Alexandros G. Dimakis (Alex Dimakis)

Alex Dimakis Photo 

dimakis at

I am interested in information theory, coding theory and machine learning. Resume.

Google Scholar Profile.


  • We are hiring postdocs at the new NSF Institute for Foundations of Machine Learning
    (News release)

  • Four papers accepted to NeurIPS 2023

    • S. Gadre, G. Ilharco, A. Fang, J. Hayase, G. Smyrnis, T. Nguyen, R. Marten, M. Wortsman, D. Ghosh, J. Zhang, E. Orgad,
      R. Entezari, G. Daras, S. M. Pratt, V. Ramanujan, Y. Bitton, K. Marathe, S. Mussmann, R. Vencu, M. Cherti, R. Krishna,
      P. W. Koh, O. Saukh, A. Ratner, S. Song, H. Hajishirzi, A. Farhadi, R. Beaumont, S. Oh, A.G. Dimakis,
      J. Jitsev, Y. Carmon, V. Shankar, L. Schmidt,
      “DataComp: In search of the next generation of multimodal datasets”
      Proc. of Neural Information Processing Systems (NeurIPS), Dec. 2023. Datasets and Benchmarks Track,
      Selected for Oral presentation. (Project Page)

    • G. Daras, K. Shah, Y. Dagan, A. Gollakota, A. G. Dimakis, A. R. Klivans,
      “Ambient Diffusion: Learning Clean Distributions from Corrupted Data,”
      Proc. of Neural Information Processing Systems (NeurIPS), Dec. 2023.
      (Project Page) (Arxiv), (Code).

    • L. Rout, N. Raoof, G. Daras, C. Caramanis, A. G. Dimakis, S. Shakkottai,
      “Solving Inverse Problems Provably via Posterior Sampling with Latent Diffusion Models,”
      Proc. of Neural Information Processing Systems (NeurIPS), Dec. 2023. (Project Page), (Arxiv),(Code)

    • G. Daras, Y. Dagan, A.G. Dimakis, C. Daskalakis,
      “Martingale Diffusion Models: Mitigating Sampling Drift by Learning to be Consistent,
      ” Proc. of Neural Information Processing Systems (NeurIPS), Dec. 2023. (Project Page)

  • Our paper on deep learning for time-series modeling used for electronic system design and electromagnetic (EM) interconnect analysis for signal integrity, accepted to ICCAD:

    • S. Ravula, V. Gorti, B. Deng, S. Chakraborty, J. Pingenot, B. Mutnury, D. Wallace, D. Winterberg, A. R. Klivans, A. G. Dimakis,
      “One-Dimensional Deep Image Prior for Curve Fitting of S-Parameters from Electromagnetic Solvers,”
      2023 International Conference on Computer-Aided Design (ICCAD 2023) (Project Page)

  • T. Chen, C. Gong, D. J. Diaz, X. Chen, J. T. Wells, Q. Liu, Z. Wang, A. D. Ellington, A.G. Dimakis, A. R. Klivans,
    “HotProtein: A Novel Framework for Protein Thermostability Prediction and Editing,”
    Proceedings on the International Conference on Representation Learning (ICLR 2023).

  • Elected IEEE Fellow for contributions to distributed coding and learning, 2022.

  • Selected as Commissioner. Artificial Intelligence Commission on Competition, Inclusion, and Innovation
    by the US Chamber of Commerce to provide a roadmap for tech leadership to US policy makers.

  • Faculty of the year award (for 2022). MS Program in Information Technology Management, (Voted by students).

  • Keynote speaker, 14th IEEE Image and Multidimensional Signal Processing Workshop (IVMSP) 2022.

  • Plenary speaker, 13th International Conference on the Image, 2022.

  • Best Paper Award at UAI 2021 Workshop on Tractable Probabilistic Modeling.

  • Recent talk: Generative models are the new sparsity
    Recent Berkeley Simons talk on deep generative models and how they can be used to solve inverse problems including Denoising, Missing data, Compressed Sensing and MRI.

  • G. Daras, N. Raoof, Z. Gkalitsiou and A.G. Dimakis
    ‘‘Multitasking Models are Robust to Structural Failure: A Neural Model for Bilingual Cognitive Reserve,’’
    Proc. of Neural Information Processing Systems (NeurIPS), Dec. 2022.
    (Project Page) arxiv

  • M. Jordan, J. Hayase, A. G. Dimakis, S. Oh
    ‘‘Zonotope Domains for Lagrangian Neural Network Verification,’’
    Proc. of Neural Information Processing Systems (NeurIPS), Dec. 2022.

  • G. Daras, Y. Dagan, A. G. Dimakis, C. Daskalakis
    ‘‘Score-Guided Intermediate Level Optimization: Fast Langevin Mixing for Inverse Problems.’’
    International Conference on Machine Learning (ICML), 2022.
    (Project Page)

  • J. Whang, M. Delbracio, H. Talebi, C. Saharia, A. G. Dimakis, P. Milanfar,
    ‘‘Deblurring via Stochastic Refinement.’’
    Computer Vision and Pattern Recognition (CVPR) June 2022. (Oral Presentation) (Arxiv)

  • Two papers accepted to NeurIPS 2021

  • New paper: Intermediate Layer Optimization for Inverse Problems using Deep Generative Models
    by G. Daras, J. Dean, A. Jalal and A.G. Dimakis. (Code) (Paper)

  • Four papers accepted to NeurIPS 2020

    • A. Jalal, L. Liu, A.G. Dimakis and C. Caramanis,
      Robust compressed sensing of generative models, (arxiv)

    • M. Jordan and A.G. Dimakis,
      Exactly Computing the Local Lipschitz Constant of ReLU Networks, (arxiv)

    • I. Daras, N. Kitaev, A. Odena and A.G. Dimakis
      SMYRF - Efficient attention using asymmetric clustering (arxiv)

    • M. Kocaoglu, S. Shakkottai, A.G. Dimakis, C. Caramanis and S. Vishwanath
      Applications of Common Entropy in Causal Inference (pdf)

  • New Survey: Deep Learning Techniques for Inverse Problems in Imaging
    G. Ongie, A. Jalal, C. A. Metzler, R. G. Baraniuk, A. G. Dimakis and R. Willett
    Journal on Selected Areas in Information Theory, May 2020. (arxiv), ieeeXplore

  • Your Local GAN: Designing Two Dimensional Local Attention Mechanisms for Generative Models
    Giannis Daras, Augustus Odena, Han Zhang, Alexandros G. Dimakis, CVPR 2020.
    (arxiv) | Code | Colab Notebook | Twitter Thread

  • Gradient Coding from Cyclic MDS Codes and Expander Graphs
    N. Raviv, I. Tamo, R. Tandon and A. G. Dimakis, IEEE Transactions on Information Theory

  • Five papers accepted to NeurIPS 2019

    • Matt Jordan , Justin Lewis, Alexandros G. Dimakis
      Provable Certificates for Adversarial Examples: Fitting a Ball in the Union of Polytopes

    • Qi Lei, Ajil Jalal, Inderjit S. Dhillon, and Alexandros G. Dimakis
      Inverting Deep Generative models, One layer at a time.

    • Qi Lei, Jiacheng Zhuo, Constantine Caramanis, Inderjit S Dhillon, Alexandros G Dimakis
      Primal-Dual Block Frank-Wolfe

    • Shanshan Wu, Sujay Sanghavi, Alexandros G. Dimakis
      Sparse Logistic Regression Learns All Discrete Pairwise Graphical Models (Spotlight)

    • Shanshan Wu, Alexandros G. Dimakis, Sujay Sanghavi
      Learning Distributions Generated by One-Layer ReLU Networks

  • SysML 2019 paper on adversarial attacks on text classifiers

    • Q. Lei, L. Wu, P. Chen, A.G. Dimakis, I.S. Dhillon and M. Witbrock,
      Discrete Adversarial Attacks and Submodular Optimization with Applications to Text Classification
      Systems and Machine Learning (SysML), April 2019. (pdf) slides code

    • Press coverage: Nature News, VentureBeat, TechTalks

  • ICML 2019 paper on learned compressed sensing matrices

    • S. Wu, A.G. Dimakis, S. Sanghavi, F.X. Yu, D. Holtmann-Rice, D. Storcheus, A. Rostamizadeh, S. Kumar,
      Learning a Compressed Sensing Measurement Matrix via Gradient Unrolling
      International Conference on Machine Learning (ICML), 2019. (slides) (pdf) (Tensorflow Code)

  • New Paper accepted to NeurIPS 2018

  • Preprint

  • Preprint

    • The Robust Manifold Defense: Adversarial Training using Generative Models
      A. Ilyas, A. Jalal, E. Asteri, C. Daskalakis, A. G. Dimakis

  • Paper accepted to Annals of Statistics

    • E. R. Elenberg, R. Khanna, A.G. Dimakis, and S. Negahban
      ‘‘Restricted Strong Convexity Implies Weak Submodularity’’,
      to appear in Annals of Statistics, 2018.

  • Teaching resources for Data Science and Machine learning

  • Honored to receive the James Massey award

  • NeurIPS, ICML, AISTATS Area chair

  • Compressed Sensing using Generative models Pre-trained models see also GitHub CSGM

  • ICLR 2018

    • A. Bora, E. Price, A.G. Dimakis
      AmbientGAN: Generative models from lossy measurements
      (Oral Presentation) (openreview), (code)

    • M. Kocaoglu, C. Snyder, A.G. Dimakis and S. Vishwanath
      CausalGAN: Learning Causal Implicit Generative Models with Adversarial Training
      (arxiv) (code)

  • NIPS 2017:

    • E.R. Elenberg, A.G. Dimakis, M. Feldman, and A. Karbasi
      ‘‘Streaming Weak Submodularity: Interpreting Neural Networks on the Fly’’,
      (NIPS), 2017. (Oral presentation) (arxiv) code

    • Model-Powered Conditional Independence Test R. Sen, A.T. Suresh, K. Shanmugam, A.G. Dimakis and S. Shakkottai (NIPS), 2017.

  • Preprint

    • Gradient Coding from Cyclic MDS Codes and Expander Graphs
      N Raviv, I Tamo, R Tandon, AG Dimakis (arxiv)

  • Six papers accepted to ICML 2017:

    • Compressed Sensing using Generative Models (arxiv) Code and Demo
      A. Bora, A. Jalal, E. Price, A. G. Dimakis

    • Gradient Coding (pdf)
      R. Tandon, Q. Lei, A. G. Dimakis, N. Karampatziakis. (Slides from ITA)

    • On Approximation Guarantees for Greedy Low Rank Optimization (arxiv)
      R. Khanna, E. Elenberg, A. G. Dimakis, S. Negahban.

    • Exact MAP Inference by Avoiding Fractional Vertices (arxiv)
      E. M. Lindgren, A. G. Dimakis, A. Klivans.

    • Cost-Optimal Learning of Causal Graphs (arxiv)
      M. Kocaoglu, A. G. Dimakis, S. Vishwanath.

    • Identifying Best Interventions through Online Importance Sampling (arxiv)
      R. Sen, K. Shanmugam, A. G. Dimakis and S. Shakkottai.

  • Two papers accepted to ISIT 2017:

    • Entropic Causality and Greedy Minimum Entropy Coupling (arxiv)
      M. Kocaoglu, A. G. Dimakis, S. Vishwanath and B. Hassibi.

    • Coded Caching with Linear Subpacketization is Possible using Ruzsa-Szeméredi Graphs. (arxiv)
      K. Shanmugam, A. M. Tulino and A. G. Dimakis.

  • Recent work on decoding brain signals using interpretable features:

    • H. Yi, Z. Xie, R. Reetzke, A.G. Dimakis and B. Chandrasekaran.
      Vowel decoding from single-trial speech-evoked electrophysiological responses: A feature-based machine learning approach.
      Brain and Behavior. April 2017; (Open Access)

  • Check the cool projects from our undergraduate Data Science Lab course

  • Entropic Causality paper to appear in AAAI 2017. (Arxiv)
    More information

  • Two papers accepted to AISTATS 2017:

    • Contextual Bandits with Latent Confounders: An NMF Approach (pdf)
      R. Sen, K. Shanmugam, M. Kocaoglu, A. G. Dimakis and S. Shakkottai.

    • Scalable Greedy Feature Selection via Weak Submodularity. (pdf)
      R. Khanna, E. Elenberg, A. G. Dimakis, S. Neghaban and J. Ghosh

  • Slides and notes from GraphDay overview talk on graph analytics and machine learning (pdf)

  • Upcoming talk at Canadian Workshop on Information Theory (CWIT) (CWIT link)

  • Two papers accepted to NIPS 2016:

    • Leveraging Sparsity for Efficient Submodular Data Summarization
      E. Lindgren, S. Wu, A. G. Dimakis (pdf)

    • Single Pass PCA of Matrix Products S. Wu, S. Bhojanapalli, S. Sanghavi, A. G. Dimakis (pdf)

  • Preprint: Restricted Strong Convexity Implies Weak Submodularity
    E. Elenberg, R. Khanna, A. G. Dimakis, S. Negahban (pdf)

  • Distributed Estimation of Graph 4-profiles
    E. R. Elenberg, K. Shanmugam, M. Borokhovich, A. G. Dimakis.
    in Proc. International World Wide Web Conference (WWW), 2016 (Arxiv)

  • Bipartite Correlation Clustering: Maximizing Agreements
    M. Asteris, A. Kyrillidis, D. Papailiopoulos, A. G. Dimakis, AISTATS 2016 (pdf)

  • Three papers accepted to NIPS 2015

    • Orthogonal NMF through Subspace Exploration
      M. Asteris D. Papailiopoulos A. G. Dimakis (pdf)

    • Sparse PCA via Bipartite Matchings
      M. Asteris D. Papailiopoulos A. Kyrillidis A. G. Dimakis (pdf)

    • Learning Causal Graphs with Small Interventions
      K. Shanmugam, M. Kocaoglu, A.G. Dimakis, S. Vishwanath (arxiv)

  • Stay on path: PCA along graph paths M. Asteris A. Kyrillidis A. G. Dimakis H. Yi B. Chandrasekaran International Conference on Machine Learning (ICML), Lille, France, 2015, (pdf) (slides)

I maintain the Distributed Storage Wiki, an online bibliography about theoretical problems in large-scale distributed storage systems. (Temprary offline, will need new host for this)