Extreme Microfluidics: Large-volumes and Complex Fluids
Mehmet Toner PhD
Massachusetts General Hospital, Harvard Medical School, Harvard-MIT Health Sciences & Technology
Dr. Mehmet Toner is the Helen Andrus Benedict Professor of Biomedical Engineering at the Massachusetts General Hospital, Harvard Medical School, and Harvard-MIT Health Sciences and Technology. He serves as the Director of Research at the Shriners Hospitals for Children in Boston, and the Co-Director of the Center for Engineering in Medicine and Surgery. Dr. Toner received BS degree from Istanbul Technical University and MS degree from the Massachusetts Institute of Technology (MIT), both in Mechanical Engineering. Subsequently he completed his PhD degree in Medical Engineering at Harvard-MIT Division of Health Sciences and Technology in 1989. His research involves microfluidics, nano- and micro-technologies, tissue engineering and regenerative medicine, cryobiology. Dr. Toner is also co-founder of multiple biotechnology and medical device start-ups. Dr. Toner is inducted to the U.S. National Academy of Engineering, U.S. National Academy of Inventors, and the U.S. National Academy of Medicine.
Microfluidics gained prominence with the application of microelectromechanical systems (MEMS) to biology in an attempt to benefit from the miniaturization of devices for handling of minute samples of fluids under precisely controlled conditions. Microfluidics exploits the differences between micro- and macro-scale flows, for example, the absence of turbulence, electro-osmotic flow, surface and interfacial effects, capillary forces in order to develop scaled-down biochemical analytical processes. The field also takes advantage of MEMS and silicon micromachining by integrating micro-sensors, micro-valves, and micro-pumps as well as physical, electrical, and optical detection schemes into microfluidics to develop the so-called “micro-total analysis systems (mTAS)” or “lab-on-a-chip” devices. However, the ability to process ‘real world-sized’ volumes efficiently has been a major challenge since the beginning of the field of microfluidics. This begs the question whether it is possible to take advantage of microfluidic precision without the limitation on throughput required for large-volume processing? The challenge is further compounded by the fact that physiological fluids are non-Newtonian, heterogeneous, and contain viscoelastic living cells that continuously responds to the smallest changes in their microenvironment. Our efforts towards moving the field of microfluidics to process large volumes of fluids was counterintuitive and not anticipated by the conventional wisdom at the inception of the field. We metaphorically called this “hooking garden hose to microfluidic chips.” We are motivated by a broad range of applications enabled by precise manipulation of extremely large volumes of complex fluids, especially those containing living cells or bioparticles. The use of high-throughput microfluidics to process large-volumes of complex fluids (e.g., whole blood, bone marrow, bronchoalveolar fluid) has found broad interest in both academia and industry due to its broad range of utility in medical applications.
Generative AI: How the world will evolve around it?
General Manager and VP Engineering, Cloud AI & Industry Solutions, Google Cloud
As general manager and VP of engineering at Google Cloud AI and industry solutions, Burak oversees the teams creating products and solutions that empower every enterprise to transform their business with AI, including products that require little to no AI or machine learning expertise. This unique portfolio includes a unified platform to serve large models via Vertex AI, Unified Cloud Search, DocumentAI, and Conversational AI, along with industry solutions focused on healthcare, retail, media, and financial services
He also oversees development of ML technologies such as generative AI-based large language models and their application for real-world industry problems.
Burak has been in the AI/machine learning field for more than 25 years, published more than 40 papers, and holds more than 50 patents/patent applications. Burak received his MS and PhD degrees in electrical engineering from Stanford University and double BS degrees in electrical engineering and computer science from Boğaziçi University.
Large models have recently advanced, and became readily available. Their ability to adapt to new tasks with no prior training, multimodal nature, and applicability to many use cases make them the biggest technology breakthrough of the last decade. In this talk, I will discuss key properties and short-comings of large models, and demonstrate various use cases. Finally, we will cover relevant issues, such as grounding large models with facts, multimodal models, and tuning them.
From Nano-Drones to Cars - A RISC-V Open Platform for Next-Generation Autonomous Vehicles
Luca Benini, Dr. Prof.
ETH Zürich and Università di Bologna
Luca Benini holds the chair of digital Circuits and systems at ETHZ and is Full Professor at the Universita di Bologna. He received a PhD from Stanford University. Dr. Benini’s research interests are in energy-efficient parallel computing systems, smart sensing micro-systems and machine learning hardware. He is a Fellow of the IEEE, of the ACM and a member of the Academia Europaea. He is the recipient of the 2016 IEEE CAS Mac Van Valkenburg award, the 2020 EDAA achievement Award, the 2020 ACM/IEEE A. Richard Newton Award and the 2023 IEEE CS E.J. McCluskey Award.
The next generation of autonomous vehicles, with form factors ranging from tiny palm-sized drones to full-sized cars pushes signal processing and machine learning aggressively towards the edge, near sensors and actuators, with strong energy-efficiency, safety and security requirements, while at the same time raising the bar in terms of flexibility and performance. In the talk, I will describe our experience in leveraging the Open RISC-V ISA and open hardware approaches to innovate across the board and pave the way for an open embedded computing platform for autonomous vehicles.
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