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Administrative data

Boi Faltings

boi.faltings@epfl.ch https://www.epfl.ch/labs/lia/people/boi-faltings/

Expertise

Artificial intelligence:
Constraint programming, Multi-agent systems, Computational game theory
Applications: Electronic commerce, Truthful information elicitation, Multi-agent coordination, Privacy-preserving AI

Mission

AI that empowers humans to be smarter

Current work

Some topics of recent interest include:
  • Game-theoretic techniques for truthful information elicitation
  • Adaptive software agents and multi-agent systems
  • Privacy-preserving Artificial Intelligence
  • Commonsens causality in large language models
  • Continual machine learning
Boi Faltings is a full professor of computer science and heads the Artificial Intelligence Laboratory,. He has held visiting positions at NEC Research Institute, Stanford University and the HongKong University of Science and Technology. He has co-founded 6 companies using AI for e-commerce and computer security and acted as advisor to several other companies. Prof. Faltings has published over 300 refereed papers and graduated over 40 Ph.D. students, several of which have won national and international awards. He is a fellow of the European Coordinating Committee for Artificial Intelligence and a fellow of the Association for Advancement of Artificial Intelligence (AAAI). He holds a Diploma from ETH Zurich and a Ph.D. from the University of Illinois at Urbana-Champaign.

CONTACT

boi.faltings@epfl.ch, boi@faltings.org

Selected publications

Mechanisms for Making Crowds Truthful

R. Jurca and B. Faltings
Published in Journal of Artificial Intelligence Research (JAIR), 34, 2009, pp. 209-253. in

Truthful Opinions from the Crowds

R. Jurca and B. Faltings
Published in Proceeddings of the 2008 ACM Conference on Electronic Commerce, July, 2008, pp. 119-128 in

MB-DPOP: A New Memory-Bounded Algorithm for Distributed Optimization

A. Petcu and B. Faltings
Published in Proceedings of the 20th International Joint Conference on Artificial Intelligence, IJCAI-07, Hyderabad, India, Jan, 2007, pp. 1452-1457 in

Preference-based Search using Example-Critiquing with Suggestions

P. Viappiani, B. Faltings and P. Pu
Published in Journal of Artificial Intelligence Research (JAIR), 27, 2006, pp. 465-503 in

Distributed Constraint Programming

B. Faltings
Published in In Handbook of Constraint Programming, Foundations of Artificial Intelligence, Francesca Rossi, Peter van Beek, Toby Walsh (ed.), 2006, pp. 699-729 in

Resource Allocation in Communication Networks Using Abstraction and Constraint Satisfaction

C. Frei, B. Faltings, and M. Hamdi
Published in IEEE Journal on Selected Areas in Communication, 23(2), February, 2005, pp. 304-320 in

Open Constraint Programming

B. Faltings and S. Macho-Gonzalez
Published in Artificial Intelligence, 161 (1-2), January, 2005, pp. 181-208 in

Infoscience

Teaching & PhD

Past EPFL PhD Students

Diego Matteo Antognini (2022), Marina Boia (2016), Robert Bolognesi (1999), Monique Calisti (2001), Ludek Cigler (2013), Ion Constantinescu (2006), Shaobo Cui (2025), Panayiotis Danassis (2022), Carlos Eisenberg (2003), Christian Frei (2000), Florent Frédéric Garcin (2014), Esther Gelle (1998), Naman Goel (2020), Radu Jurca (2007), Igor Kulev (2020), Thomas Léauté (2011), Beat Liver (1996), Claudio Lottaz (2000), Monika Lundell (1996), Santiago Macho Gonzalez (2004), Andreas Margelisch (1999), Fei Mi (2021), Nicoleta Neagu (2004), Quang Huy Nguyen (2008), Brammert Ottens (2012), Adrian Petcu (2007), Li Pu (2013), Goran Radanovic (2016), Adam Julian Richardson (2024), Jamila Sam (1995), Eric Sauthier (1996), Vincent Jean Fabrice Schickel (2007), Marius-Calin Silaghi (2002), Marc Torrens Arnal (2002), Aleksei Triastcyn (2020), Paolo Viappiani (2007), Rainer Weigel (1998), Steven Nicolas Willmott (2001)

Past EPFL PhD Students as codirector

Paul-Arthur Dreyfus (2022), Paul David Dütting (2013), Valentina Sintsova (2016)

Courses

EECS Seminar: Advanced Topics in Machine Learning

ENG-704

Students learn about advanced topics in machine learning, artificial intelligence, optimization, and data science. Students also learn to interact with scientific work, analyze and understand strengths and weaknesses of scientific arguments of both theoretical and experimental results.