Megan Stickler

Megan Stickler completed their doctoral degree at the University of Houston; their dissertation work considered the dynamics of evidence-based decision making in large social groups and the effect that correlation in the available information can have on overall decision quality.

Other projects they’ve worked on include analysis of political conversations on Facebook with text-mining methods, modeling nutrient exchange in fungus-plant symbiotic networks, and trying to get a computer to learn music theory via reinforcement learning algorithms.

When not doing math, they like to play with their baby, hike, play piano and banjo, roller skate, knit, listen to podcasts of literature and religious studies classes, and hopes to eventually rejoin a roller derby league. 

• B.S. in Mathematics at University of Texas at Arlington, 2016
• Ph. D. in Mathematics at University of Houston, 2022

• MTH 1030 Applied Contemporary Mathematics
• MTH 1070 Functions, Graphs, and Analysis
• MTH 200T Topics In Math - Text Mining and Natural Language Processing
• MTH 1050 Elementary Statistics

• Decision making and opinion exchange models
• Flow of opinion and topics over social networks
• Graph inference on social networks
• Correlation in stochastic models
• Text-mining applications

• B.R. Karamched, M. Stickler, B. Lindner, Z.P. Kilpatrick, W. Ott, and K. Josić. “Heterogeneity Improves Speed and Accuracy in Social Networks.” Physical Review Letters 125. (2020)