Dr. Guven is a Data Scientist and a member of the Senior Professional Staff at the Applied Physics Laboratory. His current research includes GPGPU applications, Deep Learning and its application to image, speech, text, and disease data. He is also active in cybersecurity research, graph analytics, generalized clustering techniques such as Latent Dirichlet Allocation.

Before joining JHU APL, he has been a research affiliate at The George Washington University. His research included signal processing, speech, music information retrieval, authorship attribution, plagiarism detection, swarm intelligence, statistical learning, data mining, text mining, supervised, unsupervised, reinforcement based machine learning applications. Previously his research also included bioinformatics and working with the HG18 genome, exon sequences, SEER medical databases, alternative splicing, motif and binding site detection problems. Previously, he had been working in the telecommunications field for Texas Instruments. He holds 3 U.S. patents for Voice over IP and 1 U.S. patent application describing a method of speech emotion detection.

Education History

  • Ph.D Computer Science, George Washington University

Work Experience

Senior Professional Staff, JHU Applied Physics Laboratory


• DeLeo, Michael, and Erhan Guven. “Learning Chess With Language Models and Transformers.” arXiv preprint arXiv:2209.11902 (2022).
• “An open challenge to advance probabilistic forecasting for dengue epidemics.” Proceedings of the National Academy of Sciences 116.48 (2019): 24268-24274
• “Unsupervised Graph Analytics on Heterogeneous Network Device Data,” Complex Adaptive Systems, 2018, Chicago.
• “Ensemble method for dengue prediction.” PloS one 13.1 (2018): e0189988.
• “Prediction of Peaks of Seasonal Influenza in Military Health-Care Data: Supplementary Issue: Big Data Analytics for Health.” Biomedical engineering and computational biology 7 (2016): BECB-S36277.
• “A Survey of Data Mining and Machine Learning Methods for Cyber Security,” IEEE Communications Surveys & Tutorials 18.2 (2016): 1153-1176.
• “Fuzzy Association Rule Mining and Classification for the Prediction of Malaria in South Korea,” BMC Medical Informatics and Decision Making 15.1 (2015): 47.
• “Predicting Levels of Influenza Incidence,” Online Journal of Public Health Informatics 6 (1), 2014.
• “Prediction of High Incidence of Dengue in the Philippines,” PLoS neglected tropical diseases 8 (4), e2771, 2014.
• “An OpenCL Framework for Fuzzy Associative Classification and Its Application to Disease Prediction,” Complex Adaptive Systems, 2013, Baltimore.
• “Note and Timbre Classification by Local Features of Spectrogram,” Complex Adaptive Systems, 2012, Washington D.C.
• “Speech Emotion Recognition using a Backward Context,” IEEE Applied Imagery Pattern Recognition (AIPR) Workshop, 2010, Washington D.C.
• “Recognition of Emotions from Human Speech,” Artificial Neural Networks In Eng. (ANNIE), 2010, St. Louis, Missouri.
• “Sequence Signatures of Exon Dynamics in the Evolution of Alternative Splicing,” Cold Spring Harbor New York Meeting (2007) – The Biology of Genomes, 129, 2007, New York.
• “Predicting Breast Cancer Survivability using Data Mining Techniques,” 2006 SIAM International Conference on Data Mining, 2006, Bethesda, Maryland.

Honors and Awards

  • Best Innovative Paper 3rd position at ANNIE (2010)
  • Engineering Achievement Award, Texas Instruments (2001)

Professional Organizations

ACM (Association for Computing Machinery)
IEEE (Institute of Electrical and Electronics Engineers)