Ewin Tang

Ewin Tang is a Miller Postdoctoral Fellow at the University of California, Berkeley, hosted by Umesh Vazirani. She completed her PhD at the University of Washington under James Lee and earned her undergraduate degree at UT Austin with a thesis advised by Scott Aaronson. Tang specializes in quantum computing and machine learning, focusing on quantum learning theory and dequantized/quantum-inspired algorithms.

Her research addresses fundamental questions: How to evaluate quantum computer applications, how to design algorithms for nature simulation, and quantum system engineering feasibility. She has co-authored papers in top conferences including QIP, STOC, FOCS, and SODA, with awards like the QIP 2024 Best Student Paper and UT Austin's Best Undergraduate Thesis.

Key research contributions include:

• Proving entanglement vanishes in high-temperature spin systems
• Developing quantum-inspired classical algorithms matching quantum performance
• Advancing Hamiltonian learning techniques
• Exploring computational complexity in quantum systems

Scientific achievements:

• QIP 2024 Best Student Paper
• Co-winner of UT Austin Best Undergraduate Thesis
• Published in Nature Physics and Journal of the ACM