Currently, there are 2.5 quintillion bytes of electronic data created every day and this pace is not going to decrease in the future. Scientific datasets including scientific observations, experiments, and large-scale simulations in many domains, such as earth and space science, astronomy, genomics, environment, and physics, follow the same trend. The size of these datasets typically ranges from hundreds of gigabytes to tens of petabytes. For example, the Large Hadron Collider (LHC) collects 15 petabytes of data annually. Applied science institutions and companies generate extensive experimental and observational scientific data that require computational, networking and storage resources for processing. This Guided Affinity Group will address topics related to artificial intelligence and machine learning applications in science and engineering. We will talk about the process of developing models and implementations that give high performance computers the capabilities of sifting through huge amounts of data, learning from it, and in the end guiding future scientific discoveries. Career opportunities in academia, industry and research labs will be highlighted.
What are the relevant conference themes?
- AI and machine learning for science and engineering
- Data assimilation, challenges in data science, math of AI and ML
- Model and dimensionality reduction
Alina Lazar Alina Lazar is a professor in the Department of Computer Science and Information Systems at Youngstown State University and an affiliate faculty in the Scientific Data Management Research Group at Lawrence Berkeley National Lab. Her research interests are machine learning and data science. Lately, she has been working on applying machine learning algorithms to large scientific datasets, networking and software engineering. She is also interested in adapting learning algorithms to scale well in order to deal with large datasets, missing values and noise. Dr. Lazar has been teaching database and machine learning courses at both undergraduate and graduate levels. She enjoys working with talented undergraduate students on multidisciplinary research projects.
Bethany A Lusch is an Assistant Computer Scientist working on data science at the Argonne Leadership Computing Facility (ALCF) at Argonne National Laboratory. She earned her PhD in applied mathematics from the University of Washington. Her research interests include machine learning and optimization methods for large scientific problems, especially representation learning, deep learning, dimensionality reduction, and data decompositions. Her recent application areas have primarily been dynamical systems and fluids.
Xi (Bill) Chen is a recent Bioinformatics Ph.D. graduate from the University of Kentucky. He is working as a computational biologist and machine learning engineer at Juvena Therapeutics. His current research interests focus on using deep learning algorithms to solve biopharmaceutical problems such as biomarker screening and drug candidates suggestion. Previously, he was working on developing machine learning applications for robotic surgery at one of Google’s venture companies. When time permits, he organizes deep learning workshops to the audience from both academia and industry at national-level conferences. He would like to provide insights for students on multidisciplinary research projects either in an academic or industrial setting.
Students may choose to study AI and machine learning for many reasons. Maybe they see it all the time in the media, news articles and advertisements where AI is usually portrayed as the science field just out of science fiction movies or books. The field has already impacted our lives in so many ways and things will not stop here. To build the confidence and persistence required to get through studying AI and machine learning it is helpful to have a community to rely on. Our Guided Affinity Group will serve as a community for students exploring or interested in pursuing careers in AI.