Hello everyone, my name is Aadrit Talukdar, and my project is Battle Rams to Ballistas:

An Exploration on Wartime Technology in the Classical Era. Throughout my research, I will be combining literature studies of ancient texts from Mediterranean Classics with engineering experiments on technological designs. The literature studies will be using sources from my time period (5th century BCE to 5th century CE) around the Mediterranean, and using a combination of my own language knowledge with English translations to analyze the texts, specifically on details of siege weaponry used in notable wars. With that information, I hope to engineer replicas or smaller-scale models of these technologies to investigate their efficiency more closely.

My final product is meant to be both a comprehensive comparison of ancient texts relating to the technology and time periods I will be researching, and multiple engineered models that can be compared to visualize technological improvements and design changes.

I aim to simplify patient-doctor interactions by creating a chatbot that recommends treatments based on patients’ stated problems and medical history.

As input data, I will use a list of sample data that includes patient history, medical records, the patient’s stated problem, and LLM training data. Using this information, the chatbot will be able to support the patient by providing oral-care tips or Over-The-Counter (OTC) medication if they are experiencing any pain. I will create a sample database in consultation with my external advisor that includes patient history and medical records. Based on this data, if the patient has had similar issues in the past, the chatbot will provide oral remedies.

As attention to environmental issues is increasing, food insecurity has become one of society’s fastest-growing concerns. According to the USDA, about 6 million children or 17.5 million households in the US suffered from food insecurity, and this alarming number is projected to grow. America has one of the world’s best economies, more than capable enough to provide and export enough produce. The real problem comes from how the produce is managed. By connecting restaurants and grocery stores that discard surplus produce to soup kitchens, we can combat food insecurity while protecting the environment. For my Senior Project, I’m creating a web application ScrapSaver to facilitate this relay of food. First, I will begin my research by conducting surveys with San Jose’s Martha’s Soup Kitchen and local restaurants. This project will consist of two parts: (1) design and creation of the web application in Python Django, and (2) bridging the suppliers with charities through ScrapSaver. By pursuing many restaurants that are willing to donate surplus produce, ScrapSaver can build connections to help us live more economically and environmentally.

The main goal of this project is to build an interpretable decision-support large language model that assists clinicians in immunology cases in hospitalized/ICU patients. While existing LLMs like BioGPT and Med-PaLM achieve near-expert performance on medical exams, they lack application in real-world clinical support. This research will help work toward creating an LLM that provides actual support in clinical decision-making by focusing on diagnosis and suggesting the next best steps and treatments for the patients. This work is significant because it could help future physicians rapidly narrow down potential causes for symptoms (especially in high-stress situations), improve diagnostic precision, and reduce medical error rates, which are currently estimated to contribute to up to 10% of all deaths in the U.S.. Conducted as remote computational research, I will complete this project at the Maecker Lab at Stanford University, with my External Advisor Dr. Holden Maecker, a Professor of Immunology and Microbiology.

To address this topic, I will utilize the Demo MIMIC-IV clinical dataset to build a transformer model that uses a self-attention mechanism to process the interconnected patient data. Initially, I will investigate the dataset and guideline documents, followed by implementing baseline models. I will then integrate RAG and SHAP into GPT-OSS for reasoning and recommendations by retrieving clinical guideline passages and passing them with structured patient features to the model, ensuring both credibility and interpretability. Finally, I will optimize the model through hyperparameter optimization and evaluate its performance through multiple metrics. I expect this work to produce an interpretable prototype capable of suggesting likely diagnoses and treatment steps given patient hospital notes and laboratory data, contributing to a vision of much more effective and personalized healthcare that also relieves the burden of clinician burnout and physician shortages.

This project looks at how the Soviet Union used space exploration to boost national pride and show off its power during the Cold War. While everyone remembers the big milestones—like launching Sputnik and sending the first person to space—this project goes beyond science to explore how the USSR used these moments to strengthen their political message and rival the U.S. on the global stage. By looking at Soviet media, government reports, and the political climate of the time, the project explores how space missions became tools for propaganda, emphasizing Soviet strength and ambition. The final product will be a detailed paper that dives into the cultural and political impact of Soviet space achievements, along with an interactive exhibit to bring these ideas to life, using multimedia to show how space exploration shaped Soviet identity and influenced global perceptions of the USSR. The goal is to better understand the role of space in the broader Cold War battle for influence while mainly focusing on the Soviet perspective

This research project aims to understand how state governments can effectively engage high schoolers and the 18-24 year-old voter demographic in sustained civic engagement through a combination of immersive initiatives and legislation. This work will get to the root of the issues blocking young individuals from involvement, compare and contrast states, and identify successes and pitfalls of current efforts. To do so, this research will first involve data analysis from publicly available datasets and be performed in two stages. To understand the impact of legislation on the voting population, a difference in difference analysis of twelve sample states with varying levels of voter turnout can show the effects of each new policy. Second, to measure interest among the nonvoting population, this project will explore the impact of optional engagement and volunteering opportunities on voluntary petition-signing, public commenting, and other official methods of outreach. To augment the analysis with qualitative insights, a final stage will involve studying existing interviews with local officials, students, and young voters, then conducting new ones. Thus, this project will provide a comprehensive and refined list of improved strategies that can truly engage the youth in civic issues.

The purpose of this project is to resolve a species complex within the genus Protium (Burseraceae), and to attempt to relate a specimen’s spectral reading to its phylogeny. The resolution of a Protium (Burseraceae) species complex will help scientists studying the Amazon to have a better understanding of a wide range of geographically diverse samples, and the successful use of NIR as a substitute for DNA analysis will support NIR as a faster and cheaper method of studying Protium (Burseraceae) specimens. If successful, the NIR data collected from each specimen can be related to its phylogeny and generalized through a computer model to create an effective method of placing specimens into a phylogeny without phylogenetic data.

Using NIR technology via UC Berkeley’s Fieldspec4, a sample of multiple cryptic species of Protium (Burseraceae) will be scanned for their spectra and compared to one another to identify and differentiate species within the complex. After implementing ViewSpecPro and R programs to compare and analyze the spectral data, the project will shift to creating linear relations between spectra and previously found DNA data from each specimen. An accurate relation of spectra to phylogeny has never been achieved for Protium (Burseraceae) species, however, there is strong evidence that NIR could be used to distinguish cryptic Protium (Burseraceae) species. Based on this, it is expected that the Protium (Burseraceae) species complex studied in this project will be successfully resolved, but a reliable model for predicting phylogeny is unlikely to be generated from the same sample.

India has not always followed the modern Hinduism we know today. It has evolved, changed, and adapted throughout the centuries. One example is in South India, specifically in Tamil Nadu. There has been evidence of an ancient religion practiced by Dravidians, an ancient ethnic group that continues through South Indian ethnic groups like Malayalees, Tamils, Telugus, etc. The religion of ancient Dravidians slowly adapted and their gods slowly merged into the gods in the Hindu Pantheon. And while this adaptation has been recorded in scholarships and journals, the process of these gods merging with their modern counterparts has not been explained. My project will answer that question and fill in this gap in Indian history. It explains the slow and gradual process of the transformation of three Tamil gods: Korravai, Malon, and Seyon to their modern-Hindu counterparts: Durga, Vishnu, and Murugan/Karthikeya through art and literature. My project also explores teaching this subject to the masses: Tamil communities and historians, as well as kids in lower schools which helps shed light on Indian history and culture which seems to be forgotten. In order to do this, I am reading different Dravidian and Hindu texts and art pieces and compare them with each other. I am speaking with professors in these fields and ask them about these topics. My results are documented in my research paper and potentially will be published. I am also setting up days to create a presentation and teach these findings to young students as well as Tamil communities.

As the US population surges and will continue to do so far into the future, there has been an increasing strain on our predominantly car-centric transportation system, resulting in roads clogged with traffic, negatively impacting the economy and the environment. Many cities have incentivized the construction of higher-density housing, creating destinations closer to home. And with almost 55% of daily car trips already less than 3 miles, it necessitates robust active transportation systems (cycling, walking, etc), which can help decrease unhealthy car dependence. My project intends to find ways in which traffic light signaling can be improved to better deal with congestion and create intersection designs that can encourage active transportation by prioritizing safety and efficiency to shift to more human-oriented cities. The final product will entail a simulation of the Fremont & Mowry Intersection with new signaling techniques, a design report for safe active transportation use, and a cost report for construction costs.

Mental health has been discussed more frequently in recent years, especially due to the pandemic. According to the CDC (Centers for Disease Control and Prevention), 1 in 5 Americans will experience a mental illness in a given year. However, there is a stigma around mental health, which often causes issues to go untreated, leading to more severe symptoms and other repercussions, which affect one’s work and personal life. This is why it is important that more research be done in the field in order to address mental health issues in a positive and productive way. Currently, companies provide limited resources, which are insufficient for employees to work through improving their conditions. In this project, I am researching current mental health treatment options and the level of support provided by companies. I am surveying employees, predominantly in the tech industry, regarding the status of their mental health and/or the support they may receive. Finally, I am building three types of machine learning models (1) a logistic regression model, (2) a neural network, and (3) a random forest. The models can be used to determine whether companies provide enough support for their employees’ mental health needs. The goal of this project (and my final product) is to use the results of these models to assist companies in determining the amount of resources they need to provide their employees to improve their mental health.

According to the National Institutes of Health, from 2016 to 2017, 25% of the patients that visited the Emergency Department were reported for coming in for diabetes-specific ED visits. With the rise in diabetes related E.R. visits, there is a need for a larger focus on a specific field of medicine, preventive medicine, which promotes preventive health care to improve well-being. The goal is to ultimately prevent disease, disability, and death. My research aims to focus on two aspects of preventive medicine, disease education programs and regular screenings. Specifically, diabetic related health literacy of diabetic patients is an essential focus in preventing further complications for diabetic patients. Diabetic related health literacy refers to the “extent to which patients with diabetes have the required skills and abilities to seek, understand, analyze, communicate, and enumerate diabetes-related information both in the healthcare environment.” After conducting comprehensive diabetes education sessions, I aim to survey diabetic patients’s comprehension of their specific cases of diabetes and factors that contribute to the exacerbation of their diabetes. I expect my findings to illustrate the effectiveness of practicing preventive medicine in a curative medicine setting. This may inform the development of the diabetes education program in the current healthcare system, equip physicians to further personalize their treatment plans, and enable them to further serve as an educator for preventive medicine.

Abstract: In this era of big data, vast biomedical datasets, including genomic, transcriptomic, and clinical, are being generated daily. Thus, there is an increasing need to develop robust computational tools to analyze these datasets to extract biological value, whether understanding the spread of disease in populations or modeling complex cellular processes. At the pure-biology Wang Lab at the University of California, San Francisco, I will work with Principal Investigator Dr. Biao Wang to analyze lab-produced and public datasets to understand phosphorylation regulatory mechanisms, protein shuttling, and metabolic homeostasis. I will work to understand how specific protein sequences, called nuclear localization sequences (NLSs), are phosphorylated by kinases to regulate protein nucleocytoplasmic shuttling, i.e., the movement of proteins between the nucleus and cytoplasm. My project involves conducting an extensive literature review to understand pathways of nuclear import and export. It involves handling numerous classes of data, including protein sequence, phosphoproteomic, and annotation data, as well as corresponding genomic data. Understanding phosphorylation regulatory mechanisms can help us understand how protein sub-cellular localizations are impaired in disease.

In this project, I aim to determine if the tone of news articles heavily influences the trends of technology stock market prices. As the stock market changes everyday and is a national market, all data pertaining to it will be purely online. Therefore, I will utilize online datasets, such as NASDAQ and use websites such as Yahoo Finance to gain more information about the market. Additionally, since both my statistical models are online, I will conduct online research and read computer science websites, such as Kaggle and Stack Overflow, to learn how to create and modify my models. Since online news is more common than paper news, I will be using online news outlets, such as The Washington Post and The New York Times as the ones that I am analyzing. Finally, I aim to create a Powerpoint Presentation to present at the Senior Project Presentations as a final product, presenting my findings. If time permits, I want to consolidate my research in the form of a research paper and publish it, however, for now, my main goal is to make a presentation of my findings.

"Classical reception studies involve applying concepts from antiquity to a modern context while highlighting the significance of a text with regards to readers’ experiences. By applying modern theories to Greco-Roman texts, the study of classics becomes increasingly accessible and acknowledges diversity that merits exploration beyond that in ancient tellings.
My project focuses on Medea, a character notably criticized for rebelling against the ideal image of Greek women and associated with barbarism in a play by Euripides. As numerous adaptations of her story portray her deviation from social expectations, I plan to analyze her portrayal in contemporary fiction and research how her actions are interpreted through postcolonial and feminist lenses. In addition to examining the works of classical reception analysts like Vanda Zajko, I will navigate the theories of notable postcolonial scholars like Edward Said and the feminist theorist Chandra Talpade Mohanty.
To focus my analysis, I will delve into The Hungry Woman: A Mexican Medea by Cherríe Moraga, Salvage the Bones by Jesmyn Ward, and Circe by Madeline Miller. Through virtual meetings, I will discuss my findings with Dr. Melissa Funke from the University of Winnipeg and value her insights on gender studies in antiquity.
By filling certain gaps in current scholarship regarding Medea’s foreigner status and motherhood, I plan to investigate minority representation in myths where it is often excluded from the canon and relate the themes I will explore to current events."

I have always believed that the greatest learning opportunities come from disagreement and discussion. In my senior project, I will be examining the key differences between taxing legislation in the US and in China.

I will begin with researching the fundamentals of tax laws in both countries, their respective histories, as well as their effects on corporations and individuals. I will parallel this research with interviews with corporations large and small, gaining both qualitative and quantitative insight on their experiences and potential struggles in working with various tax regulations.
I will then travel to Shanghai, China, where I will intern under a senior CPA (Certified Public Accountant). This will include learning about the auditing process and interacting with a myriad of corporations, two of which I will interview in order to compare with my findings in the US.

Finally, I will synthesize my results, comparing and contrasting my findings in the two nations. Keeping the economic contexts of the two nations in mind, I will note my key findings down in a report that details the key differences in how the two countries operate.
I hope to walk away from this experience with not only an in-depth understanding of the economic nuances of the US and China but also concrete plans of action on ways we can improve upon our inefficiencies. This includes a short-term guide for businesses to minimize their struggles in dealing with taxes, as well as more long-term policy recommendations for both nations to make lasting reforms.

AP Research

Food is important for development, especially in children. By understanding how certain ingredients in an individual’s diet can affect their activity, ability to focus, and behavior, we can have a greater understanding for what kinds of food individuals should avoid or implement into their diets. Children diagnosed with ADHD often have a difficult time focusing in schools, and research shows that artificial food additives can exacerbate this effect. My research will explore the effects of certain food additives, such as Sodium Benzoate, on activity in children, and how this is regulated in elementary public schools. School provided lunches play a major role in how children access foods with artificial food additives and dyes. I will analyze the data produced from research on the link between food additives and hyperactivity in children diagnosed with ADHD. I will then look at the results and determine the strength of the correlation. Then, I will study school lunch regulations in public elementary schools and better understand how the link between the hyperactivity and food additives is monitored.