cv
This is a description of the page. You can modify it in '_pages/cv.md'. You can also change or remove the top pdf download button.
Basics
| Name | Ping Yang |
| Label | Chemical Engineering |
| pinyang@umass.edu | |
| Phone | (541) 360 9669 |
| Url | https://PingYang16.github.io/ |
| Summary | PhD candidate in Bai Lab at UMass Amherst |
Education
-
2022.09 - Current Amherst, MA
-
2019.09 - 2022.06 Corvallis, OR
-
2013.08 - 2017.06 Shanghai, China
Certificates
| Certificate in Statistical and Computational Data Science | ||
| University of Massachusetts Amherst | 2025-05-20 (expected) |
Publications
-
2022.07.21 Classifying the toxicity of pesticides to honey bees via support vector machines with random walk graph kernels
The Journal of Chemical Physics
Pesticides benefit agriculture by increasing crop yield, quality, and security. However, pesticides may inadvertently harm bees, which are valuable as pollinators. Thus, candidate pesticides in development pipelines must be assessed for toxicity to bees. Leveraging a dataset of 382 molecules with toxicity labels from honey bee exposure experiments, we train a support vector machine (SVM) to predict the toxicity of pesticides to honey bees. We compare two representations of the pesticide molecules: (i) a random walk feature vector listing counts of length-L walks on the molecular graph with each vertex- and edge-label sequence and (ii) the Molecular ACCess System (MACCS) structural key fingerprint (FP), a bit vector indicating the presence/absence of a list of pre-defined subgraph patterns in the molecular graph. We explicitly construct the MACCS FPs but rely on the fixed-length-L random walk graph kernel (RWGK) in place of the dot product for the random walk representation. The L-RWGK-SVM achieves an accuracy, precision, recall, and F1 score (mean over 2000 runs) of 0.81, 0.68, 0.71, and 0.69, respectively, on the test data set—with L = 4 being the mode optimal walk length. The MACCS-FP-SVM performs on par/marginally better than the L-RWGK-SVM, lends more interpretability, but varies more in performance. We interpret the MACCS-FP-SVM by illuminating which subgraph patterns in the molecules tend to strongly push them toward the toxic/non-toxic side of the separating hyperplane.
Skills
| Programming | |
| Python | |
| Matlab | |
| C++ | |
| Julia | |
| R | |
| Bash | |
| LaTeX |
Languages
| Mandarin Chinese | |
| Native speaker |
| English | |
| Fluent |