Giacomo Nannicini, 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 for tackling critical challenges in areas as diverse as quantum computing, routing, and architecture.
Its software and algorithms have been used by one of Europe’s largest real-time traffic and mobility information groups and in the IBM Watson Studio data science platform.
Nannicini joins the USC Viterbi School of Engineering this fall as a new member of the research and teaching faculty after spending many years working on optimization algorithms in industry, including six years at IBM Research. Nannicini’s extensive background will strengthen the Daniel J. Epstein Department of Industrial and Systems Engineering’s capacity in optimization and algorithms for quantum computing.
“It is very difficult to get bored with optimization. These are the kind of tools and frameworks that can be applied to many different problems,” Nannicini said.
From his early days as a Ph.D. Fascinated by optimization, student Nannicini began working on large-scale shortest-path problems – in which an algorithm was developed to find the optimal path between points. This is the theoretical framework underlying map navigation technology such as Google Maps, taking into account many variables such as terrain and traffic information to find the most efficient path between two points.
Nannicini quickly realized that optimization challenges were everywhere, and his background could be applied to a range of fields including energy, transportation and supply chains.
“Another area I have worked in is architectural design optimization. Some problems in architecture, especially in building design, can be informed by performance metrics. These are generally energy efficiency metrics or various metrics related to the quality of life in a building such as: B. the brightness inside and how the heat changes throughout the day,” said Nannicini. “All of these things can be figured out through simulation, with complex simulation software where you can put in your parameters for your building design, your windows and your location, and then you can see what the heat profile and energy consumption will be like.”
Some of Nannicini’s work involves algorithms to find optimal values for these input parameters.
Nannicini said these algorithms are also useful for machine learning. He said that training machine learning models to tackle problems often involves a time-consuming process of figuring out the right parameters to input into a model.
“The way you set the parameters determines how well you can train the model,” Nannicini said. “During my time at IBM, I worked on algorithms and software to automate this process.”
Nannicini holds a Ph.D. in Computer Science from the École Polytechnique in France. He has held Visiting Positions at the Tepper School of Business at Carnegie Mellon University and the Sloan School of Management at MIT, as well as an Assistant Professorship in the Engineering Systems and Design Pillar at the Singapore University of Technology and Design.
He has received a number of awards for his research, including the 2021 Beale Orchard Hays Prize, the 2015 Robert Faure Prize and the 2012 Glover Klingman Prize.
Nannicini’s recent work in IBM’s Quantum Algorithms group has focused on developing and understanding the power of quantum optimization algorithms.
Starting at USC Viterbi, Nannicini strives to build collaborations with experts working in the field of quantum computing and optimization algorithms both within USC and in the industry.
“USC has a strong group of people in other departments like electrical engineering and physics, but I think it’s a fairly new area for industrial and systems engineering,” Nannicini said. “I plan to start developing classes and see how I can incorporate collaborations with other departments and industry.”
Published on 09/26/2022
Last updated on September 26, 2022