Yuchen Shen

I am a master’s student at the Language Technologies Institute, Carnegie Mellon University. My research aims to address the efficiency & accuracy challenges in science via AI, developing systematic methodologies at both data & model levels. Guided by this principle, I am interested in AI + drug discovery, particularly:

1. from the data perspective: building general-purpose models from large-scale DNA/RNA data for target identification;
2. from the model perspective: developing multimodal conditional generative models for lead optimization.
3. to advance ML: designing efficient & interpretable biology-informed ML algorithms by studying the computational patterns of the brain and cells.

Currently, I am working with Professor Aran Nayebi on brain-inspired ML. I am fortunate to work with Professor Barnabás Póczos on generative models for molecules and with Professor Leman Akoglu on outlier detection for efficient discovery from scientific data. Additionally, I am interested in optimization and have the privilege of working with Professor Xiaorui Liu on decentralized algorithms.

During my undergraduate years, I concentrated on Natural Language Processing (NLP) and worked on summarization, few-shot sentiment analysis, and chatbots.

Email is the best way to reach me and please feel free to send me an email to discuss research! I try to read all my emails carefully, but don’t hesitate to send another one if you don’t receive my reply after one week!