AI Bio Lab Faculty & Students

Nate Steffen is a PhD candidate in Aerospace Engineering at the University of Cincinnati. He previously earned his Bachelor of Science degree in Aerospace Engineering with certificates in Mathematics and Computer Science from UC.

During his undergraduate studies, Nate completed multiple co-op and research rotations that shaped his interest in AI and space robotics. His early work included two semesters as a research assistant in mechanical engineering, where he conducted molecular dynamics simulations. He then completed a co-op semester with the Air Force Research Laboratory, focusing on machine learning applications. Following this, Nate undertook two co-op rotations at NASA Glenn Research Center as a Pathways Intern, developing optimization code and supporting the design of cislunar low-thrust spacecraft trajectories. He continues to work at NASA Glenn during academic breaks.

Nate’s research centers on the development and assurance of responsible AI for space robotics. As part of the Strategic Space Technologies Initiative (SSTI), he works on the verification and validation of AI techniques used in space proximity operations. Much of his research involves optimization of robotic arms for on-orbit servicing and inspection procedures, and he is also exploring the use of AI for path planning and autonomy in other key space robotics missions.

He earned his M.S. in Aerospace Engineering at the University of Cincinnati in March 2026 with the thesis Fuzzy Ordinary Differential Equations for Intelligent Aerospace Applications.

Hugo Henry is a Ph.D. researcher in Artificial Intelligence and Aerospace Systems at the University of Cincinnati. His research focuses on explainable AI, genetic fuzzy systems, and decision-support methods for safety-critical aerospace applications, with particular emphasis on interpretable machine learning for autonomous systems. Prior to his doctoral studies, he gained industrial experience in aerospace through roles at Safran Helicopter Engines, where he contributed to integration, verification, validation, and qualification (IVVQ) of embedded engine control software, and at Sopra Steria Aeroline, where he supported qualification testing of the Rafale mission system. He holds a dual Master's degree in avionics and international aerospace engineering from the University of Bordeaux. His work has resulted in peer-reviewed publications, conference presentations, and the 2026 NAFIPS Outstanding Student Paper Award.

Jared is an Aerospace Engineering PhD student at the AI Bio Lab and a Digital Futures Fellow. He earned his B.S. in Aerospace Engineering & Engineering Mechanics from the University of Cincinnati in 2021. Previously, he worked as a Machine Learning intern at THALES Avionics, where he explored new applications of Evolutionary Fuzzy Systems (EFS) and contributed to expanding their core methodological capabilities.

His current research focuses on the verification of machine-learning-based and classical control systems, with a specialization in Fuzzy Logic Control (FLC). His work spans three verification paradigms: (1) Formal Methods — providing mathematical guarantees of requirement satisfaction (2) Probabilistic Methods — offering statistical evidence of safety and robustness and (3) Interventionist Methods — runtime strategies that anticipate and mitigate requirement violations. Promising techniques within these paradigms include reachability analysis, stochastic robustness analysis, and runtime assurance.

Currently, he is applying reachability analysis tools to assess the safety of satellite servicing and inspection missions and is developing new tools to enable formal analysis of fuzzy logic controllers.

Lohith works on advancing explainable artificial intelligence (XAI) for cybersecurity, aiming to build machine learning models that are both effective and accountable. His research focuses on transforming opaque ML systems into auditable, analyst-ready tools capable of surfacing threats early while providing faithful, attack-resistant explanations. These explanations support incident response and regulatory compliance, ensuring that AI decisions can be trusted and verified rather than treated as black-box alerts.

Additionally, Lohith works on autonomous AI decision-making for adversarial and uncertain environments. His work emphasizes the development of verifiable, human-aware AI agents that integrate uncertainty estimation, causal reasoning, and safety guardrails. These mechanisms ensure that autonomous systems can operate independently under adversarial conditions while still deferring control to humans when appropriate.

His overarching goal is to build AI systems that detect and respond effectively to security threats while also justifying their reasoning — earning human trust in real-world deployments.

Magnus Sieverding is a Ph.D. candidate at the University of Cincinnati’s College of Engineering and Applied Science. His work spans fuzzy-logic-based artificial intelligence, control theory, dynamical modeling, and simulation of complex engineering systems. He assists Dr. Kelly Cohen in projects involving validation, performance evaluation, safety assurance, and robustness analysis of intelligent systems, with a particular focus on improving the scalability and applicability of Fuzzy Logic to large, real-world engineering problems.

Tri Nguyen is a PhD student in Aerospace Engineering at the University of Cincinnati, where he researches explainable artificial intelligence (XAI) for medical and safety-critical applications. His work explores predictive modeling and transparency techniques for clinical decision support and AI safety.

In his first year of PhD, Tri authored multiple peer-reviewed conference papers, including four at the 2025 Joint IFSA/NAFIPS Conference, where he received the Outstanding Paper Award, the Outstanding Student Paper Award, and Honorable Mention for Student Paper. He also has two accepted publications for the 2026 International Conference on Artificial Intelligence and Computational Intelligence (AICI 2026), to appear in Springer’s edited proceedings, covering explainability assessment for retinal OCT image diagnosis and sampling methods for psychiatric evaluation data.

Tri completed his B.S. in Aerospace Engineering at UC with a 3.98 GPA, supported by multiple research fellowships and scholarships including the UGR Fellowship, UPRISE Fellowship, Co-op/Research of the Year Award, Tau Beta Pi Scholarship, and the SRIDE Undergraduate Fellowship. His goal is to advance interpretable AI systems that can be responsibly deployed in clinical environments.

John P. Cavanaugh is a founder, privacy technologist, and PhD candidate at the University of Cincinnati's AI BIO Lab, where he builds privacy-by-design AI systems rooted in explainability and human accountability. After nine years co-founding and leading Plunk Student Community, he spent five years as Executive Director of the Plunk Foundation, serving 30 million children, women, and veterans through consent-first digital safety technology. Today he serves on boards across nonprofit, civic, and technology sectors and advises organizations on responsible AI, privacy strategy, and ethical leadership.

Joshua Metcalf is a Ph.D. student in Aerospace Engineering at the University of Cincinnati and a graduate researcher in the AI Bio Lab. He earned his B.S. in Mechanical Engineering, with a minor in Aerospace Engineering, from Trine University and is concurrently completing his M.S. at UC. His research focuses on interpretable and explainable artificial intelligence for healthcare, with particular emphasis on fuzzy inference systems, evolutionary optimization, and clinical classification. His current work applies Pittsburgh Genetic Algorithm-evolved Takagi-Sugeno-Kang fuzzy systems to breast cancer Fine Needle Aspiration data, with the goal of developing accurate classification systems that remain interpretable for clinical use. Alongside his research, Joshua serves as a computer science faculty member at Mount St. Joseph University, where he teaches Python programming and introductory artificial intelligence. His background also includes engineering industry experience in fire protection system design.

Haidar Bin Hamid is a PhD Student in Biomedical Engineering at the University of Cincinnati, working in Dr. Kelly Cohen's AI Bio Lab. His research centers on explainable AI, fuzzy logic systems, and interpretable fuzzy inference, applied to wearable devices for neurodegenerative disease monitoring. He holds Masters in AI Engineering from the University of Cincinnati. Haidar serves as the president of FLAIR, a student organization focused on promoting explainable AI research and researchers at the University of Cincinnati.

Additionally, he is the founder and CEO of Xtainable LLC, a company built on the thesis that AI should be explainable first, then configured to be Sustainable, Maintainable, Attainable, and Containable for regulated industries such as healthcare, finance, and government. Its flagship product, EXI (Explainable Executive Intelligence), delivers auditable executive decision support.

Cleopatra Douglas is a PhD student in Aerospace Engineering at the University of Cincinnati, where she conducts research in Dr. Kelly Cohen's AI Bio Lab. Her work focuses on explainable AI and interpretable fuzzy inference systems, applying methods from safety-critical engineering to high-stakes financial decision-making. She holds a Master's in Computer Science from Northeastern University and a Bachelor's in Mathematics.

Cleopatra is the co-founder and CEO of Recital Finance, an agentic financial operations startup built on transparency and human accountability. She also serves as treasurer of FLAIR, a University of Cincinnati student organization that promotes explainable AI research. Her goal is to advance AI systems that financial institutions can trust, audit, and deploy responsibly.

Reka Patel is an Aerospace Engineering MS student at the University of Cincinnati, with research focuses on trustworthy and interpretable artificial intelligence for safety-critical aerospace systems in the AI Bio Lab. Reka got her undergraduate degree in Mechanical Engineering from Southern Methodist University. Her work explores genetic fuzzy systems, explainable AI, runtime assurance, intelligent flight-control and aircraft-monitoring applications, with an emphasis on developing AI systems that are both high-performing and understandable to human operators. Her current thesis investigates genetic fuzzy trees as an interpretable surrogate for the ACAS Xu aircraft collision avoidance system, including their performance, robustness, interpretability, and potential path toward certification. 

Connor Horn is an Aerospace Engineering MS student whose work focuses on unmanned aircraft systems, autonomy, flight controls, sensor fusion, modeling and simulation, and fuzzy logic. His research centers on ground-referenced UAV localization using vision-based pose estimation and complementary sensors, with an emphasis on characterizing real-world measurement behavior and evaluating navigation performance in simulation.

Korinne “Rin” Newcamp is an Aerospace Engineering student at the University of Cincinnati with experience spanning engineering, sales, and aerospace business development. Through multiple co-op rotations with The Timken Company, she has gained experience in technical problem-solving, data analysis, customer support, and cross-functional collaboration. As a recipient of the OSCG 2026–2027 STEM Scholarship, Rin is conducting independent research with Dr. Kelly Cohen focused on airport gate optimization, exploring methods to improve the efficiency of airport operations. She has also contributed to aerospace projects involving aircraft, rockets, and payload design. Rin enjoys applying her technical background to real-world challenges and is particularly interested in opportunities that combine engineering, analytical problem-solving, and communication. Following graduation, she plans to begin her engineering career while continuing to expand her technical knowledge and leadership skills.