URMC Briggs Lab
University of Rochester
Research Group
Biological Sciences: Neuroscience
Natural and Biomedical Sciences
Thank you for your interest in working in the Briggs lab at URMC!
We are a vision research lab and detailed information about ongoing lab projects can be found at our lab webpage here: briggsneurolab.urmc.edu
Positions begin in the summer or fall. Please note that positions for the 2023-24 academic year have already been filled. At this time, we are NOT accepting applications until March 2024. If you are interested in a position starting summer 2024 or Fall 2024, please apply in March 2024.
Undergraduates working in the Briggs lab do not receive a salary. However, Dr. Briggs supports applications for course credit and/or paid internships (e.g., BCS summer internship, offered through the university).
Active Perception Laboratory
University of Rochester
Research Group
Biological Sciences: Neuroscience, Biomedical Engineering, Brain and Cognitive Sciences, Computer Science, Data Science, Electrical and Computer Engineering, Engineering Science, Optics, Visual Science
Engineering/ Math/ Computer Science, Natural and Biomedical Sciences
A research assistant position is available in the Active Perception Laboratory (https://aplab.bcs.rochester.edu) in the Department Brain and Cognitive Sciences at the university of Rochester. Research in the lab focuses on understanding the interplay between eye movements and vision using a combination of behavioral, computational, high-resolution retinal imaging and evoked potentials (EEG) techniques (https://aplab.bcs.rochester.edu/facilities.html#).
Responsibilities will depend on the applicant interests and background. They could include any of the following:
experimental data collection with human subjects with eyetracking and/or EEG,
implementation of experimental protocols,
contribution to the development of novel eyetracking techniques (for candidates with an Optics and /or Engineering background),
analysis of behavioral data,
collection and analysis of high-resolution retinal images,
alignment and calibration of optical devices for eyetracking and retinal imaging (for candidates with an Optics background).
Quantitative skills and some computer programming skills are desirable.
This position is ideal for someone interested in obtaining experience in vision and neuroscience research, and in improving quantitative and computational skills, with the goal of applying to graduate school.
En-Ability
Rochester Institute of Technology
Research Group
Human Computer Interaction
Engineering/ Math/ Computer Science
The En-Ability Lab is about enabiling, enhancing, and empowering people. Our research areas cover accessibility and HCI, more specifically we investigate topics on design, immersive technologies, and networking. Our lab’s mission is to foster a collaborative environment that values diversity—not only diversity in the topics we research, but also the diversity in our research team, and the communities our research is made to serve.
Porosoff Lab
University of Rochester
Research Group
Engineering/ Math/ Computer Science
The Porosoff group focuses on developing new catalysts for upgrading C1 and C2 resources (CO2, CO, CH4, C2H6) for efficient energy storage and low-cost production of plastics, chemicals and fuels. Understanding the relationships between chemical reactivity and catalyst electronic/structure properties is extremely important for developing catalysts that exploit particular reaction pathways. This approach requires controlled synthesis of catalysts combined with in situ techniques and theoretical calculations. In particular, target areas of research are three types of catalytic reactions for improved shale gas utilization and lowering CO2 emissions: (I) Catalyst development for CO2 hydrogenation, (II) Selective synthesis of light olefins from CO and H2 and (III) Catalytic dehydrogenation of light alkanes to olefins by CO2. Experimental work combines a mix of catalyst synthesis and characterization, reactor studies and in situ spectroscopy.
VIStA (Visual Intelligence & Social Multimedia Analytics)
University of Rochester
Research Group
Engineering/ Math/ Computer Science, Social Sciences, Natural and Biomedical Sciences
[Computer Vision]: recognition of objects, scenes, people, locations, actions, and events from images and videos
[Vision and Language]: description and explanation of visual content; language-based search, retrieval, and generation
[Social media data mining]: prediction, nowcasting, forecasting, profiling, and recommendation using open-source data
[Machine Learning]: learning with large-scale loosely labeled web data, cross-domain learning, few-shot learning
[Health informatics]: healthcare and wellness analytics using text and visual data; surgical video analysis
[Pervasive computing]: context-aware applications; multimodal inference from multiple sensors
[Media experience]: multimodal reliving; aesthetics, emotion, sentiment, and influence of multimedia
[Note]: Undergraduate students should seek research opportunities after having done well in the related courses (240/440 Data Mining and/or 249/449 Machine Vision).
Ultrasound Tomography Center
University of Rochester
Research Group
Applied Mathematics, Biomedical Engineering, Computer Science, Electrical and Computer Engineering, Mathematics, Mechanical Engineering, Physics
Engineering/ Math/ Computer Science
We are a multidisciplinary group of scientists, engineers, and physicians working to bring a new ultrasound-based medical imaging platform to the clinic. Most conventional ultrasound systems only use reflected waves to create images of the tissue. This approach can be limited in its capability to quantitatively characterize tissue. Ultrasound tomography uses both the waves reflected by AND transmitted through tissue to fully characterize the material properties of the tissue. Specifically, we observe that these material properties distort the ultrasound wave as it passes through the tissue. These same distortions allow us to interrogate and recover the material properties within the tissue of interest. Our group integrates the latest advances in hardware development and algorithm design to translate these ideas to a clinically relevant imaging modality.
We are looking for highly motivated students for both hardware development and algorithm design. Interested students should have a strong interest in some or all of the following categories: acoustics, numerical modeling, signal processing, inverse problems, waveform inversion, computational imaging, and/or imaging hardware design. We expect students to come with a background in MATLAB (or an equivalent language). C/C++ experience (especially CUDA) would be an additional bonus as we also plan to accelerate existing algorithms using GPUs.
MixingLab
University of Rochester
Research Group
Biological Sciences: Neuroscience, Biomedical Engineering, Mechanical Engineering, Physics
Engineering/ Math/ Computer Science
Fluid mixing is both beautiful and devilishly difficult to understand, predict, or control. Our research team, led by Prof. Douglas H. Kelley, studies how flows and the materials they carry change over space and time, primarily with application to cerebrospinal fluid flow in the brain and to liquid metals technologies. Brain cerebrospinal fluid flows through the recently-discovered glymphatic system, which evacuates metabolic wastes to prevent diseases like Alzheimer's, but can also malfunction in situations like stroke or traumatic brain injury. Fluid flow affects the performance of liquid metal batteries, a grid-scale storage technology, and the efficiency of aluminum manufacture, which uses 3% of worldwide electricity. Our research team studies these problems with a combination of experiments, simulations, and theory. Undergraduate researchers work in collaboration with each other and/or with PhD students and postdoctoral researchers, building skills and taking creative ownership of their own efforts. Undergraduate researchers on the team frequently coauthor peer-reviewed journal articles and present at international research conferences. Valuable skills for undergraduate applicants include -- but are not limited to -- coding, machining / fabrication, computer simulation / drawing, and writing. We value interpersonal diversity and encourage all to apply. Students need not be upperclassmen to apply. More information is available on the team website.
Photoacoustic and Ultrasonic Research & Engineering (PURE)Laboratory
University of Rochester
Research Group
Biomedical Engineering, Computer Science, Data Science, Electrical and Computer Engineering, Mechanical Engineering, Optical Engineering, Optics
Engineering/ Math/ Computer Science, Natural and Biomedical Sciences
The primary focus of the PURE lab at the University of Rochester is to develop novel, hybrid, and ultrasound-based diagnostic methods, and define the clinical utility of the developed technologies as it applies to detection, diagnosis, and therapy of various pathologies.
Our ultimate goal is to help physicians and patients by providing more accurate and multi-parametric information about diseases that can help:
to detect pathologies at their early stages of development
to more accurately locate the diseased tissue
to better plan for individualized therapy
to monitor the outcome of the therapeutic procedures
These developments will serve to improve the diagnosis and treatment guidance of high impact diseases, such as cancer.
Almost every project in the lab utilizes ultrasound imaging. Ultrasound imaging (aka sonography) is the most-widely available medical imaging modality in clinical practice due to its notable advantages, including using non-ionizing energy, providing real-time information, portability, and low cost. However, it is limited to imaging tissue morphology and structure, without any functional, cellular, or molecular information. That is why our lab explores a newly born modality known as "Photoacoustic Imaging". Photoacoustic imaging utilizes lasers to complement ultrasound imaging, providing functional and molecular information to the morphological images obtained from ultrasound.
Our research team works closely with the School of Medicine. This collaboration has helped us to better identify the real clinical needs and direct our efforts to overcome clinical limitations. We are closely working with several industry-leading imaging companies, such as Verasonics and Siemens, to implement our technologies on existing clinical devices. We believe this could be a key to enable faster clinical translation of the developed methods.