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I will speak to how students can proactively engage with professors and industry professionals through the classroom and through industry experiences. I will point out a few exemplars showing best practices for our students, illuminated by my experience as a professor, industry professional, and startup founder. I will also discuss some pitfalls to avoid, which I have seen slow down the progress of highly talented students.
The session will be highly interactive with comments and discussion with the students built into the session as an integral part.
Relief and rescue operations of the near and the far future will involve autonomous operations among multiple cyber, physical, and kinetic assets, together with interactions with humans. Such autonomous operation will rely on a pipeline of machine learning (ML) algorithms executing in real-time on a distributed set of heterogeneous platforms, both stationary and maneuverable. The algorithms will have to deal with both adversarial control and data planes. The former means that some of the nodes on which the algorithms will execute cannot be trusted and have been compromised for leaking information or violating the integrity of the results. An adversarial data plane means that the algorithms will have to operate with uncertain, incomplete, and potentially, maliciously manipulated data sources. This talk will show the basics of how to design secure algorithms that can provide probabilistic guarantees on security and latency, under powerful, rigorously quantified adversary models. It will cover three pillars that are needed to achieve the above desired outcome — robust adversarial algorithms, interpretable algorithms aiding the trust of the humans on the results of the autonomous algorithms, and secure, distributed execution of the autonomy pipeline among multiple platforms.
Many of our critical distributed infrastructures (transportation, distributed manufacturing, power grid, etc.) comprise multiple interdependent assets, and a set of defenders, each responsible for securing a subset of the assets against an attacker. The practical question that arises is how should the defenders make their security investments and if they should cooperate. While prior work has answered these questions, they have been under the assumption of perfect rationality of the decision makers. In this talk, we will show that these answers can be dangerously sub-optimal when the defenders exhibit characteristics of human decision-making that have been identified by the behavioral psychology and economics communities. In particular, humans have been shown to perceive probabilities in a nonlinear manner, typically overweighting low probabilities and underweighting high probabilities. By applying results from two Nobel Prize winning economists (Kahneman-2002 and Thaler-2017), we get glimpses of where a biased defender can be beneficial for the other defenders in the network. We want to spur discussion of where we should, and should not, learn from behavioral economists in securing our distributed infrastructures.