Anthony Vetro

  • President and CEO, Mitsubishi Electric Research Laboratories (MERL)


Mitsubishi Electric Research Laboratories (MERL) is the North American research arm of Mitsubishi Electric Corporation, one of the world’s largest companies. Founded in 1921, the tech giant has a long string of firsts to its credit, including introducing the first plasma television to the U.S. 

Many of the technological advancements made by the company are due to work carried out by MERL, whose mission is to generate new technology and intellectual property in vital areas and to create new and improved products in Mitsubishi Electric’s sectors such as automotive equipment and energy systems.

Now, that mission is being overseen by an NYU Tandon alum, Anthony Vetro, who earned his Ph.D. in Electrical Engineering from the school in 2001. (It was Mitsubishi, he explains, who funded his doctoral degree after he joined the company.) Before ascending to the post of President and CEO, Vetro held a variety of roles, starting in 1996 when he joined as a member of the technical staff, conducting research on multimedia signal processing, with a focus on video compression. He became deeply involved in helping set industry standards for MPEG compression formats and much of his work was ultimately incorporated into such products as digital television receivers and displays, surveillance and camera monitoring systems, automotive equipment, and satellite imaging systems. 

Named an IEEE Fellow in 2011 for his contributions to video coding, three-dimensional television, and multimedia adaptation, Vetro now has dozens of Ph.D.-level researchers working under his guidance, and among their top priorities is to investigate how the company can best leverage the power of artificial intelligence in trustworthy, transparent ways. 

Ensuring reliability and explainability is especially needed for deployment in real-world systems, especially safety-critical systems, he explains. As such, one area that Vetro is excited about is the use of physical models to ground machine learning frameworks. He says, “We have a very good understanding of the mechanics of a moving car, fluid dynamics that are relevant for air condition systems, and wave propagation in radar imaging systems. It is important to leverage these models to ensure that the machine learning output is consistent with what we know about the physics of different systems.” These technologies will greatly support sustainability initiatives that the company is pursuing, including products that support decarbonization and sustainable use and reuse of resources.

In an effort to showcase a more intuitive interaction system, MERL created an automotive AI system capable of reacting to the physical environment; “When you’re sitting next to the driver, you don’t say, ‘Turn right in 20 meters,’” Vetro explained in an 2022 article for IEEE Spectrum.  “Instead, you’ll say, ‘Turn at that Starbucks on the corner’ or ‘Follow that black car turning right.’ Vetro predicts: “Three to five years from now, cars will be equipped with scene-aware virtual assistants that engage drivers and passengers in conversations about surrounding places and events.”

While the idea for a more intelligent navigation system and autonomous driving systems is not entirely new, many of the technologies required to bring that vision to fruition, were not yet sufficiently sophisticated; however, MERL scientists bided their time, intently researching the most challenging aspects of these system from robust object tracking and localization methods to path planning and control techniques–all of which were rapidly advancing thanks to machine learning. Vetro notes, “MERL has delivered key components of vehicle safety and autonomous driving systems in recent years and offers a strong pipeline of new technology that will propel the industry further. Our work in this area is something that we are quite proud of.”

Vetro envisions a future in which humans and machines interact naturally, not only through language based systems but also through visual demonstration and prediction of human movements and intentions. This will have broad applicability in numerous business sectors including automation, home and building systems, automotive, and healthcare. “Achieving the type of fluid interactions between robots and humans as portrayed on TV or in movies may still be some distance off. But now, it’s at least visible on the horizon.”

Professor Yao Wang, Vetro’s thesis advisor, is confident that and much more is on the horizon with Vetro at the helm of MERL. “It has been truly gratifying for me to play a small part in Anthony’s incredible professional growth,” she says. “I got to watch him as an undergraduate participating in a doctoral candidate’s research and as a Ph.D. conducting his own research, which won him an award for best thesis in his cohort. He went on to play a major leadership role in media compression standardization efforts and in the technical community at large, and I have followed with pride his ascension to his current role.” She concluded, “I recall speaking to him back when he had just begun his doctoral studies, and he expressed hopes of becoming a research manager one day; that was quite a noteworthy ambition since most Ph.D. candidates did not set their sights quite that high, but as we now see, he made it there — and quite a bit beyond! In my mind, it’s equally noteworthy that he has remained a humble, kind person despite his meteoric success; he truly exemplifies what it means to be Tandon Made.”