Nikos Karanikolas — Academic Profile

Teaching

Courses

Communication Algorithms

Adjunct Lecturer · Computer Engineering & Informatics, University of Patras (2019 – Present)

Course Materials

Cryptography

Adjunct Lecturer · Computer Engineering & Informatics, University of Patras (2019 – Present)

Course Materials

Experience

Academic Appointments

2017 – Present

Department of Computer Engineering and Informatics, University of Patras, Greece.
Subject: Computational Social Choice and Argumentation for Collective Decision-making.
Supervisor: Professor Christos Kaklamanis

November 2016 – October 2017

GraphIK, LIRMM (University of Montpellier) and INRA, France.
Subject: Argumentation and Computational Social Choice in Agricultural applications.
Collaborators: Patrice Buche, Madalina Croitoru, Rallou Thomopoulos, Pierre Bisquert.

October 2014 – October 2015

Laboratoire d'Informatique de Grenoble, France.
Subject: Interactive visualization of voting procedures.
Supervisors: Renaud Blanch and Sylvain Bouveret.

Education

Academic Degrees

June 2014 · University of Patras

Thesis: Computational Aspects in Social Choice (in Greek)
Advisor: Professor Christos Kaklamanis

January 2009 · University of Patras

Thesis: Computational Aspects in Social Choice: A study of Dodgson elections
Advisor: Professor Christos Kaklamanis

2006 · University of Patras

Thesis: Routing algorithms in P2P Networks
Advisor: Professor Christos Kaklamanis

Research

Publications

1

I. Caragiannis, J. A. Covey, M. Feldman, C. M. Homan, C. Kaklamanis, N. Karanikolas, A. D. Procaccia, and J. S. Rosenschein. On the approximability of Dodgson and Young elections.

Artificial Intelligence, 187–188, pp. 31–51, 2012.

2

I. Caragiannis, C. Kaklamanis, N. Karanikolas, and A. D. Procaccia. Socially desirable approximations for Dodgson's voting rule.

ACM Transactions on Algorithms, Vol. 10, Issue 2, February 2014.

3

N. Karanikolas, P. Bisquert, P. Buche, C. Kaklamanis, and R. Thomopoulos. A Decision Support Tool for Agricultural Applications Based on Computational Social Choice and Argumentation.

International Journal of Agricultural and Environmental Information Systems (IJAEIS), Vol. 9, Issue 3, pp. 54–73, 2018.

4

P. Bisquert, M. Croitoru, C. Kaklamanis and N. Karanikolas. A Decision-Making approach where Argumentation added value tackles Social Choice deficiencies.

Progress in Artificial Intelligence, Vol. 8, Issue 2, pp. 229–239, June 2019.

5

I. Caragiannis, C. Kaklamanis, N. Karanikolas, G. A. Krimpas. Evaluating Approval-Based Multiwinner Voting in Terms of Robustness to Noise.

Journal of Autonomous Agents and Multi-Agent Systems (JAAMAS), Vol. 36, Issue 1, 2022.

6

N. Karanikolas, C. Kaklamanis, and S. Nikolopoulos. AQUA: A blockchain based multi-winner e-voting system.

Technium, vol. 11, pp. 32–43, July 2023.

1

I. Caragiannis et al. On the approximability of Dodgson and Young elections.

SODA 2009, pp. 1058–1067.

2

I. Caragiannis, C. Kaklamanis, N. Karanikolas, A. D. Procaccia. Socially desirable approximations for Dodgson voting rule.

EC 2010, pp. 253–262.

3

N. Karanikolas, R. Blanch and S. Bouveret. Edge-Compressed Majority Graph: Where Social Choice Meets Information Visualization.

COMSOC 2016.

4

P. Bisquert, P. Buche, M. Croitoru, N. Karanikolas and R. Thomopoulos. Selection of agro-waste valorisation routes based on a computational social choice and argumentation decision support tool.

EFITA WCCA 2017 (Extended Abstract).

5

P. Bisquert, M. Croitoru, N. Karanikolas. A Qualitative Decision-Making Approach Overlapping Argumentation and Social Choice.

ADT 2017, pp. 344–349.

6

N. Karanikolas et al. Multi-Criteria Decision Making with Existential Rules using Repair Techniques.

AI-2018 (SGAI), pp. 177–183.

7

N. Karanikolas, P. Bisquert and C. Kaklamanis. A Voting Argumentation Framework: Considering the Reasoning behind Preferences.

ICAART 2019, Vol. 1, pp. 42–53. Best Paper Runner-up

8

I. Caragiannis, C. Kaklamanis, N. Karanikolas and G. A. Krimpas. Evaluating Approval-Based Multiwinner Voting in Terms of Robustness to Noise.