Closing Ceremonies for Computational Sensing and Medical Robotics (CSMR) REU
Aug 6 @ 9:00 am – 3:00 pm

The closing ceremonies of the Computational Sensing and Medical Robotics (CSMR) REU are set to take place Friday, August 6 from 9am until 3pm at this Zoom link. Seventeen undergraduate students from across the country are eager to share the culmination of their work for the past 10 weeks this summer.

The schedule for the day is listed below, but each presentation is featured in more detail in the program. Please invite your students and faculty, and feel free to distribute this flyer to advertise the event.

We would love for everyone to come learn about the amazing summer research these students have been conducting!


2021 REU Final Presentations
Time Presenter Project Title Faculty Mentor Student/Postdoc/Research Engineer Mentors

Ben Frey


Deep Learning for Lung Ultrasound Imaging of COVID-19 Patients Muyinatu Bell Lingyi Zhao

Camryn Graham


Optimization of a Photoacoustic Technique to Differentiate Methylene Blue from Hemoglobin Muyinatu Bell Eduardo Gonzalez

Ariadna Rivera


Autonomous Quadcopter Flying and Swarming Enrique Mallada Yue Shen

Katie Sapozhnikov


Force Sensing Surgical Drill Russell Taylor Anna Goodridge

Savannah Hays


Evaluating SLANT Brain Segmentation using CALAMITI Jerry Prince Lianrui Zuo

Ammaar Firozi


Robustness of Deep Networks to Adversarial Attacks René Vidal Kaleab Kinfu, Carolina Pacheco
10:30 Break

Karina Soto Perez


Brain Tumor Segmentation in Structural MRIs Archana Venkataraman Naresh Nandakumar

Jonathan Mi


Design of a Small Legged Robot to Traverse a Field of Multiple Types of Large Obstacles Chen Li Ratan Othayoth, Yaqing Wang, Qihan Xuan

Arko Chatterjee


Telerobotic System for Satellite Servicing Peter Kazanzides, Louis Whitcomb, Simon Leonard Will Pryor

Lauren Peterson


Can a Fish Learn to Ride a Bicycle? Noah Cowan Yu Yang

Josiah Lozano


Robotic System for Mosquito Dissection Russel Taylor,

Lulian Lordachita

Anna Goodridge

Zulekha Karachiwalla


Application of dual modality haptic feedback within surgical robotic Jeremy Brown
12:15 Break

James Campbell


Understanding Overparameterization from Symmetry René Vidal Salma Tarmoun

Evan Dramko


Establishing FDR Control For Genetic Marker Selection Soledad Villar, Jeremias Sulam N/A

Chase Lahr


Modeling Dynamic Systems Through a Classroom Testbed Jeremy Brown Mohit Singhala

Anire Egbe


Object Discrimination Using Vibrotactile Feedback for Upper Limb Prosthetic Users Jeremy Brown

Harrison Menkes


Measuring Proprioceptive Impairment in Stroke Survivors (Pre-Recorded) Jeremy Brown



3:00 Winner Announced
Dissertation Defense: Debojyoti Biswas
Aug 9 @ 10:00 am
Dissertation Defense: Debojyoti Biswas

Note: This is a virtual presentation. Here is the link for where the presentation will be taking place.

Title: Stochastic Models of Chemotaxing Signaling Processes

Abstract: Stochasticity is ubiquitous in all processes. Its contribution in shaping the output response is not only restricted to systems involving entities with low copy numbers. Intrinsic fluctuations can also affect systems in which the interacting species are present in abundance. Chemotaxis, the migration of cells towards chemical cues, is one such example. Chemotaxis is a fundamental process that is behind a wide range of biological events, ranging from the innate immune response of organisms to cancer metastasis. In this dissertation, we study the role that stochastic fluctuations play in the regulatory mechanism that regulates chemotaxis in the social amoeba Dictyostelium discoideum. It has been argued theoretically and shown experimentally that stochastically driven threshold crossings of an underlying excitable system, lead to the protrusions that enable amoeboid cells to move. To date, however, there has been no good computational model that accurately accounts for the effects of noise, as most models merely inject noise extraneously to deterministic models leading to stochastic differential equations. In contrast, in this study, we employ an entirely different paradigm to account for the noise effects, based on the reaction-diffusion master equation. Using a modular approach and a three-dimensional description of the cell model with specific subdomains attributed to the cell membrane and cortex, we develop a detailed model of the receptor-mediated regulation of the signal transduction excitable network (STEN), which has been shown to drive actin dynamics. Using this model, we recreate the patterns of wave propagation seen in both front- and back-side markers that are seen experimentally. Moreover, we recreate various perturbations. Our model provides further support for the biased excitable network hypothesis that posits that directed motion occurs from a spatially biased regulation of the threshold for activation of an excitable network.

Here we also consider another aspect of the chemotactic response. While front- and back-markers redistribute in response to chemoattractant gradients, over time, this spatial heterogeneity becomes established and can exist even when the external chemoattractant gradient is removed. We refer to this persistent segregation of the cell into back and front regions as polarity. In this dissertation, we study various methods by which polarity can be established. For example, we consider the role of vesicular trafficking as a means of bringing back-markers from the front to the rear of the cell. Then, we study how BAR-domain proteins that are sensitive to membrane curvature, can amplify small shape heterogeneities leading to cell polarization. Finally, we develop computational models that describe a novel framework by which polarity can be established and perturbed through the alteration of the charge distribution on the inner leaf of the cell membrane.

Committee Members

  • Pablo A. Iglesias, Department of Electrical and Computer Engineering
  • Noah J . Cowan, Department of Mechanical Engineering
  • Enrique Mallada, Department of Electrical and Computer Engineering
  • Peter N. Devreotes, Department of Cell Biology
Dissertation Defense: Yufan He
Aug 12 @ 1:00 pm
Dissertation Defense: Yufan He

Note: This is a virtual presentation. Here is the link for where the presentation will be taking place.

Title: Retinal OCT Image Analysis Using Deep Learning

Abstract: Optical coherence tomography (OCT) is a noninvasive imaging modality which uses low coherence light waves to take cross-sectional images of optical scattering media. OCT has been widely used in diagnosing retinal and neural diseases by imaging the human retina. The thickness of retinal layers are important biomarkers for neurological diseases like multiple sclerosis (MS). The peripapillary retinal nerve fiber layer (pRNFL) and ganglion cell plus inner plexiform layer (GCIP) thickness can be used to assess global disease progression of MS patients. Automated OCT image analysis tools are critical for quantitatively monitoring disease progression and explore biomarkers. With the development of more powerful computational resources, deep learning based methods have achieved much better performance in accuracy, speed, and algorithm flexibility for many image analysis tasks. However, without task-specific modifications, these emerging deep learning methods are not satisfactory if directly applied to tasks like retinal layer segmentation.

In this thesis, we present a set of novel deep learning based methods for OCT image analysis. Specifically, we focus on automated retinal layer segmentation from macular OCT images. A first problem we address is that existing deep learning methods do not incorporate explicit anatomical rules and cannot guarantee the layer segmentation hierarchy (pixels of the upper layers should have no overlap or gap with pixels of layers beneath it). To solve this, we developed an efficient fully convolutional network to generate structured layer surfaces with correct topology that is also able to perform retinal lesion (cysts or edema) segmentation. A second problem we addressed is that the segmentation uncertainty reduces the sensitivity of detecting mild retinal changes in MS patients overtime. To solve this, we developed a longitudinal deep learning pipeline that considers both inter-slice and longitudinal segmentation priors to achieve a more consistent segmentation for monitoring patient-specific retinal changes. A third problem we addressed is that the performance of the deep learning models will degrade when test data is generated from different scanners (domain shift). We address this problem by developing a novel test-time domain adaptation method. Different than existing solutions, our model can dynamically adapt to each test subject during inference without time-consuming retraining. Our deep networks achieved state-of-the-art segmentation accuracy, speed, and flexibility comparing to the existing methods.

Committee Members

  • Jerry Prince, Department of Electrical and Computer Engineering
  • Archana Venkataraman, Department of Electrical and Computer Engineering
  • Vishal Patel, Department of Electrical and Computer Engineering
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