Forward and Inverse Causal Inference in a Tensor Framework

M. Alex O. Vasilescu
Institute of Pure and Applied Mathematics, UCLA

1-2:00 pm Monday, March 29, 2021
via WebEx

Developing causal explanations for correct results or for failures from mathematical equations and data is important in developing a trustworthy artificial intelligence, and retaining public trust.  Causal explanations are germane to the “right to an explanation” statute, i.e., to data-driven decisions, such as those that rely on images.  Computer graphics and computer vision problems, also known as forward and inverse imaging problems, have been cast as causal inference questions consistent with Donald Rubin’s quantitative definition of causality, where “A causes B” means “the effect of A is B”, a measurable and experimentally repeatable quantity. Computer graphics may be viewed as addressing analogous questions to forward causal inference that addresses the “what if” question, and estimates a change in effects given a delta change in a causal factor. Computer vision may be viewed as addressing analogous questions to inverse causal inference that addresses the “why” question which we define as the estimation of causes given a forward causal model, and a set of observations that constrain the solution set.  Tensor algebra is a suitable and transparent framework for modeling the mechanism that generates observed data.  Tensor-based data analysis, also known in the literature as structural equation modeling with multimode latent variables, has been employed in representing the causal factor structure of data formation in econometrics, psychometric, and chemometrics since the 1960s.  More recently, tensor factor analysis has been successfully employed to represent cause-and-effect in computer vision, and computer graphics, or for prediction and dimensionality reduction in machine learning tasks.   

M. Alex O. Vasilescu received her education at the Massachusetts Institute of Technology and the University of Toronto. She is currently a senior fellow at UCLA’s Institute of Pure and Applied mathematics (IPAM) that has held research scientist positions at the MIT Media Lab from 2005-07 and at New York University’s Courant Institute of Mathematical Sciences from 2001-05.  Vasilescu introduced the tensor paradigm for computer vision, computer graphics, and machine learning. She addressed causal inferencing questions by framing computer graphics and computer vision as multilinear problems. Causal inferencing in a tensor framework facilitates the analysis, recognition, synthesis, and interpretability of data. The development of the tensor framework has been spearheaded with premier papers, such as Human Motion Signatures (2001), TensorFaces (2002), Multilinear Independent Component Analysis (2005), TensorTextures (2004), and Multilinear Projection for Recognition (2007, 2011). Vasilescu’s face recognition research, known as TensorFaces, has been funded by the TSWG, the Department of Defenses Combating Terrorism Support Program, Intelligence Advanced Research Projects Activity (IARPA), and NSF. Her work was featured on the cover of Computer World and in articles in the New York Times, Washington Times, etc. MIT’s Technology Review Magazine named her to their TR100 list of honorees, and the National Academy of Science co-awarded the Keck Futures Initiative Grant.  

Continuing with our Innovation, Collaboration, Job Search, and Career help theme, the ACM UMBC chapter is back again, hosting another session on the coming Friday with Sameer Ahirrao, a Founder and CEO of Ardent Privacy. He will be talking about Innovation, Collaboration, and Career Opportunities in Data Privacy. Find more on how you can get a part-time off-campus or full-time internship under MIPS (Maryland Industrial Partnership) Program with Ardent Privacy.

Join us for insights from him and a Q&A session with Sameer.
See you on Friday, March 16 at 3:00 pm EST on WebeX.  For more information, contact:  *protected email*.

The UMBC Cyber Defense Lab presents

Transparent Dishonesty: Front-Running Attacks on Blockchain

Professor Jeremy Clark
Concordia Institute for Information Systems Engineering
Concordia University, Montreal, Canada

12–1 pm ET Friday, March 26, 2021
online via WebEx

We consider front-running to be a course of action where an entity benefits from prior access to privileged market information about upcoming transactions and trades. Front-running has been an issue in financial instrument markets since the 1970s. With the advent of blockchain technology, front-running has resurfaced in new forms we explore here, instigated by blockchain’s decentralized and transparent nature. I will discuss our “systemization of knowledge” paper which draws from a scattered body of knowledge and instances of front-running across the top 25 most active decentral applications (DApps) deployed on Ethereum blockchain. Additionally, we carry out a detailed analysis of Status.im initial coin offering (ICO) and show evidence of abnormal miner’s behavior indicative of front-running token purchases. Finally, we map the proposed solutions to front-running into useful categories.

Jeremy Clark is an associate professor at the Concordia Institute for Information Systems Engineering. At Concordia, he holds the NSERC/Raymond Chabot Grant Thornton/Catallaxy Industrial Research Chair in Blockchain Technologies. He earned his Ph.D. from the University of Waterloo, where his gold medal dissertation was on designing and deploying secure voting systems including Scantegrity—the first cryptographically verifiable system used in a public sector election. He wrote one of the earliest academic papers on Bitcoin, completed several research projects in the area, and contributed to the first textbook. Beyond research, he has worked with several municipalities on voting technology and testified to both the Canadian Senate and House finance committees on Bitcoin. email: *protected email*

Host: Alan T. Sherman, *protected email* Support for this event was provided in part by the National Science Foundation under SFS grant DGE-1753681. The UMBC Cyber Defense Lab meets biweekly Fridays. All meetings are open to the public. Upcoming CDL Meetings: April 9, (UMBC), MeetingMayhem: A network adversarial thinking game; April 23, Peter Peterson (University of Minnesota Duluth), Adversarial thinking;
May 7, Farid Javani (UMBC), Anonymization by oblivious transfer.

Sensing the Physical World using Pervasive Wireless Infrastructures

Elahe Soltanaghaei
Carnegie Mellon University

1:00-2:00 pm ET Thursday, 25 March 2021
online via WebEx

Emerging applications such as smart cities, autonomous vehicles, and mixed reality rely on embedded systems that are engaging with the physical environment through sensors. Building upon this connection, my vision is to advance Omnipresent Sensing by harnessing the wireless infrastructure in and around buildings and cities to act as a non-intrusive sensing platform. This is possible by innovating at the crossroads of two recent trends in mobile computing: (1) Wireless technologies such as Millimeter-wave (mmWave) and Massive MIMO systems can now support higher bandwidth for communication and improved resolution for RF sensing applications. (2) Advancements in CPUs and RF front-ends are making it easier to develop software-defined sensing and communication systems. This is affording edge devices the ability to do more advanced signal processing and machine learning.  

In this talk, I will focus on how to design an RF-equivalent of optical retro-reflectors and use them as fiducial markers in autonomous vehicles, robotics, and mixed reality applications. I will then discuss how nuances from the environment itself can be leveraged to improve sensing quality in the context of human sensing, object tracking, and indoor localization. I will conclude this talk with a roadmap of combining radar-style RF sensors and wireless communication links for the wireless embedded systems of the future. 

Elahe Soltanaghaei is a postdoctoral researcher at Carnegie Mellon University in the Wireless, Sensing, and Embedded Systems (WiSE) lab. She received her Ph.D. in Computer Science from the University of Virginia. Her research spans the areas of wireless sensing and networking with applications in IoT and Cyber-Physical Systems. Reflecting the multidisciplinary nature of her research, her work has been published in premier conferences and journals in the areas of mobile computing, wireless networks, and energy and infrastructure. She is the recipient of the 2020 ACM SIGMOBILE Dissertation Award, 2019 EECS Rising Stars, and the 2019 N2-Women Young Researcher Fellowship (more at https://www.andrew.cmu.edu/~esoltana/)

TIPPERS is an IoT data management middleware system developed at UCI that manages IoT smart spaces by collecting sensor data, inferring semantically meaningful information, and providing developers with data for intelligent applications.

(Don’t) Mind the Gap: Bridging the Worlds of People and IoT Devices

Dr. Roberto Yus
University of California, Irvine

1:00-2:00 pm ET, Tuesday, 23 March 2021
online via WebEx

The Internet of Things (IoT) has the potential to improve our lives through different services given the diversity of smart devices and their capabilities. For example, the IoT can empower services to make the re-opening of business during the current pandemic safer by monitoring adherence to regulations. But the large amounts of highly heterogeneous data captured by IoT devices typically require further processing to become useful information. The challenge is thus for IoT systems to determine which sensor data has to be captured/stored/processed/shared to, for instance, determine the occupancy of a specific office building or the spaces in which a potential exposure took place. This becomes even more challenging when IoT systems have to take into account the privacy preferences of individuals, such as the need to prevent sharing data about their daily patterns or habits.

In this talk, I will discuss my efforts into helping IoT systems bridge the gap between the world of IoT devices and the world where people act. First, I will introduce a model to represent knowledge about sensors/actuators, people, spaces, events, and their relationships. Based on the model, I will explain an algorithmic solution to translate user requests and privacy preferences defined in a high-level, more semantically meaningful way into operations on IoT devices and their captured data. Second, I will talk about the enforcement of privacy preferences in the context of the IoT. Finally, I will overview my experience building and deploying an IoT data management system, TIPPERS, which has been deployed at UC Irvine and two US Navy vessels and is soon to be deployed on other campuses. I will conclude the talk by discussing the exciting future work opportunities towards supporting the next generation of ubiquitous IoT data management systems and technologies that autonomously, transparently, and at scale, balance the trade-off between providing users with high utility and respecting people’s privacy requirements.

Roberto Yus is a Postdoctoral Researcher in the Computer Science department at the University of California, Irvine working with Prof. Sharad Mehrotra. Before that, he spent a year as a visiting researcher at the University of Maryland, Baltimore County working with Prof. Anupam Joshi and Prof. Tim Finin. He obtained his Ph.D. in Computer Science from the University of Zaragoza, Spain, funded through a 4-year fellowship from the Spanish Ministry of Science and Innovation. His research interests are in the fields of data management, knowledge representation, privacy, and the Internet of Things (IoT). His research focuses on the design of semantic data management solutions to empower IoT systems to understand user information requirements and user privacy preferences and adapt their operations taking those into account. Roberto’s research has been published in top-tier conferences and journals such as VLDB and the Journal of Web Semantics. He is part of the editorial board of the “Sensors” and “Frontiers in Big Data” journals and has served as part of the organizing and program committee of several conferences and workshops in addition to serving as an external reviewer for multiple conferences and journals.

UMBC expands live online peer tutoring to include computing courses

When Amanda Knapp heard last fall from Anupam Joshi, professor and chair of computer science and electrical engineering (CSEE), that his department wanted to offer online tutoring to students in their courses, she was ready to help make it happen. COVID or no COVID, she says, “It just made sense.”

Knapp is associate vice provost and assistant dean of Undergraduate Academic Affairs, and she manages UMBC’s Academic Success Center (ASC). “We already had an established system in place and could provide the administration, training, staffing, and assessment,” she explains. “CSEE provided the funding and identified potential tutors for a variety of computer science courses, and we did the rest.”

Just a few months after the partnership began, it expanded to include courses in the information systems (IS) department. And this semester, tutors are supporting ten IS computing courses.

In total, the ASC’s new Computing Success Center now offers tutoring for 21 courses from across the College of Engineering and Information Technology (COEIT). And the center continues to grow, supported by COEIT Dean Keith J Bowman and Dean Katharine Cole, Undergraduate Academic Affairs.

Peer tutors help students of any major, from computing fields to the arts and life sciences, learn coding and other computing skills.

“The ethos of UMBC is to share knowledge and collaborate with others instead of being proprietary,” says Helena Mentis, associate professor of information systems. Mentis is COEIT’s associate dean of academic programs and learning, and one of the College leads on the partnership.

“At the end of the day,” she says, “our goal at UMBC is to ensure that there are multiple pathways for students to get the assistance they need to succeed and remove any barriers for access to help.”

You can read more about UMBC’s new Computing Success Center in this UMBC News article.

Adapted from a UMBC News article by Gregory J. Alexander and Dinah Winnick