Dr. Alexios Balatsoukas-Stimming holds Dipl.-Ing. (2010) and MSc (2012) degrees in Electronics and Computer Engineering from the Technical University of Crete (Greece). He received his PhD in Computer and Communications Sciences from the École polytechnique fédérale de Lausanne (Switzerland) in 2016. He then spent a year as Marie Skłodowska-Curie postdoctoral fellow at the European Laboratory for Particle Physics (CERN), before joining the Telecommunications Circuits Laboratory of EPFL (Switzerland) as postdoctoral researcher. Dr. Balatsoukas-Stimming started as a tenure-track assistant professor at Eindhoven University of Technology in 2019 and also holds an honorary adjunct assistant professor position at Rice University (USA).

Dr. Balatsoukas-Stimming has spent time in the USA on other occasions, as visiting researcher at Cornell University and the University of California at Irvine and as a graduate intern at Intel Labs.

Dr. Alexios Balatsoukas-Stimming is an assistant professor on the Electronic Systems group of the Department of Electrical Engineering at Eindhoven University of Technology.  

The work of Dr. Balatsoukas-Stimming lies on the intersection between communications, hardware design, and machine learning. His specific research interests include VLSI circuits for communications, error-correction coding theory and practice, non-linear signal processing, massive MIMO, as well as applying approximate computing and machine learning to signal processing for communications.

In addition to his research, teaching and student supervision activities, Dr. Balatsoukas-Stimming is a dedicated scientific community builder. He is currently a member or chair of several conference and program committees. He is also a founding editor of an IEEE journal and an active editor and reviewer for several other journals and conferences.

To make future communications systems faster, more reliable, and more energy efficient, I believe that interdisciplinary research is essential, ranging from information-theoretic principles to machine learning and VLSI implementation.