Syed Ghazi Sarwat           

Short Biography

Syed Ghazi Sarwat is a Research Staff Member in IBM Research in Zurich, Switzerland. He received a Bachelor of Engineering degree from PSG College of Technology in 2015, and a Ph.D. degree from the University of Oxford in 2019, in Metallurgical Engineering and Materials Science, respectively. His current research focus is primarily in the exploration and use of processing in-memory technologies for deep learning accelerators, both in the electronics and optics domain. The research often combines device design, new functional materials, and algorithmic breakthroughs.

He is a recipient of an Innovator Award by the Indian National Academy of Engineering, a Felix Scholarship by Oxford University, a Foundation Award by the Society of Automotive Engineering, a Commonwealth Scholarship (not continued), a top-20 science project in India award by Indian Science Congress, and a World Steel Association award in Asia Category. He is also a recipient of a best literature review award by Taylor and Francis, the best thesis and best outgoing student award, and a Brahm Prakash Medal. He is an innovator of 14 International Patents and his work has been featured widely over the last several years in reputed Journals and Conferences, including in Nature Nanotechnology, Nature Communications, Science Advances, Advanced Materials, Advanced Functional Materials, Nano letters, primarily on the topics of materials science, nanoelectronics and neuromorphic algorithms.  

He practices interdisciplinary and collaborative science: with a worldwide IBM team he works to develop chipsets for real-world artificial intelligence workload demos, and with leading European Universities he architects functional components and algorithms for silicon photonics-based in-memory computing. He is an active reviewer for leading journals and conferences, and currently sits in the organizing committe of the MRS Spring 2023 Phase Change Materials Symposium. Dr Ghazi Syed is an advocate of communication of scientific results, and STEM education and is always open to strike discussions and initiatives to support the cause:

Invited Talks/Lectures

-"Tutorial: A Gentle Introduction to In-Memory Computing", Mauterndorf Winterschool (2023-Austria)*

-“Chalcogenide Computational Memory” at European Phase Change and Ovonics Symposium, EPCOS (2022-Oxford)

-“Concepts of In-memory Computing” at ETH University (2022-Zurich)

-“Neuromorphic Computing and Neural Networks” at Club House (2022-Podcast)

-“Phase Change Computational Memory” at The Materials Research Society of India, MRSI (2022-India)

-“Biomimetic Phase Change Memtransistive Synapse” at European Phase Change and Ovonics Symposium, EPCOS (2021-UK)

-“Brain-Inspired Photonics Computing using Non-Volatile Memories” at The European Conference on Optical Communication, ECOC (2021-France)

-“In-memory Computing using Phase Change Materials” at Photonics North (2021-Canada)

-“Nanoelectronics with Low-dimensional Thin Films” at IBM Research (2019-Zurich)

-“Light-Matter Interaction in Phase Change Materials” at European Phase Change and Ovonics Symposium, EPCOS (2018-Catania)

-“Two Dimensional Materials for Nanoelectronics” at Public Engagement Lectures (2018-Oxford)

-“Phase Change Materials for Optical Applications” at Symposium on Amorphous Chalcogenides (2018-Lake District)

-“Truly Flexible Technologies” at Wearable and Flexible Technology Symposium, WAFT (2018 and 2017-Oxford)

-“Graphene Nanogaps: It's Performance Limits" at The International Union of Materials Research Societies, IUMRS (2018-Bangalore)

-“Nanogap Electrodes for Nanoelectronics" at The Micro and Nano Engineering, MNE (2017-Sweden)

-“Phase Change Memory using graphe Nanogaps" at The Micromechanics and Microsystems Europe Workshop, MME (2017-Portugal)

In the News

-Designing and Writing a Review Paper

-IBM scientists give artificial neural networks a new look for neuromorphic computing

-An “Optomemristor” for Neural-Net Computing

-An Integrated Photonics Engine for Unsupervised Correlation Detection

-Lighting up artificial neural networks

-Photonics offers a solution to latency issues for AI

-What ultimately limits the scaling of graphene nanogap electrodes

-Studying strain effects in 2D materials using Kelvin Probe Microcopy

-Writing a scientific literature review

-Techian To Oxonian