To Manage Engineers, You Must Be a Manager and an Engineer

Professional Image of Carlo Lipizzi

With a Ph.D. in Systems Engineering, Carlo Lipizzi understands the theory and technology underlying modern computing, particularly in his specialization areas of machine learning and natural language processing. As a Stevens Institute of Technology instructor, Lipizzi also recognizes the need to highlight the practical applications of these technologies. That's what he does in his Online Master of Engineering in Engineering Management classes.

Lipizzi draws on 25 years of professional experience as a working engineer, entrepreneur, and industry consultant to bring the theoretical to life. An Executive MBA from IMD Business School in Lausanne, Switzerland means he has the business acumen to teach engineering management as well as applied engineering.

Lipizzi recently shared his insights on the online program: its curriculum, its impact on students' careers, and the type of student most likely to succeed here.

To be a successful engineering manager, which skill set is more critical: management or engineering?

Engineering management is a combination of skills. You cannot be a successful engineering manager without management skills. However, you cannot manage a highly skilled team and complex engineering projects without knowing the language of engineering.

We have courses in leadership. We teach people to lead and manage projects, but also how to identify underlying engineering problems. Engineering can be complex, and, as a manager, you are in charge of the project, so you need to understand everything down to the details.

On what core skills does the StevensOnline Master of Engineering in Engineering Management focus?

The program targets three areas. One is the foundations of management: things like project management, financial management — the skills you need to manage any problem or project. The second is data. When you work in complex environments, you need to rely on data. We teach students how to handle data and organize, represent, and draw insights from it.

The third is complexity. At the School of Systems & Enterprises, we address problems through a lens of complexity. If you build an engine or an aircraft or an information system, those are complex environments. Within the School of Systems & Enterprises, we have two divisions: Systems Engineering and Software Engineering. We want students to leverage all these assets because they are essential to success in engineering management.

Who is the ideal candidate for this program?

Someone with some experience in the field. They have worked for a few years and realize they need something extra. They need to formalize their knowledge to move forward in their career.

That said, we have students who enter with minimal or no experience other than a background in quantitative applications. That's fine because, at the end of the day, engineering is about measuring things. Newton once said, "If you don't measure, you don't know." That is the reality.

It's not just a matter of measuring. You need to have a scope for the measurement. Why are you measuring the size of, let's say, a screen in a car? Because the screen has to fit in a dashboard, and the dashboard has to be in a car, and the car has to be compliant with codes, and so on.

That's the point. Either you need to have some experience in an engineering-related area and you want to move forward, or you need to have some quantitative background in statistics and mathematics. That's what you bring to the program and what you want to leverage to become a more successful engineering manager.

Why should students choose this program over an MBA?

We get this question quite often. I generally answer with an example from the movie industry. When you are a director of a traditional Hollywood movie, you need management skills because it's a complex environment. There are a lot of parts: financial aspects, different personalities to manage, deadlines to meet. These are skills you can learn in an MBA program.

Now, if you are directing a Pixar movie, you need different skills, and some of them have to be technology-related because technology is an essential component. You need to know the refresh rate of screens, the rendering efficiency of the machines you use, and which software works best to extrapolate images. You need traditional management skills — budgeting, people management, all of that — but on top of that, there's a technical component you don't need for live-action movies. That's why you get an engineering management degree instead of an MBA.

Your professional career includes over 25 years of consulting and leadership work. How do you incorporate that experience into your teaching?

In my opinion, my experience is absolutely essential to the way I teach. At the School of Systems & Enterprises, we don't teach just theory: We teach theory to be used in a given field and in given applications. The majority of the students we have are not looking for a career in academia. They are looking for a better job in their current industry. Having someone who can give them examples from real life that I experienced firsthand is quite helpful to them.

That is all within the context of rigorous academics. I have a Ph.D., which I completed relatively recently. After 25 years in industry, at age 50, I went back to academia and earned my doctorate. I'm up-to-date on the latest developments in technology and engineering, and I bring that to the classroom. Students appreciate that.

My real-world experience also includes my current research, which is very oriented toward product delivery. I bring my consulting background to my research projects, and the people who work with me in my projects visit my class and present what they do. I try to make my class very interactive and very example-driven, with many real-life cases. I think that is what my students need.

You conduct a lot of research in technologies that have undergone paradigm shifts in the last decade or so. Please break out the crystal ball and tell us: have we seen the most significant changes in these fields, or does the future hold even bigger surprises and opportunities?

I can give you my opinion, that's all! I think AI and machine learning are changing very rapidly. I was fortunate to start my career working in AI machine learning. In 1986, I was with an information technology group in Europe that worked closely with MIT. My company was developing natural language processing and also expert/knowledge-based systems. My field was knowledge-based systems, which have changed dramatically over time.

These changes stem from two causes. First, we have better computing now: more powerful processors, larger storage capacity, and better software. The systems I used then are long obsolete; they were quite low-level compared to what we have now. We didn't have libraries to draw from when we wrote algorithms. We had to write it from the tiniest portion on up. Writing algorithms is still a lot of work, but now you can focus more of that effort at a higher level.

The second cause is the availability of data. We never had it before, and now we have so much. No matter how intelligent a system is, it's still data-driven. If you don't have data, you can't do forecasts. Take a facial recognition algorithm, for example. You may have the most brilliant algorithm, but it won't recognize faces without a sufficient number of cases. You need the data. That's why countries like China and cities like London have higher success rates in recognizing faces: they have more extensive data sets.

The combination of the two — computing power and data — that's what's driving progress. Everything we do now in our daily lives is digitally coded with TCP IP, the same protocol we use for the internet. That's the game-changer in AI. The algorithms are not really that much different than the ones we used in 1986.

What advice would you give a mid-career engineer looking to advance?

Don't take shortcuts. They don't pay in the longer term. Come up with a career strategy and work hard to pursue it.

I recently participated in a LinkedIn discussion where some participants argued that formal education is becoming non-essential because you can learn everything for free. My counterargument was this: would you get surgery from someone self-taught from YouTube videos?

Engineering management, data science and data analytics are not any easier than surgery. Yes, you can find all the information online. By the same reasoning, libraries have existed since ancient Egypt, almost 3,000 years ago. Just because all the knowledge is available doesn't mean you can learn it all or discriminate between what's valuable and what isn't.

You need someone to guide you. Find a mentor. That can be a formal education, which is something I would definitely recommend.

Whether you're a mid-level engineer looking to advance to a leadership role or a mathematics and statistics expert looking for exciting opportunities, the 30-credit hour Master of Engineering in Engineering Management from StevensOnline can help you achieve your career goals. Students can choose between specializations in Managerial Analytics and Supply Chain and Logistical Management. Both options include coursework in engineering economics, project management, operations research, modeling and simulation, risk analysis, and informatics.

Regardless of the track you choose, you'll receive a world-class education in business management and engineering from an internationally renowned technological institution. US News & World Report ranks the school's online programs highly in multiple technology, engineering, and business fields. Business Insider ranks the school among its top 25 schools at which students "get the most for their money."

You can apply for admission online today to jumpstart your engineering management career on the next program start date.