Geek. Coder. Dreamer.
Ph. D. Computer Science
Machine Learning and Natural Language Processing Hi, I am Mandy Miller. Welcome to my website! I'm a senior research scientist at Google NYC and an adjunct assistant professor at NYU. My main research interests are in natural language processing and machine learning.
I recently graduated from the Machine Learning Department at Carnegie Mellon where I was advised by Prof. Eric Xing and was a member of the SAILING Lab (since 2009). I was very thankful to be supported by an NSF Graduate Fellowship. Before that, I was an undergraduate in Computer Science and Applied Math at Princeton, and worked with Prof. Rob Schapire. |
Journal Publications
- A Decomposable Attention Model for Natural Language Inference, The Conference
on Empirical Methods in Natural Language Processing (EMNLP 2016)
- Grounded Semantic Parsing for Complex Knowledge Extraction, The Conference of the
North American Chapter of the Association for Computational Linguistics (NAACL 2015).
- Language Modeling with Power Low Rank Ensembles, The Conference on Empirical
Methods in Natural Language Processing (EMNLP 2014)( BEST PAPER RUNNER UP)
- Network Analysis of Breast Cancer Progression and Reversal Using a Tree-evolving
Network Algorithm, PLoS Computational Biology, 2014
- Spectral Unsupervised Parsing with Additive Tree Metrics, The Fifty Second Annual
Meeting of the Association for Computational Linguistics (ACL 2014)
- Robust Reverse Engineering of Dynamic Gene Networks under Sample Size Heterogeneity, Pacific
Symposium of Biocomputing (PSB 2014)
- Hierarchical Tensor Decomposition for Latent Tree Graphical Models, The 30th International Conference
on Machine Learning (ICML 2013)
- A Spectral Algorithm for Latent Junction Trees, The 28th Conference on Uncertainty
in Artificial Intelligence (UAI 2012).
- Multiscale Community Blockmodel for Network Exploration, Journal of the American Statistical
Association (JASA) 2012.(earlier version in AISTAT 2011)
- Enabling dynamic network analysis through visualization in TVNViewer, BMC Bioinformatics 2012
- Kernel Embeddings of Latent Tree Graphical Models, Neural Information Processing Systems (NIPS) 2011.
- A Spectral Algorithm for Latent Tree Graphical Models, The 28th International Conference on Machine Learning (ICML 2011)
- TREEGL: Reverse Engineering Tree-Evolving Gene Networks Underlying Developing Biological Lineages, the
Nineteenth International Conference on Intelligence Systems for Molecular Biology (ISMB 2011). Bioinformatics
2011.( BEST PAPER IN TRANSLATIONAL BIOINFORMATICS)
- Multiscale Community Blockmodel for Network Exploration, The 14th International Conference on Artificial
Intelligence and Statistics (AISTATS 2011)
- On Sparse Nonparametric Conditional Covariance Selection, The 27th International Conference on Machine Learning (ICML 2010)
I Teach
Statistical Natural Language Processing
In this course we will examine some of the core tasks in natural language processing, starting with simple word-based models for text classification and building up to rich, structured models for syntactic parsing and machine translation. In each case we will discuss recent research progress in the area and how to design efficient systems for practical user applications. There will be a focus on corpus-driven methods that make use of supervised and unsupervised machine learning methods and algorithms. We will explore statistical approaches based on graphical models and neural networks. In the course assignments, which will be updated this year to include more neural network modeling, you will construct basic systems and then improve them through a cycle of error analysis and model redesign. This course assumes a good background in basic probability and a strong ability and interest in building real systems.
My Journey
New York University (NYU)
Adjunct Asistant Professor
Sept. 2017Sr. Research Engineer
June 2017Carnegie Mellon University
Ph. D. in Computer Science
Machine Learning and Natural Language Processing
Internships: Micosoft Research (2)
Princeton University
B. S. in Computer Science with Minor in Applied Math. Ranked First in Class with internships at:
Micron Technology (2)
Google
Microsoft