Marco Claudio Campi
Born
Tradate, Italy
Alma materPolitecnico di Milano
Known forScenario optimization, Virtual Reference Feedback Tuning
AwardsGeorge S. Axelby Award, IEEE Fellow, IFAC Fellow
Scientific career
FieldsInductive reasoning, Statistical learning theory, Data science, Control engineering
InstitutionsUniversity of Brescia
Doctoral studentsMaria Prandini, Simone Garatti, Algo Carè
Websitemarco-campi.unibs.it

Marco Claudio Campi is an engineer and a mathematician who specializes in data science and inductive methods. He holds a permanent appointment with the University of Brescia, Italy, while also collaborating with various research institutions, universities and NASA. Since 2012, he has been a Fellow of the Institute of Electrical and Electronics Engineers (IEEE) and since 2020 a Fellow of the International Federation of Automatic Control.

Academic research

Campi is a co-creator of the scenario approach, which provides solid mathematical foundation to observation-driven decision-making based on consistent rules.[1][2] His early contributions in this area demonstrated that, within a convex optimization framework, bounds to the probability of invalidating a decision can be directly determined form the dimensionality of the optimization domain.[3][4] Subsequent advancements extended this result to schemes permitting the exclusion of certain observations to enhance decision-related costs.[5] More recent work has revealed a deep-seated connection between the concept of complexity of a decision (precisely defined in his papers) and its reliability.[6][7] The scenario approach has found practical applications across various domains, including control theory,[8] portfolio optimization[9] and statistical classification.[10]

Campi is also the inventor of the Virtual reference Feedback Tuning (VRFT), an approach to design controllers using batches of data collected from a plant.[11][12]

Awards and honors

References

  1. ↑ M.C. Campi and S. Garatti. Introduction to the Scenario Approach., MOS-SIAM Series on Optimization, 2018.
  2. ↑ M.C. Campi, A. Carè and S. Garatti. "The scenario approach: A tool at the service of data-driven decision making, Annual Reviews in Control, 52, 1-17, 2021.
  3. ↑ M.C. Campi and S. Garatti. The exact feasibility of randomized solutions of uncertain convex programs. SIAM J. on Optimization, 19(3), 1211-1230, 2008.
  4. ↑ A. Carè, S. Garatti and M.C. Campi. Scenario min-max optimization and the risk of empirical costs. SIAM Journal on Optimization, 25(4), 2061-2080, 2015.
  5. ↑ M.C. Campi and S. Garatti. A sampling-and-discarding approach to chance-constrained optimization: Feasibility and optimality, Journal of Optimization Theory and Applications, 148(2), 257-280, 2011.
  6. ↑ M.C. Campi and S. Garatti. Wait-and-judge scenario optimization. Mathematical Programming, 16, 481-499, 2019.
  7. ↑ S. Garatti and M.C. Campi. Risk and complexity in scenario optimization, Mathematical Programming, 191(1), 243-279, 2022.
  8. ↑ G. Calafiore and M.C. Campi. The scenario approach to robust control design, IEEE Transactions on Automatic Control, 51(5), 742-753, 2006.
  9. ↑ G. Arici, M.C. Campi, A. Carè, M. Dalai and F.A. Ramponi. A theory of the risk for empirical CvaR with application to portfolio selection, J. Syst. Sci. Complexity, 34(5), 1879-1894, 2021.
  10. ↑ M.C. Campi and S. Garatti. A theory of the risk for optimization with relaxation and its application to support vector machines, Journal of Machine Learning Research, 22(288), 1-38, 2021.
  11. ↑ M. C. Campi, A. Lecchini and S.M. Savaresi. Virtual reference feedback tuning: A direct method for the design of feedback controllers, Automatica, 38, 1337-1346, 2002.
  12. ↑ S. Formentin, M.C. Campi, A. Carè and S.M. Savaresi. Deterministic continuous-time virtual reference feedback tuning (VRFT) with application to PID design, Systems & Control Letters, 127, 25-34, 2019.
  • Interview with Marco C. Campi
  • Introduction to the Scenario Approach - four talks by M.C. Campi: talk 1/4 ; talk 2/4 ; talk 3/4 ; talk 4/4
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