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Mehrdad Mahdavi

Dorothy Quiggle Career Development Assistant Professor

Affiliation(s):

  • School of Electrical Engineering and Computer Science
  • Computer Science and Engineering

Research Areas:

Data Science and Artificial Intelligence

Interest Areas:

Large-scale optimization, High-dimensional learning, Convex and non-convex Optimization, Distributed optimization, Statistical and computational learning theory, Online (adversarial) learning, Applications of machine learning in social graphs, recommender systems, text analysis, etc

 
 

 

Education

  • Ph D, Computer Sceince, Michigan State University, 2014
  • , Toyota Technological Institute at Chicago, 2016

Publications

Journal Articles

  • Mengran Fan, Jian Wang, Huaipan Jiang, Yilin Feng, Mehrdad Mahdavi, Kamesh Madduri, Mahmut T Kandemir and Nikolay Dokholyan, 2020, "Gpu-accelerated Flexible Molecular Docking", The Journal of Physical Chemistry B, 125, (4)
  • Huaipan Jiang, Mengran Fan, Jian Wang, Anup Sarma, Shruti Mohanty, Nikolay Dokholyan, Mehrdad Mahdavi and Mahmut T Kandemir, 2020, "Guiding Conventional Protein–Ligand Docking Software with Convolutional Neural Networks", Journal of Chemical Information and Modeling
  • Jialei Wang, Jason D Lee, Mehrdad Mahdavi, Mladen Kolar, Nathan Srebro and others, 2018, "Sketching meets random projection in the dual: A provable recovery algorithm for big and high-dimensional data", Electronic Journal of Statistics, 11, (2), pp. 4896--4944
  • Mehrdad Mahdavi, 2015, "Random Projections for Classification: A Recovery Approach", IEEE Transactions on Information Theory
  • Tianbao Yang, Mehrdad Mahdavi, Rong Jin and Shenghuo Zhu, 2014, "An efficient primal dual prox method for non-smooth optimization", Machine Learning Journal, pp. 1--38
  • Rana Forsati, Mehrdad Mahdavi, Mehrnoush Shamsfard and Mohamed Sarwat, 2014, "Matrix factorization with explicit trust and distrust side information for improved social recommendation", ACM Transactions on Information Systems (TOIS), 32, (4), pp. 17
  • L Zhang, Mehrdad Mahdavi, R Jin, Tinglu Yang and S Zhu, 2014, "Random Projections for Classification: A Recovery Approach", IEEE Transactions on Information Theory
  • Tianbao Yang, Mehrdad Mahdavi, Rong Jin and Shenghuo Zhu, 2014, "Regret bounded by gradual variation for online convex optimization", Machine Learning Journal, pp. 1--41
  • Rana Forsati, Mehrdad Mahdavi, Mehrnoush Shamsfard and Mohammad Reza Meybodi, 2013, "Efficient stochastic algorithms for document clustering", Information Sciences, 220, pp. 269--291
  • Mehrdad Mahdavi, Rong Jin and Tianbao Yang, 2012, "Trading regret for efficiency: online convex optimization with long term constraints", Journal of Machine Learning Research (JMLR), 13, pp. 2503--2528
  • Mehrdad Mahdavi and Hassan Abolhassani, 2009, "Harmony k-means algorithm for document clustering", Data Mining and Knowledge Discovery, 18, (3), pp. 370--391
  • Mahamed GH Omran and Mehrdad Mahdavi, 2008, "Global-best harmony search", Applied Mathematics and Computation, 198, (2), pp. 643--656
  • Mehrdad Mahdavi, Mohammad Fesanghary and Ebrahim Damangir, 2007, "An improved harmony search algorithm for solving optimization problems", Applied Mathematics and Computation, 188, (2), pp. 1567--1579

Conference Proceedings

  • Yuyang Deng and Mehrdad Mahdavi, 2021, "Local stochastic gradient descent ascent: Convergence analysis and communication efficiency", International Conference on Artificial Intelligence and Statistics (AISTAT)
  • Farzin Haddadpour, Mohammad Mahdi Kamani, Aryan Mokhtari and Mehrdad Mahdavi, 2021, "Federated learning with compression: Unified analysis and sharp guarantees", International Conference on Artificial Intelligence and Statistics (AISTAT)
  • Mohammad Mahdi Kamani, Sadegh Farhang , Mehrdad Mahdavi and James Z Wang, 2020, "Targeted Data-driven Regularization for Out-of-Distribution Generalization", ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
  • Weilin Cong, Rana Forsati, Mahmut T Kandemir and Mehrdad Mahdavi, 2020, "Minimal variance sampling with provable guarantees for fast training of graph neural networks", ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD)
  • Huaxiu Yao, Yingbo Zhou, Mehrdad Mahdavi, Zhenhui Li, Richard Socher and Caiming Xiong, 2020, "Online Structured Meta-learning", Advances in Neural Information Processing Systems (NeurIPS)
  • Yuyang Deng, Mohammad Mahdi Kamani and Mehrdad Mahdavi, 2020, "Distributionally Robust Federated Averaging", Advances in Neural Information Processing Systems (NeurIPS)
  • Morteza Ramezani, Weilin Cong, Mehrdad Mahdavi, Anand Sivasubramaniam and Mahmut T Kandemir, 2020, "GCN meets GPU: Decoupling “When to Sample” from “How to Sample”", Advances in Neural Information Processing Systems (NeurIPS)
  • Hanlin Lu, Ting He, Shiqiang Wang, Changchang Liu, Mehrdad Mahdavi, Vijaykrishnan Narayanan, Kevin S. Chan and Stephen Pasteris, 2020, "Communication-efficient k-Means for Edge-based Machine Learning", IEEE International Conference on Distributed Computing Systems (ICDCS)
  • Farzin Haddadpour, Mohammad Mahdi Kamani, Mehrdad Mahdavi and Viveck Ramesh Cadambe, 2019, "Local SGD with periodic averaging: Tighter analysis and adaptive synchronization", Advances in Neural Information Processing Systems (NeurIPS), pp. 11080--11092
  • Mohammad Mahdi Kamani, Sadegh Farhang, Mehrdad Mahdavi and James Z Wang, 2019, "Targeted meta-learning for critical incident detection in weather data"
  • Samet Oymak, Mehrdad Mahdavi and Jiasi Chen, 2019, "Learning Feature Nonlinearities with Non-Convex Regularized Binned Regression", IEEE International Symposium on Information Theory (ISIT)
  • Farzin Haddadpour, Mahdi Kamani, Mehrdad Mahdav and, Viveck Cadambe , 2019, "Trading Redundancy for Communication: Speeding up Distributed SGD for Non-convex Optimization", International Conference on Machine Learning (ICML)
  • Ofer Meshi, Mehrdad Mahdavi, Adrian Weller and David Sontag, 2016, "Train and Test Tightness of LP Relaxations in Structured Prediction", International Conference on Machine Learning (ICML)
  • Mehrdad Mahdavi, Lijun Zhang and Rong Jin, 2015, "Lower and upper bounds on the generalization of stochastic exponentially concave optimization", Conference on Learning Theory (COLT), pp. 1305--1320
  • Ofer Meshi, Mehrdad Mahdavi and Alex Schwing, 2015, "Smooth and strong: MAP inference with linear convergence", Advances in Neural Information Processing Systems (NeurIPS), pp. 298--306
  • Tianbao Yang, Yu-Feng Li, Mehrdad Mahdavi, Rong Jin and Zhi-Hua Zhou, 2013, "Nystrom method vs random fourier features: a theoretical and empirical comparison", Advances in Neural Information Processing Systems (NeurIPS), Advances in Neural Information Processing Systems
  • Lijun Zhang, Mehrdad Mahdavi and Rong Jin, 2013, "Linear convergence with condition number independent access of full gradients", Advances in Neural Information Processing Systems (NeurIPS), pp. 980--988
  • Mehrdad Mahdavi, Lijun Zhang and Rong Jin, 2013, "Mixed optimization for smooth functions", Advances in Neural Information Processing Systems (NeurIPS), pp. 674--682
  • Mehrdad Mahdavi and Rong Jin, 2013, "Passive learning with target risk", Conference on Learning Theory (COLT)
  • Mehrdad Mahdavi, Tianbao Yang and Rong Jin, 2013, "Stochastic convex optimization with multiple objectives", Advances in Neural Information Processing Systems (NeurIPS), pp. 1115--1123
  • Tianbao Yang, Mehrdad Mahdavi, Rong Jin, Lijun Zhang and Yang Zhou, 2012, "Multiple kernel learning from noisy labels by stochastic programming", International Conference on Machine Learning (ICML)
  • Mehrdad Mahdavi, Tianbao Yang and Rong Jin, 2012, "Online decision making under stochastic constraints", Discrete Optimization in Machine Learning, NeurIPS
  • Tianbao Yang, Mehrdad Mahdavi, Rong Jin, Jinfeng Yi and Steven CH Hoi, 2012, "Online kernel selection: algorithms and evaluations.", AAAI
  • Lijun Zhang, Mehrdad Mahdavi, Rong Jin and Tianbao Yang, 2012, "Recovering the optimal solution by dual random projection", Conference on Learning Theory (COLT)
  • Jinfeng Yi, Tianbao Yang, Rong Jin, Anil K Jain and Mehrdad Mahdavi, 2012, "Robust ensemble clustering by matrix completion", IEEE International Conference on Data Mining (ICDM), pp. 1176--1181
  • Mehrdad Mahdavi, Tianbao Yang, Rong Jin, Shenghuo Zhu and Jinfeng Yi, 2012, "Stochastic gradient descent with only one projection", Advances in Neural Information Processing Systems (NeurIPS)
  • Chao-Kai Chiang, Tianbao Yang, Chia-Jung Lee, Mehrdad Mahdavi, Chi-Jen Lu, Rong Jin and Shenghuo Zhu, 2012, "Online optimization with gradual variations.", Conference on Learning Theory (COLT), 23, pp. 6--1
  • Rana Forsati, Mehrdad Mahdavi, Abolfazl Torghy Haghighat and Azadeh Ghariniyat, 2008, "An efficient algorithm for bandwidth-delay constrained least cost multicast routing", IEEE Canadian Conference on Electrical and Computer Engineering, pp. 001641--001646
  • Rana Forsati, MohammadReza Meybodi, Mehrdad Mahdavi and AzadehGhari Neiat, 2008, "Hybridization of k-means and harmony search methods for web page clustering", IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology-Volume 01, pp. 329--335
  • Rana Forsati, Mehrdad Mahdavi, Mohammadreza Kangavari and Banafsheh Safarkhani, 2008, "Web page clustering using harmony search optimization", IEEE Canadian Conference on Electrical and Computer Engineering, pp. 001601--001604
  • Jialei Wang, Jason Lee, Mehrdad Mahdavi, Mladen Kolar and Nathan Srebro, , "A provable recovery algorithm for big and high-dimensional data", International Conference on Artificial Intelligence and Statistics (AISTATS)
  • Md Fahim Faysal Khan, Mohammad Kamani, Mehrdad Mahdavi and Vijay Narayanan, , "Learning to Quantize Deep Neural Networks: A Competitive-Collaborative Approach", Design Automation Conference (DAC)

Research Projects

Honors and Awards

  • Mark Fulk Best Student Paper Award, Conference on Learning Theory, 2012 - 2012
  • Top Cited Paper Award, Elsevier, 2010 - 2010

Service

Service to Penn State:

Service to External Organizations:

 


 

About

The School of Electrical Engineering and Computer Science was created in the spring of 2015 to allow greater access to courses offered by both departments for undergraduate and graduate students in exciting collaborative research fields.

We offer B.S. degrees in electrical engineering, computer science, computer engineering and data science and graduate degrees (master's degrees and Ph.D.'s) in electrical engineering and computer science and engineering. EECS focuses on the convergence of technologies and disciplines to meet today’s industrial demands.

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