YSP Alumni

’15 | ’16 | ’17 | ’18 | ’19 | ’20 | ’21

2021 YSP Program Coordinators: Gabriella Gonzalez and Franklin Ollivierre III


Participating Labs

Program: Final Presentations
Final Remarks

LabYSP StudentsTitleAbstract
Faculty:
Amirabadi, Mashid

Mentors:
Junhao Luo, Anran Wei
Ali Noamany,
Nandana Alwarappan
Solar Powered Charger

Presentation | Poster
We became familiar with solar modules and their behaviors, then designed and simulated a charger circuit for charging a cellphone from solar modules. We used the software PSIM for our simulation, where we would create circuits and test them at various temperatures that could occur in a real-life scenario. The final circuit used in our solar powered charger design was a linear regulator, consisting of a resistor voltage divider and Zener diode. Our goal is to help in creating a more efficient and environmentally friendly method of powering our everyday electronics. If we can learn how to create a circuit that works purely with solar energy, we can help reduce dependence on nonrenewable energy and decrease pollution.
Faculty:
Hashmi, Sara

Mentor:
Sabrina Marnoto
Logan Armstrong,
Emma Bocquillion
Particulate Flows in Small Channels

Presentation
Margination is a biological phenomena in which particles are pushed towards the outermost wall of blood channels. Margination is important to further understand, as knowing more about it allows us to create drugs and treatments which are better at reaching their desired location. Additionally, they can allow us to better understand when people may be in danger with channel issues like stenoses. To learn more about this phenomena we used an online representation of a blood vessel and adjusted key factors - particle size, particle shape, particle density, particle stiffness, geometry of blood channels, shear rate, and hematocrit, and RBC aggregation - to test their effect on margination. The bulk of our research was focused on the geometry of channels, where we tried to explore how bifurcations and stenoses could impact margination and flow.
Faculty:
Jornet, Josep

Mentors:
Duschia Bodet, Priyangshu Sen
Andres Scully Morales,
Connie Yang
Designing and Testing New Communications Signals for 6G Networks

Presentation
With the commercialization of 5G networks, the race for the future 6G systems becomes more competitive than ever. One of the key building blocks of future wireless networks is the adoption of frequencies above 100 GHz in the electromagnetic spectrum, the terahertz spectrum, where large bandwidths are available. Using the concepts of digital modulation and coding in a communication system, we utilized the numerical tool MATLAB to define new waveforms. Among these included designing a 1024 QAM simulation in the THz band with a transmitting and receiving end that accounted for external factors such as loss and noise. Upon completing the theoretical design and analysis, a 1024 QAM signal will be sent through the one-of-a-kind TeraNova testbed developed by the UN Lab to experimentally test the performance. Additionally, we created new constellations to evaluate the potential benefits and disadvantages of designs for modulations.
Faculty:
Kaeli, David

Mentors:
Kaustubh Shivdikar
Jerry Xia,
Yurika Tarui
Accelerating Clustering for Breast Cancer Tumor Diagnosis

Presentation | Poster
Machine learning and clustering algorithms have been increasingly utilized to analyze the exponentially growing quantity of data produced in daily life - Big Data - that are beyond human capabilities. Biomedical applications have enjoyed the benefits of machine learning algorithms, which has revolutionized the study, treatment, and prevention of illnesses and diseases. Tumor classification can effectively leverage biomedical machine learning algorithms to achieve robustness in detection. This project will use the Breast Cancer Wisconsin Diagnostic Data Set and use k-means clustering to identify tumors. Our focus is on performance of k-means, which will accelerate the pace of cancer screening. Parallel programming can exploit the inherent parallelism k-means by concurrently running threads code. Thus, parallelizing breast cancer tumor detection can reduce data bottlenecks, enabling faster diagnosis.
Faculty:
Koppes, Ryan

Mentors:
Kyla Nichols, Katelyn Neuman
Rohan Meier,
Sofia Arboleda
Automating Live/Dead Cell Quantification to Determine the Biocompatibility of Hydrogels

Presentation
Hydrogels are integral to organ-on-a-chip research serving as the 3D scaffolding that outclasses simple 2D in vitro cultures. de, alginate). The focus of our lab was assessing the viability and biocompatibility of neural and gut cells in hydrogels, such as Gelatin Methacrylate and Choline Acrylate (Gel-Amin) hydrogels. These assessments allow us to evaluate the biocompatibility of these gels in future organ-on-a-chip research, as well as their effect on specific functions of these cells. Our task was to create an automated python program that could quickly and efficiently analyze the viability of neural and gut cells in Gel-Amin hydrogel through determining the live-dead ratio of cells in stain microscopy images.This automated program will speed up research to finding rates with cell counts and determination of viability coming almost immediately.
Faculty:
Milane, Lara/
Amiji, Mansoor

Mentors:
Raquel Sevilla, Aashray Bhavsar
Daniel Becker,
Alice Han
Novel Nanomedicine for Cancer

Presentation | Poster
Triple Negative Breast Cancer (TNBC) is a multi-drug-resistant form of breast cancer that is negative for excess estrogen, progesterone, and HER2 receptors and is therefore resistant to HER2 targeting or hormonal therapies. In part due to this multi-drug resistance, TNBC is a particularly hard to treat and aggressive form of cancer. The goal of this study was to find a novel treatment for TNBC that uses nanomedicine and design ex vivo and in vitro studies to evaluate our treatment.
Faculty:
Onabajo, Marvin

Mentors:
Safaa Abdelfattah
Barthelemy Lagene,
Lanai Carey
Digitally-Programmable Analog Filter Configuration

Presentation
Purpose
Develop systems-on-a-chip to measure electroencephalography (EEG) signals for automatic seizure detection using low-power analog circuits.
Provide the ability to tune the performance of integrated circuits to compensate for variations due to manufacturing process imperfections.
Enable digital user control to modify circuit characteristics after fabrication.
Challenges/problems
Manufacturing variations change performance after fabrication
Investigation of a digitally-programmable analog filter configuration
Evaluation of the filter design through simulation software
Faculty:
Platt, Robert

Mentors:
David Klee, Kevin Esslinger
Joseph Durasingh,
Abigail Lussier
Applying Reinforcement Learning Algorithms to Novel Environments

Presentation
Deep reinforcement learning (RL) has had major successes in the last several years, including robotic manipulation, self-driving cars, and superhuman-level video game performance. We designed, developed, and implemented novel environments to challenge state of the art RL algorithms. We tested the environments with our own hand-designed solvers, tabular RL methods, and deep RL algorithms and compared performances.
Faculty:
Su, Lili
Jedidiah Nelson,
Yelissa Burgos
Self-Driving Cars Control that is Robust to Environmental Error-Prone Human Drivers

Presentation
Our point of focus was Autonomous vehicles’ ability to detect abnormal actions of drivers and prevent accidents. We took time to understand how autonomous vehicles can help react to dangerous interactions on the road. We researched the development of autonomous cars in the last century. We learned about a state of the art algorithm that can detect abnormal behavior of neighboring cars while doing this in a way that doesn't intrude on the privacy of others
Faculty:
Tiwari, Devesh

Mentors:
Rohan Basu Roy
Shoumik Kundu,
Kirya Caine
Quantum Computing: What and Why?

Presentation
We recently were introduced to the topic of Quantum Computing, an emerging field with the potential to greatly expand and diversify the computational power accessible to the scientific community. Despite the promise of quantum computing, more widespread and accurate comprehension of its principles and capabilities are necessary in order to gather more consumer interest and outside investment as well as further improve the technology. The goal of this project is to simply and effectively demonstrate basic quantum computing concepts to both increase its relevance among circles that do not directly interact with this technology as well as discuss its potential future challenges and relevant possibilities.
Faculty:
Willits, Rebecca

Mentors:
Yang Hu
Stefan Nguyen,
Claire Stipp
Schwann Cell Migration Enhanced by Laminin-Derived Peptide Gradients

Presentation | Poster
Neuroregeneration following peripheral nerve injury is largely mediated by Schwann cells (SC), the principal glial cell that supports neurons in the peripheral nervous system. Current treatments for PNS injuries are costly and do not ensure a full return of motor and sensory functioning in the damaged area. Enhancing Schwann cell migration to the injury site could both speed up nerve regeneration and prevent lasting effects. The goal of this project was to understand cell migration behavior, analyze original data for different research purposes, and make scientific graphs of the data. We analyzed raw data of the velocity and displacement of Schwann cells and fibroblasts on several peptide gradients. Through reading literature and our data analysis, we found that YIGSR peptide gradients directed the most migration with a strong bias to the concentration profile. On the YIGSR peptide gradients, the overall speed of SC migration increased with the steepness of the peptide concentration profile. YIGSR gradients had no influence on fibroblast migration, in contrast to fibroblast migration on RGD gradients.