My primary interest in is the use of machine learning, particularly deep learning, to solve problems in collaboration with domain experts across a wide range of disciplines, including astronomy, biology, chemistry, climate science, cybersecurity, education, energy efficiency and materials science. I am also interested in multimodal and fewshot learning, spoken language processing and optimization. In the past I have worked on low resource language processing, low rank matrix and tensor methods, detecting deception in social media, sports analytics, prosodic feature extraction and structure detection, detecting social phenomena in language (e.g. subgroups), articulatory feature detection and formant tracking.
If you are a graduate student or motivated undergraduate student with research interests aligned with or adjacent to mine, feel free to send me an email or stop by during office hours to chat about possible research projects.
- Current students
- Graduate students
- Undergraduate students
- Former students (w/ 6+ months of collaboration)
- Graduate students
- Undergraduate students
Conference and Workshop
- Graham Roberts, Simon Haile, Rajat Sainju, Danny Edwards, Brian Hutchinson and Yuanyuan Zhu. Deep Learning for Semantic Segmentation of Defects in Advanced STEM Images of Steels. Scientific Reports, Volume 9, issue 1, pages 1-12, 2019.
- Richard Olney, Aaron Tuor, Filip Jagodzinksi and Brian Hutchinson. A Systematic Exploration of ΔΔG Cutoff Ranges in Machine Learning Models for Protein Mutation Stability Prediction. Journal of Bioinformatics and Computational Biology. Volume 16, no. 5, 2018.
- Ramin Dehghanpoor, Evan Ricks, Katie Hursh, Sarah Gunderson, Roshanak Farhoodi, Nurit Haspel, Brian Hutchinson and Filip Jagodzinski. Predicting the Effect of Single and Multiple Mutations on Protein Structural Stability. Molecules 23, no. 2: 251, 2018.
- Yanzhang He, Peter Baumann, Hao Fang, Brian Hutchinson, Aaron Jaech, Mari Ostendorf, Eric Fosler-Lussier and Janet Pierrehumbert. Using Pronunciation-Based Morphological Subword Units to Improve OOV Handling in Keyword Search. IEEE Transactions on Audio, Speech and Language Processing. Volume 24, issue 1, pages 79-92, 2016.
- Brian Hutchinson, Mari Ostendorf and Maryam Fazel. A Sparse Plus Low-Rank Exponential Language Modeling for Limited Resource Scenarios. IEEE Transactions on Audio, Speech and Language Processing. Volume 23, issue 3, pages 494-504, 2015.
- Brian Hutchinson, Li Deng and Dong Yu. Tensor Deep Stacking Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence. Volume 35, issue 8, pages 1944-1957, 2013.
- Bin Zhang, Alex Marin, Brian Hutchinson and Mari Ostendorf. Learning Phrase Patterns for Text Classification. IEEE Transactions on Audio, Speech and Language Processing. Volume 21, issue 6, pages 1180-1189, 2013.
- Brian Hutchinson, Mari Ostendorf and Maryam Fazel. Low Rank Language Models for Small Training Sets. IEEE Signal Processing Letters. Volume 18, issue 9, pages 489-492, 2011.
- Svitlana Volkova, Ellyn Ayton , Dustin L. Arendt, Zhuanyi Huang and Brian Hutchinson. Explaining Multimodal Deceptive News Prediction Models. ICWSM 2019.
- Robin Cosbey, Allison Wusterbarth and Brian Hutchinson. Deep Learning for Classroom Activity Detection from Audio. ICASSP 2019.
- Nicholas Majeske, Brian Hutchinson, Filip Jagodzinski and Tanzima Islam. Low Rank Smoothed Sampling Methods for Identifying Impactful Pairwise Mutations. Computational Structural Bioinformatics Workshop at ACMBCB 2018.
- Andy Brown, Aaron Tuor, Brian Hutchinson and Nicole Nichols. Recurrent Neural Network Attention Mechanisms for Interpretable System Log Anomaly Detection. Machine Learning for Computing Systems Workshop at HPDC 2018.
- Gracie Ermi, Ellyn Ayton, Nolan Price and Brian Hutchinson. Deep Learning Approaches to Chemical Property Prediction from Brewing Recipes. IJCNN 2018.
- Richard Olney, Aaron Tuor, Filip Jagodzinski and Brian Hutchinson. Protein Mutation Stability Ternary Classification using Neural Networks and Rigidity Analysis. International Conference on Bioinformatics and Computational Biology 2018. Best Paper Finalist.
- Aaron Tuor, Ryan Baerwolf, Nicolas Knowles, Brian Hutchinson, Nicole Nichols and Robert Jasper. Recurrent Neural Network Language Models for Open Vocabulary Event-Level Cyber Anomaly Detection. AI for Cybersecurity Workshop at AAAI 2018.
- Roshanak Farhoodi, Max Shelbourne, Rebecca Hsieh, Nurit Haspel, Brian Hutchinson and Filip Jagodzinski. Predicting the Effect of Point Mutations on Protein Structural Stability. ACM BCB 2017.
- Caleb Nelson, Yulo Leake and Brian Hutchinson. Low n-Rank Tensor Log-Linear Models for Classification. IJCNN 2017.
- Aaron Tuor, Samuel Kaplan, Brian Hutchinson, Nicole Nichols and Sean Robinson. Deep Learning for Unsupervised Insider Threat Detection in Structured Cybersecurity Data Streams. AI for Cybersecurity Workshop at AAAI 2017.
- Aaron Tuor, Samuel Kaplan, Brian Hutchinson, Nicole Nichols and Sean Robinson. Predicting User Roles from Computer Logs using Recurrent Neural Networks. AAAI 2017. Extended abstract. Best Poster finalist.
- David Palzer and Brian Hutchinson. The Tensor Deep Stacking Network Toolkit. IJCNN 2015.
- Curtis Fielding, Joshua Weaver and Brian Hutchinson. Phonetic Variation Analysis Via Multi-Factor Sparse Plus Low Rank Language Model. NW-NLP 2014. Extended abstract.
- Yanzhang He, Brian Hutchinson, Peter Baumann, Mari Ostendorf Eric Fosler-Lussier and Janet Pierrehumbert. Subword-based Modeling for Handling OOV Words in Keyword Spotting. ICASSP 2014.
- Brian Hutchinson, Mari Ostendorf and Maryam Fazel. Exceptions in Language as Learned by the Multi-Factor Sparse Plus Low-Rank Language Model. ICASSP 2013, invited to the special session on sparsity in web information processing.
- Brian Hutchinson, Mari Ostendorf and Maryam Fazel. A Sparse Plus Low Rank Maximum Entropy Language Model. Interspeech 2012.
- Li Deng, Brian Hutchinson and Dong Yu. Parallel Training of Deep Stacking Networks. Interspeech 2012.
- Brian Hutchinson, Li Deng and Dong Yu. A Deep Architecture With Bilinear Modeling Of Hidden Representations: Applications to Phonetic Recognition. ICASSP 2012.
- Bin Zhang, Alex Marin, Brian Hutchinson, Mari Ostendorf. Analyzing Conversations Using Rich Phrase Patterns. ASRU 2011.
- Emily M. Bender, Jonathan T. Morgan, Meghan Oxley, Mark Zachry, Brian Hutchinson, Alex Marin, Bin Zhang, Mari Ostendorf.
Annotating Social Acts: Authority Claims and Alignment Moves in Wikipedia Talk Pages. ACL-HLT LSM Workshop 2011.
- Brian Hutchinson and Jasha Droppo. Learning Non-Parametric Models of Pronunciation. ICASSP 2011.
- Jonathan T. Morgan, Meghan Oxley, Mark Zachry, Emily M. Bender and Brian Hutchinson. Authority Claims as Identity Markers in Wikipedia Discussion Pages. Georgetown University Round Table on Languages and Linguistics 2011. Extended abstract.
- Meghan Oxley, Jonathan T. Morgan, Mark Zachry, and
Brian Hutchinson. "What I Know Is...": Establishing Credibility on Wikipedia Talk Pages. WikiSym 2010. Extended abstract.
- Bin Zhang, Brian Hutchinson, Wei Wu and Mari Ostendorf. Extracting Phrase Patterns with Minimum Redundancy for Unsupervised Speaker Role Classification. NAACL 2010.
- Brian Hutchinson, Bin Zhang and Mari Ostendorf. Unsupervised Broadcast Conversation Speaker Role Labeling. ICASSP 2010.
- Amy Dashiell, Brian Hutchinson, Anna Margolis and Mari Ostendorf.
Non-segmental duration feature extraction for prosodic classification.
- Brian Hutchinson and Jianna Zhang. Multiclass support vector machines for articulatory feature classification. AAAI-06. Extended abstract.
- Brian Hutchinson. Rank and Sparsity in Language Processing. PhD thesis, University of Washington, Aug 2013.
- Associate Professor, Computer Science Department, Western Washington University (Fall 2018 - )
- Assistant Professor, Computer Science Department, Western Washington University (Fall 2013 - Spr 2018)
- Course Instructor, EE 235 "Continuous Time Linear Systems," Electrical Engineering Department, University of Washington (Win 2012)
- Seminar Co-Instructor, ENGR 498 B "Preparing for Graduate Education," College of Engineering, University of Washington (Win 2010, 2011)
- Graduate TA, EE 578 "Convex Optimization," Electrical Engineering Department, University of Washington (Win 2009)
- Lecturer, Computer Science Department, Western Washington University (Fall 2006 - Spr 2007)
- Graduate TA, Computer Science Department, Western Washington University (Fall 2005 - Spr 2006)
PhD, Electrical Engineering, University of Washington, 2013
MS, Electrical Engineering, University of Washington, 2009
MS, Computer Science, Western Washington University, 2006
BS, Computer Science, Western Washington University, 2005
BA, Linguistics, Western Washington University, 2005