A Brief Introduction About Me

Hi, I'm Yuyang Qiu (仇裕洋), currently a 5th-year Ph.D. candidate in the department of Industrial & Systems Engineering, Rutgers University. My advisor is Prof. Farzad Yousefian. My research spans federated learning (FL) for nonsmooth, nonconvex, and hierarchical optimization, as well as distributed optimization over networks. I am currently interested on integrating federated learning with foundation models, where I aim to develop memory and communication-efficient FL for foundation models. I'm a student member of SIAM, MOS, INFORMS, and IEEE. I'm also the treasurer of INFORMS Rutgers Student Chapter since Sep. 2022. I have completed a summer internship as Givens Associates at Argonne National Laboratory, where I worked on memory and communication-efficient asynchronous federated learning. [CV]

Education

Ph.D. student, Industrial and Systems Engineering, Fall 2020 - Spring 2025 (expected), Rutgers University, US.
M.S., Applied Mathematics, Fall 2018 - Summer 2020, Northeastern University, US.
B.S., Mathematics & Applied Mathematics, Fall 2014 - Spring 2018, Jiangsu University, China.


You can find a simple Github webpage tutorial & template here, other templates can be found here. The background image is cluster "Westerlund 2", taken by the Hubble Space Telescope.

Academic Events

I will present our recent work on federated simple bilevel optimization at ICCOPT 2025 (details will be posted soon), see you in LA!

Past events
2024 INFORMS annual meeting presentation: Zeroth-order federated methods for stochastic MPECs and nondifferentiable nonconvex hierarchical optimization.

ISMP 2024 presentation: Zeroth-order federated methods for stochastic MPECs and nondifferentiable nonconvex hierarchical optimization.

NeurIPS 2023 paper presentation in poster session: Zeroth-order methods for nondifferentiable, nonconvex, and hierarchical federated optimization. [full paper] [poster & video]

2023 INFORMS Annual Meeting presentation: Randomized Zeroth-order federated methods for nonsmooth nonconvex and hierarchical optimization.

SIAM Conference on Optimization (OP23) Minisymposia presentation: Randomized methods for nonsmooth and nonconvex federated optimization.

Google Scholar

Publications

Yuyang Qiu, Uday V. Shanbhag, Farzad Yousefian. Zeroth-order methods for nondifferentiable, nonconvex, and hierarchical federated optimization. 37th Conference on Neural Information Processing Systems (NeurIPS 2023). [arXiv]

Yuyang Qiu, Uday V. Shanbhag, Farzad Yousefian. Zeroth-order federated methods for stochastic MPECs and nondifferentiable nonconvex hierarchical optimization. Mathematics of Operations Research (under first major revision).


Publications Before 2020 👇

Qian, L., Attia, R.A., Qiu, Y., Lu, D. and Khater, M.M., 2019."The shock peakon wave solutions of the general Degasperis–Procesi equation," International Journal of Modern Physics B, 33(29), p.1950351.

Li, J., Qiu, Y., Lu, D., Attia, R.A. and Khater, M., 2019."Study on the solitary wave solutions of the ionic currents on microtubules equation by using the modified Khater method," Thermal Science, 23(Suppl. 6), pp.2053-2062.

Qian, L., Attia, R.A., Qiu, Y., Lu, D. and Khater, M.M., 2019."On Breather and Cuspon waves solutions for the generalized higher-order nonlinear Schrodinger equation with light-wave promulgation in an optical fiber," Comp. Meth. Sci. Eng, 1, pp.101-110.


Contact Information

WeChat: Eric-Qyy
Linkedin
Twitter/X

Please feel free to contact me through this email: yuyang.qiu(at)rutgers(dot)edu.

Some Reference Books

Optimization Theory & Algorithms 👇

Bazaraa, M.S., Sherali, H.D. and Shetty, C.M., 2006. Nonlinear programming: theory and algorithms, 3rd edition, John Wiley & Sons.
Beck, A., 2017. First-order methods in optimization, Society for Industrial and Applied Mathematics.
Beck, A., 2023. Introduction to nonlinear optimization: Theory, algorithms, and applications with Python and MATLAB, 2nd edition, Society for Industrial and Applied Mathematics.
Bertsekas, D., 2016. Nonlinear Programming, 3rd edition, Athena Scientific.
Bertsekas, D., Nedic, A. and Ozdaglar, A., 2003. Convex analysis and optimization, Athena Scientific.
Boumal, N., 2023. An introduction to optimization on smooth manifolds, Cambridge university press.
Boyd, S. and Vandenberghe, L., 2004. Convex optimization, Cambridge university press.
Nesterov, Y., 2018. Lectures on convex optimization, Berlin: Springer.
Nocedal, J. and Wright, S.J., 2006. Numerical optimization, 2nd edition, New York: Springer.
Ryu, E. and Yin, W., 2022. Large-scale convex optimization: algorithms & analyses via monotone operators, Cambridge university press.
...


Mathematics & Optimization 👇

Clarke, F.H., 1990. Optimization and nonsmooth analysis, Society for Industrial and Applied Mathematics.
Facchinei, F. and Pang, J.S., 2003. Finite-dimensional variational inequalities and complementarity problems, New York, NY: Springer New York.
Folland, G.B., 1999. Real analysis: modern techniques and their applications, 2nd edition, John Wiley & Sons.
Rockafellar, R.T., 1970. Convex analysis, Princeton university press.
Rockafellar, R.T. and Wets, R.J.B., 1998. Variational analysis, Springer Science & Business Media.
Ross, S.M., 2019. Introduction to probability models, 12th edition, Academic press.
...


Machine Learning & Data Analysis & Optimization 👇

Deisenroth, M.P., Faisal, A.A. and Ong, C.S., 2020. Mathematics for Machine Learning, Cambridge University Press.
Géron, A., 2022. Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow, 3rd edition, O'Reilly Media, Inc..
Goodfellow, I., Bengio, Y. and Courville, A., 2016. Deep learning, MIT press.
Murphy, K.P., 2022. Probabilistic machine learning: an introduction, MIT press.
Wright, S.J. and Recht, B., 2022. Optimization for data analysis, Cambridge University Press.