An experienced data scientist working to improve health and policy.

Originally trained as a computer scientist and computational biologist, my career has led to multidisciplinary partnerships with economists, doctors, and policymakers to solve challenges in human health and public policy. Currently, I serve as the Director of Research and Technology for the not-for-profit organization Research Improving People's Lives. I recieved my M.S. in Computer Science from UC Berkeley.



Predicting Opioid Dependence

As many as 80% of those suffering with an opioid use disorder had a legitimate opioid prescription from a doctor prior to their diagnosis. I helped develop a predictive model for the risk of developing a disorder if given an opioid prescription. We are partnering with policymakers to provide this information to doctors when weighing the risks and benefits of opioid therapy for new patients.


Big Data for Policy Innovation

I lead a data science team that integrated over 800 data sets from Rhode Island government agencies into an anonymized and secure database for delivering policy insights.


The Rhode to College

I am the technnology director for Rhode2College, an innovative program announced on Sep 24, 2018 by Governor Gina Raimondo to help Rhode Island high school students succeed on their path to college.


Mining Data for Research

Data in the real world is messy and not always ready for research. I have developed computer-vision methods to extract historical data on changes in industrial land use from printed directories, and a comprehensive directory of FDA drug codes for understanding prescriptions in medical claims.


Disrupting HIV Transmission

The actual transmission network between HIV-infected individuals is unknown, but gene sequencing of new infections can reveal patterns of transmission. I serve as the bioinformatician on an NIH-funded project to use these patterns to help public health officials disrupt HIV transmission.


Measuring HIV Drug Resistance

Modern gene sequencing technologies can monitor HIV infections with high precision and have the potential to improve and personalize drug therapies for treating HIV. I research methods for measuring drug resistance in HIV-infected individuals and worked with an international group of HIV researchers to recommend future standards for clinical applications.


Technology for Research

I understand the technological needs of researchers and can translate them into IT solutions. I was the technical architect for a secure computing environment at Brown University that serves over 150 researchers across 17 labs and centers in fields such as public policy, economics, public health, and biomedical informatics. I have also conducted performance studies on large computing clusters and developed methods for managing research software and tracking complex analyses of big data.

Publications

* = alphabetical author order

2019

* Hastings JS, Howison M, Inman SE. 2019. Predicting High-Risk Opioid Prescriptions Before they are Given. NBER Working Paper No. 25791.

* Hastings JS, Howison M, Lawless T, Ucles J, White P. Unlocking Data to Improve Public Policy. Forthcoming. Communications of the ACM. Preprint available from: https://osf.io/28krq/.

2018

Howison M, Coetzer M, Kantor R. 2018. Measurement error and variant-calling in deep Illumina sequencing of HIV. Bioinformatics: bty919. doi:10.1093/bioinformatics/bty919

Ji H, Enns E, Brumme CJ, Parkin N, Howison M, et al. 2018. Bioinformatic data processing pipelines in support of next‐generation sequencing‐based HIV drug resistance testing: the Winnipeg Consensus. Journal of the International AIDS Society 21(10): e25193. doi:10.1002/jia2.25193

2017

Guang A, Howison M, et al. 2017. Preserving Intra-Patient Variance Improves Phylogenetic Inference of HIV Transmission Networks. Poster presented at the 24th International HIV Dynamics & Evolution, 23-26 May 2017, Scotland, UK.

Howison M, Bethel EW. 2017. GPU-accelerated denoising of 3D magnetic resonance images. Journal of Real-Time Image Processing 13(4): 713-724. doi:10.1007/s11554-014-0436-8

Berenbaum D, Deighan D, Marlow T, Lee A, Frickel S, Howison M. 2016. Mining Spatio-temporal Data on Industrialization from Historical Registries. arXiv: 1612.00992

2016

Guang A, Zapata F, Howison M, Lawrence CE, Dunn CW. 2016. An Integrated Perspective on Phylogenetic Workflows. Trends in Ecology & Evolution 31(2): 116-126. doi:10.1016/j.tree.2015.12.007

2015

Zapata F, Goetz F, Smith S, Howison M, et al. 2015. Phylogenomic Analyses Support Traditional Relationships within Cnidaria. PLOS ONE 10(10): e0139068. doi:10.1371/journal.pone.0139068

2014

Zapata F, Wilson NG, Howison M, et al. 2014. Phylogenomic analyses of deep gastropod relationships reject Orthogastropoda. Proc. R. Soc. B 281(1794): 20141739. doi:10.1098/rspb.2014.1739

Howison M, Zapata F, Edwards EJ, Dunn CW. 2014. Bayesian Genome Assembly and Assessment by Markov Chain Monte Carlo Sampling. PLoS ONE 9(6): e99497. doi:10.1371/journal.pone.0099497

Howison M, Zapata F, Dunn CW. 2013. Toward a statistically explicit understanding of de novo sequence assembly. Bioinformatics 29(23): 29592963. doi:10.1093/bioinformatics/btt525 [pdf]

2013

Dunn CW, Howison M, Zapata F. 2013. Agalma: an automated phylogenomics workflow. BMC Bioinformatics 14(1): 330. doi:10.1186/1471-2105-14-330

Howison M, Shen A, Loomis A. 2013. Building Software Environments for Research Computing Clusters. In Proceedings of the 27th Large Installation System Administration Conference (LISA '13), 3-8 November 2013, Washington, DC, USA. [website]

Howison M. 2013. High-throughput compression of FASTQ data with SeqDB. IEEE/ACM Transactions on Computational Biology and Bioinformatics 10(1): 213-218. doi:10.1109/TCBB.2012.160 [pdf]

2012

Bethel EW, Howison M. 2012. Multi-core and many-core shared-memory parallel raycasting volume rendering optimization and tuning. International Journal of High Performance Computing Applications 26(4): 399-412. doi:10.1177/1094342012440466

Howison M, Sinnott-Armstrong NA, Dunn CW. 2012. BioLite, a lightweight bioinformatics framework with automated tracking of diagnostics and provenance. In Proceedings of the 4th USENIX Workshop on the Theory and Practice of Provenance (TaPP '12), 14-15 June 2012, Boston, MA, USA. [website]

Howison M, Bethel EW, Childs H. 2012. Hybrid Parallelism for Volume Rendering on Large-, Multi-, and Many-Core Systems. IEEE Transactions on Visualization and Computer Graphics 18(1): 17-29. doi:10.1109/TVCG.2011.24

2011

Howison M, Trninic D, Reinholz D, Abrahamson D. 2011. The Mathematical Imagery Trainer: From Embodied Interaction to Conceptual Learning. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '11), pp. 1989-1998, 7-12 May 2011, Vancouver, BC, Canada. doi:10.1145/1978942.1979230

2010

Childs H, Pugmire D, Ahern S, Whitlock B, Howison M, Prabhat, Weber GH, Bethel EW. 2010. Extreme Scaling of Production Visualization Software on Diverse Architectures. IEEE Computer Graphics and Applications 30(3): 22-31. doi:10.1109/MCG.2010.51

Uselton A, Howison M, Wright NJ, Skinner D, Keen N, Shalf J, Karavanic KL, Oliker L. 2010. Parallel I/O performance: From events to ensembles. In Proceedings of the 2010 IEEE International Symposium on Parallel & Distributed Processing (IPDPS), 19-23 April 2010, Atlanta, GA, USA. doi:10.1109/IPDPS.2010.5470424

2009

Howison M, Séquin CH. 2009. CAD Tools for the Construction of 3D Escher Tiles. Computer-Aided Design and Applications 6(6): 737-748. doi:10.3722/cadaps.2009.737-748