Event Calendar Links

  • College of Science and Engineering
  • Computer Science and Engineering
  • Electrical and Computer Engineering
  • Institute for Mathematics and its Applications
  • School of Physics and Astronomy
  • Statistics
  • September 18, 2020, 2:30 p.m. - MIfA Colloquim 

    • Speaker: Salvatore Vitale
    • Title: The first 5 years of gravitational-wave astrophysics
    • Zoom link: https://umn.zoom.us/j/92030995506?pwd=bWlGV0g5V2ZQSmxYam1hS2ZITkFTUT09, Meeting ID:920 3099 5506, Passcode: h@d0f3
    • Professor Vitale is also available for some meetings on Friday, 9/18 morning.  You can sign up for a time at  this link. Please enter your name and e-mail in the spaces provided and a zoom link will be created and sent to everyone who signs up.  
    • Abstract: In 2015 the LIGO observatories discovered gravitational waves. Five years later, signals from tens of binary black holes and two binary neutron stars have been detected. With a detection rate of roughly one source a week, it is now possible to start inferring the properties of the underlying population of black holes in binaries. Meanwhile, new groundbreaking discoveries have been made, such as the observation of an intermediate-mass black hole and of a compact object that could be either the lightest black hole or heaviest neutron star ever detected. In this talk I will present an overview of what we have learned in these first five years, and a summary of what comes next.
  • October 9, 2020, 2:30 p.m. - MIfA Colloquium 

    • Presenter: David Spergel, Astrophysics, Flatiron Institute
    • Title: Determining the Universe’s Initial Conditions
    • Zoom link: https://www.google.com/url?q=https%3A%2F%2Fumn.zoom.us%2Fj%2F98636692905&sa=D&ust=1604512724069000&usg=AOvVaw05M2MkyTJkyrfgL7EH5nRW
    • Abstract: Observations of the cosmic microwave background and measurements of the large-scale structure of the universe have revealed the initial fluctuations that grew to form galaxies. I will review measurements showing that these fluctuations were Gaussian random phase and that the basic properties of the universe appear to be described by the Lambda Cold Dark Matter model.I will report recent results from the Atacama Cosmology Telescope that probe not only the initial conditions but also map the integrated matter density, integrated pressure and integrated electron monentum through gravitational lensing and the Sunyaev-Zel’dovich Effects. I will then discuss the use of machine learning techniques to enable rapid forward modeling of the universe and discuss how these can be used in the coming years to recover the initial conditions from observations of large-scale structure
  • November 10, 2020, 4:00 p.m.- Seagate Presentation

    • Presenters: Nicholas Propes, Addishiwot Woldesenbet, Seagate
    • Title: Applied Data Science in Seagate Manufacturing
    • Presentation Slides
    • Zoom Link: https://umn.zoom.us/j/91859416813?pwd=bzN3S3F6NnlXSDNwZ3NkSUZsTkdldz09 Meeting ID: 918 5941 6813 Passcode: JXN6TJ
    • Abstract: Seagate Technology manufacturers world-class, precision-engineered data storage solutions.  Data science offers many opportunities to optimize manufacturing operations.  In this presentation, we provide an overview of Seagate manufacturing and highlight some example use cases of applied machine learning to various systems.  We discuss some of the associated challenges that machine learning presents in manufacturing and internship opportunities at Seagate.
  • December 11, 2020, 2:30 p.m. - MIfA Colloquium

    • Presenter: Roberto Trotta, Imperial College London 
    • Title: New Statistical Tools Improve Supernova Type Ia Cosmology
    • Zoom link: https://umn.zoom.us/j/98636692905
    • Abstract: One of the observational pillars of the accelerating expansion of the universe attributed to dark energy is the measurement of the redshift-distance relation with type Ia supernovae (SNIa), thermonuclear explosions of CO white dwarfs. While the original discovery of acceleration in 1998 relied on just ~40 SNIas, current samples of over 1,000 spectroscopically confirmed SNIas require a more sophisticated statistical approach to account for systematics, selection effects and intrinsic variability of the SNIas' properties. In the near future, the Vera Rubin Observatory will deliver ~10,000 candidates per year -- too many for complete spectroscopic typing: SNIa cosmology will have to rely on photometric data only, and thus contend with the possibility of non-Ia interlopers which could bias cosmological constraints. In this talk, I will present in a pedagogic fashion the basics of supernova type Ia cosmology. I will then focus on three open questions that need resolving in order to achieve precise and accurate constraints on dark energy in the coming decade: the problem of selection bias in the training set used for machine learning classification of photometric candidates; the question of accurate modeling of selection effects; and the investigation of the dependency of SNIa's brightness on their environment.
  • March 9, 2021, 4:00 p.m. - DSMMA Journal Club

    • Presenter: Vipin Kumar, University of Minnesota
    • Title: Physics Guided Machine Learning: A New Framework for Accelerating Scientific Discovery 
    • Zoom link: https://umn.zoom.us/j/94712430475?pwd=SjduWXh1NGRySzV0MGlQa21IdElrdz09
    • Abstract: Physics-based models of dynamical systems are often used to study engineering and environmental systems. Despite their extensive use, these models have several well-known limitations due to incomplete or inaccurate representations of the physical processes being modeled. Given rapid data growth due to advances in sensor technologies, there is a tremendous opportunity to systematically advance modeling in these domains by using machine learning (ML) methods. However, capturing this opportunity is contingent on a paradigm shift in data-intensive scientific discovery since the “black box” use of ML often leads to serious false discoveries in scientific applications.  Because the hypothesis space of scientific applications is often complex and exponentially large, an uninformed data-driven search can easily select a highly complex model that is neither generalizable nor physically interpretable, resulting in the discovery of spurious relationships, predictors, and patterns. This problem becomes worse when there is a scarcity of labeled samples, which is quite common in science and engineering domains. This talk makes a case that in a real-world systems that are governed by physical processes, there is an opportunity to take advantage of fundamental physical principles to inform the search of a physically meaningful and accurate ML model.  Even though this will be illustrated for a few problems in the domain of aquatic sciences and hydrology, the paradigm has the potential to greatly advance the pace of discovery in a number of scientific and engineering disciplines where physics-based models are used, e.g., power engineering, climate science, weather forecasting, materials science, and biomedicine.
  • March 23, 2021, 4:00 p.m. - Travelers Presentation

    • Presenters: Nathan Hubbell, Kathy Ziff - Travelers 
    • Title: Analytics and Data Science at Travelers
    • Zoom Link:  https://umn.zoom.us/j/92436304324?pwd=UHB2UVZEZ3FWdDYxdGw5WWxJYUtmZz09 Meeting ID: 924 3630 4324 Passcode: 03G543
    • Abstract: Data Scientists at Travelers Insurance employ AI/ML solutions for solving both traditional insurance pricing and reserving problems but also an expanding array of applications from imagery feature extraction to fraud detection.  We will provide an overview of Travelers, cover some of these AI/ML applications and also discuss career opportunities.
  • April 16, 2021, 2:30 p.m. - MIfA Colloquium

    • Presenter: Zeljko Ivezic, University of Washington
    • Title: The Greatest Movie of All Time 
    • Zoom link:https://umn.zoom.us/j/98191500828 Meeting ID: 981 9150 0828
    • Abstract: The Legacy Survey of Space and Time (LSST), the first project to be undertaken at the new Vera C. Rubin Observatory, will be the most comprehensive optical astronomical sky survey ever undertaken. Starting in a few years, Rubin Observatory will obtain panoramic images covering the sky visible from its location in Chile every clear night for ten years. Close to a thousand observations of each position across half of the celestial sphere will represent the greatest movie of all time: it would take 11 months of  uninterrupted viewing to see it! About 20 billion galaxies and a similar number of stars will be detected using this 60 petabyte image dataset — for the first time in history, the number of cataloged celestial objects will exceed the number of living people. LSST data will be used for investigations ranging from cataloging dangerous near-Earth asteroids to fundamental physics such as characterization of dark matter and dark energy. I will briefly describe scientific goals behind this project, show lots of pretty pictures to illustrate the progress of its construction, and finish with a discussion of data analysis challenges that need to be tackled to make the best use of LSST data. 
  • April 30, 2021, 2:30 p.m. - MIfA Colloquium

    • Presenter: Sara Algeri, University of Minnesota
    • Title: Searching for new phenomena with likelihood ratio tests: an overview on non-standard scenarios 
    • Zoom link: Zoom:https://umn.zoom.us/j/98191500828 Meeting ID: 981 9150 0828. Zoom Recording
    • Abstract: The likelihood ratio test is a standard statistical tool widely used in astrophysics to perform tests of hypotheses. The null distribution of the likelihood ratio test statistic is often assumed to be χ2, following Wilks’ theorem. However, in many circumstances relevant to modern experiments, this theorem is not applicable. Examples include detecting signals whose location is unknown, distinguishing new sources from known astrophysics mimicking them, and searches for new physics under background mismodeling. In this talk, I will overview practical ways to identify situations of non-regularity and  I will discuss solutions to construct valid inference in these settings.