Research Experiences for Undergraduates (REU)
Advancing Human Health, From Nano to Network
Human health research at the University of Rochester (UR) covers a wide range of engineering and science disciplines and a breadth of topics including accessibility, drug delivery and the development of therapeutic and novel medical devices.
Students who are interested in understanding human health as a continuum of interrelated complex systems and questions will excel in this REU program.
Collaborating UR schools include:
- Hajim School of Engineering
- University of Rochester Medical Center (URMC)
- School of Medicine and Dentistry (SMD)
Our best research students come through recommendations from faculty and fellow students who recognize talent at all levels.
Faculty mentors have appointments in:
- Biomedical engineering
- Chemical engineering
- Electrical and computer engineering
- Mechanical engineering
Our 10 core faculty mentors are 60 percent women and 10 percent underrepresented minority. Collectively, they have mentored over 140 undergraduate students and produced 38 publications with undergraduate students.
The Advancing Human Health, from Nano to Network program is funded by the National Science Foundation (NSF) and is currently in a renewal year. Program operation for Summer 2020 is contingent upon successfully being renewed by the National Science Foundation.
This program runs from May 27-August 1, 2020.
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.
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.
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.
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:
- Detection of acoustic signals in background noise
- Coding of complex sounds, such as speech, by fluctuations in neural responses
- Signal processing to enhance fluctuation cues for listeners with hearing loss
- 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.
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.
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).
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.
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.
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:
- Identify combinations of mechanical and biochemical cues that can instruct stem cell differentiation toward specific musculoskeletal tissue lineages
- Understand what specific characteristics of stem cells play critical roles in these responses
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Applications will be accepted through an NSF-sponsored website beginning on Monday, December 2, 2019.
Follow the steps below to apply:
- Register for an REU ID and then create an REU account at https://www.nsfreu.org/login. (You can skip this step if you've already created an account).
You can select up to ten REU programs and apply to them using a “Common App” format. A common portion will prompt you for a CV, a transcript, contact information for letter writers (they will be notified automatically), and a 500-word statement about your interest in pursuing an REU in general. You will also have to answer some program-specific questions via a Supplemental Application for this REU.
- Complete the common application questions on the NSF website at https://www.nsfreu.org/login. You will need to select "Human Health REU at University of Rochester" in the Site Selection section of the application to apply for this REU.
- Submit the required "Supplemental Application for Advancing Human Health, From Nano to Network REU Program 2020" at https://form.jotform.com/93014015256952.
The application will close on Monday, January 27, 2020.
The REU program concludes with a day-long Summer Research Symposium that brings together over 80 summer research scholars. The symposium includes lightening talks by students in the Advancing Human Health, From Nano to Network REU program as well as an opportunity to engage with the University of Rochester community and community at large through poster presentations. REU students are also required to submit a final paper, as specified by their faculty mentor.
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
Most weeks throughout the program have a consistent rhythm to them. A week may look like this:
9 a.m. —GRE class
6 p.m.—Talk (optional, dinner included)
Noon—Meet with mentor
JazzFest (optional, multiple events)
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 will receive a housing allowance to pay for a local rental unit or sublet.
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.).
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.
Who handles the travel arrangements for REU students?
The Kearns Center arranges and pays for travel round-trip travel for most REU students to/from the city of their choice in the continental U.S. Baggage fees are the responsibility of each student. If a student wishes to drive to campus, the Center will reimburse the student for travel costs up to $450 and payment is made at the end of the summer. The reimbursement is based on the mileage rate established by the University (the rate was $0.58 per mile for the 2018-2019 academic year).
How is the stipend paid?
The stipend is paid out over 4 – 5 equal payments, depending on the pay cycle, and payments are made on the 15th 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.
How are REU students selected?
The selection process is based on the objectives of each grant and seems to match students with research that interests them. REU programs funded by the National Science Foundation generally seek to attract a diverse pool of talented students.
When will I know if I have been selected for an REU?
Award notices typically come in two phases – initial selection and waitlist selection – with initial notices going out in late February/early March and waitlist matches going out in mid-March. 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.
The REU Experience at the University of Rochester Webinars
This fall the Kearns Center is hosting four virtual 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, and Q&A with a student and faculty mentor team from 2019.
- Wednesday, October 9 at 2 p.m. EDT (1 p.m. CDT/12 p.m. MDT/11 a.m. PDT)
- Thursday, October 24 at 12 p.m. EDT (11 a.m. CDT/10 a.m. MDT/9 a.m. PDT)
- Wednesday, November 6 at 3:30 p.m. EST (2:30 p.m. CST/1:30 p.m. MST/ 12:30 p.m. PST)
- Friday, November 15 at 3 p.m. EST (2 p.m. CST/1 p.m. MST/12 p.m. PST)
To RSVP and receive the Zoom login for your session complete and submit the Webinar Registration Form.
All sessions are hosting 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.