AI for Science/Engineering, Machine Learning, System Engineering, Modeling and Simulation, Heterogeneous Integration, Quantum Photonics, Semiconductor Devices, Nanotechnology

Dr. Linsen Li

I am a system engineer skilled in integrating cutting-edge technologies in electrical engineering (EE), computer science (CS), and artificial intelligence (AI) to achieve co-designed engineering systems pushing the boundaries of physical-limited performance. My notable achievements include work from my Ph.D. thesis at MIT in EECS, which was published in the prestigious Nature journal, focusing on large-scale quantum system engineering.

I completed my B.S. at Tsinghua University in electrical engineering in 2019, followed by an M.S. in 2021 and a Ph.D. in 2024 at MIT EECS. I have authored over 30 scientific papers, appearing in top-tier publications such as Nature, Physical Review Letters, Optica, ACS Nano, and Nano Letters. I have a solid physics and math background with a gold medal in the Asian Physics Olympiad and a first-place award in the American Mathematical Modeling Contest. My expertise allows me to provide a broad overview of system architectures as well as a deep dive into the details at the atomic physics level. I can efficiently develop the system from initial theoretical modeling and simulations to demonstrate experimental results. In June 2024, I will begin my career as a Display System Engineer at Apple Inc.

EE-CS-AI Co-design Vision

Our world is based on the physical system, where EE plays an essential role in computation, sensing, control, and communication. However, the physical system has limited development speed due to physical law limitations for material/manufacturing.

Beyond the physical system, the digital system is essential by providing large programmable flexibility to reveal the power of the same physical system by reconfiguration. Various software can run on the same general hardware platform without marginal cost for duplicating.

The digitized information generated during digital services forms the data that becomes the foundation for AI. Learning from the mass volume of digitized data, AI reduces the barriers and starts to become the infrastructure and toolset for different fields. The fast development speed also benefits from the open-source environment for the AI community.

In this AI Era, the future engineering system solution having the competitive business need will be based on EE-CS-AI co-design, which integrates the strength of EE as a physical world sensor and executor, CS in digitization and programmability, and AI for trained complicated interference. The future engineering product will push toward physical-limited capability with an easy-to-use interface to provide the optimum cost performance.

My milesotnes that support my vision

EECS: Nature Journal with novel hardware and system architecture for quantum application, 2024

AI: Machine learning for science paper with Prof. Kaiming He, 2024

EECS: MIT EECS Ph.D., 2024

EE: Tsinghua University Undergraduate Presidential Scholarship (清华大学本科生特等奖学金)​, the highest honor awarded to 10 undergrads, 2018

Math: Meritorious Winner of the American Mathematical Contest in Modeling (top 10% internationally), 2017

Physics: Gold Medal in the 16th Asian Physics Olympiad (only 8 Students can Represent China Annually), 2015

Physics: Gold Medal in the 31st China Physics Olympiad (Ranked 10th Nationwide), Best Score in the Experiment, 2014



MIT EECS Ph.D. Thesis

Highlighted Milestone

Background: General quantum information processing holds the promise for quickly solving extremely complex problems that might take the world’s most powerful supercomputer decades to crack. However, achieving that performance involves building a quantum system with millions of physical qubits. Delivering a system with such many qubits and controls in a hardware architecture is an enormous challenge. I introduced an EE-CS-AI codesign architecture solution for this research topic in my Ph.D. thesis at MIT EECS.

EE: We introduced a "Quantum System-on-Chip" (QSoC) hardware architecture that integrates thousands of individually addressable spin qubits in two-dimensional quantum microchiplet arrays into an integrated circuit, supporting full connectivity for quantum memory arrays by optical routing. We designed and built the EE hardware platform and setup combining the foundry tapeout, cleanroom fabrication, and in-house manufacturing.

CS: We proposed a protocol of “entanglement multiplexing” for large-scale quantum computing resource state generation fully utilizing the capability of the QSoC. We also built a digital twin model that connects the physical and the digital worlds for root cause identification and implementation optimization. The achievement of EE and CS parts of this work is published in the Nature journal.

AI: We proposed a "Transformer-on-QuPairs" architecture that uses machine learning technology to outperform the rule-based approach for the “entanglement multiplexing” quantum resource scheduling operation for the inhomogeneous quantum resource in paper.