Engineering Intelligence (EI) – AI for Engineering and Engineering for AI

Engineering Intelligence (EI): AI for Engineering and Engineering for AI reflects the College of Engineering’s comprehensive approach to uniting artificial intelligence with the practice and infrastructure of modern engineering. AI for Engineering focuses on how data-driven methods and intelligent tools enhance design, prediction, optimization, and control in systems such as autonomous vehicles, smart grids, and resilient infrastructure. Engineering for AI emphasizes the complementary role of engineers in creating the physical and computational foundations that enable AI — including high-performance computing, secure data centers, advanced sensors, and energy-efficient hardware. Together, these efforts converge in Embodied Intelligence—the integration of sensing, computation, and actuation within systems that learn, adapt, and interact safely with the physical world. Through this initiative, the College seeks to prepare a new generation of engineers who can harness intelligence responsibly, advance the technologies that sustain it, and apply it to solve the most pressing scientific, environmental, and societal challenges.

Strategic Vision

Our vision is to unite the two dimensions of Engineering Intelligence to drive innovation, economic growth, and workforce development. By linking artificial intelligence with core engineering disciplines, we aim to accelerate technology transfer, fuel entrepreneurship, and strengthen industry–academic partnerships. This integrated approach uses AI to enhance the design and performance of complex systems while advancing the physical, computational, and cyber infrastructure that enables intelligent technologies. Through EI, the College will position engineering as both a driver and enabler of innovation, turning discovery into impact and preparing a workforce ready to lead in an AI-powered economy.

Through EI, we will:

Integrate AI into engineering education to produce graduates fluent in data-driven design, simulation, and decision-making, ready to lead in an AI-powered economy.
Advance interdisciplinary research and industry partnerships that apply AI to strengthen key sectors such as energy, mobility, manufacturing, healthcare, and infrastructure.
Engineer embodied intelligence through innovations in robotics, autonomous systems, smart materials, and human–machine interfaces—creating systems that sense, learn, and act safely in complex environments.
Build the enabling infrastructure for AI, including high-performance computing, secure data systems, intelligent sensors, and edge technologies that support real-time learning and control.
Promote innovation and entrepreneurship by translating discoveries into technologies, startups, and workforce opportunities that drive regional and national economic growth.
Embed ethics, security, and resilience across all applications to ensure intelligent systems remain transparent, trustworthy, and aligned with societal values.
Develop a sustainable talent pipeline by cultivating engineers who not only use AI responsibly but also engineer the physical, digital, and cyber systems that make intelligence real.

National Leadership

Engineering Intelligence (EI) positions the College of Engineering and the Tallahassee region as a growing hub for intelligent engineering, applied AI innovation, and workforce development. Faculty and students are collaborating with local industries, utilities, and government agencies to apply AI to challenges that directly affect the community—forecasting harmful algal blooms along Florida’s coast, improving traffic safety and transportation systems, enhancing energy grid resilience, and monitoring the health of regional infrastructure. Supported by advanced computing resources, interdisciplinary research, and experiential learning, these efforts are building a regional ecosystem where intelligent technologies drive environmental sustainability, economic growth, and quality of life while preparing a highly skilled workforce for Florida’s innovation economy.

Global Significance

Across the world, engineering is being redefined by AI-enabled transformation—in manufacturing, transportation, defense, healthcare, and infrastructure. Engineering Intelligence (EI) positions the College to contribute directly to these global trends by connecting digital intelligence with the physical systems that sustain modern economies. Faculty and students collaborate with international partners to advance embodied intelligence in robotics and autonomous systems, accelerate materials innovation, strengthen supply-chain resilience, and develop digital twins for critical infrastructure. These efforts address the central challenge noted by Moravec’s paradox—bridging the gap between what machines can compute and what they can physically perceive and do. Through these collaborations, the College is helping to shape a global framework for intelligent, secure, and resilient engineering.

Research Focus

Research in Engineering Intelligence spans the full spectrum of discovery, design, and deployment across multiple engineering domains:

AI-accelerated modeling and simulation using surrogate models, multiscale learning, and physics-informed neural networks to speed complex analyses and design.
Intelligent materials and manufacturing applying machine learning for defect detection, process optimization, and performance enhancement in additive manufacturing and advanced materials.
Autonomy and embodied intelligence integrating sensing, control, and adaptive learning in robotics, autonomous vehicles, and cyber-physical systems for safe, reliable operation.
Infrastructure resilience and smart systems improving transportation safety, monitoring structural health, and enhancing energy grid reliability and disaster response.
Environmental and natural systems leveraging predictive models and machine learning to forecast harmful algal blooms, improve water quality, and monitor ecological health.
Computational and data infrastructure developing high-performance computing, secure data networks, and edge platforms that support scalable, trustworthy AI.
Human–AI collaboration and decision systems combining human judgment with machine intelligence for design, manufacturing, and operational decision-making.
Innovation, industry engagement, and workforce development translating discoveries into technologies, startups, and experiential learning opportunities that strengthen the engineering economy.

Research Opportunities for Students

Students in the initiative engage directly in frontier research on AI-integrated engineering systems, participate in design challenges, internships with industry and labs, and collaborate on interdisciplinary teams that merge computational intelligence with hardware and physical models.

Course Offerings

In their future careers, students may work in areas that use or develop artificial intelligence and machine learning (AI/ML) methods and techniques for data analysis, code generation, prediction, optimization, and control – such as in the areas of robotics, self-driving cars, industrial automation, predictive maintenance, and smart grid management. At the FAMU-FSU College of Engineering, we believe it is necessary to equip our undergraduate students with the knowledge to use AI tools and methods for success in industry and delivering the next generation of solutions.

EEL 4347 - Introduction to Cybersecurity (3)

Prerequisites: COP 3014 and EE: 3705.

This course is an introduction to computer security: symmetric ciphers, public-key cryptosystems, digital signatures, hashes, message authentication codes, key management and distribution, authentication protocols, vulnerabilities and malware, access control, and network security.

EEE 4773 - Machine Learning (3)

Prerequisites: EEL 3135, MAS 3105, knowledge of Matlab and/or Python, and instructor permission.

This course is designed for senior undergraduate students from engineering disciplines and introduces students to the theory and engineering applications of machine learning including neural networks, fuzzy logic, genetic algorithms, supervised and unsupervised learning algorithms. This course places emphasis on engineering applications in controls, power systems, and robotics.

EEE 4872 - Artificial Intelligence (3)

Prerequisites: COP 4530 and EEL 4021.

This course instructs students in basic artificial intelligence (AI) techniques of search, machine learning, natural language 6 of 12processing, robotics, and image processing. In this course, potential/current limitations are analyzed; as are human interaction in a decision-making environment.

EGN 4060 - Big Data Analytics in Engineering (3)

Prerequisite: EGN 3443.

This course introduces fundamentals of big data analytics, including data loading, cleaning, transformation, visualization, predictive analytics, and data-driven decision making. An emphasis is placed on computer implementation using state-of-the-art data analytics language.

EIN 4936 - Introduction to Data Engineering (3)

Prerequisites: This course is intended for undergraduate students who have taken COP 3014 Programming I - C++ or its equivalent.

Introductory course for undergraduate students to master high-level programming and database query languages for data extraction, transformation, and loading (ETL). Emphasis on utilizing computers to perform ETL operations relevant to industrial and systems engineering. Covers fundamentals of programming using a high-level language (such as Python) and introduces SQL for database management.

ESI 4234 - Quality Control and Reliability (3)

Prerequisite: EGN 3443.

This course is an introduction to quality and reliability engineering. This course examines statistical quality control techniques, process capability analysis, design and analysis of experiments for quality and reliability improvement.

ESI 4682 - Introduction to Machine Learning (3)

Prerequisites: EGN 3443, ESI 3312, and MAS 3105.

This course is an introduction to machine learning geared toward advanced undergraduates or first-year graduate students.

ESI 4686 - Deep Learning in Practice (3)

Prerequisites: EGN 3443, ESI 3312C, and MAS 3105.

This course introduces three main neural networks (ANN, CNN, and RNN) and the realization in Python. Students learn the basics such as forward propagation, backward propagation, and gradient descent algorithms, as well as up-to-date neural network projects like (YOLO, VGG19, Resnet50, etc.).

Foundational courses oﬀered at the college that could prepare students for later courses in machine learning include:

EEE 4510 - Digital Signal Processing (3)

Prerequisite: EEL 3135.

This course covers topics such as sinusoids, periodic signals, and Fourier spectra. Sampling of continuous-time signals, aliasing. Impulse response of linear, discrete-time systems, convolution. FIR filters and implementation. Frequency response of FIR filters. Z-transforms, IIR filters, poles and zeros, frequency response. Realization of IIR filters. Discrete Fourier transform and the FFT algorithm. MATLAB exercises are assigned.

EEL 4759 - Digital Image Processing (3)

Prerequisite: MAP 2302.

This course is an introduction to image processing techniques, including theoretical development, analysis, and practical implementation. A project that includes implementation grounds the successful student in current engineering practice.

Faculty Research

Engineering Intelligence (EI): AI for Engineering and Engineering for AI is a college-wide effort involving faculty from multiple departments at the FAMU-FSU College of Engineering. The following list highlights faculty actively engaged in EI/AI research (not a complete list).