Two new Humboldt Professorships in the field of Artificial Intelligence
TILOS Foundations team members Stefanie Jegelka and Suvrit Sra were honored with Germany’s most highly endowed research award.
2023 IEEE Communications Society & Information Theory Society Joint Paper 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.
Congratulations to TILOS graduate students Zhili Xiong and Rachel Selina Rajarathnam for winning the Best Paper Award at FCCM 2024
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.
Congratulations to TILOS graduate student Zhishang Luo for winning a 2024 Qualcomm Innovation Fellowship
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.
Fan Chung Graham elected to the National Academy of Sciences
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.
How Do Machines ‘Grok’ Data?
Quanta Magazine || By apparently overtraining them, researchers have seen neural networks discover novel solutions to problems.
Amin Karbasi granted 2024 Roberts Innovation Fund Award
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.
AI Researchers Present “Average Gradient Outer Product (AGOP)” to Explain how Neural Networks Learn
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.
How Do Neural Networks Learn? A Mathematical Formula Explains How They Detect Relevant Patterns
UC San Diego Today || The insights, published in the journal Science, can also be used to make other types of machine learning architectures more effective.
Mechanism for feature learning in neural networks and backpropagation-free machine learning models
Science || Work by TILOS Foundations team member Mikahil Belkin and TILOS graduate students presents a unifying mathematical mechanism called Average Gradient Outer Product (AGOP) that characterizes feature learning in neural networks.