PEARC23 has a goal of amplifying insights important to the computational science community. This year’s theme is Computing for the Common Good, which provides a timely chance to showcase how research computing and data science can address issues with broad societal impact in areas such as health, energy, climate, equity, and education.
Upcoming opportunities and deadlines include:
Request for Information on Developing a Roadmap for the Directorate for Technology, Innovation, and Partnerships at the National Science Foundation
The National Science Foundation (NSF) requests input from the full range of institutions and organizations across all sectors—industry, academia, non-profits, government, venture capital, and others—to inform the development of a roadmap for its newly-established Technology, Innovation, and Partnerships (TIP) Directorate, in accordance with the CHIPS and Science Act of 2022. This legislation tasks the TIP Directorate to develop a roadmap to guide investment decisions in use-inspired and translational research over a 3-year time frame, working towards the goal of advancing U.S. competitiveness in the identified key technology focus areas and addressing the identified societal, national, and geostrategic challenges. Investments would be in use-inspired research, translation of research results to impact, and education, training, and development of talent in the key technology areas and societal, national, and geostrategic challenges. Interested persons or organizations are invited to submit comments on or before Thursday, July 27.
2023 CIC Student Paper Challenge
The COVID Information Commons (CIC) invites students from higher education institutions around the world to leverage the resources available from the CIC to synthesize a paper for the 2023 CIC Student Paper Challenge. Papers should consider research, trends, or topics that could contribute to COVID recovery, health and scientific insights, future discoveries, and innovations. Student submissions are due by Monday, July 31.
NSDC 2023 Data Science Symposium
The NEBDHub and National Student Data Corps (NSDC) invite undergraduate, graduate, and recently-graduated students to join the 2023 Data Science Symposium (DSS)! Participants will discover the best practices for academic research, connect with other data science students, and expand their data science knowledge and community. Learn more on the DSS website. Student submissions are due by Monday, July 31.
WiCyS Virtual Career Fair
The WiCyS Virtual Career Fair will be held on Wednesday, August 16, at 9am PT. Interact with WiCyS strategic partners and learn about future career opportunities. Members can chat at virtual booths with different businesses that devote time and attention to hiring women in cybersecurity. Whether you're seeking a part-time or full-time job, aiming for entry-level or senior positions, or interested in internships, participants should update their profiles and upload their resumes to the Job Board++. Members can update their profile and resume here. Not a WiCyS Member? Join now and gain access to the Virtual Career Fair and many other WiCyS member benefits.
Strategies for Machine Learning Reproducibility Webinar
This AI reproducibility webinar series presented by FARR will kickoff with a focus on ML reproducibility. Dr. Odd Erik Gundersen and Kevin Coakley will highlight the sources that can lead to unintentional irreproducibility. In this webinar, you will learn valuable insights and practical ideas to help achieve reproducibility in machine learning research. Additionally, you will gain a solid grasp of the common pitfalls that can undermine the reproducibility of your research. Join us on August 22nd at 8AM PT.
Odd Erik Gundersen is an adjunct associate professor at the Norwegian University of Science and Technology (NTNU) in Trondheim, Norway, where he teaches courses and supervises master students in AI. He received his PhD from the Norwegian University of Science and Technology. Gundersen has applied AI in the industry, mostly for startups, since 2006. He has conducted several analyses of reproducibility in the artificial intelligence and machine learning literature, and has developed guidelines for reproducibility in data science. Currently, he investigates how AI can be applied in the renewable energy sector and for driver training.
Kevin Coakley is a Senior Systems and Cloud Integration Engineer at the San Diego Supercomputer Center, UC San Diego where he supports the cloud infrastructure for multiple projects. Kevin’s research interest is in Computer Science where he focuses on reproducibility in Machine Learning.