Giacomo Nanchini, an associate professor of industrial and systems engineering, agrees that his field of research — quantum algorithms and optimization — is never boring. His work gives him a roadmap to help tackle critical challenges, in fields as diverse as quantum computing, routing and architecture.
Its software and algorithms have been used by one of Europe’s largest real-time navigation and motion information suites and by the IBM Watson Studio data science platform.
Nannicini comes to USC Viterbi School of Engineering This fall as a new faculty member for research and education, after many years working in the industry on optimization algorithms, including six years with IBM Research. Nannicini’s extensive background will strengthen Daniel C. Epstein Department of Industrial and Systems EngineeringOptimization ability and algorithms for quantum computing.
“It is very difficult to get bored of optimization. This is the kind of tools and frameworks that can be applied to many different problems,” said Nanishini.
From his early days as a Ph.D. The student, Nannicini was fascinated by optimization, and began working on large-scale shortest path problems – an algorithm designed to find the optimal path between points. This is the theoretical framework supported by map navigation technology such as Google Maps, taking into account many variables, such as terrain and traffic information, in order to find the most efficient path between two points.
Nanshini soon discovered that optimization challenges are everywhere, and his background can be applied to a range of areas, such as energy, transportation and supply chains.
“Another area I’ve worked in is improving architectural design. Some of the issues in architecture, especially building design, can be captured by performance metrics. These are generally energy performance metrics or different metrics about the quality of life inside a building, such as the luster inside and how the temperature changes during Daylight,” Nanishini said. “All of these things can be learned through simulation, using a complex simulation software that allows you to enter your parameters for your building design, windows, and location, and then you can see what the heat and energy consumption profile will look like.”
Some of Nannicini’s work relates to algorithms for finding optimal values for these input parameters.
These algorithms are useful for machine learning, too, said Nanishini. He said that training machine learning models to handle problems often requires a time-consuming process of figuring out the correct parameters to enter into the model.
“The way you define the parameters determines how well the model is trained,” Nanishini said. “While I was at IBM, I worked on algorithms and software to automate this process.”
Nanishini has a Ph.D. in Computer Science from École Polytechnique in France. He has held visiting positions at Carnegie Mellon’s Tepper School of Business and MIT’s Sloan School of Management, as well as an assistant professor role in systems engineering and design at Singapore University of Technology and Design.
He has been honored with a number of awards for his research, including the 2021 Bell-Orchard-Hayes Prize, the 2015 Robert Fore Prize, and the 2012 Glover-Klingman Prize.
Nannicini’s recent work within IBM’s Quantum Algorithms Group has focused on designing and understanding the power of quantum optimization algorithms.
When he began his role at USC Viterbi, Nannicini was keen to establish collaborations with experts working on quantum computing and optimization algorithms, both within USC and in the industry.
“USC has a strong group of people in other departments, such as electrical engineering and physics, but I think it’s a whole new area of industrial engineering and systems engineering,” Nanishini said. “I plan to start developing the classroom, and also see how I can connect collaborations from other departments and industry.”
Posted on September 26, 2022
Last updated on September 26, 2022