Publications

Student collaboration indicated with underlined authorship

Journal Publications [Peer-Reviewed]

• Murph A, Flynt A, King BR Comparing finite sequences of discrete events with non-uniform time intervals. Sequential Analysis, 291-313 (2021) https://doi.org/10.1080/07474946.2021.1940491
• Kim T, King BR. Time series prediction using deep echo state networks. Neural Computing & Applications (2020) https://doi.org/10.1007/s00521-020-04948-x [link]
• Hare-Harris AE, Mitchell MW, Meyers SM, Mitchel A, King BR, Ruocco B, Martin CL, Flax JF, Brzustowicz LM. Within-task variability on standardized language tests predicts autism spectrum disorder: A pilot study of the Response Dispersion Index. J Neurodevelop Disord 11, 21 (2019) doi:10.1186/s11689-019-9283-z  [link]
• Lipsky E, King BR, Tromp G. Node-Oriented Workflow (NOW) – A command template workflow management tool for high throughput data analysis pipelines. Journal of Data Mining in Genomics and Proteomics 2014; 5(2) [link] [PDF]
• King BR, Aburdene M, Thompson A, Warres Z. Application of discrete Fourier inter-coefficient difference for assessing genetic sequence similarity. EURASIP Journal on Bioinformatics and Systems Biology; 2014; 8 [link]
• Srinivasan SM, Vural S, King BR, Guda C. Mining for class-specific motifs in protein sequence classification. BMC Bioinformatics; 2013; 14(96) [link] [PDF]
• King BR, Vural S, Pandey S, Barteau A, Guda C. ngLOC: software and web server for predicting protein subcellular localization in prokaryotes and eukaryotes. BMC Research Notes; 2012; 5(351) [link] [PDF]
• Guda C, King BR, Pal LR, Guda P. A top-down approach to infer and compare domain-domain interactions across eight model organisms. PLoS One; 2009; 4(3) [link] [PDF]
• King BR, Latham L, Guda C. Estimating the subcellular proteome of prokaryotes. The Open Applied Informatics Journal 2009; 3(1) [link] [PDF]
• King BR, Guda C. Semi-supervised learning for classification of protein sequence data. Scientific Programming; 2008; 16(1) [link][PDF]
• King BR, Guda C. ngLOC: an n-gram based Bayesian method for estimating the subcellular proteomes of eukaryotes. Genome Biol 2007; 8(5):R68 [link] [PDF]

International Conferences
[Peer Reviewed]

• Zeng Z, Hatzinger B, Torres G, Dutcher D, Raymond T and King BR. On the Development and Setup of a Hygroscopic Tandem Differential Mobility Analyzer for Aerosol Studies. Presented at AIChE 2021 Annual Meeting, Nov 7-11, Boston MA, USA [link]
• Murph A, Flynt A, King BR Comparing finite sequences of discrete events with non-uniform time intervals. Presented at CS2021: The Classificaton Society Annual Meeting, Nov 5-7, Lewisburg, PA, USA [link]
• Pirmann C, Acharya B, King BR, Faull K. Training Algorithms to Read Complex Collections: Handwriting Classification for Improved HTR Models. Presented at DH2020: Digital Humanities, July 22-24, Ottawa, Canada [link]
  • Due to COVID-19 pandemic, to be presented remotely
• King BR, Troiani V. Identifying Scanpath Trends using a Frequent Trajectory Pattern Mining Approach. Journal of Vision September 2019, Vol.19, 307a. doi:https://doi.org/10.1167/19.10.307a
  • Presented at VSS19: Vision Sciences Society 19th Annual Meeting, St. Pete Beach, FL May 17-22, 2019
• Hare-Harris AE, Mitchel MW, King BR, Myers SM, Greene B, Martin CL, Flax JF, Brzustowicz LM. Developmental Deviance of Item-Level Responses on Standardized Language Measures Correlates with Autism Spectrum Disorder Diagnosis. Presented at the International Meeting for Autism Research, IMFAR 2016, May 11-14, Baltimore, MD [link]
• Hare-Harris AE, King BR, Easse E, Ledbetter DH, Martin CL. Building a dosage map of the genome to assist in CNV interpretation. Presented at ASHG 2015: American Society of Human Genetics, Oct 6-10, Baltimore, MD [link]
  • Reviewer’s Choice Abstract winner (top 10% of posters selected – Congrats to Abby!)
  • Voted #24 out of 300 Reader’s Choice Abstracts
• Ren C, King BR. Predicting Protein Contact Maps by Bagging Decision Trees. Proceedings of BCB ’14: ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, Sept 20-23, Newport Beach, CA [link] [PDF]
• Hofmeister B, King BR. ngPhylo – N-gram Modeled Proteins with Subsitution Matrices for Phylogenetic Analysis. Proceedings of BCB ’13 : ACM International Conference on Bioinformatics, Computational Biology and Biomedicine, Sept 22-25, Washington DC [link] [PDF]
• Cole C, King BR. Using Machine Learning to Predict the Health of HIV-Infected Patients. Proceedings of BCB ’13 : ACM International Conference on Bioinformatics, Computational Biology and Biomedicine, Sept 22-25, Washington DC [link] [PDF]
• King BR, Satyanarayana A. Teaching Data Mining in the Era of Big Data. Presented at American Society for Engineering Education, ASEE 2013, June 23-26, Atlanta GA [link] [PDF]
• Segar M, King BR. An n-gram based probabilistic method for de novo sequence assembly. Presented at 20th Annual International Conference on Intelligent Systems for Molecular Biology, ISMB 2012, July 15-17, Long Beach, CA [PDF]
• King BR, Guda C. Semi-supervised learning for protein classification. ROCKY 07 — Presented at 5th Annual Rocky Mountain Bioinformatics Conference, November 30 – December 2, 2007, Aspen, CO [PPT]

Local and Regional Conferences

• Pirrman CM, King BR, Acharya B, Faull K: Collaborating on Machine Reading: Training Algorithms to Read Complex Collections. Presented at Bucknell University Digital Scholarship
  Conference, BUDSC19, Oct. 11-13, Bucknell University, Lewisburg, PA (Talk)
• Yang T, Troiani V, King BR: Using Deep Learning to Analyze Images That Have High Interest from Children with Autism. Presented at 16th Annual Kalman Research Symposium, April 1, 2017, Bucknell University, Lewisburg, PA (Poster)
• Eckenroth M, Troiani V, King BR: Assessing the Utility of Virtual Reality on Selective Attention Bias in Children with Autism Spectrum Disorder. Presented at 6th Annual Susquehanna Valley Undergraduate Research Symposium, SVURS 2016, July 29, Bloomsburg University, Bloomsburg, PA (Poster)
• Yang T, Troiani V, King BR: Using Deep Learning to Analyze Images That Have High Interest from Children with Autism. Presented at 6th Annual Susquehanna Valley Undergraduate Research Symposium, SVURS 2016, July 29, Bloomsburg University, Bloomsburg, PA (Poster)
• Yang T, Troiani V, King BR: Using Deep Learning to Analyze Images That Have High Interest from Children with Autism. Presented at Sigma Xi Summer Research Symposium 2016, July 21, Bucknell University, Lewisburg, PA (Poster)
• Pham S, King BR: Using Machine Learning to Automatically Predict Feature Representation on Sequential Data. Presented at 15th Annual Kalman Research Symposium, April 2, 2016, Bucknell University, Lewisburg, PA (Poster)
• Hammett J, King BR: Using Data Mining to Construct More Practical Weather Forecasting Models. Presented at 15th Annual Kalman Research Symposium, April 2, 2016, Bucknell University, Lewisburg, PA (Poster)
• Cowen R, Mitchel M, Hare-Harris AE, King BR: Application of n-gram prediction and Brown’s Stages of Syntactic and Morphological Development to design augmentative and alternative communication for children with autism. Presented at 5th Annual Susquehanna Valley Undergraduate Research Symposium, SVURS 2015, August 4, Bucknell University, Lewisburg, PA. (Poster)
• Pham S, King BR: Using Deep Learning to Automatically Learn Feature Representation and Build a Better Classification Model on Protein Sequential Data. Presented at 5th Annual Susquehanna Valley Undergraduate Research Symposium, SVURS 2015, August 4, Bucknell University, Lewisburg, PA. (Poster)
• Hammett J, King BR: Using Data Mining to Construct More Practical Weather Forecasting Models. Presented at Sigma Xi Summer Research Symposium 2015, July 23, Bucknell University, Lewisburg, PA (Poster)
• Rogge M, King BR. Analysis of Spike Timing Dependent Neural Networks for More Efficient Starting State Learning. Presented at 14th Annual Kalman Research Symposium, March 28, 2015, Bucknell University, Lewisburg, PA
• Gonthier S, King BR. Using statistical learning to improve word prediction for augmentative and alternative communication. Presented at 14th Annual Kalman Research Symposium, March 28, 2015, Bucknell University, Lewisburg, PA
• Ren C, King BR. Predicting a protein contact map from protein sequence. Presented at 4th Annual Susquehanna Valley Undergraduate Research Symposium, SVURS 2014, August 5, Geisinger Research, Danville, PA
• Gonthier S, King BR. Using statistical learning to improve word prediction for augmentative and alternative communication. Presented at 4th Annual Susquehanna Valley Undergraduate Research Symposium, SVURS 2014, August 5, Geisinger Research, Danville PA
  • Winner for oral presentation – One of three chosen out of 86 submissions
• Gonthier S, King BR. Using statistical learning to improve word prediction for augmentative and alternative communication. Presented at Sigma Xi Summer Research Symposium, July 24, 2014, Bucknell University, Lewisburg, PA
• Ren C, King BR. An improved method for protein contact map prediction. Presented at 13th Annual Kalman Research Symposium 2014, March 29, Bucknell University, Lewisburg, PA.
• Dwornik L, King BR. Regular expressions for named entity recognition. Presented at 13th Annual Kalman Research Symposium 2014, March 29, Bucknell University, Lewisburg, PA.
• Cole C, King BR. Using Machine Learning to Predict the Health of HIV-Infected Patients. Presented at Third Annual Susquehanna Valley Undergraduate Research Symposium, SVURS 2013, August 6, Geisinger Research, Danville, PA
  • Winner for oral presentation – One of three chosen out of 67 submissions
• Cole C, King BR. Identification of Genetic Variance in HIV Associated with Varying Stages of AIDS to Assess a Patient’s Prognosis. Presented at 12th Annual Kalman Research Symposium 2013, April 13, Bucknell University, Lewisburg, PA.
• Segar M, King BR. Genome Assembly from Next Generation Sequencing Instrumentation, Presented at Susquehanna Valley Undergraduate Research Symposium, August 9, 2011 Geisinger Research, Danville, PA
• Stahlfeld P, Tromp G, King BR. Installation and deployment of caBIG for cancer research, Presented at Sigma Xi Summer Research Symposium, July 27, 2011, Bucknell University, Lewisburg, PA
• Segar M, King BR. Genome Assembly from Next Generation Sequencing Instrumentation, Presented at Sigma Xi Summer Research Symposium, July 27, 2011, Bucknell University, Lewisburg, PA
• King BR, Barteau A, Guda C: ngLOC: software for proteome-wide prediction of subcellular localization, Presented at Sigma Xi Summer Research Symposium, July 27, 2011, Bucknell University, Lewisburg, PA
• Kagle J, King BR. Identification of a Putative Chlorocatechol Catabolic Pathway in the Triclosan Degrader Sphingomonas sp. RD1. Presented at NEMPET 2010, June 25-27 2010, Blue Mountain Lake, NY [PPT]

Honors Theses

Below is a list of students who completed an undergraduate honors thesis during their senior year under my advisement:

• Taehwan Kim ’20 – Chaotic Time Series Prediction using Deep Echo State Networks. Bucknell University Honors Thesis, 2020
• Chuqiao Ren ’15 – Predicting Protein Contact Maps by Bagging Decision Trees. Bucknell University Honors Thesis, 2015
• Charles Cole ’14 – Using Machine Learning to Predict the Health of HIV-Infected Patients. Bucknell University Honors Thesis, 2014
• Matthew Segar ’12 – Utilization of Probabilistic Models in Short Read Assembly from Second-Generation Sequencing, Bucknell University Honors Thesis, 2012 [PDF]
  • Winner of Harold W. Miller Prize for Outstanding Honor’s Thesis.

Invited Talks and Panels

• [INVITED TALK]: “Bioinformatics and Genomics”, CHEG 470, Spring 2018, Spring 2019.
• [INVITED TALK]: “Data Science and Analytics, and Bucknell”. Presented before the Board of Trustees with Abby Flynt and Matt Bailey. October 11, 2018.
• [INVITED TALK]: “Genomics”. Foundations Seminar, Fall 2018.
• [INVITED TALK]: “Agile Design Methodology”, ENGR 452 – Interdisciplinary Senior Design, Fall 2018.
• [INVITED TALK]: “Genomics and Your Future” – a talk introducing the movie GATTACA. Campus Theater, March 21, 2018.
• [INVITED TALK]: “Using eye-tracking to identify gaze behaviors in autistic children”, Pecha Kucha Night 2019, January 24, 2019
• [INVITED TALK]: “SCRUM for Design and Engineering” ENGR 452 – Interdisciplinary Senior Design, Fall 2017.
• [INVITED TALK]: “IP Course Perspectives” w/ Abby Flynt. TLC / Faculty Learning Series, Fall 2016.
• [INVITED TALK]: “Sequential Data Mining”, September 15, 2016. Mathematics Dept. Student Colloquium Series, Bucknell University, Lewisburg, PA
• [PANEL MEMBER]: “Conducting research with students”, November 13, 2014. Teaching and Learning Center, Bucknell University, Lewisburg, PA
• [PANEL MEMBER]: “Artificial Intelligence: An Interdisciplinary Conversation”, November 21, 2013, Bucknell University, Lewisburg, PA
• [INVITED TALK]: “Bioinformatics: Increasing knowledge in healthcare and medicine.” Presented at Bending the Curve: Innovative Approaches to Controlling Health Care Costs,November 2, 2013, Bucknell University, Lewisburg, PA
  • Included students Charles Cole, Brigitte Hofmeister, and Chuqiao Ren in talk to give 5 minute snapshot of their research
• [INVITED TALK]: “Big Biological Data: The Grand Challenge in Modern Biology and Medicine.” Presented at Grand Challenges Foundation Seminar, October 2013, Bucknell University, Lewisburg, PA
• [INVITED TALK]: “Analysis of biological sequence data” Fall 2012, Presented in ELEC 402 – Genomic Signal Processing, Bucknell University, Lewisburg, PA
• [INVITED TALK]: “Bioinformatics for the Analysis of Biological Data”, Fall 2011, Presented at the Biology Department Seminar Series, Fall 2011, Bucknell University, Lewisburg, PA
• [PANEL MEMBER]: “What I wish I had known”,  August, 2011. Presented at the Teaching and Learning Center discussion for new faculty, Bucknell University, Lewisburg, PA