Osama Hanna

Osama Hanna

Contact

Lab:
63-149 ENG IV UCLA Campus

Postal Address:
UCLA
EE Department
420 Westwood Plaza
Los Angeles, CA 90095-1594
ATTN: 63-149, ENG IV
UCLA Main Campus

Email:
ohanna@ucla.edu

Biography

Osama Hanna is a Research Scientist at Meta, AI, and Ph.D. candidate at the Electrical and Computer Engineering Department at UCLA. He works with Prof. Christina Fragouli (advisor) and Prof. Lin F. Yang (co-advisor) as a member of ARNI lab, and he closely collaborates with Prof. Suhas Diggavi. 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

My research explores the intersection of online learning and information theory. Machine learning techniques guide in devising better algorithms, while information theory sets fundamental barriers for optimal performance. However, things happened to work in reverse for me:) my struggles with machine learning techniques to enhance performance identified the challenging aspects, hence, helped me to establish performance lower bounds, while struggling with information-theoretic lower bounds led to improved algorithms. I enjoy the process of thinking about the mathematical and modeling aspects of these problems, and hope my research will advance interaction between humans and AI, enhancing decision-making in areas such as healthcare, inclusive prosperity, and economic development.

Education

  • Ph.D., Electrical and Computer Engineering, University of California Los Angeles (Advisors: Prof. Christina Fragouli, and Lin F. Yang)
    Although I am with the ECE department you were most likely to find me in Math department halls. Why? I completed 7 ECE courses, but my love affair with Math reached 12 courses.
    Collaborators at UCLA: Christina Fragouli (advisor), Lin F. Yang (co-advisor), and Suhas Diggavi
  • M.Sc., Electrical Engineering, Nile University, Egypt
  • B.S., Electrical, Electronics and Communications Engineering, Cairo University, Egypt

    Experience

    • Reserach Scientist, Meta AI (2024-current)
    • Research Intern, Meta AI (Summer 2022)
    • Research Intern, Aalborg University, Denmark (Advisor: Petar Popovski) (Summer 2017)

      Publications

      generated by bibbase.org
        2024 (3)
        2 (1)
      Osama Hanna; Antonious M. Girgis; Christina Fragouli; and Suhas Diggavi. Differentially Private Stochastic Linear Bandits: (Almost) for Free. IEEE Journal on Selected Areas in Information Theory, 5: 135-147. 2024.
      doi   link   bibtex   13 downloads  
        4 (2)
      Osama A Hanna; Merve Karakas; Lin Yang; and Christina Fragouli. Multi-Agent Bandit Learning through Heterogeneous Action Erasure Channels. In International Conference on Artificial Intelligence and Statistics, pages 3898–3906, 2024. PMLR
      link   bibtex  
      Osama A. Hanna; Xinlin Li; Suhas Diggavi; and Christina Fragouli. On the Relation Between the Common Information Dimension and Wyner Common Information. In 2024 IEEE International Symposium on Information Theory (ISIT), pages 2311-2316, 2024.
      doi   link   bibtex  
        7 (3)
      Osama A. Hanna; Lin F. Yang; and Christina Fragouli. Contexts can be Cheap: A Reduction from Stochastic Contextual Bandits to Linear Bandits. under submission. 2024.
      link   bibtex  
      Osama A. Hanna; Xinlin Li; Christina Fragouli; and Suhas Diggavi. Common information dimension. under submission. 2024.
      link   bibtex  
      Osama A. Hanna; Merve Karakas; Lin F. Yang; and Christina Fragouli. Multi-Arm Bandits over Action Erasure Channels. under submission. 2024.
      link   bibtex  
        2023 (2)
        2 (1)
      Osama A Hanna; Lin F Yang; and Christina Fragouli. Compression for Multi-Arm Bandits. IEEE Journal on Selected Areas in Information Theory. 2023.
      link   bibtex  
        4 (6)
      Osama A Hanna; Xinlin Li; Suhas Diggavi; and Christina Fragouli. Common information dimension. In 2023 IEEE International Symposium on Information Theory (ISIT), pages 406–411, 2023. IEEE
      link   bibtex  
      Xinlin Li; Osama A. Hanna; Christina Fragouli; Suhas Diggavi; Gunjan Verma; and Joydeep Bhattacharyya. Feature Compression for Multimodal Multi-Object Tracking. In MILCOM 2023 - 2023 IEEE Military Communications Conference (MILCOM), pages 139-143, 2023.
      doi   link   bibtex  
      Xinlin Li; Merve Karakas; Osama A. Hanna; Mehrdad Kiamari; Jared Coleman; Christina Fragouli; Bhaskar Krishnamachari; and Gunjan Verma. Online Allocation of Sensing and Computation in Large Graphs. In 2023 IEEE 9th International Conference on Collaboration and Internet Computing (CIC), pages 28-34, 2023.
      doi   link   bibtex  
      Osama A Hanna; Lin Yang; and Christina Fragouli. Contexts can be cheap: Solving stochastic contextual bandits with linear bandit algorithms. In The Thirty Sixth Annual Conference on Learning Theory, pages 1791–1821, 2023. PMLR
      link   bibtex  
      Osama A Hanna; Merve Karakas; Lin F Yang; and Christina Fragouli. Multi-Arm Bandits over Action Erasure Channels. In 2023 IEEE International Symposium on Information Theory (ISIT), pages 1312–1317, 2023. IEEE
      link   bibtex  
      Osama Hanna; Lin Yang; and Christina Fragouli. Efficient Batched Algorithms for Contextual Linear Bandits with Large Action Space via Soft Elimination. Advances in Neural Information Processing Systems. 2023.
      link   bibtex  
        2022 (1)
        4 (3)
      Osama A. Hanna; Xinlin Li; Christina Fragouli; and Suhas Diggavi. Can we break the dependency in distributed detection?. In 2022 IEEE International Symposium on Information Theory (ISIT), pages 2720-2725, 2022.
      doi   link   bibtex   7 downloads  
      Osama Hanna; Lin Yang; and Christina Fragouli. Learning from Distributed Users in Contextual Linear Bandits Without Sharing the Context. Advances in Neural Information Processing Systems, 35: 11049–11062. 2022.
      link   bibtex  
      Osama A. Hanna; Lin F. Yang; and Christina Fragouli. Solving Multi-Arm Bandit Using a Few Bits of Communication. In International Conference on Artificial Intelligence and Statistics, pages 3220-3225, 2022.
      link   bibtex  
        2021 (3)
        2 (1)
      Osama A. Hanna; Yahya H. Ezzeldin; Christina Fragouli; and Suhas Diggavi. Quantization of Distributed Data for Learning. IEEE Journal on Selected Areas in Information Theory, 2(3): 987-1001. 2021.
      doi   link   bibtex   10 downloads  
        4 (1)
      Rasmus Vestergaard; Osama Hanna; Linqi Song; Daniel E Lucani; and Christina Fragouli. On Coded Broadcasting for Wireless Recommendation Systems. In ICC 2021-IEEE International Conference on Communications, pages 1–6, 2021. IEEE
      link   bibtex  
        8 (1)
      Osama A. Yang Hanna Lin F.; and Christina Fragouli. Solving Multi-Arm Bandit Using a Few Bits of Communication. In spotlight talk in ICML Workshop on Reinforcement Learning Theory, 2021.
      link   bibtex  
        2020 (1)
        2 (1)
      O. A. Hanna; Y. H. Ezzeldin; T. Sadjadpour; C. Fragouli; and S. Diggavi. On Distributed Quantization for Classification. IEEE Journal on Selected Areas in Information Theory. 2020.
      link   bibtex   4 downloads  
        2018 (2)
        2 (1)
      Osama A. Hanna; Mohammed Nafie; and Amr El-Keyi. Cache-Aware Source Coding. IEEE Communications Letters, 22(6): 1144-1147. 2018.
      doi   link   bibtex  
        4 (1)
      Anders E. Kalor; Osama A. Hanna; and Petar Popovski. Random Access Schemes in Wireless Systems with Correlated User Activity. In 2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), pages 1-5, 2018.
      doi   link   bibtex  
        2017 (1)
        4 (1)
      Osama A. Hanna; Amr El-Keyi; and Mohammed Nafie. Degrees of freedom in cached MIMO relay networks with multiple base stations. In 2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC), pages 2056-2061, 2017.
      doi   link   bibtex  

       

      Invited Talks

      • [April 2024] University of California, Riverside
      • [April 2024] Stanford University
      • [Feb 2024] University of California, Berkeley
      • [Jan 2024] University of Southern California
      • [Nov 2023] The Reinforcement Learning Theory Seminar Series
      • [Nov 2023] University of California, Irvine
      • [Jul 2023] The Chinese University of Hong Kong
      • [Jul 2023] City University of Hong Kong
      • [Feb 2023] ITA Graduation Day Talk
      • [Jan 2022] Spotlight talk at ICML Workshop on Reinforcement Learning Theory

       

      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

      Mentoring

      • PhD students: Merve Karakas, Xinlin Li
      • Undergraduate students: Tara Sadjadpour, Dylan Lee, Yiming Guo, Karim Saraipour, Zixiang Ji, Jianxing
        Zhao

      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)