Publications

Microfluidic cap-to-dispense (μCD): a universal microfluidic–robotic interface for automated pipette-free high-precision liquid handling
Wang Jingjing, Ka Deng, Chuqing Zhou, Zecong Fang, Conary Meyer, Kaustubh Deshpande, Zhihao Li, Xianqiang Mi, Qian Luo, Bruce D. Hammock, Cheemeng Tan, Yan Chen, and Tingrui Pan. “Microfluidic Cap-to-dispense (μCD): A Universal Microfluidic–robotic Interface for Automated Pipette-free High-precision Liquid Handling.” Lab on a Chip, 19(20), 3405–3415. https://doi.org/10.1039/c9lc00622b
View Publication

Accurate Detection of RNA Stem-Loops in Structurome Data Reveals Widespread Association with Protein Binding Sites
Pierce Radecki, Rahul Uppuluri, Kaustubh Deshpande, Sharon Aviran bioRxiv 2021.04.28.441809; https://doi.org/10.1101/2021.04.28.441809
View Publication

Enlisting 3D Crop Models and GANs for More Data Efficient and Generalizable Fruit Detection
Contributed to API development that enables a user to generate synthetic images as needed for computer vision research
View Publication

Research Experience

I have previously worked at 3 research labs. My work primarily involved programing, deep learning, computer vision and statistical analysis.

  • Plant AI & Biophysics Lab : At the PAIBL my research was centered around generating and utilizing synthetic data for computer vision tasks. Through my work I gained familiarity implementing and customizing basic computer vision architectures such as Mask R-CNN, Faster R-CNN, FCN, U-Net. I additionally worked on with various instances of object detection, semantic and instance segmentation as well as multi-model regression tasks.

  • Computational RNA Genomics Lab My research at this lab primarily involved statistical analysis of large RNA data sets and software development of the lab’s primary product: PatteRNA. I worked on the experimentation and integration of a binary classifier into the PatteRNA pipeline which resulted in a rapid and accurate method for automatically detecting families of RNA structure motifs.

  • Micro-nano Innovations Lab At the MiNi lab, the aim of our project was to develop a robotic–microfluidic interface for complete pipette-free liquid handling automation. I worked on the computer vision segment of this project and the software I developed was utilized by a DOBOT robotic arm to identify chemicals in a laboratory setting and dispense them as needed.

Back to home page ⮕