Pending WSCUC Approval
Build on today’s breakthroughs. Lead tomorrow’s innovation.
The Online METL curriculum combines deep technical specialization with leadership and management training—preparing you to solve complex problems and lead the teams tackling them. With 38 total units, the program is structured around four components that build expertise and impact.
Program Structure
Core Track Courses (16–20 units)
Dive deep into your chosen specialization—Semiconductor Technology, Quantum Information, AI & Machine Learning for Science and Technology, or Technology Management—through courses taught by renowned UCSB engineering faculty and industry experts.
Technology Leadership Courses (12 units)
Develop the strategic thinking, communication, and decision-making skills that move engineers into leadership roles. Taught by faculty in UCSB's Department of Technology Management, these courses bridge technical expertise with organizational impact.
Electives (2-8 units)
Explore modern and emerging topics while connecting with faculty, industry experts, and peers. These flexible learning experiences keep you at the forefront of innovation and expand your professional network.
Capstone Project (2 units)
Apply everything you've learned to a real-world challenge. Complete your capstone online, at your employer site, or at a UCSB lab facility during summer residency.
Online METL Courses at UCSB
All METL courses include a variety of asynchronous learning material that students complete on their own schedule. Certain select courses include a weekly live online session, in which students interact with UCSB faculty, industry professionals, and one another. The remaining courses each include regular instructor office hours, in which students can meet with course instructors to receive assistance and feedback on course material and assignments.
Technology Leadership Core Courses
Technology is a critical function in modern organizations, and managing it effectively is essential to strategic success. This course examines the distinct challenges and opportunities involved in formulating technology strategies that align with and drive overall business strategy. Students will explore key economic and strategic considerations that shape firms’ decisions on the development, commercialization, protection, and adoption of technological innovations.
This course examines the key processes and strategies for fostering and managing innovation within organizations. Students will learn how to cultivate innovation through the development of organizational cultures, structures, and incentives, grounded in relevant theories. In addition, the course explores the broader processes of innovation diffusion and the adoption of new ideas. Through case studies and practical exercises, students will gain insights into best practices for driving innovation at all levels. The course is designed to enhance students’ managerial effectiveness and equip them to advance their careers in dynamic, innovation-driven environments.
This course provides emerging inventors, entrepreneurs, and scientists with a working knowledge of intellectual property (patents, copyrights, trademarks, and trade secrets). Topics covered include the basic functions of patents, structure of patent applications, patent prosecution, patent coverage in foreign jurisdictions, trade-offs between patent and trade secret protection, branding and trademark strategy, and general patenting and IP strategies for businesses.
Digital disruption is reshaping entire industries in today’s global economy. In this dynamic environment, organizations must be agile and innovate with emerging technologies to generate new value propositions and create sustainable growth. Technology leaders need to build new perspectives and capabilities to navigate this changing digital landscape and ensure that their organizations remain competitive. In this course, students will learn about the challenges of digital transformation and how to best deal with this process to ensure the best outcome for their organizations.
Semiconductor Technology and Quantum Information Courses
Students will obtain an overview of important topics in semiconductor and quantum information technology, including technology needs for different applications, overview of materials and device technology for meeting these needs, and a history of quantum mechanics and solid-state physics and their development.
Students will obtain an overview of important topics in semiconductor and quantum information technology, including technology needs for different applications, overview of materials and device technology for meeting these needs, and a history of quantum mechanics and solid-state physics and their development.
Prerequisite: ENGR W 411 (may be taken concurrently) or consent of instructor
Students will learn the fundamentals of quantum mechanics, including introduction to core concepts of the Schrodinger wave equation in one dimension, and Dirac notation.
Prerequisite: ENGR W 412 (may be taken concurrently) or consent of instructor
Students will be introduced to advanced topics in quantum mechanics, including two-level systems and operator formalism.
Prerequisite: ENGR W 413 (may be taken concurrently) or consent of instructor
Students will learn concepts in semiconductor physics, including properties of materials, band theory, charge transport, absorption and emission, and tunneling.
Prerequisite: ENGR W 413 (may be taken concurrently) or consent of instructor
Students will learn the fundamental physics and operation of semiconductor electronic devices, including p-n homojunctions and heterojunctions, bipolar junction transistors (BJTs) and heterojunction bipolar transistors (HBTs), junction field-effect transistors (JFETs), metal semiconductor field-effect transistors (MESFETs), metal-oxide-semiconductor field-effect transistors (MOSFETs), and heterojunction field-effect transistors (HFETs).
Prerequisite: ENGR W 414 (may be taken concurrently) or consent of instructor
Students will learn techniques and tools used in the fabrication of semiconductor materials and devices, including epitaxy, lithography, oxidation, diffusion, ion implantation, etching, and thin film deposition.
Prerequisite: ENGR W 415 (may be taken concurrently) or consent of instructor
Students will learn the fundamental physics and operation of semiconductor optical devices, including LEDs, laser diodes, photodetectors, and modulators.
Prerequisite: ENGR W 416 (may be taken concurrently) or consent of instructor
Students will learn about physical layout of devices and integrated circuits, CAD tools for layout, and characterization techniques and test structures for analysis of materials and device performance.
Prerequisite: ENGR W 417 (may be taken concurrently) or consent of instructor
Students will learn about concerns in scaling and manufacturing, packaging of LEDs, LDs, and photodetectors, encapsulation schemes for circuits, and package characterization and analysis.
Prerequisite: ENGR W 418 (may be taken concurrently) or consent of instructor
Students will learn about data analysis and statistical methods for semiconductor technology, statistical process control, and design of experiment (DOE) Methodology.
Prerequisite: ENGR W 413 (may be taken concurrently) or consent of instructor
Students will learn important topics related to the quantum theory of measurement, including uncertainty relations, Bell’s theorem, state preparation, and state measurement.
Prerequisite: ENGR W 421 (may be taken concurrently) or consent of instructor
Students will learn the fundamentals of quantum information, including an introduction to qubits, superposition, and entanglement.
Prerequisite: ENGR W 422 (may be taken concurrently) or consent of instructor
Students will receive an overview of qubit platforms, including superconducting qubits, molecular qubits, cold atom systems, and spin qubits.
Prerequisite: ENGR W 423 (may be taken concurrently) or consent of instructor
Students will learn fundamental principles of quantum photonics, including networking and communications, quantum states of light, and photon generation/manipulation/detection.
Prerequisite: ENGR W 424 (may be taken concurrently) or consent of instructor
Students will learn the fundamentals of quantum sensing, including a high-level discussion of quantum vs. classical sensing, and modern implementations, including magnetometry, geodesy, and gravimetry (LIGO).
Prerequisite: ENGR W 425 (may be taken concurrently) or consent of instructor
Students will learn the fundamentals of quantum computing, architectures, and algorithms.
Prerequisite: ENGR W 426 (may be taken concurrently) or consent of instructor
Students will learn the fundamentals of quantum materials and about future directions in quantum information, including topics such as many-body states and nonlocal order as hosts for quantum information, and new materials with nontrivial band topologies and correlation effects.
AI and Machine Learning for Science and Technology Courses
Students will be introduced to AI methods and implementations across a variety of applications in scientific research and development.
Prerequisites: Basic working knowledge of and ability to code in at least one programming language.
This course covers multilayered neural networks, early models of perceptrons and associative memory, back-propagation learning, convolutional neural networks, recurrent neural networks, attention models, and applications to natural language processing and computer vision.
Prerequisites: Basic working knowledge of and ability to code in at least one programming language.
This course explores AI's capability to understand human language, focusing on machine learning algorithms and large language models. It covers language modeling, sentiment analysis, tagging, and machine translation, discussing lexical, syntactic, and semantic processing. Key models include Naïve Bayes, logistic regression, and neural networks like RNNs and CNNs.
Prerequisites: Basic working knowledge of and ability to code in at least one programming language.
This course explores the integration of multiple modalities in artificial intelligence, focusing on how different forms of data, such as text, images, audio, and video, can be combined to enhance machine understanding and decision-making. Students will examine techniques in multimodal representation learning, deep learning architectures, and applications in areas like natural language processing, computer vision, and robotics.
This course examines the ethical considerations surrounding artificial intelligence, emphasizing the societal, legal, and philosophical implications of AI technologies. Students will explore topics such as bias and fairness, accountability, privacy, transparency, and the impact of AI on employment and human rights.
This course explores the role of data in advancing scientific research and technological innovation. Students will examine how data is generated, analyzed, and interpreted across various fields. Key topics include data collection methodologies, statistical analysis, and machine learning techniques.
Technology Management Courses
Work in technology companies is teamwork. Whether it is a software development team developing a new app, an innovation team developing a new product, or an entrepreneurial team launching a new venture, working in a team is the way that difficult problems are solved in technology companies. In this course, we will discuss how to create, lead, and troubleshoot technology teams. We will begin by discussing what we know about teams generally before doing a deep dive into innovation teams in established companies and entrepreneurial teams in new ventures. Students will have a broad knowledge of the potential of teams and how to manage them following the completion of the course.
In this course, students will learn about advanced behavioral science theories and applications in management. Organizational behavior (OB) is an interdisciplinary field drawing from numerous disciplines including psychology, sociology, economics, organization theory, statistics, and many others. After an overview of OB and how to assess the value-added of "soft" management interventions, topics will include work motivation, work attitudes, newcomer socialization, "natural" and nominal work teams, leadership, decision making, and management of change.
In this course, students will learn about theoretical models and mathematical tools for quantitative analysis, statistics, decision theory, and management. They will also learn how various business situations are modeled and optimized effectively using mathematical modeling and quantitative techniques.
The age of intelligent technologies is transforming work in uncertain ways. This means success will favor those who can adapt. This course involves practical use of AI to explore research- backed approaches for developing technological skills in ourselves and others. Students will gain hands-on experience with cutting-edge AI tools while building the managerial capabilities needed to design work that optimally combines human and machine intelligence. Through projects and critical analysis, students will learn to lead in AI-rich environments where human-AI collaboration is standard. The focus is on innovative problem-solving and creating work processes that effectively integrate human capabilities with intelligent technologies to maximize both productivity and human potential.
This course covers the fundamentals of entrepreneurship, including product ideation, forming and building a startup team, market validation, business model development, go-to-market strategies, capital acquisition, and building competitive insulation.
Design thinking is a dynamic, user-centered approach to innovation that fosters creativity and leads to breakthrough solutions for complex challenges. In this course, students will explore the five core stages of design thinking: empathize, define, ideate, prototype, and test. Assessments will focus on developing key skills that are critical to the design thinking process, including deep observation to understand user behaviors and needs, conducting user interviews, synthesizing insights, generating creative solutions, building and refining prototypes, and performing usability tests to gather meaningful feedback. These assessments will ensure that students not only grasp the theoretical aspects of design thinking but also gain hands-on experience in applying these skills to real-world challenges, equipping them with valuable skills for innovation and problem-solving in any field.
Learn more. Connect with us.
There’s so much to know about UCSB’s Online METL program that you’re bound to have questions. We’re here to help. For information and support, contact the METL Admissions Counselor at metl@engineering.ucsb.edu. We’ll make sure you get all the answers.