About Me:

I am a research scientist at Meta AI where I am a part of the AI integrity team. I work on creating scalable and responsible AI systems that can solve challenging integrity problems such as hate speech and low-quality e-commerce detection. Prior to joining Meta AI, I was an AI Research Scientist at Electronic Arts where I was working on applying deep generative models in automating tasks in game design and computer graphics.

I used to be a postdoctoral scholar at USC Data Science and Operations Department, working with Jason Lee. Prior to joining USC, I was a postdoctoral scholar at UCLA Computer Science Department, working with Ameet Talwalkar. Before that, I was an R&D engineer at Starkey Hearing Technologies Research and Development department. I was fortunate to receive my Ph.D. and Masters in Electrical Engineering with a minor in Computer Science from the University of Minnesota under the supervision of Tom Luo.

Research Interests:

My current research interests broadly include the design of scalable & responsible AI for real-world challenging problems. This has meant working on areas such as self-supervised & multi-modal learning, generative modeling, federated learning, and efficient/scalable, fair, robust & explainable AI models.

Current/Past Interns (Co-)Mentored:

Yaodong Yu (UC Berkeley)

Nimit Sohoni (Stanford -> Citadel)

Aaron Chan (USC -> Meta AI)

Khalil Mrini (UC San Diego -> Meta AI)

Krishna Pillutla (University of Washington -> Google Research)

Woojeong Jin (USC)

Ajinkya Tejankar (UC Davis)

Neha Kalibhat (UMD)

Samuel Horvath (KAUST -> MBZUAI)

Jingwen Liang (UCSD -> EA Data & AI)

Recent Publications and Patents (Google Scholar) :

The items are color-coded based on the following topics*:

1. Scalable, and Efficient Learning 🔵

2. Robustness, Fairness, and Explainability 🟢

3. Private & Federated Learning 🔴

4. Self-Supervised, and Multi-Modal Learning 🟣

5. Generative Modeling 🟡

*Some items might belong to multiple topics

• Accepted/Published:

  • K. Pillutla, K. Malik, A. Mohamed, M. Rabbat, M. Sanjabi, and L. Xiao, "Federated Learning with Partial Model Personalization," ICML, 2022 🔴🔵

  • A. Chan, M. Sanjabi, L. Mathias, L. Tan, S. Nie, X. Peng, X. Ren, and H. Firooz, "UniREx: A Unified Learning Framework for Language Model Rationale Extraction," ICML, 2022 🟢🔵

  • B. Joshi, A. Chan, Z. Liu, S. Nie, M. Sanjabi, H. Firooz, and X. Ren “ER-TEST: Evaluating Explanation Regularization Methods for NLP Models,” EMNLP, 2022 🟢

  • S. Horvath, M. Sanjabi, L. Xiao, P. Richtarik, and M. Rabbat, "FedShuffle: Recipes for Better Use of Local Work in Federated Learning," TMLR, 2022 🔴

  • K. Mrini, S. Nie, J. Gu, S. Wang, M. Sanjabi, and H. Firooz, "Detection, Disambiguation, Re-ranking: Autoregressive Entity Linking as a Multi-Task Problem," ACL, 2022 🔵

  • A. Tejankar, M. Sanjabi, B. Wu, S. Xie, M. Khabsa, H. Pirsiavash, and H. Firooz, "Can we train vision and language zero-shot classification models without syntax?", Self-Supervised Learning: Theory and Practice workshop, NeurIPS, 2022 🟣

  • N. Sohoni, M. Sanjabi, N. Ballas, A. Grover, S. Nie, H. Firooz, and C. Re, "BARACK: Partially Supervised Group Robustness With Guarantees," Spurious correlations, Invariance, and Stability (SCIS) Workshop, ICML, 2022 🟢

  • T. Li, A. Beirami, M. Sanjabi, and V. Smith. "Tilted Empirical Risk Minimization," ICLR, 2021 🟢

  • T. Huang, P. Singhania, M. Sanjabi, P. Mitra, M. Razaviyayn, "Alternating Direction Method of Multipliers for Quantization," AISTAT, 2021 🔵

  • W. Jin, M. Sanjabi, S. Nie, L. Tan, X. Ren, H. Firooz, "MSD: Saliency-aware Knowledge Distillation for Multimodal Understanding," EMNLP, 2021 🔵🟣

  • T. Li, M. Sanjabi, A. Beirami and V. Smith. "Fair Resource Allocation in Federated Learning," ICLR, 2020 🔴🟢

  • T. Li, A. K. Sahu, M. Zaheer, M. Sanjabi, A. Talwalkar, and V. Smith, "On the convergence of federated optimization in heterogeneous networks," MLSYS, 2020 🔴

  • J. Liang, H. Liu, Y. Zhao, M. Sanjabi, "Building Placements In Urban Modeling Using Conditional Generative Latent Optimization," IEEE International Conference on Image Processing (ICIP), 2020 🟡

  • M. Razaviyayn, T. Huang, S. Lu, M. Nouiehed, M. Sanjabi, M. Hong, "Nonconvex min-max optimization: Applications, challenges, and recent theoretical advances," IEEE Signal Processing Magazine, 2020 🔵

  • M. Nouiehed, M. Sanjabi, T. Huang, J. D. Lee, and M. Razaviyayn, "Solving a Class of Non-Convex Min-Max Games Using Iterative First-Order Methods," NeurIPS, 2019 🔵🟢

  • Y. Zhao, H. Liu, I. Borovikov, A. Beirami, M. Sanjabi, and K. Zaman, "Multi-Style Generative Adversarial Terrain Amplification," ACM SIGGRAPH Conference on Computer Graphics and Interactive Techniques in Asia (SIGGRAPH Asia) [Published in ACM Transactions on Graphics (TOG)], 2019 🟡

  • B. Barazandeh, M. Razaviyayn, and M. Sanjabi, "Training Generative Networks using Random Discriminators," IEEE Data Science Workshop (DSW), 2019 [Best Paper Award] 🔵🟡

  • M. Sanjabi, J. Ba, M. Razaviyayn, and J. D. Lee, "On the Convergence and Robustness of Training GANs with Regularized Optimal Transport," NeurIPS, 2018 🔵🟡

  • V. Smith, C. K. Chiang, M. Sanjabi, and A. Talwalkar, "Federated Multi-Task Learning," NeurIPS, 2017 🔴

  • M. Razaviyayn, M. Sanjabi, and Z.-Q. Luo, "A Stochastic Successive Minimization Method for Nonsmooth Nonconvex Optimization with Applications to Transceiver Design in Wireless Communication Networks," Mathematical Programming, 2016 🔵

• Preprints:

  • C. Guo, A. Sablayrolles, M. Sanjabi, "Analyzing Privacy Leakage in Machine Learning via Multiple Hypothesis Testing: A Lesson From Fano," arXiv preprint:2210.13662, 2022 [submitted to AISTATS'23] 🔴

  • N. M. Kalibhat, K. Narang, L. Tan, H. Firooz, M. Sanjabi, and S. Feizi, "Understanding Failure Modes of Self-Supervised Learning," arXiv preprint arXiv:2203.01881, 2022 [submitted to CVPR'23] 🟢🟣

  • T. Li, A. Beirami, M. Sanjabi, and V. Smith, "On tilted losses in machine learning: Theory and applications," arXiv preprint arXiv:2112.08802 [submitted to JMLR] 🟢

  • M. Sanjabi, S. Baharlouie, M. Razaviyayn, and J. D. Lee, "When Does Non-Orthogonal Tensor Decomposition Have No Spurious Local Minima?" arXiv preprint arXiv:1911.09815 🔵

• Patents:

  • Y. Zhao, I. Borovikov, M. Sanjabi, M. Sardari, H. Chaput, N. Aghdaie, and K. Zaman, "Systems and methods for supervised and unsupervised animation style transfer," US Patent App. 16/896,541 🟡

Recent Talks

• Fair Resource Allocation in Federated Learning, INFORMS 2020

• Challenges of Modern Machine Learning at Scale: An Optimization Perspective, Facebook AI, Amazon Alexa AI, Bosch AI, UIC, Electronic Arts Data & AI, Samsung Research, JP Morgan AI (2019-2020)

• Generative Adversarial Network Formulations with Convergence Guarantees, UCL, and INFORMS 2018

Contact:

Email: my first name dot my last name "at" gmail "dot" com