Upcoming Presenters

2021 – IEEE Denver Computer, Information Theory, and Robotics Society



 

Virtual Meeting

IEEE Denver Computer, Information Theory, and Robotics Society – Technical Meeting

13 May 2021 @ 6:00 PM – 7:00 PM MT

Denver IEEE Computer Society Guest Lecturer,

Dr. Jyotirmoy V. Deshmukh

Assistant Professor of Computer Science at University of Southern California Viterbi, School of Engineering

Principle Research Engineer

 
 

Dr. Jyotirmoy V. Deshmukh joined the Department of the University of Southern California as a new faculty in August 2017. He transitioned to his role as an educator after five years of work as a Principal Research Engineer at Toyota Motors North America R&D. At Toyota, Dr. Deshmukh helped bridge the gap between academic research and industrial practice through requirement engineering and testing methods. Before joining Toyota, Dr. Deshmukh was the 2010 Computing Innovation Post-Doctoral Fellow at the University of Pennsylvania under the mentorship of Rajeev Alur.

Dr. Deshmukh  was awarded his Ph.D. in computer engineering from the University of Texas at Austin in 2010, where he was advised by E. Allen Emerson – a recipient of the prestigious A.M. Turing Award. Dr. Deshmukh’s current research interests include the application of formal methods to reason about cyber-physical systems, verification and testing of embedded control systems, real-time temporal logic, and analyzing time-series data. He is particularly interested in studying cyber-physical systems that use machine learning based components, such as autonomous driving vehicles.

 

Presentation: Specification-driven Design of Autonomous Systems

Abstract: Machine learning-based techniques such as deep reinforcement learning (Deep RL) have shown a lot of promise in automatically synthesizing controllers for autonomous cyber-physical systems that operate in uncertain environments. The basic idea in RL is to treat the control software as an agent to be trained; in each state, the agent performs some action, and the environment probabilistically determines its next state and the reward that it should receive. RL algorithms try to maximize the payoff that the agent would receive over a long-term horizon. Deep RL algorithms extend RL by using deep neural networks to model functions that map states (and actions) to expected rewards, and/or the control policy for the agent. In general, RL algorithms crucially rely on an expert user to design state-based localized reward functions with the understanding that desired system behavior occurs when the reward payoff is optimized. Unfortunately, there are documented cases of reward hacking: where the agent learns to maximize the reward while exhibiting undesirable or unsafe behavior. Thus, for adoption of deep RL algorithms in the design of safety-critical autonomous systems, it is important to carefully design reward functions. In this talk, we propose a new paradigm for (deep) RL algorithms, where instead of providing state-based rewards, the user specifies the desired behavior of the system using a formal specification language (such as Signal Temporal Logic or STL). We then explore two different approaches for a systematic inference of reward functions: the first in which we map STL specifications to localized state-based rewards, and the other in which we combine the system specification with user-provided demonstrations to learn safer control policies. We demonstrate the efficacy of our technique on examples from the autonomous driving domain.

Location: – Virtual – WebEx

Invited: IEEE members, guests, students, walk-ins are welcome.

Cost: Free

 



 

Virtual Meeting

IEEE Denver Computer, Information Theory, and Robotics Society – Technical Meeting

12 Aug 2021 @ 6:00 PM – 7:00 PM MT

Denver IEEE Computer Society Guest Lecturer,

 

Dr. Haadi Jafarian

 Professor of Computer Science and Engineering

Director of Active Cyber and Infrastructure Defense (ACID) lab at University of Colorado Denver

 

Dr. Jafarian is an assistant professor, and the director of Active Cyber and Infrastructure Defense (ACID) laboratory at the CSE department. His primary research includes active cyber defense, resilient infrastructures, and data analytics for cyber threat intelligence. He has authored over 30 scholarly conference and journal publications (IEEE TIFS, IEEE INFOCOMM, ESORICS), including several noteworthy works on moving target defense, cyber deception, web security, and network security. He is currently advising five Ph.D. students and several master students in the ACID lab, where most of lab’s research is focused on data-driven cybersecurity analytics.

 
 
 
Presentation: Defeating traffic analysis attacks: challenges and countermeasures

Abstract: While encryption protects communications from on-path eavesdropping and man-in-the-middle attacks, it does not protect them against privacy threats realized via advanced traffic analysis algorithms. Traffic analysis refers to ever-growing side-channel attacks that rely on sophisticated machine learning algorithms to enable an on-path attacker to classify the encrypted packets based on unencryptable features of the traffic, such as packet sizes, counts, and timings which are not modified by the encryption algorithms, to infer sensitive information from eavesdropped encrypted communications. Examples include traffic analysis attacks that identify the spoken language or even the speaker in a conversation over encrypted VoIP packets or visited web pages or other user activities from encrypted HTTP packets. In this talk, we first provide an overview of traffic analysis attacks and show that existing countermeasures like packet padding are in no way sufficient or effective in addressing them. We then discuss the characteristics of an ideal defense mechanism for obfuscating footprints of encrypted traffic. Finally, we discuss a novel approach based on proactive cyber defense paradigms, including moving target defense and cyber deception, to realize this ideal countermeasure against traffic analysis attacks.

 

Location: – Virtual – WebEx

Invited: IEEE members, guests, students, walk-ins are welcome.

Cost: Free

 



 

Virtual Meeting

IEEE Denver Computer, Information Theory, and Robotics Society – Technical Meeting

09 Sept 2021 @ 6:00 PM – 7:00 PM MT

Denver IEEE Computer Society Guest Lecturer,

 

Mr. Kevin Havis

Industrial Engineer, Data Visualization Expert, & Engineering Leader

Lockheed Martin Operations

 
 

Presentation: Data Driven Decisions and Continuous Improvement

 

Location: – Virtual – WebEx

Invited: IEEE members, guests, students, walk-ins are welcome.

Cost: Free

 



 

Virtual Meeting

IEEE Denver Computer, Information Theory, and Robotics Society – Technical Meeting

14 October 2021 @ 6:00 PM – 7:00 PM MT

Denver IEEE Computer Society Guest Lecturer,

 

Mr. Paul Walker

Agile leader and teacher, Computer Scientist, Software Engineering

Lockheed Martin

 
 

Presentation: Agile Management in Code Development

 

Location: – Virtual – WebEx

Invited: IEEE members, guests, students, walk-ins are welcome.

Cost: Free

 



 

2022 – IEEE Denver Computer, Information Theory, and Robotics Society



 

Virtual Meeting

IEEE Denver Computer, Information Theory, and Robotics Society – Technical Meeting

10 February 2022 @ 6:00 PM – 7:00 PM MT

Denver IEEE Computer Society Guest Lecturer,

Dr. Tarek El-Ghazawi

Professor High-Performance Computing at George Washington University 

IEEE Computer Society Distinguished Speaker

 
 

Tarek El-Ghazawi is a Professor in the Department of Electrical and Computer Engineering at The George Washington (GW) University, where he leads the university-wide Strategic Academic Program in High-Performance Computing. He is the founding director of The GW Institute for Massively Parallel Applications and Computing Technologies (IMPACT) and was a founding Co-Director of the NSF Industry/University Center for High-Performance Reconfigurable Computing (CHREC). Dr. El-Ghazawi’s research interests include high-performance computing, computer architectures, reconfigurable and embedded computing, nano-photonic based computing, and computer vision and remote sensing. Dr. El-Ghazawi is also one of the pioneers of the area of High-Performance Reconfigurable Computing (HPRC).

Dr. El-Ghazawi was also one of the early researchers in Cluster Computing and has built the first GW cluster in 1995. At present, he is leading efforts for rebooting computing based on new paradigms including analog, nano-photonic, and neuromorphic computing. He has served on many boards and served as a consultant for organizations like CESDIS and RIACS at NASA GSFC and NASA ARC, IBM, and ARSC. He has published over 250 refereed research publications in his area and his work is funded extensively by government organizations like DARPA, NSF, AFOSR, NASA, DoD, and industrial organizations such as Intel, AMD, HP, SGI. Dr. El-Ghazawi has served in many editorial roles and has chaired numerous IEEE international conferences and symposia, including IEEE PGAS 2015, IEEE/ACM CCGrid2018, DSS 2017 to name a few.

Presentation: Rebooting Computing — The Search for Post-Moore’s Law Breakthroughs

Abstract: The field of high-performance computing (HPC) or supercomputing refers to the building and using computing systems that are orders of magnitude faster than our common systems. The top supercomputer, Summit, can perform 148,600 trillion calculations in one second (148.6 PF on LINPAC). The top two supercomputers are now in the USA followed by two Chinese supercomputers. Many countries are racing to break the record and build an ExaFLOP supercomputer that can perform more than one million trillion (quintillion) calculations per second. In fact, the USA is planning two supercomputers in 2021 one of which, when fully operational (Frontier), will perform at 1.5 EF. Scientists however are concerned that we are reaching many physical limits and we need new innovative ideas to make it to the next generation of computing. This talk will consider where we stand and where we are going with the current state of supercomputing with emphasis on future processors, and some of the ideas that scientists are looking at to re-invent computing. A comparative understanding of Neuromorphic and Brain-Inspired Computing, Quantum Computing, and innovative computing paradigms will be provided along with an assessment of progress so far and the road ahead. Further, I will cover some of our own progress on Nanophotnonic PostMoore’s law processing efforts.

Location: – Virtual – WebEx

Invited: IEEE members, guests, students, walk-ins are welcome.

Cost: Free