Science Project Summaries

SPRING 2025

Andrej Prsa

Astrophysics and Planetary Science
College of Liberal Arts and Sciences

NASA's space missions Kepler and TESS provided us with ultra-precise data on tens of thousands of eclipsing binary stars. Analyzing their data allows us to determine fundamental stellar parameters: masses, radii, temperatures and luminosities. These are inferred using modeling codes that attempt to reproduce observed data. PHOEBE is an in-house developed modeling suite used to this end; but even an experienced modeler will take upwards of two weeks to solve a single binary system. To overcome that bottleneck, we have been developing an artificial intelligence-powered engine to reduce the modeling time by a factor of ~million. Using this state-of-the-art approach, we can solve thousands of systems in a short amount of time. The results are subsequently used to test and calibrate models of stellar evolution and gain a better understanding of binary and multiple star populations in our Galaxy.

The Match research assistant would learn to perform fluorescence immunohistochemistry and microscopy. This will involve sectioning brain tissue, staining brain tissue for markers of neurogenesis (BrdU and doublecortin), taking pictures on a microscope, and counting the number of BrdU+ and doublecortin+ neurons. Eventually, the student would analyze the data and produce graphs depicting the results.

The research assistant (RA) will work with the team to understand, directly participate, and contribute to the project. In particular, the RA will learn how to download satellite data, how to interpret the observables, how to formulate a model using PHOEBE, and how to adjust the parameters to match the model with the observables.

Kelly Prsa

Astrophysics and Planetary Science
College of Liberal Arts and Sciences

Akin to seismology, asteroseismology is the study of stellar pulsations to understand the interiors of stars. Most stars in the sky pulsate, and through the application of asteroseismic analysis, fundamental parameters such as stellar mass, radius, internal rotation, and stellar structure can be determined.

The selected student will work with NASA's Kepler and TESS space data to identify pulsations in a list of known heartbeat stars. Heartbeat stars are double star systems where the stars undergo gravitational distortions at their closest approach, causing their light variations to resemble a cardiogram (hence the name). When analyzing the light variations of these stars, extensive analysis is required to differentiate between pulsations and gravitational distortions. Positive detections require detailed analysis using periodograms and wavelet transforms.

The selected student will work closely with the professor to ensure clear detection for each object. The tasks involve downloading the data, modeling the double star features using pre-written code, applying periodograms, and applying filters and wavelet transforms. While much of the code is pre-written for this task, the student will collaborate with the professor to identify areas where improvements could be made as they analyze the sample.

The student will first need to learn about binary stars and pulsations to gain an understanding of the project. They will then learn how to use Jupyter notebooks to extract information from the light curves. The student will be provided with a list of approximately 150 objects and will work to identify which ones have pulsations. The student will work closely with the professor to ensure the project's success.

The student will have the opportunity to work in the professor's office at select times during the week to propel the project forward and encourage open communication.

Dan Kraut

Chemistry and Biochemistry
College of Liberal Arts and Sciences

The proteasome is a macromolecular machine that unfolds and degrades proteins in all eukaryotic cells. In the Kraut lab we study the processivity of the proteasome – that is, its ability to unfold and degrade substrates containing folding domains without falling off the substrate. Substrates to be degraded are polyubiquitinated, meaning a chain of small proteins called ubiquitin are attached to lysines within the substrate. In addition to targeting the substrate to the proteasome, we have previously shown that ubiquitin seems to activate the proteasome for better unfolding of its substrates. In this project, we will compare the degradation of substrates containing an internal ubiquitin-like-domain (UBL) with substrates that contain an internal stretch of ubiquitin domains. Our hypothesis, based on preliminary evidence, is that the UBL domain will activate the proteasome’s unfolding ability to a greater extent than ubiquitin does. Depending on the results of initial experiments, we may then generate new DNA constructs encoding new protein substrates to determine which portions of the proteasome interact with the substrate, or we may use proteasome ubiquitin receptor mutants to examine the mechanism behind any observed differences.

The research assistant will learn how to make substrate proteins for use in degradation assays, how to purify proteins, how to do degradation assays (enzyme kinetics), run protein gels, quantify and analyze data from the degradation assays, and ultimately how to design new experiments based on the results of previous experiments. Research is a significant time commitment. Early on, the research assistant will work alongside me, but as they master various techniques, will become increasingly independent. This project might extend beyond the timeline of the First-Year Match Program. Applicants should be willing to continue working in the lab in the summer (with a stipend) and, ideally, throughout their time at Villanova. Ultimately, research may lead to publications and/or presentations at national meetings.

 

Kevin Minbiole

Chemistry
College of Liberal Arts and Sciences

This collaborative and multidisciplinary project utilizes synthetic organic chemistry, physical organic chemistry, and microbiological analysis to develop novel disinfectant compounds to combat pathogenic bacteria, and shed light on why bacteria can resist the traditional disinfectants that have been used for nearly a century. Moving beyond traditional structural classes of disinfectants that have become susceptible to bacterial resistance, we will develop fundamentally different structures that, when paired with careful analyses of their physical properties and solution behavior, will shape the next generation of antimicrobials.

The Match student will work in the synthetic organic chemistry lab of the Minbiole group. Because no research experience or knowledge of organic chemistry is necessary, the student will be trained in the discipline of organic synthesis, building novel disinfectants. This will require advanced reaction planning, reaction execution, then purification of characterization of products. Work will initially be alongside a more senior undergraduate or Masters student.

 

Mauricio Gruppi

Computing Sciences
College of Liberal Arts and Sciences

This project aims at characterizing the mentions to named entities by various US-based news producers. A named entity is a reference to a real-world object, such as a person, institution, or location. For example, “New York City” is a named entity that references a location. The goal is to provide a quantitative description of the entities mentioned by the news publishers with respect to the categories of entities that are referenced, as well as the sentiment of the text around such entities. We seek to answer the following questions:

  1. What categories of entities are mentioned the most by each news producer?
  2. What named entities are mentioned more frequently overall?
  3. Which entities are mentioned in a positive context, and which are mentioned in a more negative context?

To answer these questions, we will utilize a large corpus of news publications containing over 7 million articles published by more than 500 news websites. Language models will be used to perform named-entity recognition (NER), and sentiment analysis of the target data. Once the named entities are identified, statistical analyses will be performed to determine the most frequent entities, and the correlations between entity and sentiment (positive or negative).  With these results, we will proceed to ask deeper research questions, such as whether correlations between entity, sentiment and the credibility of the news producers exist.

The student’s responsibilities are:

  1. To develop code (in Python 3) to extract the named entities from the news data. This includes becoming familiar with Python 3 and other necessary NLP tools, as well as reading instructions on how to implement named-entity recognition.
  2. To implement code to analyze of the observed NE’s, including frequency count and sentiment analysis.
  3. To meet with the faculty mentor on a regular basis (to be determined by the student and faculty) to report on the progress and challenges encountered during the project, and to receive advice from the faculty on how to proceed.
  4. To document the work and results by writing a technical report.

Nathaniel Chodosh

Computing Sciences
College of Liberal Arts and Sciences

Now, more than ever, we experience much of the world remotely. This requires us to communicate our experiences to people through digital media. For example, to describe to a remote person what it is like to be on Villanova's campus, we might use words, send them pictures or videos, or even "walk" them around while on a video call. These modes of communication effectively communicate *our* experiences but do not tell someone what it would be like for *them* to explore the campus. It does not give them agency. The simplest way to give someone a taste of this experience would be to let them point the camera and move around campus freely in a virtual environment.

The classic way of creating these virtual environments is to collect a large amount of video data of the location, construct a 3D model, and then put that model in a rendering or game engine. This has already been done for the campus! However, extracting 3D models from video data is tricky and often gives the final product a synthetic video game feel. Furthermore, many elements of the visual world (water, glass, shiny objects, etc) are difficult to extract or even impossible to represent with a 3D surface model. Recent work in Computer Vision has addressed many of these concerns with a new paradigm for representing 3D worlds: novel-view synthesis. In novel view synthesis, one skips the step of extracting a complex 3D surface model and instead creates an implicit 3D model that can be directly queried for high-resolution renderings from any viewpoint. This project aims to apply these new techniques to the existing Villanova campus data.

The project will involve:

  1. Accessing and manipulating the image data for the campus.
  2. Evaluating and refining existing novel-view synthesis applied to this data.
  3. Deploying the final model.

The Match student research assistant will:

  1. Access the video survey data and transfer it to a remote computing machine.
  2. Manipulate this data into a format applicable to novel-view synthesis methods.
  3. Experiment with existing code bases and parameters for novel view synthesis methods.
  4. Create a final deployment website for the finished model.

 

Xue Qin

Computing Sciences
College of Liberal Arts and Sciences

The virtual reality (VR) industry continues to experience significant growth and expansion, with approximately USD 32.64 billion market by the end of 2024. Due to its unique immersive experience and simulation ability, VR technology is increasingly being adopted in fields such as gaming, healthcare and education. Therefore, comprehensive graphic user interface (GUI) testing is crucial to validate user experience and designed functions. Unfortunately, many of the existing testing approaches are white-box testing and require the app’s source code. With over six major VR brands and over five VR engines on the market, it is challenging to design universal testing support for all VR apps.

Recent Generative AI (GenAI), such as GPT-4 and BERT, shows its ability to train, process, and generate data in mixed formats, including text and images. In this project, we want to explore the potential of using GenAI to assist VR exploration testing. In particular, we plan to conduct a case study using GPT-4o and try to answer three questions:

  1. How can GenAI help in test entity selection in the user’s view?
  2. How can GenAI help in suggesting test actions across multiple views?
  3. What are the limitations and potential solutions?

The view is known as the field of view (FOV). The initial question in our study focuses on the fundamental operation of exploration testing: selecting the testing target. Efficient target selection involves accurately identifying objects, organizing them effectively, and correctly localizing them within the scene. Once the test targets are determined, a sequence of actions is applied accurately to complete the exploration task. Given that previous actions can influence subsequent ones, we aim to utilize GenAI's memory capabilities to explore how contextual information across multiple interactions affects the accuracy of the suggested results. The final question addresses our findings on GenAI's limitations and potential improvements for future research.

The research assistant will perform a variety of tasks associated with the project, including:

  1. Collect the VR app’s screenshots to form the experiment database.
  2. Prepare the ground truth of the detected targets and test actions in these screenshots.
  3. Write a simple GenAI API-based tool to process the captured screens. In particular, learn to use GPT-4 APIs to send screens and requirement prompts and get results automatically.
  4. Design a comparison tool to analyze the results systematically.
  5. Write a statistical report for the empirical study results.
  6. Learn to explore and discuss the potential solutions.

More importantly, the students will build crucial preliminary work that can be extended to numerous future works and will have the opportunity to keep working on the research topics in future semesters.

 

Xue Qin

Computing Sciences
College of Liberal Arts and Sciences

Voice Assistant (VA) in smartphones has become very popular with millions of users nowadays. A key trend is the rise of custom VA embedding, which enables users to perform the customized tasks of their favorite app through voice control. However, with such a great demand, little effort has been made to support app developers in VA development. Moreover, many user-oriented VA control approaches even increase the programming burden on developers. To reduce the workload and improve code efficiency, we will design and evaluate a novel approach that reuses the test code of an application to support its VA development.

In this project, we assume the VA function has been built and will design a mapping system that links the received user command to the functions. In particular, we will first generate the textual description (short sentences or phrases) of the pre-built functions by adopting the Large Language Model (LLM) powered code-to-text model and then using the NLP-related techniques to find the target functions for given user commands. We will compare the similarity between the sentences using different similarity measuring approaches. Moreover, we will explore the solution to identify or extract the variable values of functions from the user command. For instance, when a user requests to start a ten-minute timer, we need to invoke the setTimer() method and pass a duration variable that represents “10” minutes. After finishing the design of the approaches, we plan to evaluate the system by letting real users speak out the query and observe the task completion status.

The research assistant will perform a variety of tasks associated with the project, including:

  1. Learn how to program with the LLM models such as GPT by following documents.
  2. Design a mapping system using LLM so that it can: generate textual descriptions for the function code, compare the similarity between the user commands and the descriptions, and identify the variable value if it exists.
  3. Prepare evaluation task set based on the pre-build functions.
  4. Conduct a user study by inviting university students to finish the task.
  5. Record the study data, such as user commands and the task completion status.
  6. Write a summary report for the study results.

More importantly, the research assistant will build crucial preliminary work that can be extended to numerous future works and will have the opportunity to keep working on the research topics in future semesters.

 

Steven Goldsmith

Geography and the Environment
College of Liberal Arts and Sciences

Each year, over 20 million metric tons of road salt (e.g., sodium chloride) are applied to U.S. roadways as a de-icing agent. Much of this salt makes its way into rivers and streams where it can have harmful impacts on aquatic life and on downstream municipalities who rely on these waterways as a drinking water resource. Additionally, sodium in road salts deposited on the side of roadways can infiltrate into soils where it displaces base cations (calcium, magnesium, and potassium) and potentially harmful trace metals (copper, lead, and zinc) into shallow groundwater, which are then discharged into streams as baseflow. These increased metal concentrations can also have a negative influence on aquatic life.

For this study, we will characterize the impacts of road salt on headwater streams in the Villanova campus area. We will carry out the collection of streamwater samples and discharge measurements in several suburban watersheds. Water samples will be analyzed for major anions and cations using ion chromatography and select trace metals using inductively coupled plasma mass spectrometry. The analytical data will be subsequently compared to water quality standards for the protection of aquatic life and drinking water quality.

The Match student would be required to meet with the faculty mentor on a weekly basis to discuss all aspects of the project, including reading relevant literature, sample preparation, and analysis techniques. In particular, the student should set aside a 2-3 hour block of time to work with the mentor on stream sampling and water sample analysis, as well as data analysis techniques. It is anticipated that the student will gain more independence with the data analysis techniques over the course of the semester. Though the student would also be accompanied during field sampling.

Nathaniel Weston

Geography and the Environment
College of Liberal Arts and Sciences

The Weston lab works on questions around the resilience of tidal wetland ecosystems in the face of climate change and land use change. Tidal wetlands provide many critical ecosystem services, but many wetland systems are threatened by sea-level rise, changing inputs from the watershed, and other impacts of human activities. The First Year Match students will work with the Weston lab on research in support of several externally-funded projects that examine carbon and nitrogen cycling in coastal wetland ecosystems and the response of coastal wetlands to sea-level rise and changing land use. The laboratory conducts fieldwork in the Spring, Summer, and Fall in Plum Island Sound, MA and more locally in the Delaware River estuary. Samples collected in the field are brought to the laboratory for processing.

The First Year Match students will gain laboratory experience in environmental sample processing. The Match student will help process soil and water samples that have been collected during field research trips. Laboratory work will consist of analytical analysis of nutrients (ammonium, nitrate, phosphate), dissolved organic carbon and nitrogen, and dissolved inorganic nitrogen. The Match student will also assist with laboratory organization, cleaning, and other tasks needed to keep the lab functional. The Match student may also assist with several ongoing graduate student projects. There may be opportunities for the Match student to undertake additional field and laboratory research beyond the Match program (during the spring, summer, fall) with the Weston lab, if so desired.

Ryan Almeida

Geography and the Environment
College of Liberal Arts and Sciences

The trade of reptiles and amphibians (i.e. herpetofauna) for exotic pets is an expansive industry that threatens biodiversity. While much of this trade is both legal and sustainable, it can simultaneously contribute to species declines by incentivizing the overexploitation of wild populations, facilitating the spread of invasive species, and acting as a vector for the transmission of zoonotic pathogens. In the United States, herpetofauna are frequently sold at trade shows, where breeders, consumers, hobbyists, and other enthusiasts convene to sell and purchase animals and supplies, often traveling long distances and across state lines. Importantly, these shows are often used by breeders to debut new species and/or morphs; however, the role these expositions play in broader US exotic herpetofauna trade network, in terms of species composition and consumer profile, has yet to be studied.

This project seeks to fill this knowledge gap by establishing a monitoring program for herpetofauna trade shows throughout the state of Pennsylvania. Pennsylvania, relative to other states in the northeastern United States, which has relatively little restrictions on which species can be sold, making it a popular destination for consumers looking for rare, venomous, and/or “risky” species; as a result, there are a relatively large number of long-term trade shows held in cities throughout the state (e.g. Morgantown, Oaks, Hershey, Scranton, Carlisle, Pittsburgh). Information on species composition, selling prices, and the geographic distribution of participants collected from trade shows will be compared to existing large datasets on herpetofauna trade collected by governmental organizations and from online retailers to characterize the contributions of trade shows to the broader exotic herpetofauna trade network.

The student will work towards creating a master reference guide of potential species, morphs, and vendors of trade shows in Pennsylvania, and will conduct at least one pilot survey of a trade show, with an emphasis on workshopping methodological approaches and collecting preliminary data to inform future surveys. To achieve these goals, the student will collate information from vendor websites, social media, in situ surveys of trade shows, and existing datasets to create a comprehensive list of potential species, selling prices, and relevant logistic information about trade shows. Additionally, the student will meet weekly with the faculty mentor, read relevant scientific literature, and discuss aspects of the project.

Vikram Kamat

Mathematics and Statistics
College of Liberal Arts and Sciences

The project is broadly in the field of extremal combinatorics, which is a field in discrete mathematics dealing with certain optimization-type problems -- i.e. maximizing or minimizing the size of a given discrete structure subject to a given constraint. The constraint is typically provided in the form of a forbidden substructure that may not be contained inside the larger structure being optimized. The project will aim to investigate generalizations and applications of a seminal extremal theorem known as the Erdos--Ko--Rado (EKR) theorem, a foundational result that has found wide-ranging applications in the fields of probability theory, discrete geometry, combinatorial designs and computer science.

The project will particularly focus on a structurally intricate graph-theoretic generalization of the EKR theorem, and its connections to a related, longstanding conjecture of Chvatal, from 1974.

The student is first expected to carry out a thorough literature survey, one that involves studying the foundational results and proof methods in the field of extremal set theory. They would then begin investigating—using theoretical and computational methods—generalizations of known results in the field. Theoretical methods will include developing new extensions of known proof methods commonly used in the field and using them to discover new theorems. Computational methods will involve making use of a computer algebra system such as MAPLE to generate and interpret relevant data that would allow us to make and/or refine conjectures on the mathematical objects being studied.

Becka Phillipson

Physics
College of Liberal Arts and Sciences

X-ray Binaries (XRBs) are binary star systems in a close orbit containing a regular star and a companion that is a compact object – a black hole or neutron star. These exotic systems are given their name because they are very bright at X-ray frequencies. The basic picture of an XRB consists of a compact object surrounded by hot plasma that orbits and falls onto the compact object in the shape of a disk, called an accretion disk. At the innermost regions around the compact object is a coronal plasma producing X-ray emission at even higher frequencies, sometimes close to the gamma-ray regime. In addition to the accretion disk and corona, many XRBs also contain collimated jets that emit at radio frequencies. Each component of the XRB environment is dynamic: the inner edge of the accretion disk contracts and recedes, the corona can grow or shrink, and the radio-emitting jet may appear and disappear. The evolution of the emission over time offers a particularly powerful window into the structure of the accretion environment and the relationship between different emitting regions in the system. Astrophysicists can use techniques from time series analysis to connect the variations in brightness over time to the dominant physical mechanisms at play.

This project will focus on exploring creative approaches to presenting results from the analysis of X-ray data of a black hole XRB from the Rossi X-ray Timing Explorer. Our current preliminary results include snapshot images of the XRB at different points in time and show that the brightness variability of the source connects to the geometry and changes in the accretion environment. This project will aim to explore techniques such as sonification to make our results accessible and interesting to the diverse science community.

The student will meet with the mentor on a weekly basis, and as needed, during which time the student and mentor will discuss the project and how to proceed with each step of the analysis. The student will perform the analysis and data visualization work using a coding environment that is suitable for a personal laptop. At the end of each week, the student will submit a 1-page summary to the mentor detailing the accomplishments and challenges that occurred during the week and goals for the following week. The student will keep the 1-page summaries as a work log and compile it into a final report at the end of the semester. The only prerequisite for the student is an enthusiasm for astrophysics. Although programming experience is not necessary, some familiarity with Python will be very helpful, as will familiarity with physics concepts (but again, not required). Majors in subjects outside of physics or astronomy are welcome!

David Chuss

Physics
College of Liberal Arts and Sciences

Dust grains in the Milky Way become aligned by interstellar magnetic fields. As a result of this alignment, the resulting far-infrared radiation emitted by these grains is polarized. By measuring the polarization of this light, we can probe the physics of these interstellar magnetic fields and by doing so increase our understanding of the role of magnetic fields in dynamical Galactic processes. This is especially true in the central region of our Milky Way where densities, velocities, and field strengths all exceed median values in the Galactic disk. Our group has completed a survey of the Galactic Center using NASA's Stratospheric Observatory for Infrared Astronomy (SOFIA). This polarimetric survey covers the inner 500 light-years of the Galaxy and provides clues as to the complexity of the magnetic field in this region. In this project, we will compare this data set with other multi-scale tracers of magnetic field structure to better understand the 3-dimensional configuration of the magnetic field. This will involve investigating trends in polarimetric quantities (magnitude and direction) with respect to properties of the central Milky Way such as temperature, density, and radiation environment.

The student will learn to work with astronomical data (FITS) files, which contain the data and the telemetry.  They will compare the polarimetric data from our survey with other data sets quantitatively using Python to display images and graphs. Model fitting will also be employed to quantify relationships between quantities from disparate data sets.

Scott Dietrich

Physics
College of Liberal Arts and Sciences

A student working on this project will develop a machine learning algorithm to automate the categorization and analysis of optical images of van der Waals (vdW) material flakes, which are vital for flexible electronics and nanoscale devices due to their diverse properties. Currently, optical images of flakes are manually captured and tagged so that they can be used for various other research projects. The student will photograph these flakes and measure their thickness using an Atomic Force Microscope to create a dataset that correlates thickness with optical properties such as color. The student will then train a machine learning model to predict the thickness of flakes based on their optical characteristics. In addition, image processing techniques will be applied to segment regions of uniform thickness and automatically calculate their area and dimensions. This project aims to streamline what is currently a labor-intensive process used in laboratories around the world.

Students will gain hands-on experience working in the 2D Materials Laboratory, engaging in both lab work and Python programming tasks. Previous experience programming in Python is useful but not necessary. They will develop skills in nanoscale data collection and analysis using an AFM, as well as image processing techniques, such as color analysis and segmentation. The project also offers experience in machine learning algorithm development and training, specifically focused on optical image analysis. Additionally, students will interact with peers working on related projects involving vdW flakes, gaining insights into their broader applications. The lab component typically requires two to six hours per week, with additional hours depending on the nature of the work each week. Programming tasks can be done remotely, allowing for flexible scheduling based on project needs.

Georgia Papaefthymiou and Scott Dietrich

Physics
College of Liberal Arts and Sciences

Atomic Force Microscopy (AFM) has emerged as an indispensable tool in the study of low-dimensional materials of both hard and soft matter. In this study we use AFM to study human ferritin heteropolymers over-expressed in E. coli bacteria by genetic engineering techniques. Ferritin is the iron storage protein in living systems where iron, an essential element for life, is sequestered within a spherical protein shell of 8nm inner and 12nm outer diameter, composed of two types of amino-acid chains, H and L. These two types of chains play complementary roles in iron reduction, nucleation and nanoparticle growth within the shell. H-rich ferritins are associated with heart and brain tissue, where fast iron trafficking occurs, while L-rich ferritins are associated with liver and lung tissue, for long-term iron storage. Ferritin malfunction is associated with a host of iron-related disorders, including neurological disorders.  Thus, ferritin is of great interest to the physiology of iron homeostasis in health and disease. In addition to ferrihydrite, other metal oxide nanoparticles can be grown within the protein shell, which presents itself as a robust nanotemplate to produce monodisperse nanoparticles, of interest to nanoscience and nanoengineering (nano biomedicine, targeted drug delivery, nano-architectural designs for device applications, etc.).

The nanomechanical properties of heteropolymeric shells will be studied by investigating the elasticity, deformation, and Young’s modulus of H-rich and L-rich heteropolymers in the absence and presence of a ferrihydrite core. The student will be trained in the preparation of biological samples for AFM investigations and the operation of this diverse, multimode microscope to obtain topographical maps of ferritins and measure the elastic properties of various ferritin samples. This study is of relevance to biology, bioengineering and nanotechnology.

  1. Preparation of AFM samples. Dilution of the as-received samples from collaborator in DI water and deposition on Si oxide substrates.
  2. Examination and optimization of deposited samples for AFM analysis.
  3. Undergo training in the use of the Parks System AFM for obtaining topographical maps of ferritin in the optimized deposited samples.
  4. Familiarization with the Pin-Point mode of operation to obtain experimental measurements of the elasticity, deformation and Young's Modulus of samples.
  5. Interpretation of measurements.

Vasil Rokaj

Physics
College of Liberal Arts and Sciences

The forces and interactions between charged particles, for example, electrons, in nature are mediated by the quanta of the electromagnetic field known as photons. Normally, in empty space, the interaction between photons and matter is weak. However, we can enhance this interaction by changing the environment. If we place the particles in confined spaces, they can interact with the same photon many times. Some of the structures that help boost these interactions are waveguides and optical cavities. These structures are very important for quantum computing and quantum networks.

In this project, we will study how light and matter interact in optical cavities. More specifically, we will focus on how groups of quantum oscillators and systems with just a few energy levels interact with light inside these cavities. We will investigate the states of these systems, and we will study the correlations and entanglement between photons and matter. Entanglement is a special quantum property where particles become linked, so the state of one particle can instantaneously affect the state of another, no matter how far apart they are. Understanding these properties is important for quantum computing and could help us create new types of quantum materials.

The project will involve performing numerical calculations (using software like Wolfram Mathematica) to understand how entanglement scales with the number of particles that are interacting with light. We will also study what happens when the interaction between the photons and matter is perfectly in tune (resonant) and when it's not (off-resonant).

This area of research is very active and exciting, and the results we find could make important contributions to the fields of quantum optics and cavity quantum electrodynamics. The work we do here might even lead to a published paper.

With the upcoming student, we will meet twice a week. In the first meeting of the week, we will be discussing the basic principles of quantum systems, as well as introducing key mathematical tools like linear algebra and matrices. This will allow the student to get familiar with all the necessary technical tools to perform numerical calculations on quantum systems using Mathematica.

In the second meeting of the week, we will have a hands-on experience on Mathematica, focusing on the progress done by the student, as well as on the student's questions. This will solidify the conceptual understanding of quantum systems and will also teach the student how to present their results effectively. The student will learn the basics of Mathematica and will on existing code for systems where quantum particles are interacting with each other.

Joey Neilsen

Physics
College of Liberal Arts and Sciences

GRS 1915+105 is a stellar-mass black hole renowned for its strong exotic X-ray variability in a luminous outburst that lasted for decades. In 2018, this bright X-ray source began to dim sharply, and in May of 2019, its brightness dropped precipitously, as if the black hole were suddenly hidden or “obscured” behind clouds of gas with temperatures reaching up to millions of degrees. This unexpected dimming has persisted for five years! This project involves studying X-ray spectra of GRS 1915+105 from the NICER X-ray telescope, which has observed GRS 1915+105 frequently during its obscured state. These spectra can offer clues to the nature of the obscuring gas.

Students involved in this project will use data from NICER onboard the International Space Station to perform X-ray spectroscopy, fitting models to data (focusing on variable emission lines from hot iron gas), and will build their astrophysical intuition with a review of relevant literature. This work will build on work from previous group members who have studied other aspects of the obscured state.

Elizabeth Pantesco

Psychological and Brain Sciences
College of Liberal Arts and Sciences

The purpose of this study is to assess a healthy individual's cardiovascular reactions to mildly stressful laboratory tasks, and to examine how these cardiovascular reactions to stress are associated with a variety of personality and lifestyle factors.  Study participants will be asked to provide demographic, psychosocial, and health behavior information before coming to the research lab. In the lab, they will be asked to perform two different types of stress tasks (a computer task and an anger recall task) while electrocardiography and blood pressure measurements are taken.  We are interested in examining the degree to which these cardiovascular metrics, like heart rate and blood pressure, increase during the task, and how well these metrics return to baseline after the task is over.  

This study is informed by the cardiovascular reactivity hypothesis, which posits that exaggerated and repeated increases of the sympathetic nervous system in response to stress over time increases risk for the development of cardiovascular disease. Additional theories suggest that, in addition to reactivity, prolonged cardiovascular recovery from stress may also have pathophysiological effects on the cardiovascular system.  Thus, studying the correlates of stress reactivity and stress recovery can inform preventative models of cardiovascular disease. In our lab, we are particularly interested in how variables such as health behaviors (e.g., sleep) and psychosocial variables (e.g., stress, negative emotions) relate to cardiovascular reactivity and recovery.

The student research assistant would primarily assist the lead Research Assistant in collecting participant data during the in-lab stress tasks.  This would involve obtaining informed consent from study participants, ECG and blood pressure set-up, explaining tasks to participants, measuring height and weight, and helping run the computer program used to collect cardiovascular data.  Other responsibilities would be assisting with participant scheduling, data entry and basic data cleaning, and conducting literature searches. The student would be required to complete relevant IRB ethics training prior to starting the study.

Ben Sachs

Psychological and Brain Sciences
College of Liberal Arts and Sciences

The gut-brain axis has recently emerged as an important determinant of human health, including mental health. However, the precise mechanisms through which gut alterations impact the brain have not been conclusively established. This project will investigate whether an experimental mouse model of colitis (a type of inflammatory bowel disease) is characterized by alterations in adult hippocampal neurogenesis. Adult hippocampal neurogenesis is thought to contribute to stress sensitivity and cognitive function, so gut-induced alterations in this process could have important implications for mental health and cognitive acuity.

The student would be trained in the procedures required to perform fluorescence immunohistochemistry and microscopy. The student would use a cryostat to cut brain sections containing hippocampal tissue, then use specific antibodies against markers of hippocampal neurogenesis to compare the levels of neurogenesis in experimental and control mice.

    

    

Garey Hall 200 (top floor) 
800 Lancaster Avenue
Villanova, PA 19085