Richard D. Braatz
Richard D. Braatz

Lead Instructor

Bioprocess Data Analytics and Machine Learning
Professional Certificate Program in Biomanufacturing
Professional Certificate Program in Machine Learning & Artificial Intelligence

Participating Instructor

Technology Leadership Program

Dr. Richard D. Braatz is the Edwin R. Gilliland Professor of Chemical Engineering at MIT, where he conducts research into advanced biopharmaceutical manufacturing systems. In this role, he leads process data analytics, mechanistic modeling, and control systems for several projects on campus, including those focused on monoclonal antibody, viral vaccine, and gene therapy manufacturing.

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Get more from your bioprocess data. In this intensive, four-day course, designed specifically for scientists and engineers in the biopharma industry, you’ll explore best practices for translating biopharmaceutical manufacturing data into reliable models and better decisions. Working with academic and industry experts, you’ll acquire strategies for improving manufacturing accuracy, enhancing regulatory efficiency, and refining bioprocess operations.

La toma de decisiones condiciona la evolución de todas las empresas y los responsables deben ser capaces de elegir opciones de la forma más segura posible, después de eliminar el azar en el proceso. El machine learning, una vertiente de la inteligencia artificial, ha nacido para responder a esa necesidad, y se ha convirtiendo en una herramienta fundamental para la toma de decisiones fiables a través del análisis automatizado de grandes cantidades de datos y hechos.

Learn to leverage the latest deep learning advancements to create innovative solutions and solve your organization’s pressing challenges. Over the course of two intensive days, you’ll explore actionable strategies for anticipating and addressing critical issues that can impact classification performance and other hurdles, and master cutting-edge machine learning tools that process data in different modalities, including text, images, and graphs. 

The factory of the future is here—and MIT’s research and applications are leading the way in enabling this transformation. In our interactive 10-week course, you’ll draw on MIT’s more than 100 years of university-industry collaboration to learn how sensors, software, and systems can create a smart enterprise at any scale. From modeling to manufacturing systems to advanced data analytics, you’ll acquire the smart technology strategies you need to get—and stay—ahead. 

Decision-making dictates every organization’s direction and development: Those responsible for making those decisions must be empowered to do so confidently. Machine Learning, is becoming a fundamental tool for making sound decisions by analyzing large quantities of data and events. Its objective is to reduce areas of uncertainty and arbitrariness through automatic learning and efficient data analysis. 

Ready to face the revolutions in transportation systems and discover how disruptive innovations are reshaping the mobility sector? In this immersive five-day course, you will learn to model, analyze and optimize transportation systems using the latest research from MIT and beyond, delving into demand and network modelling, artificial intelligence, simulation, optimization and control. Innovative methods are explored for emerging systems that are still in an early stage of development and deployment, such as user-centric new smart mobility services, automated and AI-driven vehicles and alternative energy vectors for decarbonizing transportation. Through real-world case studies, transportation researchers and professionals from urban and mobility organizations, the automotive industry, logistics companies, and other transportation sectors can gain actionable insights to address current and future transportation challenges.
What would Artificial General Intelligence look like if its first breakthrough were not in language, but in the invention of matter? Join the frontier of superintelligence applied to agentic materials discovery. In this condensed four-day course, you will move beyond static design to master autonomous AI workflows. Through hands-on clinics, you will build multi-agent systems that do not merely predict material properties, but reason, plan, and invent next-generation smart materials - integrating large-scale computational modeling with generative AI to solve complex engineering challenges across scales, from atoms to systems, from concept to physical realization.

Improve workplace performance and achieve group objectives through people analytics—the process of using behavioral data to understand and manage organizations. Under the instruction of two of the field’s pioneering experts, you’ll learn how to use your company’s data to increase the speed and quality of your decision-making, identify organizational problems, analyze behavioral metrics, and enhance company outcomes.

Acquire the fundamental machine learning expertise you need to immediately implement new strategies for driving value in your organization. This foundational course covers essential concepts and methods in machine learning, providing the basic building blocks required to solve real tasks. You’ll also gain a deeper understanding of the strengths and weaknesses of learning algorithms, and assess which types of methods are likely to be useful for a given class of problems.
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