Abbas Rahimi received the B.S. degree in computer engineering from the University of Tehran, Tehran, Iran, in 2010, and the M.S. and Ph.D. degrees in computer science and engineering from the University of California San Diego, La Jolla, CA, USA, in 2015, followed by postdoctoral researches at the University of California Berkeley, Berkeley, CA, USA, and at the ETH Zürich, Zürich, Switzerland. He is currently a Research Staff Member at the IBM Research-Zürich laboratory in Rüschlikon, Switzerland.
His research interests include brain-inspired hyperdimensional computing, neuro-symbolic AI, distributed embedded intelligent systems, and in general approximation opportunities in computation, communication, sensing, and storage with an emphasis on improving energy efficiency and robustness. Dr. Rahimi has received the ETH Zürich Postdoctoral Fellowship, and the 2015 Outstanding Dissertation Award in the area of "New Directions in Embedded System Design and Embedded Software'' from the European Design and Automation Association. He was a co-recipient of the Best Paper Nominations at DAC (2013) and DATE (2019), and the Best Paper Awards at BICT (2017) and BioCAS (2018).
- G. Karunaratne, M. Le Gallo, G. Cherubini, L. Benini, A. Rahimi*, A. Sebastian*, "In-memory hyperdimensional computing", Nature Electronics, 2020.
- A. Burrello, K. Schindler, L. Benini, A. Rahimi, "Hyperdimensional computing with local binary patterns: one-shot learning of seizure onset and identification of ictogenic brain regions using short-time iEEG recordings,” In IEEE Transactions on Biomedical Engineering (TBME), 2020.
- M. Schmuck, L. Benini,A. Rahimi, "Hardware optimizations of dense binary hyperdimensional computing: hardware optimizations of dense binary hyperdimensional computing: rematerialization of hypervectors, binarized bundling, and combinational associative memory", In ACM Journal on Emerging Technologies in Computing (JETC), 2019.
- A. Rahimi, P. Kanerva, L. Benini, J. M. Rabaey, "Efficient biosignal processing using hyperdimensionalcomputing: network templates for combined learning and classification of ExG signals", In Proceedingsof the IEEE, 2018.
- A. Rahimi, S. Datta, D. Kleyko, E. P. Frady, B. Olshausen, P. Kanerva, J. M. Rabaey, "High-dimensional computing as a nanoscalable paradigm", In IEEE Transactions on Circuits and Systems (TCAS-I), 2017.