Attend the URCAD presentation prep session!

Wednesday, March 31, 2021

12-1pm

Presenter: Dr. Raimi Quiton

URCAD is April 19-25th

URCAD.umbc.edu

Questions? aprilh@umbc.edu

Link:

URCAD is April 19-25, 2021!

URCAD.umbc.edu

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UMBC GEARS Ideathon

11:30-1:30 Friday, April 9, 2021

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GEARS, UMBC’s Graduate Experience, Achievements, and Research Symposium, bring you its first-of-a-kind event IDEATHON that invites graduate students to describe how new or existing problems can be better tackled by using their new idea. Participants will present their ideas to the jury and fellow graduate students in UMBC.  You can participate either individually or in a group of up to three people.

This event will highlight your creative skills and the uniqueness of your idea, which can be social, environmental, IT technology, medical field related, etc. These ideas can be real or hypothetical. You create a three-minute presentation showcasing your idea and how unique it is. Up to $1000 in prize money will be available for the winning ideas. All the participants are eligible for a free UMBC logo Mask, and the first ten participants will get a chance to win UMBC merchandise T-shirts.

Sign up here.

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We welcome all department’s graduate students to come to participate and celebrate Graduate week with us on the event day i.e.  9th April 2021. For any queries contact Sulabh Sharma (+14438504311, *protected email*) or Jhansi Sankaramaddi (+14109006743, *protected email*)

What research experiences have you had? I'm fortunate to have worked on more than one major research project during my time at UMBC. This most recent project examines English nuns during the decades surrounding the dissolution of the monasteries during the English Reformation (1530s), and the ways they responded to the end of their way of life. 

Who is your mentor for your project? Dr. Amy Froide, History. She is my departmental thesis advisor and has been an excellent source of support, encouragement, and advice.

Do you get course credit for this work? How much time do you put into it? This project is my honors thesis for my History degree. It is supported by a UMBC Undergraduate Research Award and will be presented at URCAD 2021. I spend about an hour on it each day, though sometimes I spend several hours on it in one day.

What was the hardest part about your research? Working during COVID was very difficult. Many of the resources I would otherwise use were closed due to the pandemic, including spaces to write without interruption. The inability to access archives and other materials in person made this work challenging! I would have loved the opportunity to visit museums and research libraries to examine primary sources directly, but COVID made that impossible.

What was the most unexpected thing? I knew that some communities of women fled into exile on the European continent, but I was surprised and delighted to find that women from one of these communities in exile, the Carmelites in Hoogstraten (in modern-day Belgium), established the first Carmelite community in America when they arrived at Port Tobacco, Maryland, near the end of the eighteenth century. Their community is still active today and their house is just north of Towson! 

What is your advice to other students about getting involved in research? UMBC has a wonderfully supportive community of faculty to help you with your research. Don't be shy about contacting a professor who shares your interests - they are usually happy and delighted to help you get started and to offer tips and feedback along the way as your project snowballs and picks up momentum. 

What are your career goals? I plan to obtain a master's degree and, eventually, a Ph.D. so that I can pursue a career in academia. 

What else are you involved in on campus? I am a member of the campus chapters of Phi Kappa Phi, Phi Alpha Theta, and Omicron Delta Kappa. I'm also a student leader of the History Student Council. 

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Forward and Inverse Causal Inference in a Tensor Framework

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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.   

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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.