Dr. Ioerger's research interests are in the areas of Artificial Intelligence, Intelligent Agents, and Machine Learning. His work has covered diverse areas, from spatial reasoning, to simulating team-work, to modeling emotions. Currently, his primary focus is on designing multi-agent system architectures to simulate collaborative behavior and teamwork. He also applies AI and machine learning methods to various problems in the area of Bioinformatics, including the improvement of protein sequence alignments, molecular modeling, and X-ray crystallography. The latter research has lead to the development of an automated software system for protein model-building called TEXTAL, which is currently being used by crystallographers throughout the world.
- Ph.D. in Computer Science, University of Illinois at Urbana Champaign - (Urbana, Illinois, United States) 1996
- M.S. in Computer Science, University of Illinois at Urbana Champaign - (Urbana, Illinois, United States) 1992
- B.S. in Molecular and Cell Biology, Pennsylvania State University - (State College, Pennsylvania, United States) 1989
- Dragset, M. S., Ioerger, T. R., Zhang, Y. J., Maerk, M., Ginbot, Z., Sacchettini, J. C., ... Steigedal, M. (2019). Genome-wide Phenotypic Profiling Identifies and Categorizes Genes Required for Mycobacterial Low Iron Fitness. SCIENTIFIC REPORTS. 9(1), 11394.
- Carey, A. F., Rock, J. M., Krieger, I. V., Chase, M. R., Fernandez-Suarez, M., Gagneux, S., ... Fortune, S. M. (2019). TnSeq of Mycobacterium tuberculosis clinical isolates reveals strain-specific antibiotic liabilities (vol 14, e1006939, 2018). PLoS pathogens. 15(6), e1007846-e1007846.
- Farhat, M. R., Freschi, L., Calderon, R., Ioerger, T., Snyder, M., Meehan, C. J., ... Murray, M. (2019). GWAS for quantitative resistance phenotypes in Mycobacterium tuberculosis reveals resistance genes and regulatory regions.. Nature Communications. 10(1), 2128.
- Taechalertpaisarn, J., Lyu, R., Arancillo, M., Lin, C., Perez, L. M., Ioerger, T. R., & Burgess, K. (2019). Correlations between secondary structure- and protein-protein interface-mimicry: the interface mimicry hypothesis. ORGANIC & BIOMOLECULAR CHEMISTRY. 17(12), 3267-3274.
- Ballinger, E., Mosior, J., Hartman, T., Burns-Huang, K., Gold, B., Morris, R., ... Nathan, C. (2019). Opposing reactions in coenzyme A metabolism sensitize Mycobacterium tuberculosis to enzyme inhibition. Science (New York, N.Y.). 363(6426), 498-+.
- Musa, T. L., Ioerger, T. R., & Sacchettini, J. C. (2009). THE TUBERCULOSIS STRUCTURAL GENOMICS CONSORTIUM: A STRUCTURAL GENOMICS APPROACH TO DRUG DISCOVERY. Advances in protein chemistry and structural biology. STRUCTURAL GENOMICS, PART C. (pp. 41-76).
- Lai, Y. P., & Ioerger, T. R. (2017). A compatibility approach to identify recombination breakpoints in bacterial and viral genomes. 11-20.
- Haspel, N., Ioerger, T., & Al-Mubaid, H. (2016). Message from chairs. iv.
- Kieser, K. J., Baranowski, C., Chao, M. C., Long, J. E., Sassetti, C. M., Waldor, M. K., ... Rubin, E. J. (2015). Peptidoglycan synthesis in Mycobacterium tuberculosis is organized into networks with varying drug susceptibility. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA-BIOLOGICAL SCIENCES. 112(42), 13087-13092.
- DeJesus, M. A., & Ioerger, T. R. (2015). Reducing type i errors in Tn-Seq experiments by correcting the skew in read count distributions. 45-50.
- Dejesus, M. A., & Ioerger, T. R. (2013). Improving discrimination of essential genes by modeling local insertion frequencies in transposon mutagenesis data. 144-151.
- Nelson, Eric James (2016-12). Learning to Control Linear Time-Invariant Systems with Discrete Time Reinforcement Learning. (Master's Thesis)
- De Jesus Aneiro, Michael A. (2016-12). Statistical Analysis of Transposon Sequencing Data to Determine Essential Genes. (Doctoral Dissertation)
- DeJesus, Michael A. (2012-05). Bayesian Analysis of Transposon Mutagenesis Data. (Master's Thesis)
- Pai Karkala, Reetal (2009-05). Fragment Based Protein Active Site Analysis Using Markov Random Field Combinations of Stereochemical Feature-Based Classifications. (Doctoral Dissertation)
- Palmer, Victor (2007-08). Scaling reinforcement learning to the unconstrained multi-agent domain. (Doctoral Dissertation)