Research Assistant and PhD student at University of California, San Diego, CSE department

La Jolla, CA

f.mireshghallah@gmail.com


About Me!

I like mysteries and puzzles a lot, that is why I enjoy watching police mystery TV series or medical mysteries. I have lately been binge-watching National Geographic docuseries, I also do clothes designing in my free time, a refreshing activity that helps me focus and clear my mind. I sometimes go jogging after my classes, to get some air and think about ideas for my research. I usually have helpful ideas when I am running or walking and have solved a lot of the problems in my projects while exercising!

Books I Like!

  • Sapiens: A Brief History of Humankind by Yuval Noah Harari
  • The Martian by Andy Weir
  • The Solitaire Mystery by Jostein Gaarder
  • The Orange Girl by Jostein Gaarder
  • Life is Short: A Letter to St Augustine by Jostein Gaarder
  • The Alchemist by Paulo Coelho
  • The Art of Thinking Clearly by Rolf Dobelli
  • Funny in Farsi by Firoozeh Dumas
  • Curriculum Vitae



    Fatemehsadat Mireshghallah

    "Deep in the sea are riches beyond compare. But if you seek safety, it is on the shore."
    -Sa'adi, Iranian Poet

    Research Interests

  • Privacy for ML
  • Architectural Support for Machine Learning
  • Computer Architecture
  • Google Scholar
    You can find code for my projects at my GitHub.

    News

  • June 2020: I started my internship at Microsoft Research AI, with the Knowledge Technologies and Intelligent Experiences (KTX) group, where I am working on private and ethical text generation.

  • May 2020: Our paper "Divide and Conquer: Leveraging Intermediate Feature Representations for Quantized Training of Neural Networks" got accepted at the Thirty-seventh International Conference on Machine Learning (ICML 2020).

  • May 2020: Join us at OpenMined virtual Ask Me Anything (AMA) session, where I answer questions about privacy, machine learning, research and PhD life! You can find the recording here.

  • April 2020: Join us at the "Learning Representation for Cybersecurity" social, where we will be discussing Cybersecurity, ML-Based intrusion and malware detection, privacy-preserving ML and other interesting topics! You can find slides for my talk here. You can also find a reading list of papers here.

  • April 2020: I was chosen as a winner of NCWIT (National Center for Women and IT)'s AiC Collegiate award!

  • March 2020: I virtually presented my paper Shredder in ASPLOS 2020. You can find my presentation video here.

  • December 2019: I was chosen as an NCWIT (National Center for Women and IT) collegiate award finalist!

  • December 2019: Join us in Vancouver for the WiMLDS [NeurIPS Special] Talks + Panel Discussion where I'll be giving a talk on Privacy in Mahcine Learning! You can find my slides for this talk here.

  • November 2019: Our paper Shredder: Learning Noise Distributions to Protect Inference Privacy got into the 25th International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS 20) with less than 18% acceptance rate!

  • October 2019: Our paper Shredder got into NeurIPS19's Privacy in ML workshop!

  • June 2019: I am joining Western Digital's research department as a RAMP Next Generation Platform Technologies Intern.

  • June 2019: Our paper Shredder got into ICML's SPML workshop, let's meet up if you are attending ICML19!

  • April 2019: I am attending ASPLOS19, let me know if you are there!
  • Keep up with me on Twitter for more news!


    Publications

  • Shredder: Learning Noise Distributions to Protect Inference Privacy with a Self-Supervised Learning Approach, The Thirty-fourth Annual Conference on Neural Information Processing Systems (NeurIPS19), Privacy in Machin Learning Workshop (PriML19).
    Code available at shredder-v2-self-supervised

  • Shredder: Learning Noise to Protect Privacy with Partial DNN Inference on the Edge Thirty-sixth International Conference on Machine Learning (ICML19), Security and Privacy of Machin Learning Workshop (SPML19).
    Code available at shredder-v1

  • Energy-Efficient Permanent Fault Tolerance in Hard Real-Time Systems, IEEE Transactions on Computers, March 2019

  • ReLeQ: An Automatic Reinforcement Learning Approach for Deep Quantization of Neural Networks, NeurIPS ML for systems workshop, December 2018

  • Professional Services

  • Program Committee member for the LatinX in AI Research Workshop at ICML 2020 (LXAI)

  • Reviewer for the 2020 Workshop on Human Interpretability in Machine Learning (WHI) at ICML 2020

  • Program Committee member for the ML for Computer Architecture and Systems (MLArchSys) workshop at ISCA 2020

  • Mentor for COVIDathon

  • Reviewer for NeurIPS 2020 Conference

  • GHC (Grace Hopper Celebrateion) 2020 Privacy and Security Committer Member

  • Reviewer for ICML 2020 Conference

  • Reviewer for TACO Journal


  • TA Experiences, UC San Diego
    Winter and Fall 2019

  • TA of Accelerator Design for Deep Learning, Graduate Leve, Instructor: Dr. Hadi Esmaeilzadeh
  • Volunteer TA Experiences, Sharif University of Technology
    Fall 2017

  • Head TA of Digital Electronics Course, Instructor: Dr. Siavash Bayat
  • Head TA of Probability and Statistics Course, Fall of 2017, Instructor: Dr. Mohammed Gharib
  • Spring 2017

  • TA of Computer Architecture Course, Instructor: Prof. Hamid Sarbazi-Azad
  • TA of Signals and systems, Spring of 2017, Instructor: Dr. Siavash Bayat
  • Head TA of Probability and Statistics Course, Instructor: Dr. Mohammed Gharib
  • Fall 2016

  • TA of Advanced Programming course, Instructor : Mr. Omid Jafarinezhad
  • Head TA of Numerical Methods course, Instructor: Dr.Mohammed Gharib