Robert Gray

About

Robert Gray, PhD, is a Research Professor in the Department of Electrical and Computer Engineering at Boston University College of Engineering. He earned his PhD from the University of Southern California in 1969. His areas of interest include quantization theory and algorithms, information theory, and statistical signal processing. Dr. Gray has made significant contributions to these fields, as evidenced by his numerous honors and awards, including fellowships with IEEE and IMS, the IEEE Shannon Award, Signal Processing Medal, and the IEEE Centennial Medal. He has served as an editor-in-chief for prominent journals such as Foundations and Trends in Signal Processing and IEEE Transactions on Information Theory, and has been recognized for excellence through awards like the Stanford University President’s Award for Excellence Through Diversity and the Presidential Award for Excellence in Science, Mathematics and Engineering Mentoring. Dr. Gray's work has had a substantial impact on the development of theories and algorithms in signal processing and information theory, establishing him as a distinguished figure in his field.

Research topics

• Computer Science
• Artificial Intelligence
• Computational biology
• Optics
• Physics
• Genetics
• Optoelectronics
• Bioinformatics
• Biology

Selected publications

• Ultrafast neuromorphic computing with nanophotonic optical parametric oscillators

  ArXiv.org · 2025-01-28 · 2 citations

  preprintOpen access

  Over the past decade, artificial intelligence (AI) has led to disruptive advancements in fundamental sciences and everyday technologies. Among various machine learning algorithms, deep neural networks have become instrumental in revealing complex patterns in large datasets with key applications in computer vision, natural language processing, and predictive analytics. On-chip photonic neural networks offer a promising platform that leverage high bandwidths and low propagation losses associated w…

  PublisherOA PDFDOI

• Ultra-Short Pulse Biphoton Source in Lithium Niobate Nanophotonics at 2$\textμ$m

  arXiv (Cornell University) · 2024-02-07 · 2 citations

  preprintOpen access

  Photonics offers unique capabilities for quantum information processing (QIP) such as room-temperature operation, the scalability of nanophotonics, and access to ultrabroad bandwidths and consequently ultrafast operation. Ultrashort-pulse sources of quantum states in nanophotonics are an important building block for achieving scalable ultrafast QIP, however, their demonstrations so far have been sparse. Here, we demonstrate a femtosecond biphoton source in dispersion-engineered periodically pole…

  PublisherOA PDFDOI

• Mode-locked laser in nanophotonic lithium niobate

  arXiv (Cornell University) · 2023-06-08 · 2 citations

  preprintOpen access

  Mode-locked lasers (MLLs) have enabled ultrafast sciences and technologies by generating ultrashort pulses with peak powers substantially exceeding their average powers. Recently, tremendous efforts have been focused on realizing integrated MLLs not only to address the challenges associated with their size and power demand, but also to enable transforming the ultrafast technologies into nanophotonic chips, and ultimately to unlock their potential for a plethora of applications. However, till now…

  PublisherOA PDFDOI

• Two-optical-cycle pulses from nanophotonic two-color soliton compression

  ArXiv.org · 2025-01-26 · 1 citations

  preprintOpen access1st authorCorresponding

  Few- and single-cycle optical pulses and their associated ultra-broadband spectra have been crucial in the progress of ultrafast science and technology. Moreover, multi-color waveforms composed of independently manipulable ultrashort pulses in distinct spectral bands offer unique advantages in pulse synthesis and attosecond science. However, the generation and control of ultrashort pulses has required bulky and expensive optical systems at the tabletop scale and has so far been beyond the reach…

  PublisherOA PDFDOI

• Deep Variational Lesion-Deficit Mapping

  arXiv (Cornell University) · 2023-05-27 · 1 citations

  preprintOpen access

  Causal mapping of the functional organisation of the human brain requires evidence of \textit{necessity} available at adequate scale only from pathological lesions of natural origin. This demands inferential models with sufficient flexibility to capture both the observable distribution of pathological damage and the unobserved distribution of the neural substrate. Current model frameworks -- both mass-univariate and multivariate -- either ignore distributed lesion-deficit relations or do not mod…

  PublisherOA PDFDOI

Recent grants

• NIH Grant R01CA049697

  NIH · $695k · 1994

• Quantization for Signal Compression, Classification, and Mixture Modeling

  NSF · $638k · 2003–2007

• Source Coding and Simulation

  NSF · $244k · 2008–2011

Frequent coauthors

• Richard A. Olshen

  27 shared

• Pamela C. Cosman

  University of California, San Diego

  26 shared

• E.A. Riskin

  University of Washington

  23 shared

• Chee Sun Won

  Dongguk University

  19 shared

• Philip A. Chou

  Seattle University

  18 shared

• A. Gersho

  University of California, Santa Barbara

  17 shared

• Parashkev Nachev

  University College London

  17 shared

• L. Davisson

  University of Maryland, College Park

  16 shared

Awards & honors

• IEEE Fellow
• IEEE Centennial Medal
• IEEE Third Millennium Medal
• Institute for Mathematical Statistics (IMS) Fellow 2013
• Stanford University President’s Award for Excellence Through…

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