CNAS Science Lecture Series: Big Data Science
Tuesday evenings April 2022
The College of Natural & Agricultural Sciences at the University of California, Riverside is proud to present the 2022 Science Lecture Series entitled Big Data Science.
This four-part series will focus on the technologies that allow us to collect and analyze data in ways we never imagined. The big data that those technologies generate have transformed not only how scientists do their research, but also how we view our world. This series will explore the progression of new technologies in areas such as statistics, climate, environment, agriculture, biology, medicine, mathematics, and astronomy — and how it is changing our world.
This will be the fourteenth year of this popular science lecture series, which is free and open to the public. Over the years, the Science Lecture Series has covered important and timely topics such as food security, sustainability, climate change, the search for life beyond earth, gene editing, and of course, COVID-19 and it’s impact on our community.
Current undergraduate, graduate students, and the campus community will also take part in the conversation, as well as audience members from the greater City of Riverside community and beyond. Everyone is encouraged to ask questions during this live and virtual event.
All lectures are virtual, free, and open to the public. Each lecture will be held virtually via Zoom. Register for each presentation to receive the Zoom link.
Please check back as additional information will be posted.
Tuesday, April 5, 2022 | 5 p.m.
The Plurality of Worlds: Searching for Life in a Universe of Data
Professor of Planetary Astrophysics
In the space of only a few decades, the number of known planets orbiting other stars has risen from zero to over 5000. These discoveries have dramatically changed our understanding of planetary systems and further motivated the search for life in the universe. In this talk. Dr. Kane will describe the techniques that have allowed us to bring about this revolution in planetary science, the challenges in analyzing the massive amounts of data, and the future prospects in detecting life on another world.
About Stephen Kane
Stephen Kane is a Professor of Planetary Astrophysics at the University of California, Riverside who specializes in exoplanetary science. He grew up in outback Australia where his view of the night sky and fascination with solar system exploration motivated his eventual career path. He received his BS from Macquarie University in Sydney and his PhD from the University of Tasmania. His work covers a broad range of topics related to planetary astrophysics and he has discovered several hundred planets orbiting other stars. He is a leading expert on the topic of planetary habitability, the habitable zone of planetary systems, and the study of why Venus and Earth evolved so differently. He is a prominent scientific leader for several NASA missions designed to search for life in the universe. He has published hundreds of peer-reviewed scientific papers as well as several books on the topic of exoplanets and habitability. He is also a prolific advocate of interdisciplinarity science through the combination of biology, climate science, geophysics, planetary science, and stellar astrophysics.
UCR Faculty Page
Earth and Planetary Sciences Faculty Page
Tuesday, April 12, 2022 | 5 p.m.
Big Data at the Intersect of Drug Discovery and Genome Biology
Professor of Bioinformatics
Director of High-Performance Computing Center (HPCC)
Director of Genetics, Genomics and Bioinformatics (GGB) Graduate Program
Institute for Integrative Genome Biology
Botany and Plant Sciences
Dr. Girke's research focuses on the development of computational methods for extracting knowledge from complex biological data by combining discovery driven research with algorithm and software development to address fundamental research questions in agricultural and biomedical sciences ranging from stress resistance in plants to healthy aging and longevity in humans. This involves very large data sets generated by a variety of high-throughput precision technologies, such as next generation sequencing (NGS), genome-wide profiling approaches and drug screens. These technologies have transformed biology into one of the most data intensive research disciplines of all time. Dr. Girke's presentation will illustrate how this New Age of Big Data Sciences has revolutionized the way we think about biological systems by allowing us to scale research experiments from deciphering the code and measuring the abundance of single biomolecules in the past to tens of thousand of them in the present. Moreover, this new era allows us to monitor with high precision how these factors vary across thousands of cell types, individuals in populations, or soon a large proportion of all living and many extinct species of our planet. In this overview, he will highlight how the analysis methods and software environments my group has developed, help to analyze these big data sets to gain novel insights into biological processes.
About Thomas Girke
Thomas Girke is a Professor of Bioinformatics at the University California, Riverside. He received his BA, MS and Ph.D. degrees from University Hamburg in Germany, followed by post-doctoral training at Michigan State University. Subsequently, he worked for several years in industry where he directed computational research projects in agricultural high-throughput screening projects for Dow AgroSciences and the Dow Chemical Company. After rejoining academia, Dr. Girke has developed an innovative computational biology and drug discovery research program at UCR that is funded by large grants from a variety of national funding agencies including NSF and NIH. His contributions to teaching and infrastructure development at UCR include directorships of the graduate program in Genetics, Genomics and Bioinformatics (GGB) as well as the High-Performance Computing Center (HPCC). Currently, the HPCC is enabling big data research for over 150 research groups from all schools and colleges at UCR.
Tuesday, April 19, 2022 | 5 p.m.
Computational Modeling and Digital Twin of a Patient
Distinguished Professor, UCR Department of Mathematics
Director, UCR Center for Quantitative Modeling in Biology
Cooperating Faculty, UCR School of Medicine
Cooperating Faculty, UCR Department of Bioengineering
Fueled by breakthrough technology developments, the biological, biomedical, and behavioral sciences are now collecting more data than ever before. There is a critical need for time- and cost-efficient strategies to analyze and interpret these data to advance human health. The recent rise of machine learning as a powerful technique to integrate multimodality, multifidelity data, and reveal correlations between intertwined phenomena presents a special opportunity in this regard.
This technique is incredibly successful in image recognition with immediate applications in diagnostics including electrophysiology, radiology, or pathology, where clinicians have access to massive amounts of annotated data. However, machine learning often performs poorly in prognosis, especially when dealing with sparse data. Multiscale computational modeling is a successful strategy to integrate multiscale, multiphysics data and uncover biological mechanisms that explain the emergence of function. However, multiscale modeling alone often fails to efficiently combine large datasets from different sources and different levels of resolution. In this lecture we will demonstrate that machine learning and multiscale modeling can naturally complement each other to create robust predictive models that can provide new insights into disease mechanisms, help identify new targets and patient specific treatment strategies, and inform decision making for the benefit of human health [1,2].
1. Mark Alber, Adrian Buganza Tepole, William R. Cannon, Suvranu De, Salvador Dura-Bernal, Krishna Garikipati, George Karniadakis, William W. Lytton, Paris Perdikaris, Linda Petzold & Ellen Kuhl, Integrating machine learning and multiscale modeling - perspectives, challenges, and opportunities in the biological biomedical, and behavioral sciences, npj Digital Medicine, 2:115 (2019).
2. Grace C. Y. Peng, Mark Alber, Adrian Buganza Tepole, William R. Cannon, Suvranu De, Salvador Dura-Bernal, Krishna Garikipati, George Karniadakis, William W. Lytton, Paris Perdikaris, Linda Petzold & Ellen Kuhl , Multiscale Modeling Meets Machine Learning: What Can We Learn? Archives of Computational Methods in Engineering 28, 1017–1037 (2021).
About Mark Alber
Professor Mark Alber earned his Ph.D. in mathematics at the University of Pennsylvania under the direction of J. E. Marsden (UC Berkeley and Caltech). He held several positions at the University of Notre Dame including most recently Vincent J. Duncan Family Chair in Applied Mathematics. He is currently Distinguished Professor in the Department of Mathematics and Director of the Center for Quantitative Modeling in Biology, UC Riverside. Dr. Alber was elected a Fellow of the American Association for the Advancement of Science (AAAS) in 2011 and was appointed 2019 Honorary Kloosterman Professor at the Mathematical Institute, Leiden University, The Netherlands. He is currently a deputy editor of PLoS Computational Biology and member of editorial boards of Bulletin of Mathematical Biology and Biophysical Journal. His research interests include mathematical and computational multiscale modeling of blood clot formation, plants development and growth and epithelial tissue growth.
UCR Faculty Page
Tuesday, April 26, 2022 | 5 p.m.
From Sources to Space: How Big Data Can Help Us Manage Greenhouse Gas Emissions
Assistant Professor of Climate Change & Sustainability
Greenhouse gases emitted by humans present the biggest uncertainty in how much global warming the world will face over this century. While the political will to reduce emissions is beginning to be realized through laws to reduce emissions in California and new global agreements such as the 2021 Global Methane Emissions pledge, new science and technology approaches are needed to verify the success of these policies. In this talk, Dr. Hopkins will describe new observations of the most important greenhouse gases, carbon dioxide (CO2) and methane, from platforms ranging from mobile observatories to tower networks to plant samples to satellites. Along with new data systems to interpret these results and partnerships with stakeholders, these observations can help us reach our climate policy goals and ensure equity in the enactment of greenhouse gas mitigation strategies.
About Francesca Hopkins
Dr. Francesca Hopkins is an Assistant Professor of Climate Change and Sustainability in the Department of Environmental Sciences at the University of California, Riverside. Dr. Hopkins is an environmental scientist studying the effects of human activities on the global carbon cycle, with particular interest in greenhouse gas emissions and carbon cycle feedbacks to climate change. Dr. Hopkins and her research group use a range of techniques to measure emissions of greenhouse gases and air pollutants across California, including from dairy farms, vehicles, and oil and gas sources. Dr. Hopkins is also passionate about communicating the science of climate change. She led the Inland Desert chapter of the Fourth California Climate Assessment, released in 2018.
Originally from Sonoma County, California, Dr. Hopkins received Bachelor’s degrees in Environmental Science and Spanish at the University of California, Berkeley. She studied abroad at the Pontifical Catholic University in Santiago, Chile. Dr. Hopkins completed her Ph.D. in Earth System Science at the University of California, Irvine. During graduate school, she also researched at the Max Planck Institute for Biogeochemistry in Jena, Germany. After receiving her Ph.D., Dr. Hopkins was a NASA Postdoctoral Fellow at the Jet Propulsion Laboratory in Pasadena from 2014-2016. In 2016, Dr. Hopkins was recognized as one of UC Irvine’s Top 50 Graduate and Postdoctoral Scholar Alumni.
Dr. Hopkins is also a busy parent of a kindergartner and preschooler.