EMERGE

September 16, 2026

EMERGE Agenda


9:00 – 10:15 EMERGE Opening and Keynote Session
9:00 – 9:05 Opening Remarks
9:05 – 10:15 Keynote: Olga Saukh
10:15 – 10:45 Coffee Break
10:45 – 12:15 EMERGE Technical Session
10:45 – 11:03 Deploying TinyML on Commercial Smart Glasses
P. Krumpl, F. Corti, D. Wang, O. Saukh
11:03 – 11:21 Compact Topological Descriptors for Real-Time Activity Recognition on Wearable Microcontrollers
S. Dong, J. Shang, J. Pope, T. M. S. Filho, I. Craddock, G. Oikonomou
11:21 – 11:39 SyncWave: Decentralised Firefly Synchronisation for Embedded Cognitive Agentic Loops
M. Breza, L. Mehl, J. McCann
11:39 – 11:57 EdgeLLM: Towards LLM-Guided Adaptive Distributed Training at the Edge
R. Kuchida, D. Salazar, Z. Yin, H. Flores
11:57 – 12:15 How Do LLMs Reason About Distributed Systems Logs?
M. Georgiev, M. H. M. Hydher, C. A. Boano, O. Saukh
  Closing Remarks

Keynote


Olga Saukh, associate professor

Embedded Learning and Sensing Systems Group
Institute of Technical Informatics
Graz University of Technology

Bio: Olga Saukh is an associate professor and leads the Embedded Learning and Sensing Systems group at TU Graz, affiliated with the Institute of Technical Informatics (ITI). Since 2020, she holds a habilitation in Embedded Systems from TU Graz. She did her postdoctoral training at ETH Zurich in 2010-2016 working in the group headed by Prof. Lothar Thiele in the Computer Engineering and Networks Laboratory. Following her Bachelors in Applied Mathematics from the Taras Shevchenko University of Kyiv, Ukraine in 2002 and her Masters in Applied Computer Science from the University of Freiburg, Germany in 2004, she received her Ph.D. in Computer Science from the University of Bonn, Germany in 2009. She received the 2010 CONET Ph.D. Academic Award for her thesis "Efficient Algorithms for Structuring Wireless Sensor Networks". Her research focuses on efficient machine learning and the design of practical AI-based systems at the intersection of deep learning and embedded systems. She is interested in both the theoretical foundations of deep learning optimization for resource-constrained devices and their practical applications in real-world systems. She serves on program committees of leading international conferences in both the Machine Learning and Embedded Systems communities.