Current Students

My research mainly involves applying machine learning algorithms to map the gene regulation mechanism in humans. Recently, my work has expanded to finding specific genes and mechanisms they are involved in that lead to healthy aging in humans.

My primary research interests span the health data sciences. I am specifically interested in employing Artificial intelligence (AI), geospatial, and text-mining tools to address socioeconomic disparities across public health outcomes. My ultimate goal is to use fair and impactful AI tobridge inequities surrounding the public health domains.

I was born and raised in Singapore. I completed my BS in Data Science from Penn State University and my MS in the Behavioral and Data Sciences from the University of Warwick (UK). My background has provided me with the opportunity to collaborate with researchers encompassing the fields of computational & information sciences, health & recreational sciences, and decision-making. I am always grateful to my past and present advisors who have invested their time in my research endeavors.

Developing machine learning algorithms and optimization. Current works focus on Gaussian process models and Bayesian active learning applied in active cognitive testing.

My research interest lies in the interaction between Bayesian machine learning and political methodology, including but not limited to election forecasting and causal inference.

My current project is Bayesian difference in difference with mult-task gaussian process.

My paper ‘Polls, Context, and Time A Dynamic Bayesian Forecasting Model for US Senate Elections’ is under R&R of Journal of Political Analysis.

I am generally interested in applying computer vision, and multi-modal models for solving problems in the geospatial realm.

    https://alexdc.info

Adviser:
Patrick Fowler and Chien-Ju Ho

Bio:
Hi there! I’m Alex, a fifth-year PhD student in Computational & Data Sciences at WashU. I’m passionate about urban data science, and my current work focuses on evictions, specifically thinking about how we can leverage linked property data to predict where evictions will occur and how we can efficiently and equitably reach out to tenants at high-risk properties. I am advised by Patrick Fowler and Chien-Ju Ho.

I’m also fortunate to have collaborated with Sera Linardi and Michael Hamilton at the University of Pittsburgh on 412Connect, a volunteer-driven and community-based effort to design an online platform to connect local Black-owned businesses and the student community.

    https://earnestt1234.github.io/

In DCDS, I am interested in applying machine learning and data-driven approaches to study neurological and neuropsychiatric disorders. I just finished a first-year rotation in the lab of Aristeidis Sotiras, where I worked on using non-negative matrix factorization (NMF) to learn patterns of brain pathology in Alzheimer’s Disease neuroimaging.  In my next rotation with Todd Braver, I will evaluate the utility of the lab’s dynamic brain modeling method (MINDy) for predicting behavioral and psychological outcomes. 

    https://sites.google.com/view/robertjirsaraie

My research interest includes mapping developmental trajectories of psychopathology to better understand biomarkers and predict risk. My first doctoral project aims to determine how generalizable existing brain age models are to capturing within-subject changes in brain development and mental health. Towards this end, I am actively building my skills in machine learning, longitudinal analysis, and multimodal brain imaging. 

I am generally interested in the application of AI and Machine learning to solve challenges with keen interest in achieving successful Human-AI team collaborations.

    https://lee-messi.github.io

I am generally interested in how languages reflect implicit biases in society. My research specifically involves word embeddings – building relevant corpora through web scraping and APIs, training embedding models, and using association tests for bias detection. I am currently working on two projects: one is a project on perceptions of media bias trying to identify the specific proxies news consumers use to assess bias degree of news outlets, and the other is on detecting political biases in the media and studying their priming effect on the general public and political elites.

    https://sites.google.com/view/abigail-lewis/home

I am interested in using data science to understand disparities in access to health care and patient outcome. Currently I am working on a project in the Institute for Informatics which aims to understand the use of biomarker testing in Alzheimer’s diagnoses and racial disparities in access to these tests.    

My research interests include algorithms, theories, and applications of Artificial Intelligence (AI). I focus on developing new AI algorithms and improving the performance of AI models. I also focus on how to apply AI models in different fields better. At the same time, I am also very interested in physics. The following are my current research interests: Large Language Models (LLMs), Computer Vision, Deep Reinforcement Learning, AI for Science & Mathematics, Neural Networks & Deep Learning.

I am passionate about the intersection of artificial intelligence and precision medicine. My work primarily involves leveraging methods such as graph-based models and large language models to uncover the intricate relationships between genetic data, drug interactions, and diseases. By combining cutting-edge AI technologies with biomedical research, I aim to contribute to the development of personalized treatments and deeper insights into complex biological systems.

My research interest focuses on using data science and machine learning to address challenges in mental health and loneliness. I aim to identify patterns that contribute to these issues in order to develop interventions that promote well-being and reduce isolation.

“I am passionate about bridging the gap between complex data analysis and practical societal policy solutions. My current research lies at the intersection of Political Economy, Comparative Politics, Machine Learning, and Geospatial Statistics. Throughout my career and research, I have collaborated with professionals from various disciplines, including Political Science, Data Science, International Relations, and Economics, all fields in which I have formal training.

I was proudly born and raised in Mexico City and am a first-generation student. At WashU, I am honored to be a Global Scholar of the McDonnell International Scholars Academy. As part of this community, I am committed to making a positive global impact, addressing pressing challenges such as Economic Development, security, access to education, and quality health.”

Research Interests:

I’m interested in how an agent can learn quicker, and more accurately, by interacting with a complex environment. Currently, I’m exploring how causal reasoning can be used to generalize between different environments.

   http://sites.wustl.edu/davidogara

I am interested in the intersection of complex systems and public health.

My research focuses on addiction health services using secondary data such as Electronic Health Records (EHR) and insurance claims. I am interested in treatment access disparities – specifically financial and insurance barriers  – for substance use and mental health disorders.

I am interested in exploring the effects of the built environment on public health and on equity. Currently, I am working on a project to model the social and environmental determinants childhood physical activity using agent-based modeling. I am also interested in studying feedback loops in social systems, and previously presented a model of feedback mechanisms in systems of inequity.

I am a PhD student in Janine Bijsterbosch’s lab. I like using deep learning and generative models to characterize the many properties of human MRI brain data. We attempt to embed MRI data into generative models in innovative ways that might allow us to reconstruct or translate across MRI brain representation modalities.

My research interest includes addressing health disparities and bias in health care by utilizing data science methodologies and applications.

Research Interest:

My research focuses on how the structure of a social network can influence opinion dynamics and methods of reducing polarization on networks.

I am interested in utilizing big data to address the most pressing public health problems particularly HIV Stigma and mental health issues. Specifically, I am passionate about utilizing machine learning and data-driven methodologies to predict mental health disorders among adolescents living with HIV in resource-constrained communities. By identifying key risk factors and developing predictive models, I aim to inform targeted interventions and improve health outcomes for this vulnerable population.

My research interests lie at the intersection of artificial intelligence, machine learning, and causal inference. Specifically, I focus on using causal mediation analysis to explore how race impacts treatment outcomes. I am passionate about harnessing cutting-edge AI and ML techniques to address these critical healthcare disparities.

Current research explores the application of machine learning methods to synthetic data generation.

I am interested in how cognitive biases affect our decision-making strategies. My research primarily focuses on how these biases influence choices when people interact with Artificial Intelligence (AI). By understanding how these biases influence this interaction, we can implement AI to help us make optimal choices efficiently while ensuring AI properly considers how these biases affect the overall decision-making process.

I work in Dr. Dosenbach’s lab where the lab studies brain development, plasticity and precision mapping using neuroimaging technique like fMRI. I am interested in using big neuroimaging dataset to learn brain behavior correlation and how environmental/SES factors affect brain development.

I am interested in handling complex databases and applying computational methodologies to study many fields. Currently, my research focuses on analyzing datasets of complex connectivity patterns and their relation to health and disease.

I am excited to contribute to technological advances in many different research directions by utilizing my data analysis skills. To be more specific, I am interested in handling complex databases and applying computational methodologies to study many fields. Currently, my research focuses on analyzing datasets of complex connectivity patterns and their relation to health and disease. I graduated from the Ohio State University with a bachelor’s degree in data analytics. Through my undergraduate studies, I felt that data science is very useful and interesting. So, I decided to pursue the DCDS PhD at WashU. I like to play many different sports in my free time. Recently, I enjoy playing squash, badminton, tennis, golf and swimming.

I am particularly focused on applying machine learning and deep learning techniques in the field of neuroimaging, with a specific emphasis on Alzheimer’s disease pathology.

My research focuses on using statistical and data science methods to analyze environmental issues, particularly those related to environmental microbiology. Under the guidance of my advisor, I primarily explore the role and impact of microbes in environmental systems through data analysis. This research direction allows me to apply various statistical tools and models to analyze microbial diversity, community assembly processes, and environmental stress factors, providing scientific insights for environmental monitoring and management. I aim to contribute effective techniques and solutions to address real-world environmental challenges through my work.