I am Yae Jee Cho, a fifth-year Ph. D student at Carnegie Mellon University. I am on the 2024 job market!

I am advised by Prof. Gauri Joshi, in the Optimization, Probability and Learning (OPAL) Lab. Our research group is affiliated with the Parallel Data Lab. My current research interests are in distributed machine learning, on-device machine learning, and federated learning.

Scroll down to see more.

News

Selected Publications

  • Y. J. Cho, L. Liu, Z. Xu, A. Fahrezi, M. Barnes, and G. Joshi, “Heterogeneous LoRA for Federated Fine-tuning of On-device Foundation Models”, FL@FM-NeurIPS’23 [pdf]
  • Y. J. Cho, G. Joshi, and D. Dimitriadis, “Local or Global: Selective Knowledge Assimilation for Federated Learning with Limited Labels”, ICCV 2023 [pdf]
  • Y. J. Cho, P. Sharma, G. Joshi, Z. Xu, S. Kale, and T. Zhang, “On the Convergence of Federated Averaging with Cyclic Client Participation”, ICML 2023 [pdf]
  • Y. J. Cho, D. Jhunjhunwala, T. Li, V. Smith, and G. Joshi, “To Federate or Not To Federate: Incentivizing Client Participation in Federated Learning”, Under Submission, FL-NeurIPS’22 [pdf]
  • Y. J. Cho, J. Wang, T. Chiruvolu, and G. Joshi, “Personalized Federated Learning for Heterogeneous Devices with Clustered Knowledge Transfer”, IEEE Journal of Selected Topics in Signal Processing (IEEE JSTSP), Dec 2022  [pdf]
  • Y. J. Cho, A. Manoel, G. Joshi, R. Sim, and D. Dimitriadis, “Heterogeneous Ensemble Knowledge Transfer for Training Large Models in Federated Learning”, IJCAI 2022 [pdf]
  • Y. J. Cho, J. Wang, and G. Joshi, “Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategies”, AISTATS 2022 [pdf]
  • Y. J. Cho, S. Gupta, G. Joshi, and O. Yagan, “Bandit-based Communication-Efficient Client Selection Strategies for Federated Learning”, Asilomar Conference on Signals, Systems and Computers 2020 (Invited Paper) [pdf]

Research/Work Experience

  • Aug 2023 - Dec 2023 / Student Researcher / Federated Learning, Google Research (Seattle, WA)
  • May 2023 - Aug 2023 / Research Intern / Federated Learning, Google Research (Seattle, WA)
  • May 2022 - Aug 2022 / Research Intern / Privacy in AI, Microsoft Research (Redmond, WA)
  • June 2021 - Aug. 2021 / Research Intern / Language Intelligence, Microsoft Research (Redmond, WA)
  • Sep. 2018 - June 2019 / R&D Engineer / Drivers Assistance Systems at Mercedes-Benz R&D Korea (Seoul, South Korea)
  • Mar. 2016 - Aug. 2018 / Research Assistant / Intelligence Networking Lab, Yonsei Univ. (Seoul, South Korea)
  • Jul. 2017 - Aug. 2017 / Visiting Scholar / Wireless@HKU Group, Hong-Kong Univ. (Pok Fu Lam, Hong Kong)
  • Aug. 2015 / Student / Qualcomm IT class of 2015 (San Diego, CA)
  • July 2014 - Aug. 2014 / Student / Summer Development Program at Aalto Design Factory, Aalto Univ. (Helsinki, Finland)

Teaching Experience/Services

  • Reviewer for NeurIPS 22-23; AISTATS 22-23; ICML 22-23; ITR3@ICML 21; FL@NeurIPS 22; FL-ICML 23
  • Spring 2022: TA for ML Outreach for Highschools in Pittsburgh
  • Spring 2021: TA for 18-667: Algorithms for Large-scale Distributed ML and Optimization [link]

Honors

  • Doctoral Study Abroad Scholarship ($80K in total), Ministry of Education, Republic of Korea 2019-2021
  • Honor Prize in 24th Samsung Human Tech Paper Award, co-recipient, Samsung 2018
  • Outstanding Young Researcher Award, The Korean Inst. of Comm. and Info. Sciences (KICS) 2016
  • Full scholarship-graduate school ($49K in total), Inst. for Info. & Comm. Tech. Promo. (IITP) 2016-2017
  • Most Original Idea Prize for International Women’s Hackathon, Microsoft 2014
  • Honors for Academic Excellence, Yonsei University 2013-2015
  • Full scholarship-undergraduate school ($45K in total), Inst. for Info. & Comm. Tech. Promo. (IITP) 2013-2015

Previous Publications & Patent

  • Y.-G. Lim, Y. J. Cho, M. S. Sim, Y. Kim, C.-B. Chae, and R. Valenzuela, “Map-based Millimeter-Wave Channel Models: An Overview, Hybrid Modeling, Data, and Learning,” submitted to IEEE Wireless Communications Magazine, 2019 pdf

  • Y. J. Cho, K. Huang, and C.-B. Chae, “V2X Downlink Coverage Analysis with a Realistic Urban Vehicular Model,” Proc. IEEE Global Communications Conference (GLOBECOM) Workshops, Dec 2018 (Abu Dhabi, UAE) pdf

  • Y.-G. Lim, Y. J. Cho, T. Oh, Y. Lee, and C.-B. Chae, “Relationship between Cross Polarization Discrimination (XPD) and Spatial Correlation for Indoor Small Cell MIMO Systems,” IEEE Wireless Communications Letters, vol. 7, no. 4, pp. 654-657, Aug. 2018 pdf

  • Y. J. Cho, G.-Y. Suk, B. Kim, D.-K. Kim, and C.-B. Chae, “RF Lens Embedded Antenna Array for mmWave MIMO: Design and Performance,” IEEE Communications Magazine, vol. 56, no. 7, pp. 42-48, July 2018 pdf

  • Y. J. Cho, H. B. Yilmaz, W. Guo, and C.-B. Chae, “Effective Inter-symbol Interference Mitigation with a Limited Amount of Enzymes in Molecular Communications,” Trans. on Emerging Telecommunications Technologies (ETT), Special Issue on Enabling Nano-Networking via Molecular Communications, vol. 28, no. 7, pp. 1-12, July 2017 pdf

  • Y. J. Cho, H. B. Yilmaz, W. Guo, and C.-B. Chae, “Effective Enzyme Deployment for Inter-Symbol Interference Mitigation in Molecular Communication,” Proc. IEEE Wireless Communications and Networking Conference (WCNC), March 2017 (San Francisco, USA) pdf

  • H. B. Yilmaz, C. Lee, Y. J. Cho, and C.-B. Chae, “A Machine Learning Approach to Model the Received Signals in Molecular Communications,” Proc. of IEEE International Black Sea Conference on Communications and Networking (IEEE BlackSeaCom), June 2017 (Istanbul, Turkey)pdf 

  • H. B. Yilmaz, Y. J. Cho, W. Guo, and C.-B. Chae, “Interference Reduction via Enzyme Deployment for Molecular Communication,” IEEE Electronics Letters, vol. 52, no. 13, pp. 1094-1096, June 2016 pdf

  • C.-B. Chae, B. Koo, Y. J. Cho, “METHOD FOR RECEIVING DATA IN MIMO MOLECULAR COMMUNICATION SYSTEM.” U.S. Patent Application 20170346512, issued November 30, 2017

Miscellaneous

In my free time I enjoy playing the piano, squash, swimming, and spending time with PanitheCorgi.

PanitheCorgi loves


Last updated on Jan. 2024.