Computational Wellbeing Group
This interdisciplinary team comprised of data science, machine learning, behavioral science, mobile and ubiquitous computing, physics and human computer interaction studies non-clinical populations (e.g., college students, office workers) as well as clinical populations (e.g. patients with depression, schizophrenia, substance use disorders, Alzheimer, cancers) in collaboration with researchers in psychology, psychiatry, sleep and circadian disorders, engineering and behavioral science.
Associated Faculty: Sano
Data to Knowledge Lab (D2K)
Data to Knowledge Lab — The Rice D2K Lab provides students with engagement, enrichment, and experiential learning opportunities by connecting students with real-world data science challenges from companies, community organizations and researchers.
Associated Faculty: Allen
Digital Signal Processing Group
Digital Signal Processing (DSP) — the transformation of data to extract or better transmit information — has evolved from an obscure research discipline into an essential technology of everyday life. Rice has been a major force in DSP research and education and many outstanding DSP alumni now hold leadership positions in academics and industry.
Associated Faculty: Baraniuk, Burrus, Cavallaro, Frantz, Johnson, Kemere, Orchard, Patel, Pitkow, Sabharwal, Veeraraghavan
Efficient and Intelligent Computing Lab
The Efficient and Intelligent Computing (EIC) Lab at Rice University explores techniques that highlight a holistic optimization of algorithm, system, and application-level opportunities that can bring powerful machine-learning systems to daily-life devices.
Associated Faculty: Lin
Halas Research Group
The Halas Group is focused on four principal missions: to design new optically active nanostructures driven by function; to develop and implement new nanofabrication strategies to build, orient, and pattern these nanostructures into new materials and devices; to characterize and understand the physical properties of these optically active nanostructures, devices and materials; to prototype the use of optically active nanostructures in applications of potential technological and broad societal interest.
Associated Faculty: Halas
The Kono Group is currently focused on the physics and applications of semiconductor nanostructures and quantum device structures. They use state-of-the-art spectroscopic techniques to study charge, spin, and vibrational dynamics in a variety of nanostructures.
Associated Faculty: Kono
Laboratory for Nanophotonics
The goal of the Laboratory for Nanophotonics (LANP) is to invent, to understand, to develop, to simulate, to control, to optimize, to apply nanoscale optical elements, components, and systems.
Associated Faculty: Bharadwaj, Halas, Naik
Laboratory for Nanophotonic Computational Imaging and Sensing
The laboratory for Nanophotonic Computational Imaging and Sensing (NCIS) designs and builds imaging systems that can dramatically outperform systems built from traditional physical optics. The founding principle is that by co-designing nanophotonic devices and imaging algorithms, we can break free of the limitations imposed by conventional physical optics like lenses and mirrors.
Associated Faculty: Robinson, Veeraraghavan
Luan Laboratory of Integrative Neural Interface
The Luan Laboratory of Integrative Neural Interface research focuses on the development of multimodal neural interfaces that combine the state-of-art electrical, optical and other technologies to monitor and manipulate brain activity. The application of these neurotechnology advancements enables the fundamental investigation of neurological disorders and the development of novel therapies. The lab aims to develop tools to create a multifaceted picture of the brain in health and in disease, and to seek new ways to better diagnose, treat, cure, and even prevent brain disorders.
Associated Faculty: Luan
The Naik Lab explores, invents, and innovates the science and technology of extreme control of light and heat using nanotechnology. While discovering new scientific phenomena, the Naik lab addresses global challenges in energy and healthcare by developing new technologies for efficient renewable energy harvesting, compact imaging, and sensing.
Associated Faculty: Naik
Nanoscale Neural Interface Laboratory
Nanoscale Neural Interface Laboratory (Xie Lab) develops theories focused on tissue integrated neural electrodes, neural recoding, neural interfaces, and longitudinal electrophysiology in clinical research.
Associated Faculty: Xie
Neural Computation Laboratory
The Neural Computation Laboratory aims to understand how the brain works using mathematical principles. They develop theories of neural computation and collaborate with experimentalists to test these predictions.
Associated Faculty: Pitkow
Ankit Patel's Lab is a part of Rice Neuroengineering. Patel's focus is to bridge neuroscience and deep machine learning, by building theories that work in the real world.
Associated Faculty: Patel
Realtime Neural Engineering Laboratory
The Realtime Neural Engineering Laboratory focuses on forming, storing, and using memory in the hippocampus. Problems in the hippocampal circuit can lead to memory problems (e.g., Alzheimer's, PTSD) and also more complex disorders such as depression and anxiety. We'd like to understand how the hippocampal circuit works at a systems-level in healthy brains, how it goes wrong, and what can be done to change how it functions.
Associated Faculty: Kemere
Rice Integrated Systems and Electromagnetics Lab
Rice Integrated Systems and Electromagnetics (RISE) Lab focuses on integrated circuits and systems for various high-impact and emerging applications, including high-speed wireless communication, sensing, imaging, and health care.
Associated Faculty: Chi
Rice Neuroengineering Initiative
The Rice Neuroengineering Initiative is a collaborative multidisciplinary program that brings together the brightest minds in neuroscience, engineering, and related fields to improve lives by restoring and extending the capabilities of the human brain. More information can be found at https://neuroengineering.rice.edu/
Associated Faculty: Aazhang, Allen, Baraniuk, Kemere, Pitkow, Robinson, Sabharwal
Rice Networks Group
Rice Networks Group (RNG) is devoted to protocols, theory, and experimental research in next generation wireless networks. The group has deployed and operates a large-scale programmable and experimental access network in Southeast Houston.
Associated Faculty: Knightly
The Robinson Lab for Nano-neurotechnology believes that new methods to measure and manipulate the activity of specific brain cells will reveal fundamental principles of brain function and advance the treatment of neurological disorders. Using semiconductor nanofabrication and genetic engineering, the lab creates electronic, photonic, and magnetic interfaces to the brain. In addition, the lab studies millimeter-sized invertebrates with tiny nervous systems. By creating interface technologies for these tiny organisms, the lab hopes to decode the activity of the entire nervous system and uncover how simple brains operate to solve complex problems.
Associated Faculty: Robinson
Scalable Health Labs
Scalable Health Labs are focused on mobile bio-behavioral sensing, to develop novel "sensors" that can simultaneously measure both bio- and behavioral-markers for a given healthcare context. The challenge lies in measuring both bio- and behavioral-markers in-situ, away from a clinic or healthcare facility. The new sensors will be the foundation of next-generation healthcare architecture, where both the patient and healthcare providers are empowered by relevant and timely information.
Associated Faculty: Sano, Veeraraghavan, Sabharwal, Zhong
Secure and Intelligent Micro-System
The Secure and Intelligent Micro-Systems (SIMS) Lab innovates and builds radically new hardware (integrated circuits and micro-systems) to enable emerging applications that are not possible without miniaturized low-power electronics, such as internet of everything, ubiquitous computing, and bioelectronic implants and wearables. SIMS Lab pushes the limits of energy efficiency and performance in miniature hardware and seeks to address pressing system challenges on security and machine learning through cross-layer design and optimization.
Associated Faculty: Yang
Rice University Wireless Research Group have received a $1.5 million National Science Foundation (NSF) grant to develop an open-source platform to meet the urgent need of developing and validating machine-learning (ML) based innovations for future wireless networks and mobile applications. The goal of the project led by Yingyan Lin, an assistant professor of electrical and computer engineering at Rice’s Brown School of Engineering, is to develop a first-of-its-kind community platform to turbocharge the research process of inventing novel ML-based techniques for intelligent wireless network management and optimization. More information on wireless projects can be found at https://ce.rice.edu/.
Associated Faculty: Lin, Doost-Mohammady, Cavallaro, Chen, Sabharwal, and Atlas Wang at TAMU