The goal of our project is to connect climate change to personal narrative through a mobile audio installation that travels the Adirondack Park recording the stories of climate change. Research has shown that personal stories help reduce the idea that climate change will only impact people in the future. To make meaningful climate resilient communities, it’s more effective to hear and think about how climate change is impacting our local community than read scientific data. Participants were invited into this traveling recording studio to record their personal stories. To date, we've collected hundreds of climate stories from the Adirondack Park. More information about the project can be found here: https://adkclimatestories.org/ The expectation for our summer research students are that you collaborate with faculty mentors and the digital scholarship lab to catalogue and construct an audio archive of these stories that will be shared with our community partner the Adirondack Council. This research is for credit. This project can be done remotely.

Demand estimation is at the core of many economic problems. With the availability of large amounts of unstructured/high-dim data, there has been increased focus on (1) to allow for unstructured data in demand estimation settings and (2) to address endogeneity issues while using observational data. Extant workhorse models (e.g., Berry et al. 1995) are highly parameterized and rely on availability of exogeneous variations to ascertain identification. In this paper, we borrow from the literature on partial identification and propose the Deep Causal Inequalities (DeepCI) estimator that overcomes both these issues. Instead of relying on observed labels, the DeepCI estimator uses inferred moment inequalities from the observed behavior of agents in the data. This by construction can allow us to circumvent the issue of endogenous explanatory variables in many cases. We provide theoretical guarantees for our estimator and prove its consistency under very mild conditions. We demonstrate through extensive simulations that our estimator outperforms standard supervised machine learning algorithms and existing partial identification methods. Finally, we demonstrate how to use deep inequalities in the differentiated products demand estimation framework. The flexibility of the method allows for highly unstructured data like images, which we exploit in the empirical application based on the consumer-level purchase data. Job Description • Run simulations using deep learning models with image data Qualifications • Proficient in deep learning with PyTorch, and experience with computer vision • Willing to devote at least 20 hours/week to the project and participate in weekly Zoom meetings • Students who wish to pursue PhD degrees (especially in the intersection of Business and Data Science) are particularly welcome Benefit • Recommendation letters from PIs • Opportunities for coauthorship on conference papers • Opportunities to be enrolled in UW PhD programs PIs • Amandeep Singh, Assistant Professor of Information Systems, Foster School of Business, University of Washington • Jiding Zhang, Assistant Professor in Operations Management, NYU Shanghai

Project FLIGHT is a multi-method study focused on identifying how conflict between parents influences their interactions with each other and with their child. The project aims to examine the effects of interparental conflict and its potential spillover to the parent-child subsystem. The project will follow 250 families with a three-year-old child over the course of a three-year period, in hopes of better understanding the impact of interparental conflict on parenting styles. Using self-reports, interparental problem-solving tasks, parent-child interaction tasks, and neurobiological assessments, Project FLIGHT plans to: Examine whether parents’ neurobiological responses to stress impacts parent-child interactions. Identify how aspects of interparental conflict may serve as an explanation for spillover to the parent-child relationship. Explore how parents handle conflict among themselves, and how they interact with their child as a result of potential spillover. The primary goal of our research is to determine how and why interparental conflict affects parenting skills, thus ultimately influencing the parent-child system. Understanding why this happens may lead to new interventions for helping families who experience these problems. We are looking for interns who are able to commit to 2 semesters of research with our Project, starting Summer 2023.

The project is about developing a visualization tool in Web for visualizing the internal working mechanisms of digital cameras. It will be for full-time positions (40 hours/week) during the summer break. If you are a US citizen, you will receive compensation ($8K in total). If you are not a US citizen, unfortunately we don't have funding, but will support you to apply for other fundings, such as the Discover Grant.

The project investigates the use of multi-spectral cameras for computer vision tasks in self-driving cars. We will understand how to best design such cameras and deep learning algorithms that use these cameras in the context of self-driving cars. It'll be full-time (40 hours/week) position during the summer break. You will be compensated if you are a US citizen ($8K in total). If you are not a US citizen, we unfortunately don't have direct funding sources, but will support your application for other funding sources, such as the Discover Grant.

Project Description: This project studies how defects can affect phase transitions and other properties of matter at high-pressure conditions (such as those existing at the deep interiors of planets or created by planetary impacts, laser shock or diamond-anvil cell experiments). The research is conducted by computer simulations (e.g., ab initio electronic structure methods) using high-performance computing facilities. We work closely with experimentalists and theorists inhouse at UR/LLE and beyond (national labs and other tier-one universities). Students who are interested in learning more are encouraged to see our recent publications (https://scholar.google.com/citations?hl=en&user=Hzbo2JsAAAAJ&view_op=list_works&sortby=pubdate) and contact Dr. Shuai Zhang (szha@lle.rochester.edu) prior to the application.

The Laser System Science Group in the OMEGA Laser Facility Division at the LLE is looking to hire an undergraduate computer science, math, optics or physics major with a keen interest/ability in machine learning and computer applications. The project may involve laser/optical system modeling and design.

Research Assistants (RAs) are needed for the “Roc Romantic Relationship Study.” RAs will be involved in a lab study in which romantic couples are brought to a lab on campus to engage in three meaningful conversations followed by reporting on their thoughts and feelings in an online survey. RAs will help administer these sessions, greeting couples to the lab and managing the 2-hour study session. RAs will also attend team trainings and meetings. Assist with a range of tasks including interfacing with participants, administering surveys, coding recordings of lab sessions, and basic data management. RAs will be expected to work 5-20 hours per week, and need to be flexible with working evenings and weekends.

https://www.lle.rochester.edu/index.php/education/research-areas/omega_experiments/ Project: Analysis of nuclear data from cryogenic implosions with the Omega Experiments Group

The successful candidate will work to improve and automate a characterization setup for gas jet density profiles. The setup involves the pressurization of a gas jet, probed using a co-timed short-pulse probe laser which is recorded using a wavefront sensor. Measurement scans involving the firing of the gas jet, the laser, and the actuation of the wavefront sensor will be automated using LabView or MATLAB. Absolute calibration of phase accumulation will be performed using pre-characterized glass targets. Finally, an interactive phase unwrapping script will be written to extend density profile measurements beyond axisymmetric gas jet profiles. The culmination of these efforts will be to enable streamlined, accurate characterization of nontrivial gas jet density profiles for laser-plasma experiments.

Ideal candidate combines scientific/technical skills with artistic awareness to generate animations and graphical displays of radiation-hydrodynamic modeling done in our group of inertial confinement fusion experiments. https://www.lle.rochester.edu/index.php/education/research-areas/integrated-modeling-group/

We're on a mission to get every undergraduate involved in research. AURA is a platform that makes research more accessible by providing an easy way to find and apply to on campus research opportunities. We strive to be accessible, convenient, simple and fast, so if you see something that doesn't live up to those values, please let us know by emailing hello@joinaura.us.

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 2022-23 academic year have already been filled. 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).

Our research interests are in the field of cyclic nucleotide signaling and cardiovascular biology, with a particular focus on cyclic nucleotide phosphodiesterases (PDEs) that catalyze the hydrolysis of cyclic nucleotide second messengers (cAMP and cGMP). cAMP and cGMP regulate a wide variety of cardiac functions, from the short-term effects on myocyte contraction/ relaxation to long-term effects such as gene expression and structural remodeling. To identify the PDE isozymes altered in disease hearts, we have performed initial screening for PDEs that are altered in diseased hearts. The expression of a number of PDE isozymes is changed: some are up-regulated and some are down-regulated. The ongoing and future studies are aimed to determine the role and mechanism of these altered PDEs in cardiac remodeling and dysfunction through genetic and pharmacological approaches. To learn more about our research visit: https://www.urmc.rochester.edu/cardiovascular-research-institute/research/yan.aspx. We are seeking undergraduate students who are interested in cardiovascular biology and molecular biology. The student will have the opportunity to become fully involved with all the steps of research being completed in our laboratory, often working directly with Dr. Vivian Si Chen. The student can attend weekly group zoom meeting.

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.

The primary focus of Dr. Gail Johnson’s research group is on the molecular mechanisms of neurodegeneration. The lab has a longstanding interest in the pathogenic processes in Alzheimer disease, and more recently in stroke and spinal cord injury (SCI). For their studies they use a wide variety of different approaches from in vitro enzyme assays with purified proteins, to studies in whole animals. This broad-based approach allows them to translate what they learn about a process or signaling pathway at the molecular level to the in vivo situation. Currently all the positions in my lab are filled. However, when positions become available I will post them on AURA.

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.

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.

[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).

We uses recordings of ground vibrations from seismic sensors across the globe combined with high-performance computing to build high-resolution images of the sub-surface interior of the Earth.

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.

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.

The Rochester Center for Research on Children and Families seeks to better understand children’s adaptation and maladaptation within the context of family relationships and processes. Informed by the developmental psychopathology emphasis on risk and resilience, our work is focused on elucidating the costs and benefits of children’s specific patterns of responding to family processes. The center currently houses several projects, including several large scale, multi-method, multi-level longitudinal research studies funded by the National Institutes of Health.

LLE Education Program Office

Psychology lab focused on the study of relationships and well-being.