Xin Wang

Xin Wang

he/his

Trying to build something cool

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BioML Challenge 2024: Bits to Binders

11 minute read

Published:

CAR-T cell therapy is a type of cancer treatment where a patient’s own T cells， are genetically modified to fight cancer. The process involves extracting T cells from the patient, engineering them to express a Chimeric Antigen Receptor (CAR) that specifically targets cancer cells, and then reintroducing these modified cells into the patient’s body. Once inside the patient body, the CAR-T cells recognize and attack the cancer cells, helping to eliminate the tumor. An open question in the field of CAR-T cell therapy is how to design effective antigen-binding domains that can specifically target cancer antigens and trigger a robust immune response.

Molecules as Cellular Complexes

less than 1 minute read

Published:

The molecule is a complex system characterized by intricate relationships between its components, such as atoms, bonds, and rings. Topological Deep Learning (TDL) utilizes domains beyond graphs and constructs a comprehensive framework to process and extract knowledge from data associated with this kind of system. In this study, I introduce a novel representation of molecules as cellular complexes, specifically designed to accommodate ring structures. I rebuilt a topological neural network (TNN), the Cell Attention Network (CAN), and conducted a comparative analysis with standard Graph Neural Networks (GNNs) as baselines. This comparison highlighted both the strengths and limitations of our approach, offering insights into the potential and challenges of using cellular complexes for molecular modeling.

Measuring Visual Acuity in Bees from High-Resolution Images

4 minute read

Published:

Visual acuity, the ability to perceive detail, is ecologically important, as it dictates what aspects of a visual scene an animal can resolve. The study of visual acuity in bee species helps biologists understand their evolutionary history and gain insight into their foraging strategies and navigation abilities. Sponsored by the Caves Lab, this project aims to design a pipeline that uses high-resolution 2D photos taken by the NSF-funded Big Bee Project to estimate visual acuity across different bee species. In the pipeline, we develop algorithmic approaches to measure the diameter of the ommatidia and estimate the interommatidial angles on the eyeʼs surface. By achieving a significant level of automation and accuracy, our pipeline will facilitate more efficient data collection for biologists. You can access the code and poster, if you are interested.

Universal Approximation Theorem

less than 1 minute read

Published:

The most important application of neural networks is in machine learning, where neural networks are “trained” to approximate a function. Thus, a fundamental question for neural networks is whether they can approximate reasonable functions to an arbitrary degree of accuracy. This depends on the activation function \(\sigma\) and is the subject of many papers, including the paper studied for this project. This project was mentored by Zach Wagner and supported by UCSB’s Directed Reading Program. We won the People’s Choice Award luckily. You can access our presented poster via this link if you are interested.

portfolio

publications

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teaching

Undergraduate Teaching Assistant

Undergraduate course, University of California, Santa Barbara, PSTAT Department, 2022

  • 2022 Fall: Regression Analysis (PSTAT 126)
  • 2023 Winter: Statistical Machine Learning (PSTAT 131)
  • 2023 Spring: Big Data Analytics (PSTAT 134)