Yunfan Zhang

About Me

I am a PhD student in Computer Science at Columbia University. I am interested in Natural Language Processing and Multimodal Learning. I am very fortunate to be advised by Prof. Kathleen McKeown and Prof. Smaranda Muresan.

Previously, I worked with Prof. Ethan Katz-Bassett on computer networks. Before coming to Columbia, I was an undergraduate student at Duke University, advised by Prof. Maria Gorlatova and Prof. Guohao Lan. I worked on a number of projects related to computer vision, applied machine learning, augmented reality, and mobile computing.

Here is my CV.

Research Interests

• Natural Language Processing: Cross-lingual and cross-cultural understanding, reasoning, alignment.
• Multimodal Learning and Computer Vision: Text-to-Image models, 3D computer vision, depth perception.
• Computer Systems: Machine Learning for intelligent systems, efficient training and inference for large deep learning models.

News

• [Jun. 2023 - Sep. 2023] Spent an amazing summer working on language model based end-to-end bot detection at Cloudflare Research.
• [Apr. 2023] Our paper Who Squats IPv4 Addresses? appears in CCR 2023.
• [Mar. 2022] Our paper Optimal Network Protocol Selection for Competing Flows via Online Learning appears in TMC 2022.
• [Mar. 2022] Our paper InDepth: Real-time Depth Inpainting for Mobile Augmented Reality appears in IMWUT/UbiComp 2022.
• [Nov. 2021] Our paper Towards Identifying Networks with Internet Clients Using Public Data appears in IMC 2021.

Publications

• Forecasting Communication Derailments Through Conversation Generation
  Y. Zhang, K. McKeown, S. Muresan
  arXiv Preprint, 2025 arXiv 2025
  [PDF]

• Who Squats IPv4 Addresses?
  L. Salamatian, T. Arnold, Í. Cunha, J. Zhu, Y. Zhang, E. Katz-Bassett, M. Calder
  ACM SIGCOMM Computer Communication Review, Jan. 2023 CCR 2023
  [PDF]

• InDepth: Real-time Depth Inpainting for Mobile Augmented Reality
  Y. Zhang, T. Scargill, A. Vaishnav, G. Premsankar, M. Di Francesco, M. Gorlatova
  Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Mar. 2022 IMWUT/UbiComp 2022
  [PDF]

• Optimal Network Protocol Selection for Competing Flows via Online Learning
  X. Zhang, S. Chen, Y. Zhang, Y. Im, M. Gorlatova, S. Ha, C. Joe-Wong
  IEEE Transactions on Mobile Computing, 2022 TMC 2022
  [PDF]

• Edge-assisted Collaborative Image Recognition for Mobile Augmented Reality
  G. Lan, Z. Liu, Y. Zhang, T. Scargill, J. Stojkovic, C. Joe-Wong, M. Gorlatova
  ACM Transactions on Sensor Networks, Vol. 18, No 1, Feb. 2022 TOSN 2022
  [PDF]

• Towards Identifying Networks with Internet Clients Using Public Data
  W. Jiang, T. Luo, T. Koch, Y. Zhang, E. Katz-Bassett, M. Calder
  21st ACM Internet Measurement Conference IMC 2021
  [PDF]

• Towards a Traffic Map of the Internet: Connecting the Dots between Popular Services and Users
  T. Koch, W. Jiang, T. Luo, P. Gigis, Y. Zhang, K. Vermeulen, E. Aben, M. Calder, E. Katz-Bassett, L. Manassakis, G. Smaragdakis, N. Vallina-Rodriguez
  20th ACM Workshop on Hot Topics in Networks HotNets 2021
  [PDF]

• CollabAR: Edge-assisted Collaborative Image Recognition for Mobile Augmented Reality
  Z. Liu, G. Lan, J. Stojkovic, Y. Zhang, C. Joe-Wong, M. Gorlatova
  19th ACM/IEEE International Conference on Information Processing in Sensor Networks IPSN 2020
  [PDF]

• Edge-based Provisioning of Holographic Content for Contextual and Personalized Augmented Reality
  M. Glushakov, Y. Zhang, Y. Han, T. Scargill, G. Lan, M. Gorlatova
  2020 IEEE International Conference on Pervasive Computing and Communications Workshops PerCom Workshops 2020
  [PDF]

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