Conditional Synthetic Data Generation for Robust Machine Learning Applications with Limited Pandemic Data
Hari Prasanna Das, Ryan Tran, Japjot Singh, Xiangyu Yue, Geoffrey Tison, Alberto Sangiovanni-Vincentelli, and Costas J. Spanos
Proceedings of the AAAI Conference on Artificial Intelligence 2022
Ryan Tran
Brief Biography
I am a machine learning research intern at percipient.ai. I earned my master's degree in Computer Science and Engineering from UC San Diego and my bachelor's degree in Electrical Engineering and Computer Sciences from UC Berkeley. I enjoy applying machine learning solutions to problems in the fields of computer vision and natural language understanding.
News
[March 2023] Graduated with my M.S. degree in Computer Science and Engineering from UC San Diego.
[March 2023] Published my master's thesis on Spoiler Recognition as Semantic Text Matching to ProQuest.
[March 2023] Submitted a paper to ICCV 2023 (under review) together with my team at percipient.ai.
[January 2022] Returning to percipient.ai to begin a part-time internship concurrent with my graduate studies.
[December 2021] Our paper "Conditional Synthetic Data Generation for Robust Machine Learning Applications with Limited Pandemic Data" is accepted to AAAI 2022.
[December 2021] Published the open-source package sortinghatinf, a Python library that uses machine learning to infer the feature types of the columns of a Pandas dataframe.
[September 2021] Starting my master's degree in computer science at UC San Diego.
[July 2021] Our paper "CDCGen: Cross-Domain Conditional Generation via Normalizing Flows and Adversarial Training" is accepted for a Spotlight Talk at Workshop on Machine Learning for Data: Automated Creation, Privacy, Bias at the International Conference on Machine Learning (ICML) 2021.
[July 2021] Our paper "CDCGen: Cross-Domain Conditional Generation via Normalizing Flows and Adversarial Training" is accepted for an Oral Presentation at Workshop on Data-Efficient Machine Learning (DeMaL) at KDD 2021.
[May 2021] Returning to percipient.ai to begin a machine learning research internship for the summer.
[May 2021] Graduated with my B.S. degree in Electrical Engineering and Computer Sciences from UC Berkeley.
[April 2021] Our paper "Conditional Synthetic Data Generation for Robust Machine Learning Applications with Limited Pandemic Data" is accepted at ICLR 2021 workshop on Machine Learning for Preventing and Combating Pandemics.
[May 2020] Starting a machine learning research internship at percipient.ai for the summer.
[May 2019] Starting a machine learning research internship at NASA JPL for the summer.
Publications
CDCGen: Cross-Domain Conditional Generation via Normalizing Flows and Adversarial Training
Hari Prasanna Das, Ryan Tran, Japjot Singh, Yu-Wen Lin, and Costas J. Spanos
Workshop on Machine Learning for Data: Automated Creation, Privacy, Bias; International Conference on Machine Learning (ICML) 2021
Workshop on Data-Efficient Machine Learning (DeMaL); Conference on Knowledge Discovery and Data Mining (KDD) 2021
Projects
sortinghatinf: Python library that uses machine learning to infer the feature types of the columns of a Pandas dataframe. See Project SortingHat for more details.
NotaryBot: AWS cloud-native bot that automates subscribing to events based on time, distance, and monetary constraints queried from Google Cloud APIs.