Academic portfolios are essential for showcasing research, publications, and projects to potential employers, collaborators, and the academic community. However, creating a professional and visually appealing portfolio can be challenging, especially for individuals with limited web development experience. This project aims to provide a minimalistic portfolio template designed specifically for academics, researchers, and students.

The template features a clean and modern design, making it easy to customize and update with personal information, research interests, publications, and projects. Users can easily modified sections, add new pictures and texts. The template is built using Next.js, a popular React framework, and Tailwind CSS, a utility-first CSS framework, ensuring a responsive and mobile-friendly design. By providing a simple and intuitive solution for creating academic portfolios, this template empowers individuals to showcase their work effectively and professionally.

Leukemia accounts for a significant proportion of cancer cases and mortality worldwide, prompting extensive research to improve patient survival. Traditional survival analysis using the Cox proportional hazards model faces limitations with complex data structures and restrictive assumptions. This study extends the Cox model with a Bayesian framework, incorporating hierarchical priors to enhance model flexibility and reliability. Utilizing a leukemia treatment dataset from Kaggle, the Bayesian Cox model demonstrates superior parameter estimation, model fit, and inference compared to traditional methods. Through Markov Chain Monte Carlo (MCMC) sampling with Stan, the study estimates posterior distributions of parameters, revealing significant treatment effects on survival outcomes. The results indicate that the new treatment significantly improves survival time, supporting the model’s effectiveness in handling censored and uncensored data in survival analysis. The findings underscore the potential of Bayesian methods in clinical research for more accurate and informative survival predictions.

ResNet, short for Residual Network, is a deep learning model that was proposed by Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun in 2015. ResNet is a type of Convolutional Neural Network (CNN) that is widely used in computer vision tasks, such as image classification, object detection, and image segmentation.

ResNet used to be a breakthrough in the field of deep learning. It solved the problem of accuracy degradation and verify the idea that the deeper network should have better performance. It was once widely used in various computer vision tasks and has been the basis of many other deep learning models. Although Transformer has become the most popular deep learning model in recent years, it is still necessary to understand the basic principles of ResNet.

This project provides a comprehensive introduction to ResNet, including its basic theory, structure, and significance in deep learning. It also discusses advanced applications and variations of ResNet, offering insights into its practical uses and potential improvements. By presenting experimental results and analysis, this project showcases the performance and advantages of ResNet over traditional neural networks.

The opioid crisis remains a significant public health challenge in the United States, characterized by high prescribing rates and overdose deaths. This analysis examines the trends and patterns of opioid prescribing and overdose deaths from 2015 to 2021. Utilizing datasets from Medicaid, the Opioid Treatment Program (OTP) Providers, and the National Vital Statistics System, we explore the geographical distribution of prescribing rates, the availability of treatment programs, and the specific types of opioids contributing to overdose deaths. The results reveal critical insights into the relationship between opioid prescribing practices and overdose mortality, highlighting the regional disparities and the effectiveness of treatment programs. This comprehensive study provides valuable information for policymakers and public health officials to develop targeted interventions to combat the opioid epidemic.

In statistical analysis, estimating the interval of a binomial proportion has great importance, particularly in fields ranging from clinical trials to market research. In practice, we may encounter different estimation methods. We compare two different interval estimators, the Wald interval and the Agresti-Coull interval, for an unknown binomial proportion in this report. By doing simulations, we obtained the performance of those two estimators by comparing them to the nominal/stated coverage of 95% and found the Agresti-Coull interval is better than the Wald interval. We analyzed the reasons for that result from a Bayesian interpretation.