From forward-looking theoretical studies to cutting-edge experiments to world- and industry-first technology field trials, we deliver globally recognized innovation in optical communications and computing that looks into the future and translates it into present reality.
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From the Internet backbone to the data center floor, optics and photonics form the foundation of modern communications and computing infrastructure. As global data demand accelerates, optical communications and computing technologies must evolve together to keep pace. Our department advances the science of high-capacity optical transmission alongside emerging photonic computing architectures, delivering research that spans theoretical modeling, laboratory experimentation, and real-world field trials.
The advent of AI-centric data networking combined with growing demand for globally connected, always-on networks is pushing optical communication systems toward new limits. Our researchers develop coherent optical transmission techniques, agile digital signal processing (DSP) approaches, and software-defined optical networking architectures, including optical line system telemetry, network digital twins, and automation through agentic AI. These innovations expand transmission capacity and extend network reach in traditional fiber links and satellite optical communications, while giving operators the flexibility to support dynamic networking environments such as AI-compute infrastructures and augmented reality (AR)/virtual reality (VR) applications.
Beyond transmission, we explore how photonic and optical computing principles can address the growing energy and latency demands of AI workloads. Our work in high-speed optical signal processing and RF spectral sensing implemented on photonic integrated chips, together with forward-looking research in photonic AI, extends the reach of optical technology from the network core into computing architectures themselves.
Optical communications and computing will play a pivotal role in shaping the future of connectivity, data processing, and AI infrastructure across industries.
Are you interested in joining us? We are looking for the next generation of thought leaders in optical communications and computing to join our research team. If you are pursuing a career in this field, visit our careers page to learn more and apply for our available positions.
Exciting internship opportunities for Summer 2027 will be available on our internship page this fall. We are looking for students pursuing advanced degrees in Computer Science and Electrical Engineering. Internships are typically 3 months long during the summer. The benefits of working for us include the opportunity to quickly join a project team applying cutting-edge technology to industry-leading concepts.
To apply for a Summer Internship, visit our Internship page.
Want to learn more about our optical communications and computing department? Meet our team of experts, check out our blog posts, or read our latest publications.
The high-speed, high-precision, energy-efficient nature of optics and photonics makes it ideally suited for next-generation ICT-based social solutions that drive societal well-being and efficiency. With our world-leading academic partners, we are investigating state-of-the-art optics and photonics for social value innovations, including safety and security solutions and environmental sensing.
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With exponential traffic growth demand from 5G and cloud applications, optical networks powering the Internet backbone will require orders-of-magnitude improvement in capacity and latency while minimizing the carbon footprint of transmission systems. Advanced signal processing approaches aided by artificial intelligence are at the core of our innovation in this area.
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Software-defined networking (SDN) is an emerging technology that decouples network control from forwarding, making the network programmable to simplify management and enable fast provisioning. While SDN has traditionally considered small-scale networks and higher-layer applications, our research focuses on extending SDN to large-scale optical networks and the fundamental photonic, WDM, SONET/SDH and OTN layers.
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We proposed and demonstrated a new machine learning algorithm for wavelength modulation spectroscopy to enhance the accuracy of fire detection. The result shows more than 8% of accuracy improvement by analyzing CO/CO 2 2f signals.
An artificial neural network (ANN) based transfer learning model is built for quality of transmission (QoT) prediction in optical systems feasible with different modulation formats. Knowledge learned from one optical system can be transferred to a similar optical system by adjusting weights in ANN hidden
Traffic conditions of the highway, Ya traffic volume meter CCTV Because it is observed in the spot, such as the discovery of traffic disturbances which deviates from the observation spot it may be delayed. The traffic flow has a problem from the point observations data indirectly order to be estimated,
The higher-order spatial modes of a light beam are receiving significant interest. They can be used to further increase the data speeds of high speed optical communication, and for novel optical sensing modalities. As such, the classification of higher-order spatial modes is ubiquitous. Canonical classification
Fiber nonlinearity is one of the major limitations to the achievable capacity in long distance fiber optic transmission systems. Nonlinear impairments are determined by the signal pattern and the transmission system parameters. Deterministic algorithms based on approximating the nonlinear Schrodinger
A two-stage neural network model is applied on captured PS-144QAM raw data to estimate channel G-OSNR in a metro network field trial. We obtained 0.27dB RMSE with first-stage CNN classifier and second-stage ANN regressions.