Dirk Robert Englund is an Associate Professor of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology. He is known for his research in quantum photonics and optical computing.
From 2010 to 2013, Englund was an Assistant Professor of Electrical Engineering and of Applied Physics at Columbia University.[2] In 2013, he moved to Massachusetts Institute of Technology, where he is currently an Associate Professor of Electrical Engineering and Computer Science.[5]
Englund’s research focuses on photonic devices and systems for quantum information technologies and machine learning acceleration.[6][7] He has contributed to a wide range of topics in photonics including quantum dot light emission in photonic crystals,[8] solid-state quantum memories in nitrogen-vacancy centers in diamond,[9][10] graphene integration for photodetectors,[11][12] optical accelerators for machine learning,[13] and programmable photonic circuits for cryogenic environments.[14] In 2022, he and his team demonstrated power-efficient neural network inference on network edge devices using a fiber optic link and telecommunication components.[15][16][17]
His work has led to several spin-off companies: DUST Identity is developing diamond nitrogen-vacancy centers for authentication;[18] Lightmatter is developing photonic computing platforms;[19]QuEra Computing is building quantum computers using neutral atoms;[20] and Quantum Network Technologies is developing quantum repeaters for networks.[21]
Awards
Englund has received numerous awards in recognition of his research, including a Humboldt Professorship,[22][23] the Optica Adolph Lomb Medal,[24] and an IBM Faculty Award.[25] He is an Optica Fellow.[26]
^Englund, D., Fattal, D., Waks, E., et al. Controlling the spontaneous emission rate of single quantum dots in a two-dimensional photonic crystal. Phys. Rev. Lett. 95, 013904 (2005) https://doi.org/10.1103/PhysRevLett.95.013904
^Wan, N.H., Lu, TJ., Chen, K.C. et al. Large-scale integration of artificial atoms in hybrid photonic circuits. Nature 583, 226–231 (2020). https://doi.org/10.1038/s41586-020-2441-3
^Shen, Y., Harris, N., Skirlo, S. et al. Deep learning with coherent nanophotonic circuits. Nature Photon 11, 441–446 (2017), https://doi.org/10.1038/nphoton.2017.93
^Dong, M., Clark, G., Leenheer, A. J. et al. High-speed programmable photonic circuits in a cryogenically compatible, visible–near-infrared 200 mm CMOS architecture. Nature Photonics 16, 59-65 (2022),https://doi.org/10.1038/s41566-021-00903-x
^Sludds, A., Bandyopadhyay, S., Cai, Z., et al. Delocalized photonic deep learning on the internet’s edge. Science 378, 270-276 (2022). https://doi.org/10.1126/science.abq8271