Cybersecurity and Fintech Conference: Hosted by Duke University and the FBI Association of Intelligence Analysts

Join Duke University & the FBI Association of Intelligence Analysts for a Cybersecurity and Fintech conference on April 7 & 8.

Scheduled speakers include leading practitioners in areas including ransomware, APTs, threat intelligence best practices, and law enforcement & private sector cooperation.

Attend Virtually or In-Person

About the FBI AIA:


Learning environments at school and at home are diversifying every day.
Classrooms are no longer places to just listen to the lectures, but are becoming places for student-led lessons,
where students can discuss, communicate, and learn from each other.

Educational institutions are also being urged to put more focus on creating spaces to support student-led learning to maximize studentsʼ learning.

Panasonic supports creating an optimized environment for the ever-diversifying "learning" with visual system solutions.

Sennheiser Product Demo

The Sennheiser Teams meeting will include a review of TeamConnect Ceiling mic V2 by discussing its place in the market, its operating characteristics, settings and built in DSP.
We will also take a look at MobileConnect, our WiFi streaming hearing assist platform by looking at its scalability and customization.
We will also look at Speechline Digital Wireless along with Control Cockpit (our managed services platform) and the ease of campus wide systems monitoring.

Bridging the Gap Between Deep Learning and Probabilistic Modeling

Deep learning excels with large-scale unstructured data - common across many modern application domains - while probabilistic modeling offers the ability to encode prior knowledge and quantify uncertainty - necessary for safety-critical applications and downstream decision-making tasks. I will discuss examples from my research that bridge the gap between these two learning paradigms. The first half will show that insights from deep learning can improve the practicality of probabilistic models.

Towards a Foundation for Reinforcement Learning

In recent years, reinforcement learning algorithms have achieved strong empirical success on a wide variety of real-world problems. However, these algorithms usually require a huge number of samples even just for solving simple tasks. It is unclear if there are fundamental statistical limits on such methods, or such sample complexity burden can be alleviated by a better algorithm. In this talk, I will give an overview of my research efforts towards bridging the gap between the theory and the practice of reinforcement learning.

How to Handle Biased Data and Multiple Agents in Machine Learning?

Modern machine learning (ML) methods commonly postulate strong assumptions such as: (1) access to data that adequately captures the application environment, (2) the goal is to optimize the objective function of a single agent, assuming that the application environment is isolated and is not affected by the outcome chosen by the ML system. In this talk I will present methods with theoretical guarantees that are applicable in the absence of (1) and (2) as well as corresponding fundamental lower bounds.