Research
Recent Projects
Note: I have retired from active academics, graduated my last graduate student in 2024, and am not taking on any new students.
The good news is that superb young faculty have joined the EECS Department at Berkeley, and are further carrying the torch.
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Human Intranet
With the world around us rapidly becoming smarter, an extremely relevant question is how ‘we humans’ are going to cope with the onslaught of information coming at us. One plausible answer is to use the same technologies to evolve ourselves, and to equip us with the necessary tools to monitor ourselves and to interact with the smart world. Various wearable devices have been or are being developed to do just that. However, their potential to create a whole new set of human experiences is still largely unexplored. To be effective, functionality cannot be centralized and needs to be distributed to capture the right information at the right place. This project aims to realize a human intranet, a platform that allows multiple distributed input/output and information processing functions to coalesce and form a single application. In addition, it envisions the creation of capabilities to understand, interpret, reason and act on the obtained data under diverse and changing conditions, and to do so in concert with the human body and its computer, the brain.
Reading:
- Human-Centric Computing (2019)
- The Human Intranet - Where Swarms and Humans Meet (2016) -
High-Dimensional Computing
While the field of digital signal processing (as conceived in the 1970s) originally focused on single-dimensional streams (e.g. audio), advances in image and video processing rapidly led to processors that operated on two- and three-dimensional data, leading to vector and matrix processors (late 1980’s). With sensor fusion and digital neural networks gaining popularity, the need to operate on even higher-dimensional structures such as tensors has become necessary. The trend to compute in ever-higher dimensional spaces has found a sound mathematical base in the Computer Theory community. These high-dimensional (HD) data representations and computations unfortunately do not map well into the memory and processor architectures of today’s processors. In this project, we explore the opportunities of high-dimensional computing from both a theoretical and practical perspective, and apply it to real applications in the domains of sensor fusion, classification, reasoning and control. In addition, we explore the realization of an ultra-low power programmable HD processor and its memory architecture, called an HPU, optimized for operating on HD representations (with D > 1000).
Reading:
- A wearable biosensing system with in-sensor adaptive machine learning for hand gesture recognition (2021)
- Adventures in high-dimensions (2019)