Osama Hanna

Biography

Osama Hanna is a Ph.D. candidate at the Electrical and Computer Engineering Department at UCLA. He works with Prof. Fragouli as a member of the Laboratory of Algorithmic Research in Network Information Flow. Before joining UCLA, he received his Bachelor’s and Masters degrees in Electrical, Electronics and Communications Engineering from Cairo University, and Nile University respectively.

Research Interests

• Bandits and reinforcement learning
• Learning theory
• Information and coding theory
• Algorithms
• Mathematical logic

Education

• Ph.D., Electrical and Computer Engineering, University of California Los Angeles, (Current)
  • Advisor: Prof. Christina Fragouli
• M.Sc., Electrical Engineering, Nile University, Egypt, 2018
• B.S., Electrical, Electronics and Communications Engineering, Cairo University, Egypt 2014

Publications

Journal Papers

• Osama A. Hanna, Lin F. Yang, and Christina Fragouli, “Contexts can be Cheap: A Reduction from Stochastic Contextual Bandits to Linear Bandits,” under submission.
• Osama A. Hanna, Antonious M. Girgis, Christina Fragouli, and Suhas Diggavi, “Differentially Private Stochastic Linear Bandits:(Almost) for Free,” under submission.
• Osama A. Hanna, Lin F. Yang, and Christina Fragouli, “Compression for Multi-Arm Bandits,” in Journal on Selected Areas in Information Theory (JSAIT), 2023.
• Osama A. Hanna, Yahya H. Ezzeldin, Christina Fragouli, and Suhas Diggavi, “Quantization of Distributed Data for Learning”, in Journal on Selected Areas in Information Theory (JSAIT), 2021.
• Osama A. Hanna, Yahya H. Ezzeldin, T. Sadjadpour, Christina Fragouli and Suhas Diggavi, “On Distributed Quantization for Classification”, in IEEE Journal on Selected Areas in Information Theory 2020.
• Osama A. Hanna, M. Nafie, and A. El-Keyi, “Cache-Aware Source Coding”, in IEEE Comm. Letters, 2018.

Conference Papers

• Osama A. Hanna, Lin F. Yang, and Christina Fragouli, “Efficient Batched Algorithm for Contextual Linear Bandits with Large Action Space via Soft Elimination,” in NeurIPS 2023.
• Osama A. Hanna, Lin F. Yang, and Christina Fragouli, “Contexts can be Cheap: Solving Stochastic Contextual Bandits with Linear Bandit Algorithms,” in Conference on Learning Theory (COLT) 2023.
• Osama A. Hanna, Xinlin li, Christina Fragouli, and Suhas Diggavi, “Common information dimension,” in ISIT 2023.
• Osama A. Hanna, Merve Karakas, Lin F. Yang, and Christina Fragouli, “Multi-Arm Bandits over Action Erasure Channels,” in ISIT 2023.
• Xinlin li, Osama A. Hanna, Christina Fragouli, Suhas Diggavi, Gunjan Verma, and Joydeep Bhattacharyya, “Feature Compression for Multimodal Multi-Object Tracking,” in MILCOM 2023 Workshop on IoT for Adv. Operations.
• Osama A. Hanna, Lin F. Yang, and Christina Fragouli, “Learning from Distributed Users in Contextual Linear Bandits Without Sharing the Context”, in NeurIPS 2022.
• Osama A. Hanna, Lin F. Yang, and Christina Fragouli, “Solving Multi-Arm Bandit Using a Few Bits of Communication”, in AISTATS 2022.
• Osama A. Hanna, Xinlin li, Christina Fragouli, and Suhas Diggavi, “Can we break the dependency in distributed detection?,” in ISIT 2022.
• Osama A. Hanna, Lin F. Yang, and Christina Fragouli, “Solving Multi-Arm Bandit Using a Few Bits of Communication”, spotlight talk in ICML Workshop on Reinforcement Learning Theory 2021.
• Rasmus Vestergaard, Osama A. Hanna, Linqi Song, Daniel E. Lucani, and Christina Fragouli, “On Coded Broadcasting for Wireless Recommendation Systems”, in IEEE ICC, 2021.
• Anders E. Kalor, Osama A. Hanna, and Petar Popovski, “Random access schemes in wireless systems with correlated user activity”, in IEEE 19th Int. Workshop on Signal Proc. Advances in Wireless Comm. (SPAWC), 2018.
• Osama A. Hanna, Amr El-Keyi, and Mohammed Nafie, “Degrees of Freedom in Cached MIMO Relay Networks With Multiple Base Stations,” in International Wireless Communications and Mobile Computing Conference (IWCMC).

Invited Talks

• [Nov 2023] The Reinforcement Learning Theory Seminar Series: Contexts can be Cheap: Solving Stochastic Contextual Bandits with Linear Bandit Algorithms
• [Nov 2023] University of California, Irvine: Stochastic contextual bandits are not harder than linear bandits
• [Jul 2023] The Chinese University of Hong Kong: On Task Aware Compression: Common Information Dimension and Contextual Bandit Learning
• [Jul 2023] City University of Hong Kong: Contexts can be Cheap: Solving Stochastic Contextual Bandits with Linear Bandit Algorithms
• [Feb 2023] ITA Graduation Day Talk: Task Aware Compression for Multi-Armed Bandits
• [Jan 2022] Spotlight talk at ICML Workshop on Reinforcement Learning Theory: Solving multi-arm bandit using a few bits of communication

Teaching

• [Fall 2019] Teaching Assistant for Linear Programming (EE236A), University of California, Los Angeles
• [Fall 2021] Teaching Assistant for Graph Theory (EE134), University of California, Los Angeles

Reviewing

NeurIPS, ICML, ALT, ICLR, AISTATS, ISIT, GLOBECOM,  IEEE JSAIT, IEEE JSAC, IEEE Comm Letters, IEEE TWC, IEEE TC

Selected Courses from ECE Department at UCLA

Reinforcement Learning Theory(A+), Information Theory(A+), Foundations of Statistical Learning(A), Linear Programming(A), Convex Optimization(A), Large Scale Optimization(A)

Selected Courses from Math Department at UCLA

Algorithms(A+), Computability Theory(A+), Mathematical Logic(A+), Axiomatic Set Theory(A), Analysis Honors(A+), Topology(A), Optimization(A+), Model Theory(A), Measure Theory(A), Calculus of Variations(A)