Distinguished Lecture Series

College of Computing and Informatics

11:30am - 12:30pm | Woodward 106



Speaker: Professor John Stasko, Georgia Tech

Title: The Value of Visualization for Exploring, Presenting, and Understanding Data

Abstract: Everyone's talking about data these days. People, organizations, and businesses are seeking better ways to analyze, understand, and communicate their data. While a variety of approaches can be taken to address this challenge, my own research has focused on data visualization. In this talk, I'll describe the particular advantages that visualization brings to data analysis beyond other techniques. Additionally, I'll identify three key tenets for success in data visualization: understanding purpose, embracing interaction, and identifying value. To help support this premise, I will draw upon and illustrate a number of current research projects from my lab and I'll recount a few anecdotes and experiences that have helped to form my views.


Speaker: Professor Wenjing Lou, Virginia Tech

Title: Internet of Things and its Security Challenges

Abstract: Internet of Things (IoT) is an emerging technology that promises a more connected and smarter world. Things in IoT can take a wide variety of forms, from simple RFIDs attached to merchandises, smart thermostats installed in the classrooms, implantable medical devices on the patients, to video cameras on top of light poles, and automobiles with built-in sensors. The explosive deployment of IoT systems has pushed the boundary of the cyber-world to be tightly intertwined with our physical world. The IoT enables the exchange of information in a variety of application scenarios, each having unique characteristics and requiring unique performance guarantees, and together they bring potentially tremendous benefits to us- home automation, environmental monitoring, health and lifestyle, smart cities, just to name a few.

Some significant risks go along with the potential benefits of the IoT. As we add devices to our cloths, bodies, homes, and environments, more personal information will be collected. Some information is deeply sensitive. As devices are more closely connected with our physical world and some are capable of taking actions, data security and device security become critically important. Last year, IoT devices have also been exploited to launch the largest DDoS attack in history to disrupt the Internet services.

A secure and trustworthy IoT is not an easy task. It demands multiple lines of defense to thwart attacks from both the physical world and cyberspace. It also requires the integration of security and privacy mechanisms into the computing and networking infrastructures. In this talk, I will introduce the network architecture and unique characteristics of IoT systems. I will then focus on unique security and privacy challenges in the IoT. Many of the security and privacy problems are very challenging and call for interdisciplinary expertise from a number of technical domains.


Speaker: Professor Patrick McDaniel, Penn State

Title: The Challenges of Machine Learning in Adversarial Settings

Abstract: Advances in machine learning have enabled to new applications and services to process inputs in previously unthinkably complex environments. Autonomous cars, data 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 evolving use of machine learning in security-sensitive contexts and explore why many systems are 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 discoveries in the creation of adversarial samples and defenses against them, 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 offensive and defensive research in the future.

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Speaker: Professor Fred Gould, NC State

Title: Gene Drive Systems for Insect Disease Vectors

Abstract:  In this presentation, I will provide a summary of theoretical, experimental, and practical issues being addressed by researchers attempting to genetically suppress or alter characteristics of insect pest populations. The targets for genetic pest management range from mosquitoes that transmit malaria to rats that annually consume enough rice to feed 180 million people.

The first transgenetic insect control system tested at the field level involved the mosquito, Aedes aegypti that transmits the dengue virus and the Zika virus. I will give a brief history about experiences with this system that requires continual release of large numbers of engineered mosquitoes. Work on the use of gene drive systems based on engineering of selfish genetic
elements is progressing and would only require one release of a smaller number of insects. These systems could be used to suppress pest population or change their characteristics. I will discuss the status of these systems as well as their future potential for protecting crops, human health, and biodiversity.

Simple population genetic models have been developed to examine the properties of these and other insect genetic pest management tactics. These simple mathematical models are very useful, but I will demonstrate the need for more complex spatially explicit simulations models that include details of a pest’s population dynamics and population genetics.

View the Recorded Lecture