The CRA is excited to announce the launch of an Opportunity Board to enable recent new PhD graduates and members of the community that are looking for postdocs to connect. This is a continuation of the Opportunity Board used to match potential postdocs and mentors during the CIFellows 2021 process. The board allows for the posting of postdoc opportunities by potential mentors and posts by those looking for a postdoc opportunity. We encourage members of the community to use this as a resource. The Opportunity Board lists four options: Find Mentor Find Postdoc Post Mentor Profile Post Postdoc Profile  You are able to search for potential postdocs and available postdoc […]

Roughly every ten years the Theoretical Computer Science (TCS) community comes together for a visioning workshop to discuss recent accomplishments and new challenges in the field of TCS. The workshop acts as an opportunity for reflection within the community and a way of informing interested investors. The newly released Visions in Theoretical Computer Science Workshop Report, written by Shuchi Chawla (University of Wisconsin-Madison), Jelani Nelson (University of California, Berkeley), Chris Umans (California Institute of Technology), and David Woodruff (Carnegie Mellon University) and supported by the Computing Community Consortium, summarizes the key takeaways from the 2020 TCS Visioning Workshop. Organized by the SIGACT Committee for the Advancement of Theoretical Computer Science, […]

The following Great Innovative Idea is from Somali Chaterji, Assistant Professor in the Department of Agricultural and Biological Engineering at Purdue University where she leads the Innovatory for Cells and Neural Machines. The Idea The idea behind my work is that there is strength in numbers — a distributed computing system that needs to run a computationally heavy application on scarce resources can do so by pooling together many weak to moderate devices in a federated setting and with security guarantees. The secret sauce in my work is to do the right level of approximation at the right point in space (which device) and at the right point in time […]

Minka Stoyanova began her CIFellowship in January 2021 after receiving her PhD from City University of Hong Kong in October 2019. Stoyanova is at University of Colorado Boulder working with Reece Auguiste, Associate Professor of Media, Communication and Information at UC Boulder.  Current Project My current research project is focused on how digital information technologies can be used to create embodied and affective experiences in urban environments. Specifically, the project is aimed at using mobile phone augmented reality technology and digital storytelling methods to situate historic and community narratives within urban environments. Historic material held in museums and archives often becomes detached from the communities and the locations it represents. […]

Rika Antonova began her CIFellowship in January 2021 after receiving her PhD from KTH Royal Institute of Technology in Stockholm in December 2020. Antonova is at Stanford University working with Jeannette Bohg, Assistant Professor of Robotics at Stanford.  Current Project Machine learning is transforming robotics: we can now solve high-dimensional problems that have been intractable before, if given large amounts of data and ample training time. However, to go beyond structured factory settings, it is important for robots to adapt to changes in the environment/task without lengthy re-training and data collection. A related problem is closing the simulation-to-reality gap: adapting to the real world after training in simulation. My goal […]

Biresh Kumar Joardar began his CIFellowship in September 2020 after receiving his PhD from Washington State University in Summer of 2020. Joardar is at Duke University working with Krishnendu Chakrabarty, Distinguished Professor and Chair in the Department of Electrical and Computer Engineering.  Current Project The theme of my current project is “Machine Learning for Machine Learning”. The project aims to demonstrate the symbiotic relationship between machine learning (ML) algorithms and computer system design. In this new paradigm, hardware researchers benefit from new data-driven ML algorithms and ML researchers benefit from efficient computing enabled by new hardware-software co-design. More specifically, I work on designing heterogeneous manycore and in-memory computing architectures with […]