Peiwen Qiu

This researcher's publication record spans two distinct areas: an earlier line of work on bat biosonar, specifically how certain bat species move their outer ears (pinnae) to shape sound signals for navigation and sensing, and a more recent focus on distributed machine learning systems, including federated learning (training AI models across many devices without centralizing data), wireless communication-aware learning, and optimization algorithms for decentralized systems. The bat-related work appears to be earlier training or collaborative research, while the federated learning and optimization work represents the current core research direction. Prospective students would likely be joining work centered on making distributed and federated machine learning more efficient and robust, particularly under real-world constraints like unreliable client participation, wireless channel conditions, and noisy data.

Publication output has fluctuated over the past decade, with an early cluster of bat biology papers (2016-2018), a gap in 2019, and a resurgence starting in 2020 shifting toward machine learning topics, peaking in 2022 before tapering to a steady pace of 1-2 papers per year.

Generated by claude-sonnet-5 from public bibliographic data · Jul 20, 2026