad and LexisNexis.  He is currently a technical director at Symantec Center for Advanced Machine Learning. Nikolaos Vasiloglou teaches at the Executive Education and Computational Science and Engineering programs at Georgia Tech. He is also involved in the organization of the UAI conference and MLTrain workshops.

Founding Sponsor

Sponsorship

USENIX welcomes corporate sponsorship of our events. Your sponsorship exposes your brand to highly qualified and targeted attendees, funds our grant award program, supports the USENIX open access policy, and keeps USENIX conferences affordable.

For details on how you can help, please contact the Sponsorship Department via email or call +1 510.528.8649.