Artificial Intelligence
My work integrates artificial intelligence with engineering design, project-based education, robotics, embedded systems, and smart structures. The emphasis is on human-guided AI that strengthens engineering judgment, supports experiential learning, and connects computational intelligence with physical engineering systems.
AI activity portfolio
The portfolio spans development, classroom implementation, interdisciplinary engineering, physical systems, scholarship, and academic leadership.
01 AI-Enabled Engineering Design
Human-guided AI for structured engineering design decisions and scalable project-based instruction.
The project is developing an AI-guided design-methods coach integrated with openly licensed, multimodal instructional modules. The platform provides contextual guidance, helps students select and justify appropriate design methods, supports reflection on alternatives and trade-offs, and promotes more consistent access to engineering-design instruction in the MAE 451 / MAE 494 senior design sequence.
The system is being designed as an instructional and decision-support layer rather than an answer generator: students remain responsible for engineering assumptions, calculations, evidence, design choices, and validation.
The project examines how structured AI guidance can support method selection, engineering justification, consideration of alternatives and trade-offs, reflection, and instructional scalability without replacing faculty judgment or student responsibility.
Student researchers: Hadley Douglas (MAE); Bryce Lin and Sai Krishna Arthanari (CSE). Co-advising: Dr. Roshan Ayyalasomayajula.
Current development status is shown below. “Completed” indicates that the module has reached the current implemented version; remaining modules are actively in development.
02 AI-Enabled Senior Design
Senior design provides two complementary applications of AI: current research-oriented projects use the AI-enabled engineering design platform to strengthen design reasoning, while earlier projects embedded AI and machine learning directly into student-built physical systems.
A research-oriented senior design project developing a piezoelectric energy-harvesting system for a soccer-ball application while using the AI-enabled engineering design platform to support structured design decisions.
The platform supports concept generation, design-method selection, comparison of alternatives, trade-off analysis, engineering justification, and iterative refinement.
A research-oriented senior design project developing a compact, human-powered emergency energy-harvesting device while applying the AI-enabled engineering design platform throughout the design process.
The project provides a contrasting design case for evaluating how AI-supported engineering methods transfer across different requirements, constraints, alternatives, and product architectures.
AI supports the engineering design process—method selection, comparison, justification, trade-off analysis, and iteration—rather than serving as the operating mechanism of the physical devices.
A senior design team developed an affordable autonomous agricultural robot integrating AI-based photo recognition, ROS2-based navigation, laser weed removal, and mobile robotic hardware for small-scale farms.
The project addressed autonomous operation, terrain mobility, operating duration, affordability, and safety, and received the People’s Choice Award at the 2025 Panasci Technology Entrepreneurship Competition.
A senior design team developed and successfully tested an AI-enabled interactive music-lighting system using Python-based neural networks for live-audio interpretation and genre classification, with TensorFlow, PyTorch, and ONNX workflows.
The students integrated the AI software with Arduino-based hardware control, programmable LED strips, servo-driven moving-light/laser effects, projection hardware, and a Vue-based user interface. The project earned third place at the Spring 2024 Senior Design Demonstration Day.
These projects show a progression from student-built systems in which AI provides core operating intelligence to current projects in which AI also supports structured engineering-design reasoning and decision making.
03 AI-Driven Robotics
A 2025–2026 interdisciplinary collaboration connecting autonomous robotics, renewable-energy maintenance, image processing, embedded systems, and engineering education.
The project developed an intelligent robotic platform for solar-panel inspection and maintenance. Major accomplishments included rover hardware, remote manual control, autonomous obstacle avoidance, A*-based path planning, automated robotic-arm motion for solar-panel cleaning, multiple cleaning tools, AI-enabled image recognition for panel-condition assessment, and centralized web-based system coordination. Electrical work also included a custom rover power-management and safety PCB.
My role — Mechanical and robotic integration collaborator. My contributions focus on the mechanical side of the autonomous maintenance platform, including advising on the rover cleaning system, robotic-arm/tool interfaces, cleaning-tool concepts, tool-holster and attachment design, operating-range and loading constraints, design trade-offs, and mechanical integration with electrical and computing control. This role helps ensure that autonomous functions are grounded in the capabilities and constraints of the physical system.
04 AI & Distributed Embedded Control
The technical-development component of the 2026–2027 collaboration extends the earlier solar-robotics platform from centralized supervision toward a distributed, multi-agent embedded-control architecture.
In the new architecture, robotic subsystems such as the Navigator Drone and LandBot operate as embedded intelligent agents executing localized decision processes. Agents exchange telemetry, task states, and control messages through structured TCP/IP communication, enabling cooperative distributed decision making and comparison of centralized versus distributed strategies. The architecture may first be modeled in MATLAB-based multi-agent environments before deployment on Raspberry Pi platforms.
My role — Mechanical systems and physical-platform integration collaborator. My contribution focuses on advising on translating distributed-control goals into the capabilities and constraints of the physical robotic system, including rover, robotic arm, cleaning mechanisms, interfaces, reach, loading, mobility, and mechanical robustness. I also advise on design refinement and integration/testing so that control decisions can be evaluated against real mechanical-system behavior.
Localized decision processes on embedded robotic platforms.
Structured telemetry, task-state, and control-message exchange.
Centralized versus distributed control evaluated through defined performance metrics.
Simulation-informed development followed by deployment on physical platforms.
05 AI-Enabled Curriculum & OER Development
This is the educational-development component of the same 2026–2027 SUNY IITG collaboration: a new hybrid elective and modular open educational resources for broader SUNY access.
Embedded-system fundamentals, microcontroller architecture, digital and analog interfacing, sensor integration, actuator control, and systematic debugging.
Environment perception, path planning, obstacle avoidance, coordinated decision making, and networked embedded systems, supported by simulation and hands-on robotic platforms.
The course is being developed through the collaborative Buffalo State initiative using modular open educational resources and simulation tools to support hybrid delivery and broaden access to AI, robotics, and embedded-systems education.
06 Guiding Approach to Responsible AI
Across the AI-enabled design platform and curriculum work, I use AI as a support layer for reasoning and learning—not as a substitute for engineering judgment, faculty guidance, or physical validation.
07 AI in Piezoelectric & Smart Systems
My established work in piezoelectric systems, energy harvesting, sensing, and structural-health monitoring is increasingly intersecting with AI-assisted engineering design, intelligent sensing, data-driven interpretation, and learning-based control.
The two current energy-harvesting senior design projects provide real design cases for the AI-enabled engineering design platform.
Piezoelectric sensing, structural-health monitoring, and integrated smart systems provide a physical context for intelligent interpretation and decision support.
Student projects, AI-supported design methods, and smart-system research are being connected to future scholarly dissemination.
08 AI Scholarship & Editorial Leadership
Serving as Guest Editor for a Special Issue bringing together AI-assisted design, intelligent sensing, learning-based control, piezoelectric actuation, structural-health monitoring, and integrated smart systems.
Editorial activity complements ongoing work in AI-supported engineering design, robotics, embedded systems, piezoelectric devices, smart structures, and engineering education.
Current bridge: the recently published Actuators article on integrated piezoelectric crack detection and on-demand repair connects earlier smart-structure research with this broader AI-enabled systems direction.
09 Interdisciplinary AI Collaboration
The work brings together mechanical engineering, computer science, electrical engineering technology, robotics, and engineering education.
University at Buffalo · Computer Science & Engineering
Co-advising and collaboration on the AI-enabled engineering design platform.
SUNY Buffalo State University · Engineering Technology
Collaboration on AI-driven robotics, distributed embedded control, and engineering education.
Student researchers and design teams contribute across Mechanical Engineering, Computer Science & Engineering, Electrical Engineering Technology, Mechanical Engineering Technology, and related programs.