Michael Zink
Michael Zink
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Paros Professor of Geophysical Sensing Systems
University of Massachusetts Amherst
College of Engineering
Department of Computer in Electrical and Computer Engineering
Address: 151 Holdsworth Way, Amherst, MA 01003
Email: mzink@umass.edu
Office: Knowles 213 B
Phone: +1-413-545-4465
About
About
I'm the Paros Professor of Geophysical Sensing Systems in the Department of Electrical and Computer Engineering in the College of Engineering at the University of Massachusetts Amherst where I serve as the Co-Director of the Paros Center for Atmospheric Research. My research focuses on Multimedia Systems, Cyber Infrastructure, and Large-scale Sensor Networks. I am Co-Director of the graduated NSF Engineering Research Center for Collaborative Adaptive Sensing of the Atmosphere (CASA) and PI of the Massachusetts Open Cloud (MOC).
I am a Senior Member of IEEE & ACM.
My detailed CV can be found here!
cv_mzink.pdfNews
News
September 2026:
September 2026:
Two papers accepted at IEEE HPEC 2026.
Our paper "P4-Programmable SmartNIC Architecture for In-Network PTP Synchronization Across Heterogeneous Cloud Environments" was accepted at IEEE CloudNet 2026.
Our paper "High Speed Network Processing for Scientific Computing in the Open Cloud Testbed" was accepted at the SC 2026 Workshop on Heterogeneous High-performance Reconfigurable Computing (H2RC'26)
August 2026:
August 2026:
Our paper "Learning-Based Reconstruction Attacks on Coordinate-Obfuscated Point Clouds" has been accepted at ACM XRSecurity (Mobicom workshop).
July 2026:
July 2026:
Two papers accepted at eScience 2026:
Zhe Xu, Talha Mehboob, Michael Zink, and Christopher Davis, “FED-CAST: A Federated Training and Evaluation Workflow for Generative Precipitation Nowcasting Across Climate-Diverse Regions”, IEEE eScience 2026 (accepted for publication).
Komal Thareja, Paul Ruth, Berent Aldikacti, and Michael Zink, “AI-Assisted Computational Reproducibility on the FABRIC Testbed”, IEEE eScience 2026 (accepted for publication).
June 2026:
June 2026:
Our paper "Shesha: Opportunistic In-network Acceleration of Asynchronous Distributed Reinforcement Learning” has been accepted at ACM CoNEXT 2026.
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