Advised by Julian McAuley and Virginia De Sa
MS degree granted
8/2010 - 5/2014
4.0 GPA in cognitive science courses
3.87 GPA in computer science courses
Minors in Artificial Intelligence, Computer Science, and Mathematics
1/2013 - 7/2013
Computer Science and Philosophy
Building novel LSTM-based recurrent neural network to support joint inference over sequential and contextual variables
Using Tensorflow to implement model for large-scale multivariate workout dataset
- Working with Assistant Professor Julian McAuley in the UCSD Department of Computer Science
Developing computational learning theory to account more cleanly for inference in real-world problems where we have knowledge about the domain
- This provides a clear basis for optimal transfer learning
- Built web app to suggest content you have produced that is relevant to your current writing task
- Uses Latent Dirichlet Allocation NLP algorithm to represent documents in terms of topical composition
- Derived Bayesian model of pragmatic inference to account for degrees of knowledge and belief
- This model accounts for a number of observed phenomena not predicted by previous models
9/2014 – present
- Worked closely with professors Roger Levy, Michael C. Frank, and Christopher Potts to develop models of relevance inference in utterance comprehension
- Designed and implemented experiment to elicit human judgments corresponding to predictions
- Prepared detailed model comparison analysis to make an informed decision about the path to pursue
- Presented poster at XPRAG 2015
5/2012 – 4/2014
- Derived and implemented stochastic Runge-Kutta model of Hodgkin-Huxley neurons augmented with ion-channel subunit-noise to improve both the validity and the runtime of the simulation
- Designed novel spike time visualization to avoid parameter choice bias in peristimulus histograms
Representing the UCSD Department of Cognitive Science at UCSD Graduate Student Association Meetings
Developed a novel computational account of how we infer what people mean by what they say and tested it via experiments and simulations.
Designed and tested Bayesian models of how people use communicative feedback to verify that their concept structures are in accord with those of their conversation partner.
Supervised undergraduate student in building interactive visualizations of the epistemic relations in human communication.
Worked with professors Roger Levy, Michael Frank, and Christopher Potts to develop a novel computational model of how we coordinate on the context relevant to linguistic interpretation and developed experiments to test these models against human behavior.
Presented original research to the UCSD psycholinguistics community.
Supervised undergraduate artist in creating stimuli to run psycholinguistics experiments.
Organized graduate student meetings
Attended departmental faculty meetings
Designed a collaborative filtering recommender system to suggest interesting questions to users of their question answering app.
Co-taught a number of one week technology camps for elementary and middle school children. They covered web development, simple app development, programming in MIT Scratch, and programming in CMU’s Alice.
5/2011 – 8/2012
- Helped scan participants in fMRI
- Wrote software to process data, create cortical surface visualizations and produce activation graphs
Nominated by UCSD for the 2017 Microsoft Research PhD Fellowship
Led a team of four to 3rd place in the IEEE Brain Computer Interface Hackathon
Nvidia academic hardware grant
Glushko Travel Award
Published in Oxford/Cambridge poetry journal
Four times published in Case Reserve Review poetry journal
SOURCE grant for independent research
Case Alumni Association Junior/Senior Scholarship
Bank of America Joe Martin Scholarship
Case Western Trustee’s Scholarship
National Merit Scholar
Winter 2016 and Fall 2016
Languages and technologies:
Python MATLAB Java
Lisp Unix R
C Tensorflow Keras
Domains and techniques:
Natural language processing
Data structures and algorithms
Copyright Larry Muhlstein 2016