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 […]

In August 2019 the Computing Community Consortium (CCC) and the Association for the Advancement of Artificial Intelligence (AAAI), released A 20-Year Community Roadmap for Artificial Intelligence Research in the US. The Roadmap is the output of a series of three workshops that were held in late 2018 and early 2019, with the goal of identifying challenges and opportunities to effectively inform future federal priorities, including future AI R&D Investments. The CCC and AAAI shared the roadmap findings during the Artificial Intelligence Research: A Community Roadmap scientific session at the 2020 American Association for the Advancement of Science (AAAS) Annual Meeting, which took place in Seattle, Washington in February, 2020. The session was moderated […]

The last decade has seen the increased use of machine learning and data science to make decisions—from figuring out which YouTube video to recommend to deciding whom to give a loan, automated decisions are now everywhere. However, as deployment of these decision-making systems has increased so too have concerns about the transparency of the component algorithms and the fairness of their outcomes. This topic was the subject of the New Approaches to Fairness in Automated Decision Making scientific session at the 2020 American Association for the Advancement of Science (AAAS) Annual meeting in Seattle, Washington one month ago. The session was moderated by Computing Community Consortium (CCC) Director Ann Schwartz Drobnis and included: Sampath […]

A new episode of the Computing Community Consortium‘s (CCC) podcast, Catalyzing Computing, is now available. This is part two of Khari Douglas’ interview with Natesh Ganesh, a recent PhD graduate from the University of Massachussetts at Amherst and a current Research Fellow at the University of Colorado, Boulder through the NIST Professional Research Experience Program (PREP). Natesh’s research interest lie in the fields of AI, neuromorphic hardware, thermodynamics (Natesh was an organizer of the CCC’s 2019 Thermodynamic Computing workshop, read the Thermodynamic Computing workshop report here), and the emergence of consciousness. In this episode we talk about some of the key contributors to the space of artificial consciousness and discuss the ethics […]

A new episode of the Computing Community Consortium‘s (CCC) podcast, Catalyzing Computing, is now available. Khari Douglas interviews Natesh Ganesh, a recent PhD graduate from the University of Massachussetts at Amherst and a current Research Fellow at the University of Colorado, Boulder through the NIST Professional Research Experience Program (PREP), whose research interest lie in the fields of AI, neuromorphic hardware, thermodynamics, and the emergence of consciousness. In this episode we discuss some, though certainly not all, of the leading theoretical frameworks for studying artificial consciousness. You can stream the episode in the embedded player below or find it on Apple Podcasts | Spotify | Stitcher | Google Play | Blubrry | iHeartRadio | Youtube.   If you are interested in appearing in an episode […]

The following blog post was contributed by Nadya Bliss (Director, the Global Security Initiative at Arizona State University & CCC Council Member) and is reposted from the Delta 8.7 website. You can view the original post here.  Advances in computational science and artificial intelligence offer opportunities to advance Target 8.7 of the Sustainable Development Goals, but the anti-trafficking community must first establish some core building blocks that can serve as the foundation upon which new technologies can be developed and shared. Simply throwing flashy new tech at the problem is neither strategic nor effective. Key components of this foundation include a shared strategy, a common infrastructure that allows for better and […]