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10-session deep dive into a research topic of your choice

6-week research project with an expert and peers who share your passion

A career discovery experience with 3 expert mentors in your chosen fields

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This paper deals with social media companies' extensive surveillance of users’ data and concerns about user privacy and data protection. With the widespread use of social media platforms, concerns about Internet surveillance and the monitoring of the general public have grown substantially.Understanding how social media companies surveil users is crucial to understanding privacy implications and potential data misuse.

Physics, Astrophysics, Cosmology, Math, Computer Science, Machine Learning, Artificial Intelligence

Internet Surveillance on People and the Role of Social Media Companies

From late 2019, COVID-19 has sent the world into a severe health crisis, killing millions in the process. COVID-19 is a disease caused by the virus by the name of SARS-CoV-2, a highly contagious virus that spreads through droplets transmitted from an infected person’s mouth. Over the span of roughly three years, action has been taken to help reduce the spread of COVID-19 through vaccines, social distancing, and masks. 
During the pandemic, however, many refugees have been disproportionately affected by COVID-19 due to a myriad of issues including socio-economic discrimination in the American health care system, touch on other issues.
Possible Focus Question: Why have refugees been disproportionately impacted by COVID-19? What are the differences in outcomes in refugees who have COVID-19 compared to the general population (ie non-refugees)? What can be done to change this?


special interests in cardiovascular disease, eye disease, health policy, and innovations in medicine

Covid19 - dissproportionately affects the refugee population

Venturing beyond the ordinary in the world of wind energy, I embark on a journey to unlock the secrets of windmill efficiency. Imagine blades that not only harness the wind's kinetic energy but also conjure electricity through the power of piezoelectricity. Currently, piezoelectricity is generated by converting wind-induced linear motion, facilitated by a Scotch yoke mechanism and springs, into electrical energy through piezoelectric levers in a wind power harvester. This research delves into the fascinating realm of PVDF-coated Zinc Oxide windmill blades, challenging the status quo and elevating the possibilities of sustainable energy. As we’ll navigate this uncharted territory, key terms such as piezoelectricity will be defined for clarity and will set the stage for an electrifying adventure into wind energy's future. The rationale behind this study is rooted in the pursuit of maximizing energy conversion in wind turbines. By harnessing the structural and piezoelectric properties of PVDF and ZnO, I aim to augment energy generation from windmill blades. This research contributes to the ongoing efforts to make wind energy more easy, efficient, and sustainable.

mechanical engineering (thermodynamics, heat transfer, fluids). entrepreneurship (cleantech, social enterprise).

Can the application of a PVDF coating on windmill blades made out of Zinc Oxide enable the generation of piezoelectricity through mechanical vibrations induced by wind flow?

Throughout the years, tennis has seen the likes of many great athletes, such as Rod Laver, Boris Becker, and John McEnroe. Although these athletes were ahead of their time, changes in racket and string technology have changed the game altogether, making modern-day tennis almost completely different from the era of these great serve-and-volley players. At the start of the Open Era in 1968, rackets were predominantly wooden and strung with natural gut, but as new technologies emerged, these rackets grew larger and lighter, switching to a graphite frame with polyester strings. These changes allowed players to swing faster and hit bigger, introducing bigger serves, heavier forehands, and more physical baseline play.

Today, twenty-first century technology and innovations in both the tennis and medical fields have helped players develop incredible skill sets, with little difference in ability. In fact, despite a clear difference in match win percentage, data has shown that the top ranked ATP players win just slightly over 50% of their points. So what factors contribute to this rift in overall win percentage? To better understand the modern game, Python and its machine learning libraries can be used to identify differentiating factors between the best players in the world, and their less successful colleagues.

Question 1: What are the most important factors of a tennis match that separate the best players in the world from the rest of the ATP?

Recognizing such factors can help players at any level achieve improved success, by allowing them to target specific areas that are more crucial to winning tennis matches during their training sessions.

Question 2: Using match data and machine learning, can we make a model that accurately predicts a head to head winner between two ATP Players, based on statistics over the span of their careers?

After identifying the factors that distinguish more successful tennis players, we can use machine learning techniques to potentially create a model that predicts the winner of a head to head match, using data from the careers of both players.

Computer Science (my focus is in Machine Learning); Psychology/Neuroscience (my focus is in cognitive computational neuroscience)

What Factors Statistically Separate the Best Tennis Players from the Rest Using Machine Learning

The review paper will focus on explaining what negative mass cosmology is, the concepts it has been used to explain, whether those are logical applications of the cosmology, and where research on it stands today. It will include the following questions:

What is negative mass cosmology and negative mass particles?

How can a universe consisting of both negative mass particles and positive mass particles be explained? 

What are some applications of Negative Mass Cosmology and are they logical(do they separate classical physics from quantum physics)?

Where does research on NMC stand today?

experimental quantum physics/low-dimensional materials engineering

Scientific Review Paper on Current Research and Findings in Negative Mass Cosmology

Nutrition, Gut Microbiome, Preventative Medicine

Addressing orexin neuron dysfunction in sleep disorders (work in progress)

The gut microbiome has a large impact on many different facets on the human body, but they play an especially intimate role in nutrition. The diversity of the gut microbiome is affected by our diets; the specific foods we eat - especially as infants, our general diet, and fibers - change both the amount and diversity of bacteria in our gastrointestinal system.

Dentistry, Biology, Dance, Bioethics

Dietary Effects on the Human Gut Microbiome

Scientists are constantly studying outer space to expand their knowledge of the
beginnings of the solar system. Asteroids, rocky objects orbiting the sun, have been a key focus of scientific study as they can provide insights into planet formation. With a seemingly infinite number of asteroids in space, the possibility of one colliding with our planet and leading to devastating effects constantly looms large. Asteroids that could come in close proximity or collide with earth are classified as potentially hazardous asteroids, PHA. On the most general level, this classification is determined by the size of the asteroid and by how close it actually gets to earth (NASA, n.d.). However, given the quantity of data to process when determining this classification, it becomes cumbersome for humans to manually analyze the data to make a prediction. Thus, machine learning techniques are ideal to study trends and make predictions. Machine learning is a method of data analysis based on computer algorithms that model relationships and improve our ability to analyze asteroid threats. Machine learning has been applied to automate the asteroid classification process in the past, for instance by Anish Si in 2018 at the Vellore Institute of Technology in India, where it was concluded that the 15-tree Random Forest model performed the best (Si, 2018). The application of machine learning to
asteroid features is constantly evolving, emphasizing the importance of continuing to iterate on ideas that could confirm or supersede present day findings. The goal of this study was to train multiple machine learning models on asteroid features, including dimensions and orbital properties, and identify the model that most accurately classifies the asteroids as hazardous or non-hazardous. The key enhancements were that a different subset of features and significantly different list of models were used for classification. The results showed that a 50-tree Random Forest classification model had a 98.45% accuracy on the test set validating that the Random Forest is the most optimal model for asteroid classification.

Mechanical engineering, dynamics, physics, biomechanics, assistive devices, device design, machine learning, deep learning, movement disorders, promoting healthy movement

Hazardous Asteroid Classification with Machine Learning using Physical and Orbital Asteroid Properties

The study of spin glasses was inspired by the physics of structural glasses, in which there are amorphous arrangements of atoms, unlike typical solids, which have a well-defined crystalline structure. Just like how structural glass has its atoms trapped in the local minima of its energy state space, spin glasses have various random spins with frustrated bonds that can cause its energy to be trapped as well. In order to compute the ground energy state of spin glasses, the spin glass needs to be represented through the statistical Edwards-Anderson Ising model. This value can be computed by classical algorithms efficiently in two dimensions, but it becomes NP-Hard when a magnetic field is added or if 3 dimensions are considered, and so quantum computation can provide significant assistance. In this study, I will solve specific instances of 2D spin glass systems in an external magnetic field by first transforming its structure into the Max Cut problem, an NP-hard problem of finding the optimal cut between a graph’s nodes. This is done by transforming the energy Hamiltonian of the spin glass system into a graph with the edges weighted according to the Hamiltonian. I will then use the Quantum Approximate Optimization algorithm to determine the graph’s optimal cut, producing a quantum state that can be traced back to the original problem to find the ground energy state. As a next step, the quantum-statistical techniques used in this study could be applied to other NP problems to generate significant insights about their properties.

Computer Science (Data structures, algorithms, Data science, ML, AI, Backend, Databases, AWS, etc)

Solving the Max Cut Problem using the Quantum Approximate Optimization Algorithm to find the Ground State of Ising Spin Glasses in an External Magnetic Field

The study of spin glasses was inspired by the physics of structural glasses, in which there are amorphous arrangements of atoms, unlike typical solids, which have a well-defined crystalline structure. Just like how structural glass has its atoms trapped in the local minima of its energy state space, spin glasses have various random spins with frustrated bonds that can cause its energy to be trapped as well. In order to compute the ground energy state of spin glasses, the spin glass needs to be represented through the statistical Edwards-Anderson Ising model. This problem is NP-Hard, meaning that it cannot be computed by classical algorithms efficiently, and so quantum computation can provide significant assistance. In this study, I will solve specific instances of 2D spin glass lattice structures by first transforming their structure into the Max-Cut problem, an NP-hard problem of finding the optimal cut between a graph’s nodes. This is done by transforming the energy Hamiltonian of the spin glass system into a graph with the edges weighted according to the Hamiltonian. I will then use the Quantum Approximate Optimization algorithm to determine the graph’s optimal cut, producing a quantum state that can be traced back to the original problem to find the ground energy state. As a next step, the quantum-statistical techniques used in this study could be applied to other NP problems to generate significant insights about their properties.

Deep Learning, Machine Learning, Artificial Intelligence, and Robotics

Solving the Max Cut Problem using the Quantum Approximate Optimization Algorithm to find the Ground State of Ising Spin Glasses

Cybersecurity attacks are becoming increasingly sophisticated and pose a growing threat to individuals, and private and public sectors. DDoS attacks are one of the most harmful of these threats in today’s Internet, disrupting the availability of essential services. This project presents a novel deep learning-based approach for detecting DDoS attacks in network traffic using the industry-recognized CICDDoS2019 dataset, which contains packet captures from real-time DDoS attacks, creating a broader and more applicable model for the real world. The algorithm employed in this study exploits the properties of Convolutional Neural Networks (CNN) and common deep learning algorithms to build a novel mitigation technique that classifies benign and malicious traffic. The proposed model preprocesses the data by extracting packet flows and normalizing them to a fixed length. The data is then fed into a CNN architecture consisting of a 2D convolutional layer with 64 filters, kernel size of 3x3, and kernel regularization, followed by layers regulating node dropout, normalization, and a sigmoid activation function to out a binary classification. This will allow for the model to process the pcaps effectively and look for the nodes that contribute to DDoS attacks while dropping the “noise” or the distractors. The results of this study demonstrate the effectiveness of the proposed algorithm in detecting DDOS attacks in network traffic as well as being scalable for any network environment.

Social Sciences, Computational Social Sciences, Social Media, Graph Analysis, Depression on Social Media, Cyberbullying on Social Media, Hate Speech and Fake News Detection, Digital Epidemiology, Natural Language Processing, Artificial Intelligence, Machine Learning, Data Science, Threat Intelligence, Internet Security, Denial of Service Attacks, Network Traffic Analysis, Computer Science, Computer Networks

A Novel Supervised Deep Learning Solution to detect Distributed Denial of Service (DDoS) attacks on Edge Systems using Convolutional Neural Networks (CNN)

In the last 25 years, exoplanets, or planets outside the solar system, have become one of the most exciting topics in astronomy. While sending humans to a planet lightyears away from Earth remains science fiction, recent technological advancements have made the prospect of sending a small probe to an exoplanet via interstellar space not only interesting, but also realistic. In addition to time and ample resources, developing such a probe and guiding it to a world beyond our solar system requires a deep understanding of the gravitational dynamics within our solar neighborhood. Here, we use Python to simulate a hypothetical rocket trip from Earth to the nearest exoplanet, Proxima Centauri b, which is approximately four lightyears (or 23.5 trillion miles) away. Our program realistically simulates both our solar system and the Alpha Centauri system, which hosts Proxima Centauri b, and allows us to depict a visual representation of the interstellar journey. Using data from Nasa’s Horizons System, we consider the gravitational effects that all major planets and stars have on our rocket during the trip. By simulating our rocket, we determine the optimal launch angle and shortest path required to reach Proxima Centauri b in a way that minimizes the amount of exhaust from the rocket. In this way, we determine optimal trajectories for a successful voyage. Our visual program will follow the rocket and explore the travel time, the effects that various initial conditions have on the outcome of our journey, as well as the orbital dynamics and interactions between our rocket and other large bodies.

Astrophysics, Astronomy, Exoplanets, Python programming

If a hypothetical rocket was to be launched from Earth to Proxima Centauri B, the nearest exoplanet, what would be the optimal launch angle and minimum velocity for the rocket to reach the exoplanet in a relatively short amount of time?

Two brief (~750 words) blog posts will be written describing the circumstances and contributing factors to (1) contact and (2) non-contact ACL injury in American Football. The contact blog post is on Cincinnati Bengals quarterback Joe Burrow's ACL injury during the 2020-2021 NFL season. It describes what happens during a contact ACL injury and looks at risk factors for contact ACL injuries and how they apply in this case study. It also examines return to play statistics for players with similar injuries and takes a look at Burrow's future and what his team has done to help him succeed in the NFL. The non-contact blog post is on Minnesota Vikings running back Dalvin Cook's ACL injury during the 2017-2018 NFL season. Similar to the last post, it looks at risk factors for non-contact ACL injuries and how they apply to this case study. Since this injury didn't happen recently, the blog post looks at what Cook did when he returned to the field and how his injury impacted his play. Case studies allow us to understand more about actual situations by using observed risk factors. During this project, I learned about these risk factors and mechanisms that contribute to ACL injuries. I also learned about different positions the body can be in that potentially excaerbate the effect of an ACL injury. Writing these posts will allow me to continue this project into the future by looking at other contact or non-contact ACL injuries and applying what I have learned to these case studies.

Biomechanics, Exercise Physiology, Sports Performance, Sports Sci

Case Studies in American Football ACL Mechanisms of Injury

Paragraph student wrote: The COVID-19 pandemic has caused various challenges throughout our society and health care system: the most overlooked being how our youth, especially marginalized youth and minority communities have been affected. The COVID-19 pandemic has resulted in mental health deterioration across the general US population but has seemed to impact the youth/minority populations strongly. Little research has been done to investigate the pandemic's effects on these groups who are particularly vulnerable to mental health challenges. Pre-pandemic, research has shown that minority groups and youth are more susceptible to higher quantities of mental health issues, more commonly, depression and anxiety, along with economic disparity that results in inaccessibility to crucial resources. This article discusses the harmful effects the pandemic has had on youth/minority groups and raises potential solutions that may alleviate stress and suggestions to improve our healthcare/ mental health services and education system.

Cognitive psychology, cognitive training, learning and memory, working memory, music cognition, aging

The neglected mental health crisis: how the Covid-19 pandemic impacted minority groups and youth in the United States

Zombies are fictional creatures that are often portrayed as brain-eating undead humans that infect people through bites. Early zombie legends say they originated from voodoo magic, but this "origin" became more scientific as time went on. However, how scientific is the science in this sci-fi? Is it possible for a zombie-creating agent to arise from nature? Based on infectious disease studies, there are many different contestants for potential zombie viruses. These contestants include different types of pathogens including zombie fungus, prions, rabies, and toxoplasmosis. These pathogens are usually discussed by the media due to how they affect animal behavior in ways that many people consider zombie-like. In this review, we will review what is known about the “zombie-like” characteristics of each of these pathogens and how they affect animals and humans, as well as evaluate their likelihood of becoming potential zombie-creating pathogens.
 

viruses, parasites, biology, immunology

Is it possible for zombies to happen naturally?

TogaTech Core is an open-source framework for rapidly and easily building end-to-end encrypted, privacy centric, and interoperable applications. The framework utilizes a cryptography library (tEnvoy), authentication layer (TogaTech Auth), SDK, WebSockets backend, and flexible data storage mechanic (TogaTech Capsules). The first major application built on top of TogaTech Core is TogaTech Spark, a fun, feature-rich, and end-to-end encrypted collaborative chat platform. All messages are encrypted using ephemeral keys and verified with digital signatures for added security. Users can create direct or group chats but can go beyond by using organized spaces with channels, threads, reactions, typing status, profiles, and more.

Programming, Computer Science, Machine Learning, Android App Development, Proof Assistants

TogaTech Core - an open-source framework for rapidly and easily building end-to-end encrypted, privacy centric, and interoperable applications

The field of tissue
engineering and regenerative medicine has seen great advances in the last two
decades. The overall implementation of these synthesized structures is still
greatly limited, however. One of the major contributors to this stagnation is
the lack of functional vasculature within these structures. Given the role that
vasculature plays in the distribution of oxygen and nutrients within the body,
in vitro organs must also have this network of blood vessels to function
properly. Herein, we review state-of-the-art bioprinting techniques and propose
the best viable method amongst them for the creation of vascular structures.
The three methods we analyzed are extrusion-based, jetting-based, and
stereolithography. Information on specific bioinks and the overall process for
each method is provided, sourced from primary and secondary scientific
literature. While our findings indicated extrusion-based bioprinting is the
most popular bioprinting method used for printing vasculature, accounting for a
majority of the papers reviewed it was determined stereolithography bioprinting
holds the greatest potential in the creation of functional in vitro
vasculature. This decision was made given its high resolution, affordable cost,
and high cell viability percentage.  

Tissue Regeneration, Stem Cell Biology and Ethics, Cardiac Tissue Engineering

The Intricacies of Bioprinting Vascular Structures

Synthetic immunology is a field in which researchers to design constructs that will help T-cells in the immune system battle pathogens, such as cancer cells. This can take place using synthetic receptors for better recognition of pathogens so that T-cells can execute more precise functions in the body. In this project, I hope to write a literature review regarding papers in this field and have it be published in a journal. Additionally, I hope to have a blog where I talk about a subset of these papers geared towards the general public to help them better understand this field. After the completion of this project, I hope to have learned (a) more about the advancements in this field and how they can potentially impact future patients (b) how to read scientific literature (c) improve my scientific writing and (d) learning to present and explain papers. 

Cell signaling, cancer biology, mathematical modeling of biochemical networks, cancer immunotherapy

A compilation of information regarding the field of synthetic immunology

Treatments and Prophylaxis of Covid-19 - A Literature Review

There are thousands of golf balls in the ocean and water hazards. These golf balls pose a major environmental risk as they degrade and release microplastics and chemicals into the water. Golf balls are likely ending up in natural water hazards around the world. However this problem has been documented and confirmed in the Carmel area of California along the coastline that Pebble Beach golf course and Cypress Point golf course border. Modern golf balls are made of a polyurethane elastomer shell typically filled with synthetic rubber. Chemicals such as zinc acrylate, zinc oxide, and benzoyl peroxide are added to the rubber core to add flexibility and durability. Zinc oxide and zinc acrylate are poisonous in marine environments and zinc oxide has been shown to create stress reactions in fish, crustaceans, and algae. 

The goal of this project is to create an autonomous robot to remove golf balls from water hazards. This robot could be modified to remove other types of plastic debris, however for now I will start with golf balls. The robot consists of a number of systems including a movement system, a buoyancy system, an intake system, and a control system. The movement system is responsible for the movement and navigation of the robot. The bouyancy system is responsible for the bouyancy of the robot, and ensuring that the robot has the appropriate bouyancy with a load of golf balls. The intake system is responsible for collecting golf balls, and the control system manages and coordinates all the systems of the robot.

This project is intended to create a system that is capable of removing golf balls from water hazards safely and efficiently. By removing golf balls from natural water hazards we are removing a substantial environmental threat. The robot also has the possibility of generating money for golf courses as they can sell collected golf balls. This provides incentive to use the robot to clean up water hazards.

Meet Polygence Scholars and explore their projects