Fuzzy AI Lab Faculty & Students

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Faculty

Headshot of Kelly Cohen

Kelly Cohen

Professor

513-556-3523

Students

Headshot of Kyle Dunlap

Kyle Dunlap

Research Interests: Real-Time Safety Assurance, Safety Critical Control, Autonomous Systems, Reinforcement Learning, Genetic Fuzzy Systems, Robotics

Kyle Dunlap is a first-year Aerospace Engineering Ph.D. student at the University of Cincinnati. His advisors are Dr. Kelly Cohen, Brian H. Rowe Chair and Interim Head of the Department of Aerospace Engineering & Engineering Mechanics at the University of Cincinnati, and Dr. Kerianne Hobbs, Safe Autonomy Team Lead at the Air Force Research Laboratory. In 2020 Kyle worked with NASA Glenn Research Center to develop control-based fault detection and mitigation strategies for electrified aircraft propulsion systems. In both 2020 and 2021, he worked with the Air Force Research Laboratory to first apply deep reinforcement learning to safe autonomous spacecraft docking, and then to develop real-time safety assurance techniques for this task. Kyle received his BS in Aerospace Engineering from the University of Cincinnati in 2021.

Current Projects:

  • Testing Run Time Assurance for aircraft rejoin (2021 – Present) UC UAV MASTER Lab
  • Developing and comparing Run Time Assurance for spacecraft docking (2021 – Present) AFRL Safe Autonomy Team

Publications:

  • [Submitted] Kyle Dunlap, Michael Hibbard, Mark Mote, Kerianne Hobbs, “Comparing Run Time Assurance Approaches for Safe Spacecraft Docking,” American Control Conference, June 2021
  •  [Submitted] Kyle Dunlap, Mark Mote, Kaiden Delsing, Kerianne L. Hobbs, “Run Time Assured Reinforcement Learning for Safe Satellite Docking,” AIAA SciTech Forum, January 2022
  • Kyle Dunlap, Kelly Cohen, Kerianne Hobbs, “Comparing the Explainability and Performance of Reinforcement Learning and Genetic Fuzzy Systems for Safe Satellite Docking,” 2021 NAFIPS Annual Conference, June 2021
  • Donald L. Simon, Randy Thomas, Kyle M. Dunlap, “Considerations for the Extension of Gas Path Health Management Techniques to Electrified Aircraft Propulsion Systems,” 2021 ASME Turbo Expo Conference, June 2021
Headshot of Jackson Elpers

Jackson Elpers

Key Research Interests: Fuzzy Logic, Explainable Artificial Intelligence, Genetic Fuzzy Systems, Sports Medicine, Exercise Science

Jackson Elpers is starting his first years as a MS student in the Department of Aerospace Engineering at the University of Cincinnati. He completed his BS in Aerospace Engineering at the University of Cincinnati in the Spring. He completed his five co-op rotations under Dr. Kelly Cohen and Dr. Adam Kiefer applying genetic fuzzy systems to sports medicine and exercise science topics. These included concussion recovery, motion matching, and virtual reality.

Key Current Project(s):

  • Enhanced Ecological Validity of Virtual Reality for Simulated Training Environments Spring 2020 – Present

Key Publications:

  • Kiefer, A.W., Sathyan, A., Reed, C., Walker, G., Elpers, J., Gubanich, P., Cohen, K. and Logan, K., 2020. Predicting Protracted Concussion Recovery To Inform Proactive Care: A Genetic Fuzzy Machine Learning Approach: 2830 Board# 291 May 29 9: 30 AM-11: 00 AM. Medicine & Science in Sports & Exercise52(7S), p.785.
  • Armitano-Lago, C., Chaaban, C., Cain, S., MacPherson, R., Elpers, J., Padua, D., Silva, P., Wikstrom, E., and Kiefer, A., 2021. Novel portable markerless motion capture system accurately captures lower limb kinematics during functional locomotor tasks: A validation study: 529. Medicine & Science in Sports & Exercise, 53(8S), p.176.
Headshot of Lynn Pickering

Lynn Pickering

Rindsberg Fellow

Key Research Interests: Fuzzy Logic, Explainable Artificial Intelligence, Human – Centered AI, AI and Ethics, Fairness in AI, Genetic Fuzzy Systems

Lynn Pickering is starting her second year as a PhD student in this group at the University of Cincinnati, after graduating with a Bachelors of Science in Aerospace Engineering and minor in German Studies (Summa Cum Laude and Distinguished University Honors Scholar). Without trust in AI, only so much can be achieved, and so she is passionate about Fuzzy Logic as an artificial intelligence method that provides the transparency and explainability to truly advance AI. Explainability in AI is essential to AI built to work in partnerships with Humans, as well as ensure that the AI is being fair and ethical. To further promote explainable fuzzy systems, she is on the organizing board of the Explainable Fuzzy AI student competition.

Key Current Project(s):

  • Explainable Fuzzy AI Challenge – Organizing Board Fall 2020 - Present
  • Genetic Fuzzy Homicidal Chauffeur Project Spring 2021 – Present
  • Implemented the optimal control solution for the classic Homicidal Chauffeur differential game in python
  • Trained genetic fuzzy system on the optimal control solution
  • Introducing noise to test the robustness of the genetic fuzzy system compared to the optimal control solution

Key Publications:

  • Pickering, L., and Cohen, K., “Explainable AI - Genetic Fuzzy Systems - A Use Case”, 2021 NAFIPS Annual Conference, June 7-9, 2021, Purdue University, IN. To also appear as book chapter in Springer publication titled ‘Explainable AI and Other Applications of Fuzzy Technique’s Editors: Julia Rayz, Victor Raskin, Scott Dick, and Vladik Kreinovich. 
  • Pickering, L., and Cohen, K., “Genetic Fuzzy based Tetris Player”, 2020 NAFIPS Annual Conference, August 20-22, 2020 - Redmond, Washington, USA.
  • Pickering, L., Viana Perez, J., Li, X., Chhabra, A., Patel, D., and Cohen, K., “Identifying New Inputs in COVID - 19 AI Case Predictions”, Proceedings of 7th International Conference on Soft Computing and Machine Intelligence (ISCMI 2020), November 14-15, 2020, Stockholm, Sweden, pp. 192-196.
  • Chhabra, A., Patel, D., Viana Perez, J., Pickering, L., Li, X, and Cohen, K., “Understanding the Effects of Human Factors on the Spread of COVID-19 using a Neural Network”, Proceedings of 7th International Conference on Soft Computing and Machine Intelligence (ISCMI 2020), November 14-15, 2020, Stockholm, Sweden, pp. 121-125.
Headshot of Anoop Sathyan

Anoop Sathyan

Key research interests: Machine learning, Genetic fuzzy methodology, Reinforcement learning, Robotics, Dynamic systems.

Dr. Anoop Sathyan currently works as Senior Research Associate in the Department of Aerospace Engineering at University of Cincinnati. He is a data engineer with 8 years of experience doing research in the area of machine learning with a focus on developing the field of genetic fuzzy systems as it relates to supervised and reinforcement learning applications. His work focuses on using machine learning tools in various fields including robotics, unmanned systems, biomedical and neuroscience. His PhD thesis titled “Intelligent Machine Learning Approaches for Aerospace Applications” was adjudged as the Best Thesis during the years 2017-19 by North American Fuzzy Information Processing Society (NAFIPS).

  • Sathyan, A., Cohen, K. and Ma, O., 2020. “Genetic fuzzy based scalable system of distributed robots for a collaborative task”. Frontiers in Robotics and AI, 7.
  • Kiefer, A.W., Sathyan, A., Reed, C., Walker, G., Elpers, J., Gubanich, P., Cohen, K. and Logan, K., 2020. Predicting Protracted Concussion Recovery To Inform Proactive Care: A Genetic Fuzzy Machine Learning Approach: 2830 Board# 291 May 29 9: 30 AM-11: 00 AM. Medicine & Science in Sports & Exercise, 52(7S), p.785.
  • Sathyan, A. and Ma, O., 2019. “Collaborative Control of Multiple Robots Using Genetic Fuzzy Systems”. Robotica, 37(11), pp.1922-1936.
  • Sathyan, A., Cohen, K. and Ma, O. 2019. “Comparison Between Genetic Fuzzy Methodology And Q-learning For Collaborative Control Design”. International Journal of Artificial Intelligence and Applications (IJAIA).
  • Brown, B., Cohen, J., Kukreti, S., Nemati, A., Sarim, M., Sathyan, A., Wei, W., Kumar, M., and Cohen, K. 2017. “An Unmanned Aerial System for Improved Situational Awareness During Emergency Response”. International Journal of Unmanned Systems Engineering.Vol. 5, No. 3, 1-37.
  • Sathyan, A., Ernest, N.D. and Cohen, K., 2016. “An Efficient Genetic Fuzzy Approach to UAV Swarm Routing”. Unmanned Systems, 4(02), pp.117-127.
  • Cohen, J., Sathyan, A., and Kumar, M. 2016. “MatConvNet-based Convolutional Neural Networks for Classification of Humans from Infrared Images”. International Journal of Unmanned Systems Engineering.Vol. 4, No. 2, 34-45.
  • Sathyan, A., Boone, N., & Cohen, K. 2015. “Comparison of Approximate Approaches to Solving the Travelling Salesman Problem and its Application to UAV Swarming”. International Journal of Unmanned Systems Engineering.Vol. 3, No. 1, 1-16.
Headshot of Javier Viaña

Javier Viaña

Key research interests: Explainable Artificial Intelligence, Genetic Fuzzy Systems, Evolutionary Algorithms, and Intelligent Control.

Javier was born in Bilbao, Spain, in 1995. He received his B.Sc. and M.Sc. in mechanical engineering from the University of the Basque Country, Bilbao, in 2017 and 2018, and M.Eng. in aerospace engineering from the University of Cincinnati, Cincinnati, USA, in 2019. He is currently a Ph.D. candidate in artificial intelligence applied to aerospace engineering at the University of Cincinnati, Cincinnati, USA. From 2015-18 he was a space design engineer at SATLANTIS Microsatellites and IDOM. Period during which he actively participated in various mission proposals and space projects in collaboration with NASA (Florida) and ESA (Belgium). In 2019 he worked for Aurora Flight Sciences, Boeing (Lucerne, Switzerland). Where he developed multiple algorithms for the prediction of remaining useful life in critical components of an aircraft. From 2019-20 he served as instructor of Probability Theory applied to Aerospace Engineering at the College of Engineering and Applied Sciences of the University of Cincinnati, Cincinnati, USA. In 2020 he worked as a machine learning engineer at Genexia (Cincinnati, USA), developing algorithms for image processing. In 2021 he created an explainable algorithm for passenger flow prediction for CVG International Airport (Northern Kentucky, USA). He has also served as an invited professor in Cambridge University, and a visiting trainee at the European Space Agency at the European space Security & Education Centre. He has published three books, more than 10 peer-reviewed papers, book chapters, several registries of intellectual property and 2 patents. He has received awards such as the Best National Research Article of 2016, the Airport Cooperative Research Program Graduate Research Award from the Federal Aviation Administration, the "La Caixa" fellowship award, a letter of recognition from the Royal House of Spain written by HH. MM. King Felipe VI, or the Professor R. T. Davis Memorial Award for Academic Performance in Computational Sciences.

  • Viaña J., Ralescu, S., Cohen K. Ralescu, A., and Kreinovich, V. Why Cauchy Membership Functions: Efficiency. In: Advances in Artificial Intelligence and Machine 1(1), pp.1-6, 2021.
  • Viaña J., Ralescu, S., Cohen K. Ralescu, A., and Kreinovich, V. Why Cauchy Membership Functions. University of Texas El Paso 2021.
  • Viaña J., Ralescu, S., Cohen K. Ralescu, A., and Kreinovich, V. Extension to Multidimensional Problems of a Fuzzy-based Explainable & Noise-Resilient Algorithm. Proceedings of Constraint Programming and Decision Making (CoProd 2021).
  • Viaña J., Cohen K. (2022) Evaluation Criteria for Noise Resilience in Regression Algorithms. In: Rayz J., Raskin V., Dick S., Kreinovich V. (eds) Explainable AI and Other Applications of Fuzzy Techniques. NAFIPS 2021. Lecture Notes in Networks and Systems, vol 258. Springer, Cham. https://doi.org/10.1007/978-3-030-82099-2_43
  • Viaña J., Cohen K. (2022) Fuzzy-Based, Noise-Resilient, Explainable Algorithm for Regression. In: Rayz J., Raskin V., Dick S., Kreinovich V. (eds) Explainable AI and Other Applications of Fuzzy Techniques. NAFIPS 2021. Lecture Notes in Networks and Systems, vol 258. Springer, Cham. https://doi.org/10.1007/978-3-030-82099-2_42
  • O’Grady K.L., Viaña J., Cohen K. (2022) Predicting Diabetes Diagnosis with Binary-To-Fuzzy Extrapolations and Weights Tuned via Genetic Algorithm. In: Rayz J., Raskin V., Dick S., Kreinovich V. (eds) Explainable AI and Other Applications of Fuzzy Techniques. NAFIPS 2021. Lecture Notes in Networks and Systems, vol 258. Springer, Cham. https://doi.org/10.1007/978-3-030-82099-2_29
  • Viaña,J.,Scott,D.,Kumar,M.,Cohen,K.,“Dynamic Genetic Algorithm for Optimizing Movement of a Six-Limb Creature”. Dynamic Systems and Control Conference, ASME (DSCC2020) Pittsburg, PA, USA (2020).
  • Viaña, J., Cohen, K., "ExTree – Explainable Genetic Feature Coupling Treeusing Fuzzy Mapping for Dimensionality Reduction with Application to NACA 0012 Airfoils Self-Noise Data Set". In: Barnabas Bede, Martine Ceberio, Martine De Cock, and Vladik Kreinovich (eds.), Fuzzy Information Processing 2020, Proceedings of NAFIPS' 2020, Springer, Cham, Switzerland, (2020).
  • Viaña, J., Cohen, K., "Fast Training Algorithm for Genetic Fuzzy Controllers and application to an Inverted Pendulum with Free Cart". In: Barnabas Bede, Martine Ceberio, Martine De Cock, and Vladik Kreinovich (eds.), Fuzzy Information Processing 2020, Proceedings of NAFIPS'2020, Springer, Cham, Switzerland, (2020).
  • J. Viaña, K. Cohen. "Fault Tolerance Tool for Human and Machine Interaction & Applicationto Civilian Aircraft". 6th IEEE Latin American Conference on Computational Intelligence (LA-CCI), Guayaquil, Ecuador (2019). DOI:10.1109/LA-CCI47412.2019.9037045