Nicholas J. Matiasz

PhD Candidate
  • UCLA Bioengineering
  • Alcino J. Silva Laboratory

Office: 924 Westwood Blvd, Suite 420, Room Q



Nicholas J. Matiasz is a PhD student in the Department of Bioengineering and a lab member in both the Medical Imaging Informatics Group and Silva Lab. Nicholas is interested in the problems of causal discovery and experiment planning. He is developing a method to construct causal graphs from findings reported in the biological literature. In parallel, he is developing an approach to experiment planning that ranks potential experiments by their ability to inform an existing causal model. He plans to apply his work to improve the efficiency of scientific research and clinical practice. Nicholas is a developer for ResearchMaps (, a Node.js web application that allows scientists to track, integrate, and visualize causal information. His dissertation work is advised by Prof. William Hsu and Prof. Alcino J. Silva.

Nicholas received a Bachelor of Science in Electrical Engineering (BSEE) degree from Tufts University in 2010. His senior design project team developed a novel intonation method for electrolarynx devices, leading the university to apply for a patent on the design (publication number US20130294613 A1). As a graduate student at Tufts, Nicholas was a research assistant in both the Machine Learning Group (Dept. of Computer Science) and the Interpersonal Perception & Communication Lab (Dept. of Psychology), and received a Master of Science (MS) degree in electrical engineering in 2012. Before coming to UCLA, Nicholas was a research technician in the Department of Neurology Research at the MGH Institute for Neurodegenerative Disease.


  1. Matiasz NJ, Wood J, Doshi P, Beckemeyer B, Wang W, Hsu W, Silva AJ. for integrating and planning research. PLOS One. 2018;13(5):e0195271. DOI: 10.1371/journal.pone.0195271. PDF
  2. Garcia-Gathright JI, Matiasz NJ, Adame C, Sarma KV, Sauer L, Smedley NF, Spiegel ML, Strunck J, Garon EB, Taira RK, Aberle D, Bui AAT. Evaluating Casama: Contextualized semantic maps for summarization of lung cancer studies. Computers in Biology and Medicine. 2018;92. DOI: 10.1016/j.compbiomed.2017.10.034. PDF
  1. Matiasz NJ, Wood J, Wang W, Hsu W, Silva AJ. Translating literature into causal graphs: Toward automated experiment selection. Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine (BIBM). Kansas City, MO; 2017. DOI: 10.1109/BIBM.2017.8217713.
  2. Matiasz NJ, Wood J, Wang W, Silva AJ, Hsu W. Computer-aided experiment planning toward causal discovery in neuroscience. Front Neuroinform. 2017 Feb;11(12). DOI: 10.3389/fninf.2017.00012. PDF
  1. Matiasz NJ, Chen W-T, Silva AJ, Hsu W. MedicineMaps: A tool for mapping and linking evidence from experimental and clinical trial literature. AMIA Annu Symp Proc. Chicago, IL; 2016. PDF
  2. Matiasz NJ, Wood J, Hsu W, Silva AJ. A free web app for integrating and planning experiments. Proceedings of the 15th Annual Molecular and Cellular Cognition Society (MCCS) Symposium. San Diego; 2016. PDF
  3. Garcia-Gathright JI, Matiasz NJ, Garon EB, Aberle D, Taira RK, Bui AAT. Toward patient-tailored summarization of lung cancer literature. 2016 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI). 2016. p. 449–452. DOI: 10.1109/BHI.2016.7455931.
  1. Matiasz NJ, Silva AJ, Hsu W. Synthesizing clinical trials for evidence-based medicine: A representation of empirical and hypothetical causal relations. Proceedings of the 6th Annual Joint Summits on Translational Science: AMIA Summit on Clinical Research Informatics. San Francisco; 2015. PDF
  1. Matiasz NJ, Hsu W, Silva AJ., a free web application to track causal information in biology. Proceedings of the 13th Annual Molecular and Cellular Cognition Society (MCCS) Symposium. Washington, D.C.; 2014. PDF