Venue: West Hall – 340
The Women in Computational Science Symposium is the inaugural event for MICDE’s DISCOVER (Diversity and Innovation in Scientific Computing: Opportunities for Valuing Exploration and Representation) mini-symposium series. This mini-symposium provides a unique opportunity to delve into the pioneering research conducted by women in computational science while also gaining insight into their personal experiences and the challenges they face as researchers.
This year’s Women in Computational Science Symposium features:
Bio: Katrin Heitmann is the deputy director of Argonne’s High Energy Physics division, and a physicist and computational scientist. She is also a Senior Associate for the Kavli Institute for Cosmological Physics at the University of Chicago and a member of NAISE at Northwestern. Before joining Argonne, Katrin was a staff member at Los Alamos National Laboratory. Her research currently focuses on computational cosmology, in particular on trying to understand the causes for the accelerated expansion of the Universe. She is responsible for large simulation campaigns with HACC and for the tools in the associated analysis library, CosmoTools. Katrin is a member of several major astrophysical surveys that aim to shed light on this question and is currently the Spokesperson for the LSST Dark Energy Science Collaboration.
Cosmology – the study of the origin, evolution, and constituents of the Universe – is now entering one of its most scientifically exciting phases. Three decades of surveying the sky have culminated in the celebrated “Cosmological Standard Model”. Yet, two of its key pillars, dark matter, and dark energy – together accounting for 95% of the mass-energy of the Universe – remain mysterious. Next-generation observatories will open new routes to understand the true nature of the “Dark Universe”. These observations will pose tremendous challenges on many fronts – from the sheer size of the data that will be collected to its modeling and interpretation. The interpretation of the data requires sophisticated simulations on the world’s largest supercomputers. The cost of these simulations, the uncertainties in our modeling abilities, and the fact that we have only one Universe that we can observe opposed to carrying out controlled experiments, all come together to create a major test for statistical methods of data analysis. In this talk, I will give a brief introduction to the Dark Universe and outline the challenges ahead. I will describe how complex, large-scale simulations will be used to extract the cosmological information from ongoing and next-generation surveys.
Liz Livingston, PhD candidate in Mechanical Engineering and Scientific Computing at U-M
Title: Data to Differential Equations – Discovering Mathematical Models for Biological Systems
Bio: Liz Livingston is a 5th year PhD candidate in Mechanical Engineering and Scientific Computing at the University of Michigan, advised by Professor Krishna Garikipati and Professor Alberto Figueroa. Her research focuses on data-driven modeling of biological systems. This work spans a range of topics including biomechanics, numerical methods, and high-performance computing. She received her BS and MS degrees from the University of Illinois at Urbana-Champaign where she studied the strength and microstructure of bone. Liz enjoys teaching and cultivates this interest through hands-on experience, outreach, and involvement in the American Society for Engineering Education (ASEE).
Abstract: Complex phenomena, such as those observed in biological systems, can typically be modeled with partial differential equations (PDEs). Finding governing equations can be a daunting task, often involving simplifications to the system such that the PDE does not fully capture the physics of the problem. Instead of reducing the complexity of the system with successive approximations, the governing PDE can be discovered using data. One of the fastest and most popular techniques is machine learning, where a surrogate is found as an approximation to the function. Alternatively, inference techniques may be used to identify the strong or weak form of the governing equation via parameter estimation. The tools we develop for the discovery of governing equations have applications in many complex systems, including biological ones such as flow through a stenosed artery and fracture in soft tissues. The goal of my PhD thesis is to develop and improve these mathematical methods to help expand our understanding of complex biological systems.
Rachel Niemer, Managing Director of WISE (Women in Science and Engineering)
Title: Who is WISE for and what should we do? Exploring levers of change to foster equity in STEM
WISE info: The University of Michigan is at the forefront of equality in science and engineering, and our focus on diversity, equity, and inclusion spans multiple dimensions, including gender, race, SES, first generation status, to name a few. The University of Michigan’s Women in Science and Engineering (WISE) unit aims to increase the participation by women and gender minorities in careers in science, technology, engineering and mathematics, and to foster their academic and professional success. We do this by cultivating students’ skills to thrive in STEM, strengthening the community working toward STEM equity, and working to mitigate systemic forces that impede retention of women, and individuals from other historically underrepresented groups, in STEM.
Abstract: As we look at the evolving landscape of where women, and other individuals from historically marginalized groups, thrive and persist in STEM, it makes sense to ask why more progress hasn’t been made. Women in Science and Engineering has been a resource for U-M students in STEM since 1980. Over that time, WISE, and similar units at other institutions, have experimented with a range of interventions to help women thrive in STEM. What if we chose the wrong levers for change? Are there radically new ways we might support efforts to graduate more STEM majors from minoritized communities? This presentation will explore different models for advancing STEM equity.