Each REU program has its own deadline. Most applications will open in November and close in January/February. Please check individual REU websites for details.

Accepted applicants will be notified in the beginning of March. 

To apply, you will need to submit:

• Online application
• Personal statement
• Two references
• Resume
• Transcripts

REU Milestones

Students typically go through the following research stages throughout the summer program:

• Prior to REU (mid-April): Connect with faculty mentor and outline research question(s)
• Weeks 1 – 2: Read background literature, get acquainted with lab/equipment
• Weeks 3 – 4: Define research project, participate in lab meetings
• Weeks 5 – 8: Conduct research, analyze data, present initial findings
• Weeks 9 – 10: Prepare talk, poster, and final paper and participate in Symposium

REU Calendar

Most weeks throughout the program have a consistent rhythm to them. A week may look like this:

Hydrogel Culture Environments for Regenerative Medicine Applications

We can interrogate and take advantage of the critical interactions between cells and extracellular matrix (ECM) to create bioactive materials capable of controlling cell function and tissue evolution. To determine the requirements of the microenvironment, we utilize hydrogels easily modified with respect to mechanical integrity, adhesive peptides, ECM molecules, degradability, and incorporation of drugs, to direct cellular differentiation through a variety of mechanisms.

In particular, we are interested in utilizing hydrogel microenvironments to direct encapsulated mesenchymal stem cell (adult stem cell) function for applications in musculoskeletal tissue engineering. A thorough understanding of how material properties effect cell differentiation and tissue evolution is essential to tailor ‘instructive materials’ to direct cell function.

Professor Danielle Benoit
Departments of Biomedical Engineering and Chemical Engineering
benoit@bme.rochester.edu

Targeted Polymer Therapeutics to Overcome Drug Delivery Barriers

Conventional small molecule drugs and large macromolecular drugs have significant and distinctly different delivery barriers. For example, small molecule drugs, such as the chemotherapeutic doxorubicin, is highly hydrophobic, thus administration requires toxic cosolvents to aid blood solubility. Macromolecular drugs, on the other hand, suffer from enzymatic degradation and inactivation, difficulty in targeting to the appropriate cells and transversing the cell membrane, and often become degraded intracellularly once endocytosed. We are investigating polymer-drug complexes or polymer-drug conjugates to overcome these barriers and modulate drug delivery.

Professor Danielle Benoit
Departments of Biomedical Engineering and Chemical Engineering
benoit@bme.rochester.edu

Viscoelastic Heating of Soft Biological Tissues

Back pain is the leading cause of disability globally and the second most common cause of doctors’ visits. Despite extensive research efforts, the underlying mechanism of back pain has not been fully elucidated. The intervertebral disc (IVD) is a viscoelastic tissue that provides flexibility to the spinal column and acts as a shock absorber in the spine. When viscoelastic materials like IVD are cyclically loaded, they dissipate energy as heat. Thus, daily movements of the vertebral column intermittently deform the IVD and could increase disc temperature through viscoelastic heating. This temperature elevation has the potential to influence cell function, alter enzyme kinetics, drive cell death, and potentially induce nociception in innervating neurons within the IVD. Our work to date has focused on investigating the capacity of IVD to increase in temperature due to viscoelastic heating in vitro. According to our findings, the IVD can experience a measurable temperature rise (up to 2.5° C) under cyclic loading. This magnitude of temperature rise has physiological relevance as degenerative IVD tissue has been shown to produce a sensitization of nociceptive neurons that can spontaneously fire with a maximum response at just 1° C above normal body temperature. Thus, our results suggest that viscoelastic heating of IVD could interact with sensitized neurons in the degenerative IVD to play a role in back pain. Current work in this project is aimed at determining how viscoelastic heating of the disc may affect tissue structure and integrity, in addition to investigating the role of viscoelastic heating in pathologies affecting other soft biological tissues.

Professor Mark Buckley
Department of Biomedical Engineering
mark.buckley@rochester.edu

Auditory Neuroscience Lab

We combine neurophysiological, behavioral, and computational modeling techniques towards our goal of understanding neural mechanisms underlying the perception of complex sounds. Most of our work is focused on hearing in listeners with normal hearing ability. We are also interested in applying the results from our laboratory to the design of physiologically based signal-processing strategies to aid listeners with hearing loss.

We are currently studying the following specific problems:

1. Detection of acoustic signals in background noise
2. Coding of complex sounds, such as speech, by fluctuations in neural responses
3. Signal processing to enhance fluctuation cues for listeners with hearing loss
4. Neural sensitivity to fast frequency transitions

These problems are of interest because they involve tasks at which the healthy auditory system excels, but they are situations that can present great difficulty for listeners with hearing loss. We study the psychophysical limits of ability in these tasks, and we also study the neural coding and processing of these sounds using stimuli matched to those of our behavioral studies.

Computational modeling helps bridge the gap between our behavioral and physiological studies. For example, using computational models derived from neural population recordings, we make predictions of behavioral abilities that can be directly compared to actual behavioral results. The cues and mechanisms used by our computational models can be manipulated to test different hypotheses for neural coding and processing.

By identifying the cues involved in the detection of signals in noise and fluctuations of signals, our goal is to direct novel strategies for signal processors to preserve, restore, or enhance these cues for listeners with hearing loss.

Professor Laurel H. Carney
Department of Biomedical Engineering
laurel.carney@rochester.edu

Biomedical Optics for Breast Cancer Detection and Therapy Monitoring

The overall goals of this project in Professor Choe's laboratory are to assess and improve the capabilities of diffuse optical technology in breast cancer therapy monitoring and detection. In clinical measurements of human breasts with tumor, we focus on identifying functional parameters measurable with diffuse optics, which can serve as early indicators of therapy efficacy. Using a preclinical animal model, we study the metabolic mechanism of varied responses to therapy seen in the clinic, and investigate new therapeutic drugs and interventions. The students will have opportunities to participate in various aspects of research: instrumentation construction and characterization, data analysis algorithm development, preclinical experiments, and/or clinical experiments.

Professor Regine Choe
Department of Biomedical Engineering
Regine_Choe@urmc.rochester.edu

Diffuse Optical Imaging for Non-Invasive Deep-Tissue Monitoring of Bone Healing

Achieving effective revascularization is critical for successful healing of bone grafts or fractures. While various tissue engineering and regenerative medicine strategies have been proposed and tested, most revascularization assessment is performed using methods requiring destruction/sacrifice of samples. Diffuse optical imaging can quantify hemodynamic parameters of deep-tissue in vivo samples non-invasively, allowing longitudinal monitoring of bone graft vascularization process. The project will focus on development of imaging methods for in vivo preclinical experiments, which will give students exposure to various aspects of research: instrumentation construction and characterization, data analysis algorithm development, preclinical experiments, and collaboration with experts in tissue engineering field (Prof. Benoit laboratory).

Professor Regine Choe
Department of Biomedical Engineering
Regine_Choe@urmc.rochester.edu

Biomedical Ultrasound

The primary goals of Professor Dalecki’s laboratory are to advance novel diagnostic ultrasound techniques, and to discover new therapeutic applications of ultrasound in medicine and biology. For this project, students will work towards developing new ultrasound technologies for the field of tissue engineering and regenerative medicine. Specifically, students will investigate the effects of ultrasound on extracellular matrix proteins and cell functions that are key for engineering artificial 3D tissues and enhancing wound repair. Students will develop skills in acoustic field calibration, signal processing, cellular and tissue preparation procedures, cell and extracellular matrix biology, and ultrasound physics. The research is highly multidisciplinary and spans the fields of biomedical ultrasound, acoustics, medical imaging, cell and tissue engineering, and biomechanics.

Professor Diane Dalecki
Department of Biomedical Engineering
diane.dalecki@rochester.edu

Mechanobiology of Embryonic Tissue Development

How the embryo develops tendons and ligaments that transmit forces throughout the adult body is yet to be understood. The Kuo Lab harnesses the powerful tools that engineers have developed for study of synthetic materials and utilizes them to analyze living embryonic tissues. With this novel approach, our goal is to understand the mechanobiology of load-bearing tissue development, and use this knowledge to inform innovative strategies for engineering new tendons and ligaments from stem cells. Projects range from developing living embryo models, to interrogating the mechanical microenvironment of embryonic tissues, to fabricating custom-designed biomaterials and mechanical loading bioreactors to mechanoregulate tissue engineering and regeneration.

Professor Catherine Kuo
Department of Biomedical Engineering and Orthopaedics
Catherine.k.kuo@rochester.edu

3-Dimensional Hydrogel Systems to Regulate Stem Cell Differentiation

Stem cell function, such as differentiation and the regeneration of new tissues, can be controlled by the mechanical and biochemical properties of the surrounding extracellular matrix (engineered or natural). Less understood is what specific combination of such cues is required to elicit a desired response that leads to formation of a normal, functional tissue. Furthermore, why certain stem cells respond to some cues and not to others is minimally understood. Projects in the Kuo Lab are focused on developing and tailoring 3-dimensional hydrogel culture systems to:

1. Identify combinations of mechanical and biochemical cues that can instruct stem cell differentiation toward specific musculoskeletal tissue lineages
2. Understand what specific characteristics of stem cells play critical roles in these responses

Professor Catherine Kuo
Department of Biomedical Engineering and Orthopaedics
Catherine.k.kuo@rochester.edu

Assessing Predictive Language Processing in the Human Brain: Developing Tools for Research in Psychiatric and Developmental Populations

Speech is processed in the human brain a hierarchical manner – sounds are transformed into phonemes and then words and then meaning. A growing body of neurophysiological research suggests that in healthy individuals, successful comprehension is underpinned by the ability to predict impending speech. Thus, upper levels of the hierarchy (e.g. the meaning understood so far) enable predictions of which words and sounds are likely to come next (consider: The King wore a ____ ). Certain developmental and psychiatric conditions (such as autism and schizophrenia) are thought to interfere with predictive brain mechanisms. However this area is understudied, because computational methods to interrogate different levels of neural processing in natural speech comprehension have only just been introduced. In this project, we aim to further develop new methods to gain an understanding of the mechanisms behind speech perception in neurotypical people, so as they will be maximally useful in future research involving developmental and psychiatric populations.

Professor Edmund Lalor
Department of Biomedical Engineering
elalor@ur.rochester.edu

Graph Signal Processing to Study the Networks of the Brain

Recent neuroimaging advances offer unique views on brain structure and function; i.e., how the brain is wired, and where and when neural activity takes place. Data acquired using these techniques can be analyzed in terms of its network structure to reveal organizing principles at the systems level. Graph representations are versatile models where nodes are associated to brain regions and edges to structural or functional connections. Structural graphs model neural pathways in white matter, i.e., the anatomical backbone between regions which can be extracted from tractography algorithms applied to diffusion tensor imaging (DTI). Functional graphs are built based on measures of statistical interdependency between pairs of regional activity traces acquired via functional magnetic resonance imaging (fMRI). Therefore, most research to date has focused on analyzing these graphs reflecting structure or function. Graph signal processing (GSP) is an emerging area of research where functional signals recorded at the nodes of the graph are studied atop the structural graph. The fundamental GSP concepts utilized to analyze brain signals are the graph Fourier transform (GFT) and the corresponding notions of graph frequency components and graph filters.

The study of brain activity patterns expressed on brain networks is a timely application domain, where it is possible but costly to measure both structural and functional networks separately due to different spatiotemporal resolutions, running time, and acquisition methods. Thus, deciphering the relationship between structural and functional connectivity of brain networks is of great importance and a very active area of research. To bridge these gaps, the goal of this project is to develop a GSP-based graph learning framework (e.g., using graph convolutional neural networks) to estimate structural brain connectivity from functional signals measured by resting-state fMRI. The algorithms will be tested both with simulated and real neuroimaging data from the Humane Connectome Project.

Professor Gonzalo Mateos
Department of Electrical and Computer Engineering
gmateosb@ece.rochester.edu

Mechano-transduction of the Inner Ear Sensory Organ

We study the mechano-transduction of the inner ear. In the cochlea, mammalian hearing organ, mechanical stimuli (sounds) are encoded to neural signals. The identification of mechanical properties of cochlear sensory cells and tissues is crucial to better understand how we hear (or fail to hear). Students will participate in measuring mechanical responses of artificial and biological micro structures in a micro-fluidic device. Through this project, students will learn how the principles of acoustics, fluid dynamics, solid mechanics and vibrations are applied to micro-mechanical experiments with biological tissues. Students will gain experiences with vibration measurements, imaging and data acquisition devices. Students will be trained to handle experimental animals and assist in preparing tissues for experiments.

Professor Jong-Hoon Nam
Departments of Biomedical Engineering and Mechanical Engineering
jong-hoon.nam@rochester.edu

Surveying Biological Tissues with Optical Coherence Elastography

Optical coherence tomography (OCT) is a high-resolution imaging modality that uses laser light to obtain volumetric scans of a sample. OCT elastography approaches can be used to obtain the biomechanical properties of tissue, and these approaches are also called optical coherence elastography (OCE). Numerous studies using OCE have been performed in cornea, skin, heart, muscle, and breast. This highly interdisciplinary project currently involves OCE scans of brain tissue to study injury, inflammation, aging, and neurodegenerative diseases. However, there is flexibility on applications depending on the student's specific interests. In this project, the student will be reviewing key literature paper and learn to summarize them for the team, and will be engaged in experiments including data collection, analysis, and participation in future publications as appropriate according to the student contribution.

Professor Jannick Rolland
The Institute of Optics
rolland@optics.rochester.edu

Assessing Disease Progression and Treatment Efficacy for Parkinson’s and Huntington’s Diseases Using Data Analytics on Body Worn Sensors

Parkinson's and Huntington's diseases are characterized by debilitating motion irregularities: such as tremors, unsteady gain, involuntary movements, and lack of coordination. This project seeks to use analytics on data captures from minimally obtrusive sensors worn at multiple points on the body for detecting and classifying motion irregularities, for quantifying the durations of such symptoms, and for characterizing the efficacy of medication in mitigating these symptoms.

Professor Gaurav Sharma
Departments of Electrical and Computer Engineering, Computer Science, and Biostatistics and Computational Biology
gaurav.sharma@rochester.edu

Deep Learning and Data Analytics for Ophthalmic Diagnosis

Common systemic diseases, such as diabetes and hypertension, affect the body's vasculature. These vascular changes can be visualized and assessed using fundus photography (FP) and wide-field fluorescein angiography (FA), a process that involves injecting dye and taking images of it passing through the retinal blood vessels. In this project, we aim to develop an automated computer-aided method for retinal image analysis and ophthalmic diagnosis. We focus on applying deep learning techniques to detect retinal vessels in FP and FA images. We also analyze clinical data for to assess disease progression and treatment and to assist physicians.

Professor Gaurav Sharma
Departments of Electrical and Computer Engineering, Computer Science, and Biostatistics and Computational Biology
gaurav.sharma@rochester.edu

Adaptive Color Visualization for Color Deficient Observers on Android Smartphones

Around seven to ten percent of the male population in North America has some form of color deficiency. These viewers often find it difficult to tell the difference between certain colors that appear clearly different to observers with normal color vision. The color deficiency is particularly problematic when it causes a loss of discriminability of different objects or when a color deficient individual must engage in a conversation involving standard color terminology designed for color normal viewers. As increasingly popular personalized imaging devices, smart phones can be used as tools to help color-deficient users overcome their deficiency. As a participant in this research you will help develop and deploy techniques for improved color visualization on Android smartphones by specifically exploiting the adaptivity and personalization that these devices offer.

Professor Gaurav Sharma
Departments of Electrical and Computer Engineering, Computer Science, and Biostatistics and Computational Biology
gaurav.sharma@rochester.edu

Noncoding RNA Gene Search: Unlock the Hidden Information in Genomes

With the wide spread availability of high throughput sequencing technology, vast datasets of genomes are now available to researchers for exploration. Conventional protein coding genes can be located within these large genome data sets with relative ease using BLAST and other alignment tools. Noncoding RNAs (ncRNAs) that serve a direct functional role instead of providing a recipe for protein synthesis, however, present a challenge for genomic analysis. Across species ncRNAs are conserved in secondary structure rather than in sequence and they are therefore not discovered by common sequence alignment based search tools. With the discovery of an increasing number of ncRNAs it is clear that they represent the next frontier in advancing our understanding of the genomes. As a participant in this research, you will develop and evaluate new computational methods for identifying ncRNAs.

Professor Gaurav Sharma
Departments of Electrical and Computer Engineering, Computer Science, and Biostatistics and Computational Biology
gaurav.sharma@rochester.edu

Principled Machine Learning Methods for Multiple Sequence Alignment

The alignment of sequence data is a fundamental task in analyzing genomic data, which shares several commonalities with string comparison. For aligning two sequences, hidden Markov models have emerged as the model of choice for aligning two sequences. For more than two sequences, several heuristics have been proposed that operate on sequences in a pair wise fashion. In this project, we seek an alternate more principled framework for multiple sequence alignment using principled machine learning methods. As a participant in this research, you will be involved in developing, implementing, and evaluating new techniques for multiple sequence alignment.

Professor Gaurav Sharma
Departments of Electrical and Computer Engineering, Computer Science, and Biostatistics and Computational Biology
gaurav.sharma@rochester.edu

Development of Smart and Connected Healthcare Solutions

The increasing availability of mobile devices, combined with the fact that nowadays people of all ages are always carrying or within range of at least one mobile device, has opened the possibility for new healthcare solutions. These novel solutions have the potential to transform the healthcare from reactive and hospital-centered to preventive, person-centered and focused on well-being rather than disease. The need for such a transition is widely recognized by the medical community but requires large implementation efforts in order to develop suitable solutions that address the various requirements of different patients and medical conditions.

In this domain, we are currently working on different iOS and Android apps, as well as Virtual Reality (VR) apps, to help patients affected by different medical conditions such as, for example, Fetal Alcohol Spectrum Disorders (FASD), Alzheimer’s, asthma and obesity.

Students working on this project will be part of a diverse team, which includes engineers and medical professionals, and will be involved in the whole cycle of research and development, including software development and app content creation and refinement.

Professor Cristiano Tapparello
Department of Electrical and Computer Engineering
cristiano.tapparello@rochester.edu

Our past REU researchers participated from:

• Liberal arts colleges like Bucknell and Vassar
• Other R1 research universities like Columbia and Harvard
• Community colleges like Laney College (CA)
• Historically black colleges and universities (HBCUs) like Norfolk State and Morgan State
• Hispanic-serving institutions (HSIs) like Cal State Long Beach and Valencia College

Our past cohort had the following characteristics:

• Interested in pursuing an advanced degree: 49%
• Interested in pursuing medical school: 11%
• Underrepresented in STEM: 23%
• First generation students: 37%
• Female students: 43%
• Class year: Rising sophomores – 11%, juniors – 40%, seniors – 49%
• Students from outside of New York State: 86%

I am not a University of Rochester student. Can I enroll in the REU?

Our REU programs are federally funded by the National Science Foundation and intended for undergraduate students from any institution in the United States. Preference is given to students who apply from institutions other than the University of Rochester.

What is the time commitment for the REU program?

The REU programs are 10 weeks long. They begin the week prior to Memorial Day and finish the last week in July. 

What is the typical workload as an REU participant and what is expected of me?

As an REU participant, you are committed to working a minimum of 40 hours per week for the duration of the REU. Students who are selected for the REU, cannot to take courses or hold a job during the program. Accepting an REU position implies that you agree to be available for the entire program.

You should consult with your faculty advisor regarding your specific work schedule and work schedule flexibility.

I am a first-year student (freshman standing). Am I eligible for REU?

Yes, as a first‐year student you are eligible to apply our REU programs and freshman make up about 10 percent of our REU students over all programs. Most of our REU candidates apply as sophomores or juniors and have foundational engineering courses, but many projects are also accessible for first‐year students. We encourage you to apply.

How are candidates selected for your REU programs? When will I know if I have been selected for an REU? What if I am considering other offers?

Each REU program has its own selection and acceptance process so you should consult the webpage for your program of interest. Most programs make initial offers and hold a waitlist. It’s important to remember that students in both groups are high-caliber applicants who will excel in their chosen program; often the difference between an initial match and a waitlist match can be attributed to the competitiveness of the applicant pool and limited number of spaces available.

Where do students live during the REU program?

Most REU students live in on-campus residence halls (double occupancy) and are provided a meal allowance for buffet-style eating, grab-and-go options, and dining out at local restaurants.

Students who decide not to live on campus may receive a housing allowance to pay for a local rental unit or sublet, depending on the terms of the REU.

Do students have roommates? How are they assigned?

Most students who live in on-campus housing live in a double-occupancy room and have a roommate. We provide opportunities for students to meet virtually in April so that they can find and request a roommate that is a good match.

Requests for single-occupancy rooms will be reviewed and granted based on demonstrated need for privacy or accessibility.

What are the dining options on campus? Does the REU support vegetarian/vegan/kosher/gluten free and other diets?

In addition to housing and a stipend, REU scholars are given $400 in dining points to help cover the cost of meals. This stipend is not intended to cover all food expenses over the course of the 10-week program. However, food will be provided at social events and workshops throughout the summer, with accommodations for any dietary restrictions and allergens (information collected prior to arrival).

As far as places to eat, College Town houses a variety of restaurants, some of which accept our campus dining stipends as payment. For more detailed information, see the summer dining hours of operation page.

As an REU participant, are there extracurricular activities that I need to participate in during the program?

As an REU participant, you will attend seminars featuring speakers who will discuss their research and professional career paths. Several social activities also require your attendance. Additionally, the most important activity is the research conference at the close of the program. It is important to ensure that you are available for the entire REU program, start to finish. The last poster session is well attended, and it is an excellent opportunity to show off your great work while networking.

What are the social activities?

Many of our REU participants are visiting Rochester, NY for the first time. We aim to show you many of the attractions that Rochester offers, especially the wonderful sites and hallmark events of the summer. These may include:

• Rochester Public Market
• Memorial Art Gallery
• Upper Falls Park
• Underground Railroad
• Letchworth State Park
• Rochester International Jazz Festival
• Finger Lakes Region
• Rochester Rhythm & Blues Festival
• Rochester Red Wings Game

Are students able to travel off-campus?

Yes, students enjoy many opportunities to travel off-campus, both formally and informally. We have a free shuttle system that takes students to shopping and recreational outlets in/around Rochester as well as on-campus access to Pace bicycle rental and Zipcar car rental systems. Additionally, the Regional Transit System has a stop on campus for students to conveniently and economically travel throughout the region.

The Kearns Center plans trips throughout the summer for REU students and each year a handful of students have cars on campus that they use for weekend trips in the area (eg. Toronto, New York City, Adirondack Mountains, etc.).

Who handles the travel arrangements for REU students?

How is the stipend paid?

The stipend is paid out over four to five equal payments, depending on the pay cycle, and payments are made on the 15 and last day of the month. Students can elect to sign up for direct deposit into a bank account or they can receive a “live” check.

Is my REU stipend taxable?

Yes. It is taxable and you should report it in your tax filings. No deduction is taken on your stipend payment because it is not large enough to get taxed, but if you have other sources of income during the year, then you’ll end up paying some income tax on it later. Since it is a stipend and not a salary, you will not pay social security tax on it.

Are there opportunities to expand my involvement beyond REU?

Quite possibly, you should speak with your mentor/advisor. For instance, students from the University of Rochester could enroll in independent study and receive course credit. Others have been able to secure funding through their faculty mentor to return a second summer. We also encourage students to apply to the University of Rochester for graduate school.

All webinars for this semester have passed. Please reach out directly to the REU recruiter/advisor Ashley Bui, abui3@ur.rochester.edu with questions or for more information.

The REU Experience at the University of Rochester Webinars

This fall the Kearns Center will be hosting information sessions via Zoom for prospective REU students. Each webinar will feature an overview of the REU structure and programming, details about transportation, lodging, and meals.

• Webinar dates are being finalized. Please revist our website to check for updates.

All sessions are hosted on Zoom, an easy-to-use, web-based webinar tool suitable for laptops/desktops and smart phones (app available). Zoom works best when you have a microphone and speaker available, which comes standard on most laptops and phones.