Keynote Speakers

Dr. Venu Govindaraju

Vice President for Research and Economic Development
SUNY Distinguished Professor
Department Computer Science and Engineering
School of Engineering and Applied Sciences
113 Davis Hall

University at Buffalo, Amherst, New York 14260-2500



Science of Authentication: Beyond the Physical and Behavioral Biometric Modalities.


Given the pervasive use of e-commerce transactions and personal data storage in the cloud, society has an urgent need for a robust process that authenticates and protects the privacy and online assets of individuals and organizations. We recommend a totally new approach that rethinks the entire "science of authentication." The biometrics and cyber security communities have approached the challenge from different vantage points. The former focuses on "individuality" and "liveness" of human characteristics whereas the latter has primarily considered encryption and elaborate software protocols. This talk explores methods that go beyond the traditional biometrics of physical and behavioral modalities by integrating tests for humanness and identity in a cognitive framework. We are creating a comprehensive solution that will address a host of challenging AI problems ranging from OCR, object recognition to natural language understanding. We also will show how our holistic process allows for a more practical approach to security within the framework of a continuous authentication scenario.

Short Bio:

Dr. Venu Govindaraju, University at Buffalo Vice President for Research and Economic Development and SUNY Distinguished Professor of Computer Science and Engineering, is founding director of the Center for Unified Biometrics and Sensors. His research focuses on machine learning and pattern recognition primarily in Document Image Analysis and Biometrics. His pioneering work in handwriting recognition was at the core of the first handwritten address interpretation system used by the U.S. Postal Service as well as postal services in Australia and the UK. An extraordinary researcher, he has been a Principal or Co-Investigator of sponsored projects funded for nearly 65 million dollars.

He has published widely, coauthoring about 425 refereed papers. He has served on numerous professional and editorial boards, including IEEE Transactions (Pattern Analysis and Machine Intelligence; Information and Forensics Security) and as editor-in-chief of the IEEE Biometrics Councils Compendium. Dr. Govindaraju is a Fellow of the ACM (Association for Computing Machinery), the IEEE (Institute of Electrical and Electronics Engineers), the AAAS (American Association for the Advancement of Science), the IAPR (International Association of Pattern Recognition) and the SPIE (International Society of Optics and Photonics).

He received the 2001 International Conference on Document Analysis and Recognition Young Investigator award, the 2004 MIT Global Indus Technovator Award, the 2010 IEEE Technical Achievement Award and the Indian Institute of Technology (IIT) Distinguished Alumnus Award (2014). Recently, Dr. Govindaraju received the 2015 IAPR/ICDAR Outstanding Achievements Award and was named a Fellow of the National Academy of Inventors. He also has supervised the dissertations of 36 doctoral students. In Dec. 2016, he received UB’s Excellence in Graduate Student Mentoring Award, recognition of his great efforts in nurturing the next generation of scientists.

Dr. D. Janakiram

Department of Computer Science and Engineering

Indian Institute of Technology Madras
Chennai - 600 036.


Prof. Stephen Marsland

School of Engineering and Advanced Technology

Massey University, New Zealand
Private Bag 11-222
Palmerston North 4442



Automatic Birdsong Recognition


Reliably recognising species of birds from their calls detected using automatic acoustic recorders is an interesting challenge. The data is noisy (for example, there are often wind and rain as well as other animals), the birds are at varying distances from the microphone, and several of them may call simultaneously. However, it is a very useful way to know what birds are present in an area, and to detect when changes to the number of birds occurs. We have developed a series of approaches based on wavelets and machine learning for both denoising and species recognition, and I will describe these approaches in this talk, as well as suggesting areas for future research.

Prof. Agostino Cortesi

Dept. of Environmental Sciences, Informatics and Statistics

Ca'Foscari University of Venice, Italy
Via Torino 15530170
Mester, Venice, Italy



Data Leakage Analysis of Android Apps


When installing or executing an app on a smartphone, we grant it access to part of our (possibly confidential) data stored in the device. Traditional information-flow analyses aim to detect whether such information is leaked by the app to the external (untrusted) environment. We present some of our recent work where we trace not only if information is possibly leaked (as this is almost always the case), but also how relevant such a leakage might become, as an under- and over-approximation of the actual degree of values degradation, and we discuss how the analysis should be extended to deal with inter-apps communication through intents.