• Advancing Bioinspired Control Strategies Through Insect Flight Modeling: Designed time-periodic flapping flight models to study hawkmoth dynamics, addressing anatomical and kinodynamic constraints. Recorded and integrated experimental data and, with multidisciplinary collaboration, developed a trim algorithm and a closed-loop simulation environment. Contributed to validating agility metrics and advancing bioinspired control strategies, enhancing understanding of the insect flight envelope and informing perception-driven design.• Revealing the Role of Network Structure in Neural Coding for Agile Locomotion: Collaborated to develop a spiking neural network (SNN) model of the hawkmoth sensorimotor system to study temporal precision in spike-based coding and the role of convergent-divergent structures. Analyzed the structure-function relationship in sensorimotor transformations and demonstrated how network convergence affects information loss, advancing insights into neuromorphic models and neural coding strategies critical for locomotion agility.

• Unraveling Linear Dynamics in Hawkmoth Flower-Tracking for Bioinspired Flight Control: Studiedhawkmoth flower-tracking behavior to investigate how linearity emerges as simplicity in goal-directed locomotion. Conducted robotic flower experiments to test velocity and frequency limits, confirming sensorimotor control compensates for nonlinearities in flight mechanics. Identified sensory feedback and neural constraints stabilizing hover equilibrium, providing insights for bioinspired control systems.• Advancing Neuromorphic Control Through Precise Motor Spike Prediction: Developed an RNN-based sensorimotor model to predict millisecond-precise spike timing in hawkmoth flight. Combined sensory encoding, a CNN encoder, and an RNN to model wingstroke timing, validated in tethered flight experiments with robotic flower tracking. Achieved precision matching agile insects, advancing motor control understanding and neuromorphic navigation design.• Divergence in Insect Flight Strategies and Adaptations: Analyzed the relationship between wing morphology, flight behavior, and ecological adaptation in bombycoid moths by comparing hawkmoths and silkmoths. Evolutionary analysis revealed divergent wing morphologies adapted to distinct niches. Developed a quasi-steady blade-element model using 3D kinematic data to calculate aerodynamics. Findings showed hawkmoths’ high-aspect-ratio wings enable agility, while silkmoths’ low-aspect-ratio wings and slower wingbeats support energy-efficient erratic flight, highlighting the co-evolution of morphology and movement.• Unveiling Latent Representations in Neural and Behavioral Data: Collaborated in developing a multi-modal Gaussian Process Variational Autoencoder (MM-GPVAE) to analyze sensory, neural, and biomechanical data. Combined Gaussian Process Factor Analysis with GP-VAEs to partition shared and independent variability.

CYPHYNETS Lab (Dr. Abubakr Muhammad)

Digital Control SystemsFeedback Control SystemsLinear AlgebraIntroductory Physics LabIntroduction to Differential Equations

Implemented a GSM-directed GPS-tracking system and interfaced it to the control of a small autonomous vehicle.