David is an articling student at Rosen Kirshen Tax Law.
David received his J.D. from the Faculty of Law at Queen’s University in 2021. While there, he worked for the Queen’s Legal Aid Clinic and the Queen’s Business Law Clinic. He has worked on matters including landlord tenant disputes, business formation, corporate law and record maintenance, among various others.
Before joining RKTL, David was also a Student Director at the Queen’s Conflict Analytics Lab, a research-based consortium concerned with applying data science and machine learning to dispute resolution. During his tenure, he oversaw the development of the “Employee or Contractor Classification” and “Termination Compensation Calculator” tools, which has helped over 10,000 workers and businesses determine their employment rights and obligations during the COVID-19 pandemic. He also wrote two publications exploring the intersection of AI and Law, featured in the International Conference on Machine Learning’s Law and Machine Learning workshop, the McGill Law Journal, and the Queen’s Law Journal.
He received his Honours Bachelor of Science from McMaster University. During his time at McMaster University, David worked extensively with Canadian Blood Services to recruit stem cell donors.
In his spare time, he enjoys travelling, music production, and being a foodie.
Jason T. Lam, David Liang, Samuel Dahan, and Farhana H. Zulkernine, “The Gap between Deep Learning and Law: Predicting Employment Notice” (2020) 65:4 McGill LJ 711.
Samuel Dahan and David Liang, “The Case for AI-Powered Legal Aid” (2021) 46:2 Queen’s LJ 415.