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Whats New?

New projects and updates on what I've been up to!

Newest Updates as of

  • I have started a few new projects and joined some new professional organizations, to be listed in my Other Projects and Experience page soon!
  • I am currently working on updating the Skills page to be mobile-friendly.
  • I updated my CV (see here.) to the recent version.

AI Bias

The Impact of Biases in Facial Recognition Artificial Neural Networks

Circa 2014, there were a lot of conversations sparked about the potential harms of computer vision and AI uses, especially in government organizations and commercial contexts. My thesis work conducted at SUNY Oswego sought to probe how computer vision algorithms may be impacted by diversity in the datasets used for training.

In the field of artificial intelligence, the topic of neural network prejudice and bias is becoming more well-known by the day. More instances of unethical AI practices have been documented by the AIAAIC database, with instances branching outside of the scope of this project. It is now well understood that there is a major issue with how we are currently creating neural networks, because a lot of the facial recognition software (whether it be for commercial or personal use) that is being created is deeply flawed in regards to accuracy and equity between populations. For example, there have been many studies that show facial recognition and automatic gender recognition (AGR) technology’s accuracy rates are significantly worse on Black women and transgender people.

For the basis of my honors thesis at SUNY Oswego, I used two convolutional neural network (CNN) models that allowed me to probe some pressing questions on facial recognition neural network biases:

  1. How do biases infiltrate neural networks and affect their accuracy rates?
  2. What are the real-life effects of the reprecussions caused by these biases on marginalized groups of people?
  3. How can we mitigate some of the biases leading to gender prejudice in neural networks?

There have been many studies which have shown the wide array of ways that biases can infiltrate neural networks; one notable mention being through unbalanced datasets. This means that a lot of the datasets being used to train facial recognition neural networks are mostly cisgender white men, and therefore affect the accuracy rates to be better on that group. This disproportionately affects groups that are traditionally underrepresented (both in these datasets and in other places), such as transgender people and non-white individuals.

These biases can negatively affect these marginalized groups, which is shown through the real-life reprecussions that have already been discovered. Some considerable instances include police using facial recognition software to identify criminals, and instead misidentifying innocent Black individuals as criminals, as well as the potential for transgender people being misgendered by machine learning algorithms not being able to change their prescribed gender, leading to harmful mental health effects. I hypothesized that we can mitigate some of the potential biases leading to racial and gender prejudice in facial recognition technology by opting for a balanced dataset for training, as well as moving forward in the future instead of using a male/female binary gender option, opting to use a masculine/feminine continuous spectrum.

Aphantasia

Mental Rotation and Embodied Cognition

Aphantasia, or the inability to visulaize Experiences in the mind, is a condition that I Experience. Ranging from Aphantasia to Hyperphantasia, 'visual' imagery affected includes the major senses: visual, tactile, olfactory, gustatory, and auditory. During my undergraduate studies, I assisted with a project studying aphantasia using a mental rotation task. Additionally, as part of the requirements for my Cognitive Science degree, my Capstone project was completed on Aphantasia and Embodied Cognition.

Aphantasia is a relatively newly named condition which describes the lack of mental imagery in the mind. It exists on a scale from no mental imagery (aphantasia) to extremely vivid mental imagery (hyperphantasia), which for visual imagery can be quantified through a test called the , or Vividness of Visual Imagery Questionnaire. There is also the QMI, or Questionnaire of Visual Imagery, which encompasses all mental imagery, not just visual imagery.

The study that I worked on with Dr. Theo Rhodes and Dr. Sien Hu at SUNY Oswego sought to answer the questions:

  1. Is there a difference on a mental rotation task in those with aphantasia versus those without aphantasia?
  2. How can we use the differences on a mental rotation task in the aphantasic versus non aphantasic population in order to learn more about the mental processes and behaviors associated with the condition?
  3. In order to test this, we conducted a preliminary study during 2021 using the CABIN 3-T fMRI at the University of Rochester. We sought to understand the neuroanatomical differences between aphantasic and non-aphantasic individuals, as well as cognitive processing for the brains during the mental rotation task. A manuscript for publication is currently in preparation containing the results. Four adults with self-reported aphantasia and six adults without aphantasia participated in this study.

The Relationship Between Embodied Cognition (EC) and Aphantasia

The Capstone course that this project was completed for served as the culmination of my studies within the Cognitive Science major. The Cognitive Science Capstone entailed a research project on a subject relating to Cognitive Science, a final oral exam, creation and updates to my course webpage and other miscellaneous classwork that related to Cognitive Science.

Maltreatment

As of July 2023, I have been volunteering with Dr. Michelle Brown, the (PI of STARR Lab) at the University of South Carolina. I am assisting with the publication process from manuscript preparation, results refining, and miscellaneous processes to speed along the publication process. These two papers (in prep., 2024) focus on childhood maltreatment using two prospective longitudinal studies, known as the Female Adolescent Developmental Survey (FADS) and the Longitudinal Studies of Childhood Adolescent Neglect (LONGSCAN).

General Overview

Friendships and social connections are a major part of adolescence. In this project, Dr. Michelle Brown and co-authors sought to look at potential effects of how friendships may moderate the impact of childhood maltreatment in adolescent populations. As of November, 2024 there are two papers in preparation to look at two major datasets from prospective longitudinal studies (FADS and LONGSCAN).

Electroencephalography (EEG)

Electroencephalography (EEG) is a neuroimaging method to record the electrical activity (signals) coming from the brain.
The B-RAD Lab uses a special type of EEG Net that is more flexible than a Gel net. This specific EEG Net is used to better accomodate our participants. This includes participants such as:

The Reciprocity of Social Connection and Well-Being, aka the Social Connection and Well-Being (SCWB) Study

This study is an NIH R01-funded research project (PI: Caitlin Hudac, B-RAD Lab) focusing on adolescents. The main research questions:

  1. How does the reciprocity social connection and well-being change in the short-term (daily)?
  2. How does the reciprocity of social connection and well-being change in the longer-term (months - 1 year)
  3. How do behaviors and neurobiological markers link the associations between social connection and well-being?

I joined the Brain Research Across Development (B-RAD) Lab in February of 2024. Since then, I have taken on the lead role as Research Assistant for the SCWB Study, as well as minor roles including (but not limited to):

  • Lending assistance with the BBAD and Autistic Friendships Studies
  • Standing in as co-lab manager and mentoring 6-12 interns per semester; providing trainings on general EEG lab tasks and research best practices
  • Co-Author on several posters in the Spring 2024 and Fall 2024 semesters

Hierarchical Taxonomy of Psychopathology (HiTOP)

What is HiTOP?

The Hierarchical Taxonomy of Psychopathology, or, HiTOP is an alternative classification system for psychopathology and mental health conditions. Thought of as somewhat of an alternative to the DSM (due to organizational differences), HiTOP has restructured some of the ways that psychopathology is organized in Western medicine. HiTOP uses and builds upon pre-existing data and literature about psychopatology and mental health, and seeks to bring greater clarity and organization to the classification systems already in place.

Art Camp

Creative Writing and Art Camp Information

The Creative Writing and Art Camp hosted by the New Bethel Foundation was held from July 22 to July 25th, 2024 and focused on providing an enriching art education and outlet for local youth during the summer. Educational topics covered included:

  1. Shading and Values
  2. Textures and Patterns
  3. Color Theory
  4. Composition and Project Drafting

Other Projects

La Polynesie Francaise: La Faune et la Flore

Le Crise Du SIDA

Les Femmes Du Moyen Age


Does Making Narcissists "Feel Bad" Make Them Want To Be Better People?

As a sophomore at SUNY Oswego, I joined the lab of Dr. Matthew Dykas. During 2020, I was able to conduct research with him using Zoom. The project I assisted with was at the time underway with deception methods so that we could gauge the real reactions of college-aged students on how they felt about and remembered a hurtful situation from their past. For Quest Week in April of 2020, I was able to present a poster on the preliminary data we had collected. This poster presentation took a chunk of the sample we had collected and was not the exact focus of the study I assisted with as to avoid contamination effects due to the deception methods used.