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


  • Fatemehsadat Mireshghallah

    Ph.D. student at University of California, San Diego, CSE department

      Research Interests

    – Privacy-Preserving ML
    – Natural Language Processing
    – Hardware & System Design for ML

    Google Scholar | CV | GitHub | Twitter
      News
  • October 2020: I am giving an invited talk on privacy and fairness in deep neural network inference at the Machine Learning and Friends Lunch at Umass Amherst. You can find my slides for the talk here.

  • September 2020: I am giving an invited talk on Privacy-Preserving NLP at the 2020 Privacy Conference (PriCon). Checkout the talk here. You can find my slides for the talk here.

  • July 2020: I am co-leading a break out session titled Feminist Perspectives for ML & CV at the WiML 2020 Un-workshop. The reading list and the discussed material is available here.

  • 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!
      Internships
    Summer 2020
    Research Intern
    Microsoft Research AI, Knowledge Technologies and Intelligent Experiences (KTX) group, Redmond Lab
    Manager: Robert Sim
    Summer 2019
    Research Intern
    Western Digital Co. Research and Development
    Manager: Anand Kulkarni
      Publications

    [outdated, please refer to my Google Scholar page.]

  • Shredder: Learning Noise Distributions to Protect Inference Privacy, 25th International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS 20).

  • Shredder: Learning Noise Distributions to Protect Inference Privacy with a Self-Supervised Learning Approach, 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

  • Artifact Evaluation Program Committee Member for USENIX Security 2021
  • Reviewer for ICLR 2021 Conference
  • 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 MLArchSys workshop at ISCA 2020
  • Security & Privacy Committee Member and Session Chair for Grace Hopper Celebration (GHC) 2020
  • GHC (Grace Hopper Celebrateion) 2020 Privacy and Security Committer Member
  • Reviewer for ICML 2020 Conference
  • Artifact Evaluation Program Committee Member for ASPLOS 2020
  • Reviewer for IEEE TC Journal
  • Reviewer for ACM TACO Journal
  •   TA Experiences, UC San Diego
    Fall 2020

  • TA of CSE 276C: Mathematics for Robotics, Graduate Level, Instructor: Dr. Henrik I. Christensen
  • Winter and Fall 2019

  • TA of CSE 240D: Accelerator Design for Deep Learning, Graduate Level, Instructor: Dr. Hadi Esmaeilzadeh
  •   Volunteer TA Experiences, Sharif University of Technology
    Fall 2017

  • Head TA of Digital Electronics
  • Head TA of Probability and Statistics
  • Spring 2017

  • TA of Computer Architecture
  • TA of Signals and Systems
  • Head TA of Probability and Statistics
  • Fall 2016

  • TA of Advanced Programming
  • Head TA of Numerical Methods