Past events

The John V. Carlis Memorial Lecture is dedicated to the advancement of education and inclusion in the field of computing.

This year's speaker is Juan Gilbert from University of Florida, giving a talk titled "A Model for Building Diversity, Equity and Community in Computing".

Abstract

The Computer & Information Science & Engineering (CISE) Department at the University of Florida is one of, if not the most, diverse computer science departments in the nation. CISE has the nation's largest group of African-American faculty and PhD students. CISE is also in the top with respect to women tenure-track faculty. In this John V. Carlis Memorial Lecture, Dr. Juan Gilbert will discuss how this unprecedented diversity was accomplished and how it can be replicated. He will also share data on the state of African-Americans in computing.

Biography

Juan E. Gilbert received his MS and PhD degrees in computer science from the University of Cincinnati in 1995 and 2000, respectively. He also received his BS in Systems Analysis from Miami University in Ohio in 1991. Dr. Gilbert is currently the Andrew Banks Family Preeminence Endowed Professor and Chair of the Computer & Information Science & Engineering Department at the University of Florida where he leads the Human Experience Research Lab. He has research projects in election security/usability/accessibility, advanced learning technologies, usability and accessibility, Human-Centered AI/machine learning and Ethnocomputing (Culturally Relevant Computing). He is an ACM Fellow, a Fellow of the American Association of the Advancement of Science and a Fellow of the National Academy of Inventors. In 2012, Dr. Gilbert received the Presidential Award for Excellence in Science, Mathematics, and Engineering Mentoring from President Barack Obama. He also received the American Association for the Advancement of Science (AAAS) 2014 Mentor Award. He received the 2021 ACM SIGCHI Social Impact Award. Dr. Gilbert received the 2018 Computer Research Association's A. Nico Habermann Award. Dr. Gilbert has served on 3 National Academies consensus committees, "The Role of Authentic STEM Learning Experiences in Developing Interest and Competencies for Technology and Computing", "The Science of Effective Mentoring in Science, Technology, Engineering, Medicine, and Mathematics (STEMM)" and "The Future of Voting: Accessible, Reliable, Verifiable Technology"

The application deadline for the computer science integrated program (Bachelor's/Master's) is October 15.

This is exclusively available to students officially admitted to the College of Science & Engineering Bachelor’s of Science in Computer Science, Bachelor’s of Computer Engineering, the College of Liberal Arts Bachelor’s of Arts in Computer Science, and the College of Liberal Arts Second Major in Computer Science. The program allows students with strong academic performance records to take additional credits (up to 16 credits) at undergraduate tuition rates during their last few semesters which can be applied towards the Computer Science M.S. program.

Applicants must have at least 75 credits completed at the time of their application. Read more about the program eligibility requirements.

Applications must be submitted online. Before applying, students should review the application procedures.

Students will be notified of the outcome of their application via email by December 1 for a spring start. In some cases, an admission decision will be put on hold until semester grades are finalized. Students will be notified if their application is on hold.

The Minnesota Natural Language Processing (NLP) Seminar is a venue for faculty, postdocs, students, and anyone else interested in theoretical, computational, and human-centric aspects of natural language processing to exchange ideas and foster collaboration. The talks are every other Friday from 12 p.m. - 1 p.m. during the Fall 2021 semester.

This week's speaker, Philippe Laban (Salesforce Research), will be giving a talk titled "Pushing the Boundary of Unsupervised Text Generation."

Abstract

Recent progress in automated text generation relies predominantly on the use of large datasets, sometimes requiring millions of examples for each application setting. In the first part of the talk, we'll develop novel text generation methods that balance the goals of fluency, consistency, and relevancy without requiring any training data. We focus on text summarization and simplification by directly defining a multi-component reward, and training text generators to optimize this objective. The novel approaches that we introduce perform better than all existing unsupervised approaches and in many cases outperform those that rely on large datasets, showing that high-performing NLP models are possible when little data is available.

Biography

Philippe Laban is a Research Scientist at Salesforce Research, where he works on text generation projects, including summarization and interactive question answering. Previously, he obtained his Ph.D. from UC Berkeley, where he was advised by Marti Hearst and John Canny. His work in Berkeley focused on designing unsupervised methods for text generation and on building and adapting NLP techniques to a very large, noisy and evolving news dataset. He did his undergraduate education at Georgia Tech, doing research in signal processing and discrete mathematics.

The UMN Machine Learning Seminar Series brings together faculty, students, and local industrial partners who are interested in the theoretical, computational, and applied aspects of machine learning, to pose problems, exchange ideas, and foster collaborations. The talks are every Thursday from 12 p.m. - 1 p.m. during the Fall 2021 semester.

This week's speaker, Uday V. Shanbhag (Penn State), will be giving a talk titled "Probability Maximization via Minkowski Functionals: Convex Representations and Tractable Resolution."

Abstract

In this paper, we consider the maximization of a probability P{ζ∣ζ∈K(x)} over a closed and convex set X, a special case of the chance-constrained optimization problem. We define K(x) as K(x)≜{ζ∈K∣c(x,ζ)≥0} where ζ is uniformly distributed on a convex and compact set K and c(x,ζ) is defined as either {c(x,ζ)≜1−|ζTx|m, m≥0} (Setting A) or c(x,ζ)≜Tx−ζ (Setting B). We show that in either setting, P{ζ∣ζ∈K(x)} can be expressed as the expectation of a suitably defined function F(x,ξ) with respect to an appropriately defined Gaussian density (or its variant), i.e. Ep~[F(x,ξ)]. We then develop a convex representation of the original problem requiring the minimization of g(E[F(x,ξ)]) over X where g is an appropriately defined smooth convex function. Traditional stochastic approximation schemes cannot contend with the minimization of g(E[F(⋅,ξ)]) over X, since conditionally unbiased sampled gradients are unavailable. We then develop a regularized variance-reduced stochastic approximation ({\textbf{r-VRSA}}) scheme that obviates the need for such unbiasedness by combining iterative {regularization} with variance-reduction. Notably, ({\textbf{r-VRSA}}) is characterized by both almost-sure convergence guarantees, a convergence rate of O(1/k1/2−a) in expected sub-optimality where a>0, and a sample complexity of O(1/ϵ6+δ) where δ>0.

Biography

Uday V. Shanbhag has held the Gary and Sheila Bello Chaired professorship in Ind. & Manuf. Engr. at Penn State University (PSU) since Nov. 2017 and has been at PSU since Fall 2012, prior to which he was at the University of Illinois at Urbana-Champaign (between 2006–2012, both as an assistant and a tenured associate professor). His interests lie in the analysis and solution of optimization problems, variational inequality problems, and noncooperative games complicated by nonsmoothness and uncertainty. He holds undergraduate and Master’s degrees from IIT,Mumbai (1993) and MIT, Cambridge (1998) respectively and a Ph.D. in management science and engineering (Operations Research) from Stanford University (2006).

Have all your questions about the Master of Science in Software Engineering (MSSE) program answered by attending this online information session.

RSVP now to reserve your spot.

Attendees will be sent a link prior to the event.
 

Prospective students can RSVP for an information session to learn about the following graduate programs:

• Computer Science M.S.
• Computer Science MCS
• Computer Science Ph.D.
• Data Science M.S.
• Data Science Post-Baccalaureate Certificate

During the information session, we will go over the following:

• Requirements (general)
• Applying
• Prerequisite requirements
• What makes a strong applicant
• Funding
• Resources
• Common questions
• Questions from attendees

The BIPOC Voices in STEM speaker series is free and open to students of all identities who are interested in or currently studying STEM at the University of Minnesota. The series highlights the experiences of UMN alumni who have current or past careers or degrees in STEM. Registration is required.

"Working at a Startup"
Pablo Garcia, co-founder of Crowd AI
Monday, October 11, 2021
6-7 p.m. Central Time
Register for the Zoom event
 

The computer science colloquium takes place on Mondays from 11:15 a.m. - 12:15 p.m.

This week's speaker, Stephen McCamant (University of Minnesota), will be giving a talk titled "Symbolic Execution as a Flexible Tool for Binary Analysis."

Abstract

Analyzing software at the binary (machine code) level is advantageous or required in many scenarios, but it can be more challenging than source-level analysis because of the complexities of instruction sets and a lack of high-level structure. Symbolic execution is a very useful approach for building binary analyses because it encapsulates instruction-set complexity, does not require high-level structure, and serves as a foundation on which a variety of analyses can be built. I'll describe the open-source FuzzBALL system for binary symbolic execution we are developing at the University of Minnesota to discuss some design trade-offs. Then I'll briefly sketch three applications of binary symbolic execution. In test generation for security vulnerabilities, symbolic execution acts as an enhanced dynamic analysis. In enumerating the targets of jump tables, symbolic execution acts as a static analysis without conservative approximation.  And in generating tests for CPU emulators, it performs bounded-exhaustive enumeration.

Biography

Stephen McCamant is an Associate Professor of Computer Science and Engineering at the University of Minnesota, where he has been since the fall of 2012. His main research area is program analysis for software security and correctness. He is especially interested in binary code analysis and transformation, hybrid dynamic/static techniques and symbolic execution, information flow/taint analysis, and applications of decision procedures. His research on software-based fault isolation won the USENIX Security Test of Time award in 2018, and was adopted in Google's Native Client system. He received his Ph.D from the Massachusetts Institute of Technology in 2008, and from 2008-2012 he was a postdoc at UC Berkeley.

The UMN Machine Learning Seminar Series brings together faculty, students, and local industrial partners who are interested in the theoretical, computational, and applied aspects of machine learning, to pose problems, exchange ideas, and foster collaborations. The talks are every Thursday from 12 p.m. - 1 p.m. during the Fall 2021 semester.

This week's speaker, Eric Vanden-Eijnden (New York University), will be giving a talk titled "Machine Learning and Scientific Computing."

Abstract

The recent success of machine learning suggests that neural networks may be capable of approximating high-dimensional functions with controllably small errors. As a result, they could outperform standard function interpolation methods that have been the workhorses of current numerical methods. This feat offers exciting prospects for scientific computing, as it may allow us to solve problems in high-dimension once thought intractable. At the same time, looking at the tools of machine learning through the lens of applied mathematics and numerical analysis can give new insights as to why and when neural networks can beat the curse of dimensionality. I will briefly discuss these issues, and present some applications related to solving PDE in large dimensions and sampling high-dimensional probability distributions.

Biography

Eric Vanden-Eijnden is a Professor of Mathematics at the Courant Institute of Mathematical Sciences, New York University. His research focuses on the mathematical and computational aspects of statistical mechanics, with applications to complex dynamical systems arising in molecular dynamics, materials science, atmosphere-ocean science, fluids dynamics, and neural networks. He is also interested in the mathematical foundations of machine learning (ML) and the applications of ML in scientific computing. He is known for the development and analysis of multiscale numerical methods for systems whose dynamics span a wide range of spatio-temporal scales. He is the winner of the Germund Dahlquist Prize and the J.D. Crawford Prize, and a recipient of the Vannevar Bush Faculty Fellowship.

The last day to receive a 25% tuition refund for canceling full semester classes is Monday, October 4.

View the full academic schedule on One Stop.