The Robot Report || The quadrennial set of recommendations is produced and sponsored by institutions led by the University of California, San Diego. The authors sent the latest edition to presidential campaigns, the AI Caucus in Congress, and the investment community, noted Henrik I. Christensen, main editor of the roadmap.
Robohub || TILOS Robotics team members Henrik Christensen, Hao Su, and Xiaolong Wang, and graduate student Nicklas Hansen are members of the Open X-Embodiment Collaboration.
TILOS Foundations team members Stefanie Jegelka and Suvrit Sra were honored with Germany’s most highly endowed research award.
Congratulations to TILOS Networks team member Shirin S. S. Bidokhti and Foundations team member Hamed Hassani, both of University of Pennsylvania, whose paper received the 2023 IEEE Communications Society & Information Theory Society Joint Paper Award.
The IEEE International Symposium on Field-Programmable Custom Computing Machines (FCCM), is a premier conference in field-programmable gate array (FPGA) research, including computer-aided design (CAD) and architecture. Zhili Xiong and Rachel Selina Rajarathnam work with TILOS Chips team co-lead David Pan at UT Austin.
Zhishang Luo and fellow graduate student Jesse He, both of the Halıcıoğlu Data Science Institute at UC San Diego, have been awarded a 2024 Qualcomm Innovation Fellowship for their project Explainable Graph Learning for Property Prediction on Netlist Representations.
Professor Chung Graham joins a body of 2,617 active scientists who were elected by their peers to membership in the National Academy of Sciences for outstanding contributions to research.
Quanta Magazine || By apparently overtraining them, researchers have seen neural networks discover novel solutions to problems.
Yale News || TILOS Foundations team member Amin Karbasi and postdoctoral scholar Insu Han received a 2024 Roberts Innovation Fund Award for their research on making AI more powerful and cost effective.
AIthority || Considered the “black box” of machine learning, AI neural networks can expand the range of innovations and applications in different areas. AGOP is a unifying mathematical mechanism that establishes the core fundamentals explaining how neural networks learn from general machine learning models.
