IEEE Photonics Society Boston Chapter

Ms. Mihika Prabhu Massachusetts Institute of TechnologyConventional computing architectures currently have no known efficient algorithms for combinatorial optimization tasks such as the Ising problem. We present an integrated nanophotonic recurrent Ising sampler (INPRIS), using a hybrid scheme combining electronics and silicon-on-insulator photonics, that is capable of converging to the ground state of various four-spin graphs with high probability. Our architecture is compatible with optoelectronic components operating at GHz clock rates, thereby suggesting the potential for future systems that could achieve orders-of-magnitude speedups in exploring the solution space of combinatorially hard problems.Mihika Prabhu received her BS in Physics and Electrical Engineering from MIT in 2015. She was a recipient of the 2016 NSF Graduate Research Fellowship, completed an MS in Electrical Engineering and Computer Science at MIT in 2018, and is currently a PhD candidate in the Department of Electrical Engineering and Computer Science at MIT.Registration is closed.

Prof. Dirk Englund Massachusetts Institute of TechnologyRecent advances in materials, control, and nanofabrication now open the prospect for scalable photonic and quantum technologies based on solid-state quantum systems. In particular, photonic integrated circuits (PICs) now allow routing photons with high precision and low loss, and solid-state artificial atoms provide high-quality spin-photon interfaces. The first part of this talk will review progress on PICs for processing classical and quantum information in deep learning neural networks architectures.Dirk Englund received his BS in Physics from Caltech in 2002. After a Fulbright fellowship at T.U. Eindhoven, he completed an MS in Electrical Engineering and a PhD in Applied Physics at Stanford University in 2008. After a postdoctoral fellowship at Harvard University, he joined Columbia University as Assistant Professor of E.E. and of Applied Physics. He joined the MIT EECS faculty in 2013. Recent recognitions include the 2011 PECASE, the 2011 Sloan Fellowship in Physics, the 2012 DARPA Young Faculty Award, the 2017 ACS Photonics Young Investigator Award, and the OSA's 2017 Adolph Lomb Medal, and a Bose Research Fellowship in 2018.Registration is closed.

Prof. Rafael Gomez-Bombarelli Massachusetts Institute of TechnologyThe chemical space of organic optoelectronic materials is extremely vast. This allows exquisite fine-tuning of molecular designs to achieve desired properties, but hinders the systematic exploration of structure and property space. Although physics-based simulations can screen candidates much faster than chemical synthesis and device fabrication, autonomous chemical design is still a challenge. The discrete, graph-like nature of molecules presents a difficult optimization challenge; simulations may not capture all the experimental design and performance parameters and often ignore the vast amounts of pre-existing data. Recent machine learning advances have allowed progress in many of these issues. Here, I will describe an ML-accelerated design cycle of optically active compounds that combines (i) data extraction from the literature: (ii) unsupervised and semisupervised deep learning to generate discrete molecules from continuous vectors so that numerical optimization methods can be applied to chemical design; (iii) ML-based calibration of theoretical results with respect to experiment; (iv) neural simulators that replace expensive atomistic simulations. The domains of application range from organic light emitting diodes, to photodiodes or optical switches.Rafael Gomez-Bombarelli is the Toyota Assistant Professor in the Department of Materials Science and Engineering (DMSE). Rafa joined the MIT faculty in January 2018. He received a B.S., M.S., and Ph.D. in Chemistry from Universidad de Salamanca in Spain, followed by postdoctoral work at Heriot-Watt University and Harvard University after which he was a senior researcher at Kyulux NA applying Harvard-licensed technology to create real-life commercial organic light-emitting diode (OLED) products. At MIT, his research focus on the interplay between atomistic simulations and machine learning for materials design.Registration is closed.