Projects

Below is a selection of our students' mentor-guided projects, which cover a variety of topics across Humanities, Social Sciences, and STEM

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Applications of Australian Native Aquatic Plants on Purifying Wastewater Sources

  • By Nathan Nguyen, Cavendish Road State High School

  • Mentor – James Kweon, Yale University

Abstract: 

Wastewater treatment plants play an important role in maintaining the health of ecosystems and ensuring the economic, social, and political soundness of communities. However, current wastewater treatment methods are economically and environmentally unsustainable. Aquatic plant restoration has been receiving attention because of its high efficiency and eco-friendliness compared to previous methods. Three types of aquatic plants: duckweed and hornworts were tested for their effects in removing constitutes and reducing the number of bacteria colonies in a wastewater source, over the course of 7 days. The results show that all three aquatic plants were capable of recovering and removing bacteria in water. Duckweed, however, was the most effective of all three plants. A 3D digital model of a duckweed based wastewater treatment plant was devised to showcase how duckweed could be incorporated into full-scale water treatment systems. The uptake of aquatic-plant based wastewater treatment systems has been slow. The conducted research adds to the advantages and the feasibility of full-scale aquatic-plant based wastewater systems.

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Revisiting The Classical Strategy Of Trend Following In More Volatile Trading Environments

  • By Pi Rey Low

  • Mentor – Dr Eric Sakk, Cornell University

Abstract: 

Trend-following strategies (TFS) have been well-established for their effectiveness in analysing stock prices for
decades. However, there remains a pressing need to revisit and analyse their performance in today’s increasingly
volatile financial environment. First, this study investigated their profitabilities with respect to the S&P500 fund
over the past 10 years. The fund’s consistent and strong uptrend over the 10-year period resulted in TFS being
unable to outperform the passive buy-and-hold strategy. Longer moving averages and breakout lengths were more
profitable given the fund’s bullish nature. Additionally, it was found that exponential moving averages were more
effective than simple moving averages. The study also established that trading more frequently, such as daily, had no
advantage over trading weekly or monthly. TFS incorporated with stop losses were largely ineffective and were only
profitable when market prices displayed strong and consistent trends. Second, this study examined the relevance of
TFS in varying economic climates by using data across various market sectors and time periods. It was found that
TFS performed better when prices display both bullish and bearish trends as opposed to when prices only trend in
one direction or experience frequent fluctuations. Given the steady uptrend in the S&P500 fund in recent times, the
effectiveness of these strategies have deteriorated compared to the past where price patterns were less consistent.
Thus, it can be said that the relevance of TFS have diminished for funds displaying consistent one-directional trends,
like the S&P500 fund, or extremely volatile price patterns.

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A Comprehensive Overview of Epigenetics

  • By Richard Duan, Auckland Grammar School

  • Mentor – Dr Kif Liakath-Ali, Stanford University

Abstract: 

This review article focuses on the field of epigenetics. Since its inception in the 20th century, there have been several major developments which induced the change in what the word “epigenetics” actually meant. In this review, the discoveries made so far are summarised and the potential applications of these new discoveries are discussed. Some things to keep in mind are that the field of epigenetics is relatively young compared to many other fields of research, so there are bound to be areas in which the understanding of the topic is lacking. Despite this, and partly due to recent advances in technology, several epigenetic therapeutics, some of which have already been approved for use, have been developed.

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The Effect of Artificial Intelligence Models on the Diagnostic Accuracy of Liver Cancer

  • By Bianca Linares, Mountain Vista Governor’s School

  • Mentor – Mustafa Guler, University of Chicago

Abstract: 

The purpose of this study was to determine if there was a significant difference in the performance of an Artificial Neural Network (ANN) and a Support Vector Machine (SVM) for liver cancer classification. The reason to determine if there is a significant difference is to find an artificial intelligence model that can best classify liver cancer images in order to prevent future cases of liver cancer in patients. The performance of both models was compared and validated on the LiTS – Liver Tumor Segmentation Benchmark (LiTS17) dataset in terms of accuracy. The comparative results show that the SVM classifier outperforms the ANN classifier where SVM gave an classification accuracy of 82.83% whereas the ANN gave an classification accuracy of 63.48%. This result indicates that the classification capability of SVM is better than ANN and may potentially fill in a gap in the use of current or future classification algorithms for liver cancer.

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A Meta-analysis of Syndromic Autism Genes

  • By Daria Lunina, European Gymnasium

  • Mentor – Dr Kif Liakath-Ali, Stanford University

Abstract: 

Autism is a neurodevelopmental disorder, characterized by severe impairment of various behavioural functions. Mutations in several genes known to be associated with autism. In this study, a meta-analysis was performed on 126 genes that are implicated in syndromic autism by the SFARI database. Results show that several genes are associated with known human pathological conditions such as delayed speech and language development. Further analysis reveals that many genes are associated with functions related to head development, Rett syndrome (psychoneurological syndrome) and neuron projection morphogenesis. Protein network analysis revealed closely associated phenotypic terms largely clusters with neuron morphogenesis category, suggesting that mutations in these genes significantly affect neuronal development and that in turn result in autistic characters. This study sheds light into the general role of autism-related genes and how their mutation may affect normal neuronal function.

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Stem cells: Basics and Advances

  • By Savana Na, Shenzhen College of International Education

  • Mentor – Dr Kif Liakath-Ali, Stanford University

Abstract: 

Our body is made up of cells that are building blocks of various tissues. Fertilized eggs give rise to the entire body through division of embryonic stem cells. Upon development, various tissues in the body replenish and grow using their respective stem cells. Stem cells possess enormous capacity for regeneration and can be exploited for potential therapy. The aim of this review is to give a basic introduction into what stem cells are and the different types of stem cells. In addition, the concept of induced pluripotent stem cells will also be explained along with its discovery, experiments and possible applications. Furthermore, I also summarize the basic methods of direct reprogramming.

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CMOS Architecture

  • By David Xu

  • Mentor – Dr Eric Sakk, Cornell University

Abstract: 

With the current landscape of MOSFETs in mind, the approaching limits and shortages of the modern silicon transistor has lead to many concerns over the future of semiconducting technology. This paper aims to look through one possible successor to the long-standing Silicon based MOSFET architecture in the form of Carbon Nanotubes. Although a lot has been researched about the wonder material graphene, many research have not yet reached solid conclusions about the potential of graphene and carbon’s allotropes. One example is the Standford model of a 16-bit RISC-V architecture microprocessor that sets a precedent for the earliest prototypes of a CNT based system. A following prototype designed by MIT engineers also prove the possibility of CNT processors. In the time of writing this paper, no other substantial project on CNT processor have been conducted leaving much room for future findings and improvements
to existing models.

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A Novel, Integrated Computational and Synthetic Approach for the Rapid Identification of N-Heterocyclic Drug-like Small Molecules that Regulate 5-HT2

  • By Laura Debrabandere

  • Mentor – James Kweon, Yale University

Abstract: 

Human beings throughout history have been affected by various medical conditions, expediting the need for drug discovery. G-Protein Coupled Receptors (GPCRs), as the largest family of membrane receptors responsible for various physiological functions, are often valid targets for drug discovery and development. One particular example of such a GPCR is 5-hydroxytryptamine2A (5-HT2A), a serotonin receptor that is a suitable target to treat neurological disorders, such as depression, schizophrenia, PTSD, and anxiety. The first potent and selective Tetrahydropyridine (THP)-derived agonists towards 5-HT2A were identified through virtual screening and developed throughout the course of this project. These potent and selective molecules were inspired from nonselective agonists such as LSD, in order to address the question as to whether the therapeutic effects of such drugs could be divorced from their psychedelic effects.

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A Computational and Synthetic Approach for the development of the first selective agonists towards the 5-Hydroxytryptamine2A receptor

  • By Maria Zotova

  • Mentor – James Kweon, Yale University

Abstract: 

Drug discovery is a significant process that enables scientists to discover medications for the human population. Over the course of the last century or two, drug development has led to cures for numerous ailments. However, there still are many unanswered and incurable conditions that exist today, emphasizing the continued importance of these research efforts. One major target class for drug development involves the G-protein coupled receptors (GPCRs). Because GPCRs are the largest class of membrane receptors, they are in turn the largest class of druggable targets. One particular GPCR of interest is the 5-hydroxytryptamine2A receptor, which is known to be the canonical target for serotonin, but also psychedelic drugs such as LSD. Targeting 5-HT2A has the potential to treat neuropsychiatric disorders, such as PTSD and depression, but selectivity issues in drug development have hindered the production of reliable medications. Namely, lack of selectivity in drugs such as LSD leads to undesirable hallucinogenic effects, in addition to the therapeutic antidepressant effects. Our goal in this research review paper is to outline the steps for drug development by highlighting the specific example of designing potent and selective agonists towards the 5-HT2A receptor.

Hear from our mentors

Our mentors have studied at the top US & UK universities

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Brown University

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Columbia University

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Stanford University

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Georgia Tech

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Kings College London

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Princeton University

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University of Oxford

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New York University

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University of Cambridge

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University of Chicago