Group:

Visit hutchresear.ch for information on the Hutchinson Machine Learning Research group.

Interests:

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 adversarial, 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, and detecting social phenomena in language.

• Western Washington University, Bellingham, WA
  • Professor, Computer Science Department (9/2023-Present)
  • Associate Professor, Computer Science Department (9/2018-8/2023)
  • Assistant Professor, Computer Science Department (9/2013-8/2018)
• Pacific Northwest National Laboratory, Richland, WA
  • Staff Scientist, Computing and Analytics Division (3/2017-Present)
  • DOE Visiting Faculty (6/2016-9/2016)
• International Computer Science Institute, Berkeley, CA
  • Research Consultant IARPA Babel Project (9/2013-6/2014)
• University of Washington, Seattle, WA
  • Research Assistant, Electrical Engineering Department (9/2007-8/2013)
  • Instructor, Continuous-Time Linear Systems (1/2012-3/2012)
  • Huckabay Fellow (9/2009-6/2010)
  • Teaching Assistant, Convex Optimization (1/2009-3/2009)
• Microsoft Research, Redmond, WA
  • Research Intern, Deep Learning (6/2011-9/2011)
  • Research Intern, Pronunciation Modeling (6/2010-9/2010)
• Western Washington University, Bellingham, WA
  • Lecturer, Computer Science Department (9/2006-6/2007)
  • Teaching Assistant, Computer Science Department (9/2005-6/2006)

1. Sarah Coffland, Katie Christensen, Brian Hutchinson, Filip Jagodzinski. Energy Metric Prediction for Double InDel Mutants via the RoseNet Deep Learning Framework. Bioinformatics Advances, 2024 (accepted).
   
   
2. Seth Bassetti, Brian Hutchinson, Claudia Tebaldi, Ben Kravitz. DiffESM: Conditional Emulation of Temperature and Precipitation in Earth System Models with 3D Diffusion Models. Journal of Advances in Modeling Earth Systems. Volume 16, no. 10, e2023MS004194, https://doi.org/10.1029/2023MS004194, 2024.
   
   
3. Logan Sizemore, Diego Llanes, Marina Kounkel, Brian Hutchinson, Keivan G. Stassun, Vendant Chandra. A Self-consistent Data-driven Model for Determining Stellar Parameters from Optical and Near-infrared Spectra. Astronomical Journal. Volume 167, no. 4, 2024.
   
   
4. Logan Sizemore, Brian Hutchinson, Emily Borda. Use of Machine Learning to Analyze Chemistry Card Sort Tasks. Chemistry Education Research and Practice. http://dx.doi.org/10.1039/D2RP00029F, 2024.
   
   
5. Joseph Tully, Ryan Haight, Brian Hutchinson, Sen Huang, Joon-Yong Lee, Srinivas Katipamula. Dilated Causal Convolutional Neural Networks for Forecasting Zone Airflow to Estimate Short-Term Energy Consumption. Energy and Buildings. Volume 286, iss. 0378-7788, 2023.
   
   
6. Abdurro'uf et al. The Seventeenth Data Release of the Sloan Digital Sky Surveys: Complete Release of MaNGA, MaStar, and APOGEE-2 Data. The Astrophysical Journal Supplement Series. Volume 259, no. 2, 2022.
   
   
7. Dani Sprague, Connor Culhane, Marina Kounkel, Richard Olney, K. R. Covey, Brian Hutchinson, Ryan Lingg, Keivan G. Stassun, Carlos G. Roman-Zuniga, Alexandre Roman-Lopes, David Nidever, Rachael L. Beaton, Jura Borissova, Amelia Stutz, Guy S. Stringfellow, Karla Pena Ramirez, Valeria Ramirez-Preciado, Jesus Hernandez, Jinyoung Serena Kim, Richard R. Lane. APOGEE Net: An Expanded Spectral Model of Both Low-mass and High-mass Stars. Astronomical Journal. Volume 163, no. 4, 2022.
   
   
8. Aidan McBride, Ryan Lingg, Marina Kounkel, Kevin Covey, Brian Hutchinson. Untangling the Galaxy III: Photometric Search for Pre-main Sequence Stars with Deep Learning. Astronomical Journal. Volume 162, no. 6, 2021.
   
   
9. Elliott Skomski, Joon-Yong Lee, Woohyun Kim, Vikas Chandan, Srinivas Katipamula and Brian Hutchinson. Sequence-to-sequence neural networks for short-term electrical load forecasting in commercial office buildings. Energy and Buildings, Volume 226, iss. 0378-7788, 2020.
   
   
10. Jonny Mooneyham, Sean C. Crosby, Nirnimesh Kumar and Brian Hutchinson. SWRL Net: a Spectral, Residual Deep Learning Model for Improving Short-term Wave Forecasts. Weather and Forecasting. https://doi.org/10.1175/WAF-D-19-0254.1, 2020.
    
    
11. Richard Olney, Marina Kounkel, Chad Schillinger, Matthew T. Scoggins, Yichuan Yin, Erin Howard and K. R. Covey, Brian Hutchinson and Keivan G. Stassun. APOGEE Net: Improving the Derived Spectral Parameters for Young Stars Through Deep Learning. Astronomical Journal. Volume 159, no. 4, 2020.
    
    
12. Theodore Weber, Austin Corotan, Brian Hutchinson, Ben Kravitz and Robert Link. Technical Note: Deep Learning for Creating Surrogate Models of Precipitation in Earth System Models. Atmospheric Chemistry and Physics. Volume 20, pages 2303-2317, https://doi.org/10.5194/acp-20-2303-2020, 2020.
    
    
13. 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.


14. Richard Olney, Aaron Tuor, Filip Jagodzinski 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.


15. 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.


16. 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.


17. 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.


18. 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.


19. 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.


20. 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.

21. Andrew Holmes, Matt Jensen, Sarah Coffland, Hidemi Mitani-Shen, Logan Sizemore, Seth Bassetti, Brenna Nieva, Claudia Tebaldi, Abigail Snyder, Brian Hutchinson. Emulating the Global Change Analysis Model with Deep Learning. Tackling Climate Change with Machine Learning Workshop at NeurIPS, 2024.
    
    
22. Katie Christensen, Lyric Otto, Seth Bassetti, Claudia Tebaldi, Brian Hutchinson. Diffusion-Based Joint Temperature and Precipitation Emulation of Earth System Models . Tackling Climate Change with Machine Learning workshop at ICLR, 2024.
    
    
23. Sarah Coffland, Katie Christensen, Filip Jagodzinski, Brian Hutchinson. RoseNet: Predicting Energy Metrics of Double InDel Mutants Using Deep Learning. Computational Structural Bioinformatics Workshop at ACMBCB 2023.
    
    
24. Seth Bassetti, Brian Hutchinson, Claudia Tebaldi, Ben Kravitz. DiffESM: Conditional Emulation of Earth System Models with Diffusion Models. Tackling Climate Change with Machine Learning workshop at ICLR, 2023.
    
    
25. Chloe Dawson, Noah Reneau, Brian Hutchinson, Sean Crosby. Practical Advances in Short-Term Spectral Wave Forecasting with SWRL Net. ICLR Workshop on AI Earth and Space Science, 2022.
    
    
26. Alexis Ayala, Chris Drazic, Seth Bassetti, Eric Slyman, Brenna Nieva, Piper Wolters, Kyle Bittner, Claudia Tebaldi, Ben Kravitz, Brian Hutchinson. Conditional Emulation of Global Precipitation with Generative Adversarial Networks. ICLR Workshop on AI Earth and Space Science, 2022.
    
    
27. Eric Slyman, Chris Daw, Morgan Skrabut, Ana Usenko, Brian Hutchinson. Fine-Grained Classroom Activity Detection from Audio with Neural Networks. AAAI Workshop on AI for Education, 2022.
    
    
28. Alex Ayala, Christopher Drazic, Brian Hutchinson, Ben Kravitz, Claudia Tebaldi. Loosely Conditioned Emulation of Global Climate Models With Generative Adversarial Networks. Tackling Climate Change with Machine Learning workshop at NeurIPS, 2020.
    
    
29. Emily Saldanha Robin Cosbey, Ellyn Ayton, Maria Glenski, Joseph Cottam, Karthik Shivaram, Brett Jefferson, Brian Hutchinson, Dustin Arendt, Svitlana Volkova. Evaluation of Algorithm Selection and Ensemble Methods for Causal Discovery. NeurIPS Workshop on Causal Discovery and Causality-Inspired Machine Learning, 2020.
    
    
30. Piper Wolters, Chris Careaga, Brian Hutchinson and Lauren Phillips. A Study of Few-Shot Audio Classification. Grace Hopper Celebration 2020.
    
    
31. Loc Truong, Chace Jones, Brian Hutchinson, Andrew August, Brenda Praggastis, Rob Jasper, Nicole Nichols and Aaron Tuor. Systematic Evaluation of Backdoor Data Poisoning Attacks on Image Classifiers. CVPR Workshop on Adversarial Machine Learning in Computer Vision, 2020.
    
    
32. Alexandra Puchko, Robert Link, Brian Hutchinson, Ben Kravitz and Abigail Snyder. DeepClimGAN: A High-Resolution Climate Data Generator. Tackling Climate Change with Machine Learning workshop at NeurIPS, 2019.
    
    
33. Svitlana Volkova, Ellyn Ayton , Dustin L. Arendt, Zhuanyi Huang and Brian Hutchinson. Explaining Multimodal Deceptive News Prediction Models. ICWSM 2019.
    
    
34. Robin Cosbey, Allison Wusterbarth and Brian Hutchinson. Deep Learning for Classroom Activity Detection from Audio. ICASSP 2019.
    
    
35. 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.
    
    
36. 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.
    
    
37. Gracie Ermi, Ellyn Ayton, Nolan Price and Brian Hutchinson. Deep Learning Approaches to Chemical Property Prediction from Brewing Recipes. IJCNN 2018.
    
    
38. 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.
    


39. 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.


40. 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.


41. Caleb Nelson, Yulo Leake and Brian Hutchinson. Low n-Rank Tensor Log-Linear Models for Classification. IJCNN 2017.


42. 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.


43. 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.


44. David Palzer and Brian Hutchinson. The Tensor Deep Stacking Network Toolkit. IJCNN 2015.


45. Curtis Fielding, Joshua Weaver and Brian Hutchinson. Phonetic Variation Analysis Via Multi-Factor Sparse Plus Low Rank Language Model. NW-NLP 2014. Extended abstract.


46. 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.


47. 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.


48. Brian Hutchinson, Mari Ostendorf and Maryam Fazel. A Sparse Plus Low Rank Maximum Entropy Language Model. Interspeech 2012.


49. Li Deng, Brian Hutchinson and Dong Yu. Parallel Training of Deep Stacking Networks. Interspeech 2012.


50. Brian Hutchinson, Li Deng and Dong Yu. A Deep Architecture With Bilinear Modeling Of Hidden Representations: Applications to Phonetic Recognition. ICASSP 2012.


51. Bin Zhang, Alex Marin, Brian Hutchinson, Mari Ostendorf. Analyzing Conversations Using Rich Phrase Patterns. ASRU 2011.


52. 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.


53. Brian Hutchinson and Jasha Droppo. Learning Non-Parametric Models of Pronunciation. ICASSP 2011.


54. 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.


55. Meghan Oxley, Jonathan T. Morgan, Mark Zachry, and Brian Hutchinson. "What I Know Is...": Establishing Credibility on Wikipedia Talk Pages. WikiSym 2010. Extended abstract.


56. Bin Zhang, Brian Hutchinson, Wei Wu and Mari Ostendorf. Extracting Phrase Patterns with Minimum Redundancy for Unsupervised Speaker Role Classification. NAACL 2010.


57. Brian Hutchinson, Bin Zhang and Mari Ostendorf. Unsupervised Broadcast Conversation Speaker Role Labeling. ICASSP 2010.


58. Amy Dashiell, Brian Hutchinson, Anna Margolis and Mari Ostendorf. Non-segmental duration feature extraction for prosodic classification. Interspeech 2008.


59. Brian Hutchinson and Jianna Zhang. Multiclass support vector machines for articulatory feature classification. AAAI-06. Extended abstract.

60. Chris Daw, Brian Barragan Cruz, Nicholas Majeske, Filip Jagodzinski, Tanzima Islam and Brian Hutchinson. Low Rank Approximation Methods for Identifying Impactful Pairwise Protein Mutations. In: Haspel, N., Jagodzinski, F., Molloy, K. (eds) Algorithms and Methods in Structural Bioinformatics. Computational Biology. Springer, Cham. https://doi.org/10.1007/978-3-031-05914-8_4. 2022

61. Brian Hutchinson. Rank and Sparsity in Language Processing. PhD thesis, University of Washington, Aug 2013.

62. Piper Wolters, Chris Daw, Brian Hutchinson, Lauren Phillips. Proposal-based Few-shot Sound Event Detection for Speech and Environmental Sounds with Perceivers. arxiv:2107.13616 [eess.as], 2021.
    
    
63. Chris Careaga, Brian Hutchinson, Nathan Hodas, Lawrence Phillips. Metric-Based Few-Shot Learning for Video Action Recognition. arxiv:1909.09602 [cs.cv], 2019.