Projects/Publications

Parkinson's Disease

PD is a neurodegenerative disorder affecting over 1 million people in the United States alone. There is currently no known cure for PD, but medication and neurosurgical therapies can effectively manage patients' symptoms. In collaboration with North Well Health's Feinstein Institute and the Global Kinetics corporation, our work uses data from wearable sensors to optimize patients' therapies throughout the disease course.

Publications

Improving Medication Regimen Recommendation for Parkinson’s Disease Using Sensor Technology

Sensors

Watts, J., Khojandi A., Vasudevan R., Nahab F.B., Ramdhani R.A.

Machine Learning’s Application in Deep Brain Stimulation for Parkinson’s Disease: A Review

Brain Sciences

Watts, J., Khojandi, A., Shylo, O., and Ramdhani, R.

Optimizing Individualized Treatment Planning for Parkinson’s Disease Using Deep Reinforcement Learning

42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society

Watts, J., Khojandi, A., Vasudevan, R., and Ramdhani, R.

Patents

Optimizing Medication Planning Using Wearable Sensors in Chronic Diseases (provisional patent filed)

Khojandi, A., Ramdhani, R. Baucum, M. Watts, J.

Genomics

Large-scale expression quantitative trait loci (eQTL) analyses have demonstrated that most genes have a genetic component to their expression. By combining genotypes from multiple variants proximal to a gene, we build machine learning models to predict tissue-specific expression of a gene at the patient level.

Publications

Towards Faster Gene Expression Prediction via Dimensionality Reduction and Feature Selection

45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society.

Watts, J., Allen, E., Mitoubsi, A., Khojandi, A., Eales, J., and Papamarkou, T.

Adapting Random Forests to Predict Obesity-Associated Gene Expression

44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society.

Watts, J., Allen, E., Mitoubsi, A., Khojandi, A., Eales, J., Jalali-Najafabadi, F., and Papamarkou, T.

Autonomous Vechicles

To ensure the successful real-time operation of automated vehicles, it is critical to detect and isolate anomalous and/or faulty information in a timely manner. We develop a dynamic anomaly classification algorithm based on CNNs, in combination with a POMDP model. This resulting model outperforms state-of-the-art anomaly detection techniques.

Publications

Dynamic Deep Reinforcement Learning-Bayesian Framework for Anomaly Detection

IEEE Transactions on Intelligent Transportation Systems

Watts, J., van Wyk, F., Rezaei, S., Wang, Y., Khojandi, A., and Masoud, N.

Watershed Management

In collaboration with the Department of Environmental Engineering at the University of Tennessee, we are developing a predictive algorithm to dynamically determine the water level of remotely monitored retention ponds in the Knoxville area without the need for computationally expensive physics-based models.

Publications

Deep Temporal Neural Networks for Water Level Predictions of Watershed Systems

IEEE Systems and Information Engineering Design Symposium (SIEDS) 2023

Huff, J., Watts, J., Khojandi, A., and Hathaway, J.

Particle Detection

Radiation detection has applications from fundamental physics, to medicine, to nuclear security. These works, in collaboration with Oak Ridge National Laboratory, improved the efficiency and fidelity of particle detection.

Publications

Studying the Effects of Thermally Diffusing Ce into the Surface of YAlO3 for Associated Particle Imaging

Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms

Moore, M.E., Delzer, C.,Watts, J. Music ́o, B.L., Xu, C., Collette, R.M., Rack, P.D., Zhang, Y., Melcher, C.L.,McConchie, S. and Hayward, J.P.

First Precision Measurement of the Parity Violating Asymmetry in Cold Neutron Capture on 3He

Physical Review Letters

Gericke, M.T., Baeßler, S., Barron-Palos, L., Birge, N., Bowman, J.D., Calarco, J., Cianciolo, V., Coppola, C.E., Crawford, C.B., Fomin, N., Garishvili, I., Greene, G., Hale, G., Hamblen, J., Hayes, C., Iverson, E., Kabir, M., McCrea, M., Plemons, E., Mueller, P., Novikov, I., Penttila, S., Scott, E., Watts, J., and Wickersham, C.

Link to Conference and Invited Talks

University of Tennessee

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