Tareq Si Salem

Tareq Si Salem, Ph.D.

Paris, France

About Me

Tareq Si Salem (طارق سي سالم, ⵜⴰⵔⵉⵇ ⵙⵉ ⵙⴰⵍⴻⵎ) is a researcher at Huawei Paris Research Center. He received his Ph.D. (2022) in Computer Science from Université Côte d'Azur and Inria Sophia Antipolis. He was a Postdoctoral Research Associate (2024) at Northeastern University, Boston, and a visiting researcher (2022) at Delft University of Technology.

His research interests lie at the intersection of machine learning and mathematical modeling, focusing on learning under system constraints such as privacy, safety, fairness, memory, and communication. His work has been published in top venues including IEEE/ACM ToN, ACM SIGMETRICS, AAAI, and IEEE INFOCOM, and received the Best Paper Award at ITC'33 in 2021.

Recent News

Selected Publications

Bandits in Flux: Dynamic Regret under Adversarial Constraints

Author: Tareq Si Salem

International Conferences on Artificial Intelligence and Statistics (AISTATS), 2026

machine learning multi-armed bandits online learning dynamic regret non-stationary environments

Rebuttal

Goal-Oriented Time-Series Forecasting: Foundation Framework Design

Authors: Luca-Andrei Fechete, Mohamed Sana, Fadhel Ayed, Nicola Piovesan, Wenjie Li, Antonio De Domenico, Tareq Si Salem (lead researcher)

The AAAI Conf. on Artificial Intelligence (AAAI), Singapore, 2026 (A*, 17.6% AR)

machine learning multivariate time-series forecasting decision-centric forecasting inference-time task adaptation

Online Submodular Maximization via Online Convex Optimization

Authors: Tareq Si Salem, Gözde Özcan, Iasonas Nikolaou, Evimaria Terzi, Stratis Ioannidis

The AAAI Conf. on Artificial Intelligence (AAAI), Vancouver, Canada, 2024 (A*, 23% AR)

online learning bandits submodular optimization non-convex optimization

Enabling Long-term Fairness in Dynamic Resource Allocation

Authors: Tareq Si Salem, George Iosifidis, Giovanni Neglia

ACM International Conferences on Measurement and Modeling of Computer Systems (SIGMETRICS), Orlando, Florida, USA, 2023 (A*, 15% AR)

multi-criteria optimization axiomatic bargaining α-fairness resource allocation

Ascent Similarity Caching with Approximate Indexes

Authors: Tareq Si Salem, Giovanni Neglia, Damiano Carra

IEEE/ACM Transactions on Networking (ToN) (Best Paper ITC'33), 2022

information retrieval and ranking similarity search non-euclidean gradient methods

GRADES: Gradient Descent for Similarity Caching

Authors: Anirudh Sabnis, Tareq Si Salem, Giovanni Neglia, Michele Garetto, Emilio Leonardi, Ramesh K. Sitaraman

IEEE International Conferences on Computer Communications (INFOCOM), 2021 (A*, 19% AR)

machine learning systems similarity caching gradient methods