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With the rise in popularity of the recent hit show “The Last of Us,” more people than ever are interested in learning about fungal infections, specifically those that affect host behavior. Some fungal and parasitic infections are known to have an effect on hosts, exploiting them to transmit further infection amongst the host population. Toxoplasma gondii influences the rodent's behavior, making it more susceptible to contact with its predator, a cat. Toxoplasma gondii needs a feline host to reproduce. In a similar way, ophiocordyceps modifies ant host behavior to facilitate its contact with additional ant hosts. Through the use of various articles and research, this paper provides a summary of explanations for evolutionary infections that manipulate host behavior.

General Biology, Bacteriology, Human Gut Microbiome, Environmental Microbiology, Metabolomics/Lipidomics

How has evolution allowed toxoplasma gondii and ophiocordyceps to manipulate host behavior in their favor?

My project will focus on the idea of Romanitas, or Romanness, through the lens of inscriptions and how this concept changed before and after the end of the Western Roman Empire. By examining Latin inscriptions from public buildings, coins, graves, and in other contexts, I will be able to examine how Roman identity changed during the period after the collapse of their empire. By consulting the works of an ancient author like Cassiodorus and with the help of modern scholarship, I will develop my argument into a scholarly research paper. My goal is to publish this paper in an academic journal.

Ancient Greek and Latin classics, medieval literature and languages, history of religion, German

Transitioning Romanitas in Inscriptions of the Post-Roman World

Breast cancer is evidently a major public health problem and has been increasing in the United States since formal tracking began in 1930. Both environmental and genetic factors contribute to the risk of breast cancer. Given the advanced ability to sequence the genomes of cancer patients, and link gene variants to disease, this review aims to summarize the contribution of gene mutations to breast cancer.
In recent years, there have been several new genes discovered which contribute to extensive DNA damage itself. Other studies investigate gene variants and their relation to cancer. In these studies, BRCA1 and BRCA2 gene mutations correlate highly to breast cancer, with 3% of breast cancers linked to inherited mutations in the BRCA1 and BRCA2 genes. 
However, 97% of breast cancers are unrelated to familial BRCA1 and BRCA2 mutations, prompting the question: What other genes contribute to breast cancer?
By analyzing a benign and pathogenic data set for Breast Cancer, it was evident that MLH1, MSH2, and MSH6. Genes such as CDH1 are still understudied which represents future research opportunities in order to find out more about specific genes in-depth and whether they relate to breast cancer. 
 

Transcription and RNA biology, stem cell and regenerative biology, biochemistry, fluorophore discovery and environmental water science.

Analysis of Genes involved in DNA Damage and DNA damage repair and their role in Breast Cancer

The goal of this project is to consider which aspects of climate change may have contributed to the exceptional damage caused by Hurricane Harvey (2017) in Houston, Texas. The evaluation metrics include total rainfall, the rainfall return period, the rainfall pattern, and the wind speed. Data visualization with Python and Pycharm, will help illustrate the exceptional nature of this storm. I will also analyze how sea level rise, average ocean temperatures, and population changes may have contributed to excessive damage in Harvey. The final deliverable will be a scientific expository poster.

computation, hurricanes, climate change resilience, probability and statistics

Why was Hurricane Harvey (2017) so devastating?

My Polygence project is a research paper about Anti-angiogenic therapies. It is going to discuss how anti-angiogenic therapies target cancer cells. Specifically, the paper will describe monoclonal antibodies that target the vascular endothelial growth factor (VEGF) pathway, angiogenesis inhibitors that stimulate the immune system, and angiogenesis inhibitors directed at VEGF/VEGFR. Also, it will explain the involvement of the stroma in this process and how tumor cells become resistant to these therapies using parallel signaling pathways. Finally, it will delineate the future of anti-angiogenic therapies and propose my own idea for a new therapeutic strategy. My main goal is to publish this research paper in the Young Scientists Journal.

Cancer Biology, Molecular/Cellular Biology, Biochemistry, Science Communication, Immunology, Medicine, Biochemistry, Cell Death, Metabolism

Cancer Anti-Angiogenic Therapy and Mechanisms of Therapy Resistance

With an increasing number of cases every year, eating disorders have the highest mortality rate of all mental illnesses globally. However, more than half of the diagnosed patients do not receive suitable treatment. This paper identifies the delays in both mental and physical care in eating disorder treatment and their impacts on patients’ course of recovery. The reasons for these delays will be highlighted based on patients’ personal experiences and information compiled from different online sources, such as research articles and surveys found through Pubmed and Google Scholar. Research, surveys, and patients’ personal feedback highlight that treatment barriers are primarily due to high financial costs for care, stigma surrounding recovery, bias against minorities, inconsistent treatment, and insufficient outpatient care. Furthermore, from a medical standpoint, primary care physicians only have surface level knowledge on how to provide treatment to patients, but end up having to do so because there are a limited number of eating disorder specialists. If patients are diagnosed and given adequate care in the beginning stages and throughout their eating disorders, then not only will they be more likely to avoid falling into critical mental and physical conditions, but they will likely reach the point of recovery quicker and more efficiently.

Mental health research, cognitive science, public health, health policy, disability studies

Barriers to Eating Disorder Treatment

This paper demonstrates the application of machine learning in determining suitability for utility scale sites for renewable energy production. Supervised algorithms such as Random Forest Classifier are employed in a Semi-Supervised learning process that allows underlying trends present in suitable and non suitable sites to be extrapolated to a mostly unlabeled dataset. The model iteratively trains from the pseudo labels it creates throughout this process, until all data points in the data set are labeled. This allows the small percentage of hand labeled data to be leveraged for use in the larger dataset. This open source tool can be used by anyone for the quick and precise determination of suitable locations for utility scale and personal installations based on the available renewable resources. It can also be used to influence future policy decisions around renewable energy.

Cryptography, Machine Learning, Algorithm

A Machine Learning Approach for Identifying Favorable Sites for Renewable Energy Installations

Though considered a rare disease, many adults in the United States are diagnosed with some form of Irritable Bowel Disease, also known as IBD. IBD refers to autoimmune inflammation localized in the gastrointestinal tract and can include the esophagus, intestines, and colon. IBD patients commonly experience stomach pains, loose stools, frequent bathroom usage, weight loss, and anemia. The symptoms are linked to inflammation caused by the immune system. The definite cause or causes of IBD is very vague, but the pathology of the disease can be traced to its immunology. The rapid rise of inflammation in IBD is based upon an overactive immune system, in which immune cells falsely trigger defensive immune cell mediators. Such immune cell mediators can be used to understand and develop possible targets for IBD treatments. The aim of this review paper is to explore the immunology background that is linked to the cause of IBD. This paper will seek to describe the immune cells and molecules that differentiate that link the different forms of IBD together. Further, I will explain the various forms of treatment that are currently being explored to target the immune system.  The overall goal for the review article is to describe how the immune system is causing the symptoms of IBD in a manner that is understandable to the general audience.

Biology, Infectious Diseases, Microbiology, Immunology

What are the primary immune cell mediators that cause or trigger IBD symptoms and how are these being used for targeted IBD therapy?

Genetic studies have indicated that LRRK2 is the most common genetic cause of PD, but whether it is the best target for potential therapeutics will have to be determined by researchers based on the findings that are in this paper. Nearly 1 million people in just the US are affected by PD and the biggest genetic cause is changes in the LRRK2 gene. There are several possible pathways that are modified by these changes, such as the pathway involving the LRRK2 kinase, which I will explore further in my 2 focused aims. In Aim 1, I will evaluate mutations in the LRRK2 gene to discuss potential causes of Parkinson’s Disease. Building on this, in Aim 2, I will use the LRRK2 Kinase cellular pathway as a model to discuss potential therapeutics. Overall, I want readers to understand the potential causes affecting the LRRK2 gene as well as how these changes affect the interconnected cellular pathways and how this can lead to the development of potential therapeutics for PD. From already published research, I believe we are on the cusp of developing therapeutics to help PD patients.

Cell biology, neuroscience, biochemistry, scientific writing/presentation.

Cellular pathways of Parkinson’s Disease: Insights into pathogenesis and potential therapeutics

Language is coined as an ability unique to humans. However, studies have begun to show that other animals, including species of birds and primates, have complex languages with morphosyntactic syntax. Moreover, animals, especially those in symbiotic and predatory relationships, use and respond to other species' languages: as exhibited through alarm calls. Communication is any interaction between 2 or more organisms that cause a change in behavior. This paper examines the evolution of communication behaviors with hopes of better understanding language as a social behavior that’s not unique to humans. First, some similarities and differences in how animals learn and interact with their own language, including human infants, are surveyed. Differences and similarities in vocal, reinforcement, and social learning are discussed. Next, whether those comparisons contribute to the evolution of communication-related biological structures or not is discussed. Finally, some possibilities to facilitate interspecies communication using technology are explored.  Since animals in social systems that require more cooperation show more communicative behaviors, a variety of animals are reviewed to present a variety of life-history categories including slow vs fast development, dependency on parental care, immature vs mature perception systems at birth, social structure, monogamy, parental care system, etc, with a focus on multicellular vertebrates. This paper pushes the limit that language is unique to humans and focuses on non-anthropomorphizing methods, and could have great implications for improving our understanding of the evolution of language with applications in improving animal welfare and AI. Future directions such as  research in invertebrate communicative behaviors, plant, and bacteria communication, etc will be introduced. 


animal behavior, comparative cognition, animal communication and language evolution, animal social behavior, ecology and evolutionary biology, scientific communication and scientific illustration

How is language learned, processed, and used across different species, and what are some ways to facilitate cross-species communication?


Emily is creating a blog exploring drug abuse and drug law in the United States. This blog will combine storytelling, interviewing, and secondary research techniques to examine the psychology behind why some people become addicted to drugs, how federal law penalizes drug use, and the impact of drug convictions on people's lives during and after incarceration. The blog will highlight focus on the science behind a few of the drugs that are abused. The blog will also discuss the harms that accompany drug abuse, such as fatal overdoses and serious injury. This project combines Emily's interests in learning about law and psychology.

Law, Global Health, Public Health, Health, Human Rights, Criminal Defense

A Blog Exploring American Drug Law

The goal of this project is to explore what side effects anorexia causes both neurologically and physiologically. Moreover, it will focus on these effects both during and after anorexia, during recovery, to discover if recovery can in fact alleviate symptoms. The project will consist of two parts, the first being a meta-analysis. The meta-analysis will pull data from existing scholarly articles to create a clear and concise report on what has already been found about how anorexia impacts the brain and body, as well as writing it in a way easily understood by adolescents, the target audience. The second part will consist of a qualitative survey sent out on social media platforms to adolescents, in order to hear how adolescents view the prevalence of disordered eating and what they believe act as influences. It will be showcased on a poster using the Canva website, as a way for the audience to clearly view all the data.

Counseling psychology/mental health counseling

The Neurological and Physiological Side Effects of Adolescents with Active or Recovering Anorexia Nervosa 

Natural language processing is a relatively old subject in the ML field. Summarization of texts has been a significant use case meant to truncate the length of a text while retaining the meaning. In this project, Akshay will be comparing known NLP summarization models by summarizing self help book texts. Afterwards, the aim of the project is to calculate similarity scores between texts using these summaries, to test the hypothesis of whether self help books really all have the same meaning. Additional ideas for exploring include translated self-help books from other languages and comparing those, and also comparisons of full texts and comparing if there are differences in similarity scores when using full text versus summaries. Another avenue for work would be to use human subject testing to evaluate if humans who have read a self help book text are able to identify that book from the summary versus other summaries.

Robotics, Machine Learning, Generative AI, Quantitative Finance, Algorithmic Trading, Statistics, Computer Science

NLP Summary Comparisons of Self Help Books

Question: How does the individual design of governance mechanisms within digital communities support each community’s respective goal?

Thesis: The purpose of this paper is to understand how governance mechanisms are designed to achieve desired outcomes in digital communities. Additionally, this paper seeks to address the lack of existing literature on governance regarding digital social organization.

Abstract: Governance mechanisms refer to the structures, processes, and decision-making frameworks implemented within a community to regulate behavior and steer collective actions towards the achievement of goals. In digital communities, which are social networks or platforms that facilitate transactions between individuals through technology, governance mechanisms are implemented based on the platform’s goals. By applying the rules of Ostom’s Institutional Analysis and Development (IAD) framework, this paper examines how the governance mechanisms operating within digital communities emerge from, and subsequently facilitate the institutional rules of digital communities. Three specific digital communities are studied in an attempt to arrive at broader, more generalizable results: Mastodon, a Decentralized Autonomous Organization (DAO), and Linux. This comparative analysis provides a comprehensive understanding of how these communities operate and allows for a more informed analysis of digital governance structures.

American Politics, Racial and Ethnic Politics, Media, Intersectionality, Quantitative Methods, Qualitative Methods, African American and African Diaspora Studies

Governance mechanisms in digital communities: A comparative analysis using the IAD framework

For this project, Arul will use publicly available multiwavelength datasets to measure the star formation rates (SFRs) of a sample of local galaxies. This measurement begins with the selection of a sample, making cuts on stellar mass, morphology, and distance. Arul will then use custom scripts written in Python to measure the flux from each galaxy in (at least) 2 wavebands -- H-alpha recombination band, and far UV (FUV) band, both of which trace star formation. Next, these fluxes will be converted to intrinsic luminosities based on the distance to each galaxy. Given these luminosities, Arul will use scaling relations in the literature to convert these luminosities to SFRs. Finally, if time remains, Arul will attempt to quantify the effect dust (which blocks and absorbs the light used in these measurements) has on his results, and will compare the derived SFRs for galaxies that host, and don't host, active galactic nuclei (AGN).  

Astronomy: Galaxies, Astronomy:Machine Learning, Astronomy:Data Analysis

Measuring star formation rates for a sample of local galaxies

One of the primary reasons elephants are endangered is human-elephant conflict (HEC), the opposition that occurs between elephants and the humans living nearby. The violence that erupts in settings of HEC, such as crop fields, often results in both human and elephant deaths as both species struggle to coexist. Many methods are being researched to mitigate human-elephant conflict, including playing audio playbacks that deter elephants away from crop fields and reduce chances of contact and destruction. Habituation to these stimuli creates the demand for a greater number and more types of auditory deterrents, but it would be unethical and inefficient to immediately jump to field tests without verifying these playbacks are at least somewhat effective. Thus, this project aims to analyze currently used auditory deterrents to determine if any acoustic similarities exist between them, and create a generalization for what characteristics make up an effective auditory deterrent. 

To perform analysis, we used techniques in R and toolboxes such as soundgen for retrieving acoustic characteristics. Through performing principal component analysis (PCA), we were able to find that playbacks with low effectiveness on elephants are significantly different in terms of their acoustic characteristics. This contrasts the playbacks with high effectiveness, which were shown to be clustered together in the PCA. Through linear regression and calculation of p-values, we were also able to deduce that characteristics such as lowest dominant frequency band, loudness, the 25% quartile of the sound, the peak frequency, depth of amplitude modulation, and flux were all positively correlated towards the effectiveness of an auditory deterrent. We used a weighted average approach to calculate a recommended value for effectiveness of an auditory deterrent for each of these characteristics. Overall, we hope that these results will help optimize current playbacks and help create a threshold of characteristics to use before future testing, to reduce habituation and human-elephant conflict.

Conservation, ecology, wildlife ecology, human-wildlife conflict, zoology, climate change impacts on wildlife/vegetation, animal movement modeling

Investigating the Acoustic Similarities of Auditory Elephant Deterrents to Optimize Currently Used Techniques

In what ways do specific types of music (ex. classical and jazz) outline an individual's memory of an event? Furthermore, how does the application of music to these videos affect a person’s explicit memory of its contents too? To address this, I will apply a range of emotionally valenced music behind a constant video and distribute it to a large population in controlled settings. To pursue the explicit memory of the video, I will send a short follow up survey to discover how much they recalled from their videos. I hypothesize that if both classical and jazz music are tested for both lighter, happy moods and sadder, dark moods, the classical will depict the latter better because of its more serious connotations in society. However, jazz will depict the lighter moods more memorably because of its more relaxed rhythmic feel.

clinical psychology, working memory, executive functions, mood/affect.

Classical and Jazz: Where do the Memories Lie?

Ice rinks are notorious for using massive amounts of electricity, most of which are derived from conventional power sources like coal, oil, and natural gas. This contributes to the emission of greenhouse gasses into the atmosphere, further increasing climate change. To address this issue, this study explores how to integrate efficient cooling technologies in ice rinks. In the process of cooling the ice, much of the wasted energy can be redirected back into the cycle, lowering the amount of energy from outside sources and potentially producing a self-sustaining cycle. Different sustainable cooling technologies are compared and the best fit method based on efficiency, cost, energy production, required energy input, and feasibility is recommended. 

I have degrees in chemical engineering, earth & planetary science, and aerospace engineering, but my work also intersects mechanical engineering, materials science, and environmental engineering. I welcome opportunities across these fields.

Integration of sustainable cooling technologies in Southern California ice arenas

Predicting future trends in stock prices and analyzing these trends is quite difficult in its implementation. There are many factors involved in the implementation that cannot be considered such as physical or psychological behavior. However, the goal of this project will be to have a fully trained machine learning model that can forecast future trends in the International Business Machine(IBM) stock in ideal conditions. This project would be written fully in python where it is not difficult to use and train a computer with data sets. The main objectives of this project would be data harvesting, data cleaning, and training of a machine learning model to get as accurate as possible to predicting future prices for Tata Global Beverage. In specific a technical analysis would be used to measure intrinsic values of the stock and a fundamental analysis will be used to predict trends in stock price as well as volume. The data set that I will use will be from the Nasdaq website which has the historical stock data for IBM from the last 10 years. Many techniques such as moving average, Prophet, Auto Arima, kNN, and LTSM, would be used to aid in the completion of this project. Simple techniques such as moving average will be used by taking the latest “close value” for each day in the data set and while considering the predicted values, the oldest value would be removed from the data set. Another technique that would be used is Prophet which is a time series forecasting library that tries to capture the seasonality in the past data. Auto Arima is another statistical forecasting method for time series forecasting which takes in past values to predict future values that will be tested in my project. The next method that would be used in my project would be the kNN or k-Nearest Neighbor method which takes the nearest data points and uses those to predict future cases. Lastly, I would be testing LTSM or Long Short Term Memory to store information that is considered important to the model and forget information that is considered is not. Overall, through using these techniques I want to be able to have a working model that can function to predict stock prices as best as possible in the incoming years.


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