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Electrical and Computer Engineering

ECE Research

Building on more than three decades of research successes, a major goal of our research program is to prove, encourage, and support new and far-reaching initiatives that look 10+ years into the future. Driven by a core group of internationally recognized faculty, extensively and uniquely experienced in research and education, our culture fosters close collaboration, which is the major force that maximizes technology impact and direction. Through various funding programs, Rice has been able to demonstrate leadership in focused research initiatives: Computer Engineering; Data Science; Neuroengineering; Photonics, Electronics and Nano-devices; and Systems.

2016-2017 ECE Research Guide (PDF

Research Areas

Computer EngineeringData Science | NeuroengineeringPhotonics, Electronics and Nano-devices | Systems


Computer Engineering

The research in this discipline focuses on analog and mixed-signal design, computer architecture and embedded systems, hardware security and storage. Rice research in Computer Engineering touches on virtually every area that CE encompasses.

Computer Engineering research at Rice is in analog and mixed-signal design, VLSI signal processing, computer architecture and embedded systems, biosensors and computer vision, and hardware security and storage systems. Current research in VLSI signal processing, focuses on algorithms for wireless communication systems and their efficient mapping to low-power architectures on DSPs, GPUs, ASICs, and ASIPs. Analog and mixed-signal design research topics include self-healing circuits and large-scale radiating integrated circuits for medical imaging. Biosensors and mobile wireless healthcare are growing application areas in embedded systems research. Smartphones with imaging devices are leading to new areas in computer vision and sensing. Also in embedded systems, hardware security schemes are being based on physically unclonable functions. In the area of computer architecture, research interests include parallel computing for data science algorithms, large-scale storage systems, and resource scheduling for performance, power and QoS. 

Faculty: Babakhani, Cavallaro, Kemere, SimarVarman, Zhong

Data Science

Data Science is an emerging discipline that integrates the foundations, tools and techniques involving data acquisition (sensors and systems), data analytics (machine learning, statistics), data storage and computing infrastructure (GPU/CPU computing, FPGAs, cloud computing, security and privacy) in order to enable meaningful extraction of actionable information from diverse and potentially massive data sources. Data scientists in ECE use digital signal processing algorithms to collect and understand the structure in data, looking for compelling patterns, telling the story that's buried in the data. They get at the questions at the heart of complex problems and devise creative approaches to making progress in a wide variety of application domains. 

Faculty: AazhangBaraniukKemereOrchard, PatelPitkowRobinsonSabharwal, VarmanVeeraraghavanZhong

Adjunct Faculty: Dora Angelaki (Baylor College of Medicine)

Collaborating Rice Faculty: Genevera Allen (STAT), Amina Qutub (BIOE), Christopher Jermaine (CS), Anshumali Shrivastava (CS)


The brain is essentially a circuit. Neuroengineering is a discipline that exploits engineering techniques to understand, repair and manipulate human neural systems and networks. At Rice, we have a world-class team collaborating with Texas Medical Center Researchers to improve the fundamental understanding of coding and computation in the human brain as well as to develop technology for treating and diagnosing neural diseases. Current research areas include interrogating neural circuits at the cellular level, analyzing neuronal data in real-time, and manipulating healthy or diseased neural circuit activity and connectivity using nano electronics, optics, and emerging photonics technologies. 

Read more.


Faculty: Aazhang, Babakhani, Baraniuk, Halas, Kemere, PatelPitkowRobinson, Veeraraghavan

Adjunct Faculty: Dora Angelaki (Baylor College of Medicine), Giridhar Kalamangalam (UT-Health Science), Dorina Papageorgiou (Baylor College of Medicine), Nitin Tandon (UT-Health Science)

Collaborating Rice Faculty: Genevera Allen (STAT), Steven J. Cox (CAAM), Marcia O'Malley (MEMS), Amina Qutub (BIOE), Robert Raphael (BIOE)

Photonics, Electronics and Nano-devices

The focus of this program is the improved understanding of electronic, photonic, and plasmonic materials, optical physics, the interaction of light and matter, along with the application of that knowledge to develop innovative devices and technologies. The specific areas of interest cover a broad range: Nanophotonics and plasmonics, optical nanosensor and nano-actuator development, studies of new materials, in particular nanomaterials and magnetically active materials; imaging and image processing, including multispectral imaging and terahertz imaging; ultrafast spectroscopy and dynamics; laser applications in remote and point sensing, especially for trace gas detection; nanometer-scale characterization of surfaces, molecules, and devices; organic semiconductor devices; single-molecule transistors; techniques for optical communications; optical interactions with random, nanoengineered, and periodic media; and applications of Nanoshells in biomedicine.


Faculty: BharadwajHalas, Kelly, Kono, NaikRobinsonThomann, Tittel, Woods



Signal processing is the analysis and transformation of signals -- measurements taken over time and/or space -- in order to better understand, simplify, or recast their structure. Rice has a long history in digital signal processing (DSP) dating back to its inception in the late 1960s. Current research spans a wide range of areas, including image and video analysis, representation, and compression; wavelets and multiscale methods; statistical signal processing, pattern recognition, and learning theory; distributed signal processing and sensor networks; communication systems; computational neuroscience; and wireless networking. Machine Learning is a large part of our Systems Research.

Read more on Wireless.  Read more on DSP.


Faculty: Aazhang, Antoulas, Baraniuk, Cavallaro, Clark, FrantzKnightly, Orchard, PatelSabharwal, SimarVeeraraghavan