DR. Timothy Oladunni

 
Research Statement

I explore computer science fundamental and basic concepts in developing sustainable, efficient and innovative solutions to real world problems by combining theoretical analysis, computational paradigms and deductive reasoning with scientific experimentations. I have a broad research experience in Artificial Intelligence with specific expertise in predictive modelling. I am a principal investigator on an ongoing NSF funded research on the visualization, analysis and prediction of COVID-19.

Teaching Philosophy

My teaching philosophy is rooted in the belief that students learn more when they actively participate. Therefore, I focus more closely on professional, practical and hands-on skills thereby stimulating critical thinking, curiosity and creativity. I connect organizational dynamics to student outcomes by improving the learning environment and inspiring changes in curriculum for a quality computer science program. I strongly believe that computer science is for all.

Research interests

Machine Learning

Computational Biology

Deep Learning

Artificial Intelligence

Data analysis

Time series forecasting



COVID-19 cases tracking in the USA





VAPOC: Visualization, Analysis and Prediction of COVID-19

Statistical data of the ongoing COVID-19 pandemic shows a disturbing trend. While fatality and mortality rates are skyrocketing in the United States, the pandemic is just taking roots in other parts of the world. COVID- 19 pandemic seems to be the newest threat to humanity. (more...)



A Multicriteria and POC Diagnostic Imaging of COVID-19 as Independent Indicators of Unfavorable Outcomes


Since the outbreak of the coronavirus in 2019, the novel virus has infected millions of people and claimed thousands of lives. As the public embraces the inevitability of easing out the lockdown, the fear of another wave of casualty of coronavirus is real. More worrisome is the African American community; an identified vulnerable community in the United States. This is because the ethnic group has been disproportionally affected by the coronavirus pandemic. The black community seems to be hottest spot of COVID-19. (more...)

DMV MAP

The map below shows the number of COVID-19 cases, death and population of the District of Columbia, Maryland and Virginia (DMV metro in the USA).


COVID-19 in DC, Maryland and Virginia (DMV)



Research projects

Student Projects

Teaching



(spring 2021)

Machine Learning - CSCI 421/ CSCI 578

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Senior Project II

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(fall 2020)

Intro Programming (Python) - APCT 110/111

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CSCI 498 - Senior Project I

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Cryptography - 15230 - CSCI 455 - 01

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(spring 2020)

Introduction to AI: CSCI 414 - 01 / Principles of AI: CSCI 510 - 01

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Research papers

Selected Publications

  1. Denis, M., Bachoro, M., Gebreslassie, W. Oladunni, T. "Automatic Electrocardiogram Detection of Suspected Hypertrophic Cardiomyopathy: Application to Wearable Heart Monitors" IEEE Sensors Letters 2021 (submitted)
  2. Oladunni, T., Denis, M., Ososanya E. " A Machine Learning Epidemiological County-level COVID-19 Fatality Risk Predictive Model" MDPI Applied Science Journal 2021 (in preparation)
  3. Oladunni, T., Denis, M., Ososanya E., Uzoegwu E., Adesina J. "A Time Series Analysis and Forecast of COVID-19 Health Care Disparity" Plos One Journal 2021 (Submitted)
  4. Oladunni, T., Denis, M., Ososanya E., Barry A. 'Exponential Smoothening Forecast of African Americans’ COVID-19 Fatalities.' International Conference on Computing and Data Science (CONF-CDS 2021) January 28, 2021. Stanford, San Francisco.
  5. Ehsan, M., Shahirinia, A., Zhang, N., Oladunni, T., "Investigation of Data Size Variability in Wind Speed Prediction of AI Algorithms" Journal of Cybernetics and Systems 2020
  6. Tiwang, R., Oladunni, T., Mareboyana, M., ‘An Optimized Convnet-LSTM Deep Learning Probabilistic Approach to Source Code Generation with Abstract Syntax Tree and Hyper-Parameter Tuning.’ Journal of Expert Systems with Applications 2020 (submitted)
  7. Savadkoohi, M., Oladunni, T., "A Machine Learning Approach to Epileptic Seizure Prediction using Electroencephalogram (EEG) Signal.", Journal of Biocybernetics and Biomedical Engineering 2020 (accepted)
  8. Ehsan, M., Shahirinia, A., Zhang, N., Oladunni, T., "Wind Speed Prediction and Visualization Using Long Short-Term Memory Networks (LSTM)", 10th IEEE International Conference on Information Science and Technology ICIST 2020
  9. Ramirez Rochac ; Nian Zhang ; Jiang Xiong ; Jing Zhong ; Timothy Oladunni "Data Augmentation for Mixed Spectral Signatures Coupled with Convolutional Neural Networks", 9th IEEE International Conference on Information Science and Technology (ICIST 2019)
  10. Tiwang, R., Oladunni, T., "A Deep Learning Model for Source Code Generation, IEEE SoutheastCon 2019
  11. Ramirez Rochac, J., Liang, L., Zhang, N., Oladunni, T., "A Gaussian Data Augmentation Technique on Highly Dimensional, Limited Labeled Data for Multiclass Classification using Deep Learning", 10th IEEE International Conference on Intelligent Control and Information Processing (ICICIP 2019)
  12. Oladunni, T., Sharma, S., "Homomorphic Encryption and Data Security in the Cloud", 28th International Conference on Software Engineering and Data Engineering 2019
  13. Ramirez Rochac, J., Liang, L., Zhang, N., Thomson, L., Oladunni, T "A Data Augmentation-assisted Deep Learning Model for High Dimensional and Highly Imbalanced Hyperspectral Imaging Data" 9th IEEE International Conference on Information Science and Technology, Hulunbuir, China (ICIST 2019)
  14. Oladunni, T Sharma, S.," H2O Deep Learning for Hedonic Pricing", International Journal of Computers and their Applications, IJCA, Vol. 25, No. 1, March 2018.
  15. Oladunni, T., Sharma, S, Tiwang, R., "Foreclosure Sale and House Value: Correlation or Causation?", proceedings of 16th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA17) Cancun, Mexico, December 18-21, 2017.
  16. Oladunni, T., Sharma, S, Twang, R., "A Spatio – Temporal Hedonic House Regression Model", proceedings of the 16th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA17), Cancun, Mexico, December 18-21, 2017.
  17. Oladunni, T., Sharma, S, "An Occam’s Razor Approach to Hedonic Pricing Theory", proceedings of the 4th IEEE International Conference on Computational Science and Computational Intelligence, Las Vegas, USA, December 14-16, 2017.
  18. Oladunni, T., Sharma, S, "Spatial Dependency and Hedonic Housing Regression Model", 15th IEEE International Conference on Machine Learning and Applications (ICMLA 2016), Anaheim, California, USA, December 18-20, 2016. DOI 10.1109/ICMLA.2016.161. (24.69% acceptance rate).
  19. Oladunni, T., Sharma, S, "Hedonic Housing Theory – A Machine Learning Investigation", 15th IEEE International Conference on Machine Learning and Applications (ICMLA 2016), Anaheim, California, USA, December 18-20, 2016. DOI 10.1109/ICMLA.2016.103. (24.69% acceptance rate).
  20. Oladunni, T., Sharma, S. "Predictive Real Estate Multiple Listing System using MVC Architecture and Linear Regression" ISCA 24th International Conference on Software Engineering and Data Engineering (SEDE 2015), page 147- 152, San Diego, California, USA, October 12-14, 2015.
  21. Oladunni, T., Sharma, S. “Predicting Fair Housing Market Value: A Machine Learning Investigation” International Journal of Computers and their Applications, IJCA, Vol. 23, No. 3, Sept. 2016.
  22. Oladunni, T., Sharma, S, "Hedonic House Pricing Model using Deep Learning With a L1 Regularization", proceedings of ISCA 26th International Conference on Software Engineering and Data Engineering (SEDE-2017), San Diego, CA, USA, October 2-4, 2017.

Professional Services

  1. Technical program committee (member) FLAIRS-33 2020
  2. Technical program committee (Reviewer) IEEE Sarnoff 2019
  3. Technical program committee (member) SEDE 2019
  4. Technical program committee (Reviewer) IEEE Sarnoff 2016
  5. Technical program committee (Reviewer) IEEE ICMLA 2017
  6. Technical program committee (member) International Symposium on Signal Processing and Intelligent Recognition Systems 2017

Poster

  1. Oladunni, T. "Automated Accent Recognition: A Machine Learning Investigation", Google Research Laboratory, San Francisco CA, July 2014.


Grants

  1. PI - NSF Grant: RAPID: Collaborative Research: VAPOC: Visualization, Analysis and Prediction of COVID-19. Award Period: 1 June 2020 through 31 May 2021. (Awarded)
  2. Co-PI - NSA Grant: CEDI Capacity Building: Cybersecurity Research and Development (CSRD) Center (Awarded)
  3. Co-PI - NSF Grant: IUSE: EHR: Impactful and Revolutionary Design Experience by Engineering Curriculum Redesign (Submitted).
  4. PI : Data Science and Virtual Reality Modeling of COVID 19 Pandemic (In preparation)
  5. PI : COVID-19 Virtual Reality Instructional Modules for Autism Spectrum Disorder (VRIM-ASD) (In preparation)
  6. PI : Improving Student Retention Rate in undergraduate Computer Science and Engineering education of a Minority Serving Institution (In preparation)


Address

Department of Computer Science & Information Technology
School of Engineering and Applied Science
Bldg 42. Suite 112E
4200 Connecticut Ave. NW
Washington, DC 20008

Phone

202.274.5512

Email

Email: timothy.oladunni[at]udc.edu