Summary of Qualifications

• Expertise in natural language processing, with in-depth knowledge of the syntax-semantics interface.
• Experience in project management, coordinating contributions from multiple stakeholders.
• Proficient in statistical analysis software (R, Matlab), object-oriented programming (Java, C++), and scripting languages (Perl, Python, Unix shell scripting).

Relevant Experience

• Conducted a multi-year scientific investigation using functional Magnetic Resonance Imaging (fMRI) to understand brain changes accompanying language development in school-age children.
• Supervised and delegated work among three principal investigators, three postdoctoral scholars, and one graduate student in the neurology and psychology departments to ensure efforts aligned with objectives.
• Led the project through a complete cycle: experiment design, recruitment and testing of over 40 subjects, data analysis, and result presentation.
• Built statistical models to understand brain activity and connectivity during different language tasks.
• Streamlined data analysis workflow using shell scripts and grid computing.

• Conducted research aimed at leveraging semantic and context information in statistical grammar induction.
• Partnered with fellow graduate students to build a medium-scale natural language understanding system.
• Implemented a cognitive computational model of grammar learning in Java; tested on a real child language corpus.
• Translated insights from psychology and cognitive linguistics into algorithms in the model, utilizing resources such as FrameNet and Penn Treebank as guidelines.

Delivered workshops on culture in the U.S. classroom and on teaching Electrical Engineering & Computer Science, both designed to aid new graduate student teachers in acclimating to the roles and expectations of instructors.

Proposed formalized training and certification program for graduate students pursuing teaching roles. Reviewed course improvement grant applications and provided feedback to applicants. Counseled graduate student instructors on effective teaching strategies.

Psychology, Language Development
Computer Science, Linguistics, and Cognitive Science, Neural Basis of Thought and Language
Computer Science, Introduction to Artificial Intelligence

Cognitive Linguistics, A Best-Fit Approach to Productive Omission of Arguments
Mind and Language, Constructing Grammar: A Computational Model of the Acquisition of Early Constructions
Introduction to Cognitive Science, The Neural Theory of Language Project

Education

Dissertation Title: Contextual Bootstrapping for Grammar Learning .
Committee: Jerome A. Feldman (chair), Dan Klein, Carla Hudson Kam

Publications

• Eva H. Mok. (Under review). An Embodied Construction Grammar Approach to Understanding and Learning Mandarin Chinese. Computational approaches to construction grammar and frame semantics. Ed. Hans C. Boas. John Benjamins.
• Eva H. Mok (2008). Contextual Bootstrapping for Grammar Learning. Ph.D. Dissertation. Computer Science Department, University of California, Berkeley.
• Nancy Chang and Eva H. Mok. (2006). A Structured Context Model for Grammar Learning. Proceedings of the 2006 International Joint Conference on Neural Networks (IJCNN), Vancouver, BC.
• Eva H. Mok and John Bryant. (2006). A Best-Fit Approach to Productive Omission of Arguments. Proceedings of the 32nd Annual Meeting of the Berkeley Linguistics Society (BLS-32), Berkeley, CA.
• Eva H. Mok, John Bryant, and Jerome Feldman. (2004). Scaling Understanding up to Mental Spaces. Proceedings of the 2nd International Workshop on Scalable Natural Language Understanding (ScaNaLU-2004), Boston, MA.

Presentations and Invited Talks

• Eva H. Mok, Anthony Steven Dick, Anjali Raja Beharelle, Elena Zinchenko, Ozlem Ece Demir, and Steven L. Small. (Accepted). Neural processing of co-speech iconic gestures. Poster presentation at the 40th Annual Meeting of the Society for Neuroscience. San Diego, CA.
• Eva H. Mok. (2010). A computational model of the simultaneous learning of grammatical structures and statistics. Invited talk at the University of Chicago Workshop on Language, Cognition, and Computation.
• Eva H. Mok. (2009). A computational model of the simultaneous learning of grammatical structures and statistics. 33rd Stanford Child Language Research Forum, Berkeley, CA.
• Nancy Chang and Eva H. Mok. (2006). Putting Context in Constructions. The Fourth International Conference on Construction Grammar (ICCG4), Tokyo, Japan.
• Eva H. Mok and Nancy Chang. (2006). Contextual Bootstrapping for Grammar Learning. Member poster presented at the 28th Annual Conference of the Cognitive Science Society (CogSci), Vancouver, BC.

Honors and Awards