A Brief Introduction About Me
Hi, I'm Yuyang Qiu, currently a Ph.D. student in the department of Industrial & Systems Engineering, Rutgers University. My advisor is Prof. Farzad Yousefian. My research focus is on Randomized Federated Learning for Nonsmooth, Nonconvex and Hierarchical Optimization. I'm a student member of SIAM and INFORMS. I'm also the treasurer of INFORMS Rutgers Student Chapter since Sep. 2022. [CV]
Education
Ph.D. student, Industrial and Systems Engineering, Sep. 2020 - 2025 (expected), Rutgers University, US.
M.S., Applied Mathematics, Sep. 2018 - Aug. 2020, Northeastern University, US.
B.S., Mathematics & Applied Mathematics, Sep. 2014 - Jul. 2018, Jiangsu University, China.
Academic Events
NeurIPS 2023 paper presentation in poster session: Zeroth-Order Methods for Nondifferentiable, Nonconvex, and Hierarchical Federated Optimization. [full paper] [poster & video]
Past events
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.
Publications
Zeroth-Order Methods for Nondifferentiable, Nonconvex, and Hierarchical Federated Optimization
Yuyang Qiu, Uday V. Shanbhag, Farzad Yousefian
37th Conference on Neural Information Processing Systems (NeurIPS 2023). [arXiv]
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., 2015. Convex optimization algorithms, Athena Scientific.
Bertsekas, D., Nedic, A. and Ozdaglar, A., 2003. Convex analysis and optimization, Athena Scientific.
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.
...
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, K.A., 2013. Elementary analysis, 2nd edition, Springer.
Ross, S.M., 2019. Introduction to probability models, 12th edition, Academic press.
Strang, G, 2023. Introduction to linear algebra, 6th edition, Cambridge university 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.