Giselle Zeno

Dr. Giselle Zeno is a technical staff member in the Artificial Intelligence Technology and Systems Group at MIT Lincoln Laboratory. Her research interests are in machine learning and data mining, particularly focusing on graphs, temporal processes, and generative models. Her primary work involves developing and analyzing algorithms for relational domains, including social, information, and communication networks, with applications to real-world tasks.

Zeno has authored multiple papers for conferences and workshops in data mining and machine learning, specifically on collective inference methods and generative models for temporal graph-structured data. She was also an invited speaker at the Networks 2021 DynaMo satellite conference, where she discussed her research on dynamic network modeling using temporal motifs and node roles. Furthermore, she has contributed as a reviewer for IEEE journals and is an active member of the Association for Computing Machinery.

Zeno earned her BS degree in computer science from the University of Puerto Rico at Bayamón in 2010, graduating magna cum laude. She was named the Model Student by the Department of Computer Science in 2009 and received the Honors Program scholarship, throughout her degree, from 2006 to 2010. She completed her MS and PhD degrees in computer science at Purdue University in 2021 and 2023, respectively. Her doctoral studies were supported by the GEM Fellowship, sponsored by Intel Corporation, and the Frederick N. Andrews Fellowship from Purdue University.