Emmanuel Tsukerman, Machine Learning Developer in Jerusalem, AR, United States
Emmanuel Tsukerman

Machine Learning Developer in Jerusalem, AR, United States

Member since January 13, 2020
Emmanuel graduated from Stanford University. In 2017, his ML-based anti-ransomware product was awarded Top 10 Ransomware Products of 2018 by PC Magazine. In 2018, he designed an ML-based malware detection system for Palo Alto Network's WildFire service (over 30,000 customers). In 2019, Emmanuel authored the Machine Learning for Cybersecurity Cookbook and launched the Cybersecurity Data Science Course on Udemy.
Emmanuel is now available for hire




Jerusalem, AR, United States



Preferred Environment

Git, Jupyter, PyCharm, MacOS

The most amazing...

...project I've developed is an anti-ransomware product built from scratch that utilized machine learning to detect Ransomware viruses before they execute.


  • Data Scientist

    2018 - 2019
    Palo Alto Networks
    • Completed R&D of next-generation machine learning anti-malware system for WildFire production system (>30,000 enterprise users).
    • Initiated, researched, and developed a machine learning system for malicious PowerShell script detection.
    • Contributed to R&D for an AI-based NLP interface for the Panorama network security management system.
    Technologies: Hadoop, Keras, Scikit-learn, Python
  • Data Scientist

    2017 - 2018
    CyberSight Inc.
    • Completed R&D of machine learning-based anti-ransomware solution in a small startup.
    • Won Top 10 Best Ransomware Protection of 2018, Editor’s Choice (PC Magazine).
    • Developed machine learning features for an anti-ransomware solution.
    • Performed business analytics using Splunk.
    Technologies: Splunk, Boost, C++, Scikit-learn, Python
  • Machine Learning Engineer

    2015 - 2016
    SimInsights Inc.
    • Implemented online partially observable Markov decision process (POMDP) solver for personalized learning.
    • Implemented inverse reinforcement learning solution for personalized learning.
    • Created mathematical modeling for infectious disease propagation simulation for Tufts University.
    Technologies: Python
  • Team Lead

    2011 - 2011
    UCLA, Research in Industrial Projects for Students
    • Led team of four in implementing kernel density estimation (KDE) algorithm for crime prediction. The product was utilized by LAPD in the Topanga district for motor vehicle burglary prevention.
    Technologies: MATLAB


  • CyberSight RansomStopper

    I developed the product RansomStopper from conception to market, including its machine learning capabilities. There are two versions, a consumer and an enterprise version. Here's what PC Magazine had to say about the consumer version:

    "CyberSight RansomStopper offers free, dedicated ransomware protection, and it now handles ransomware that launches at Windows startup. It's a winner, and free."
    - Neil J. Rubenking (PC Magazine product reviewer) June 21, 2018

    Editors' Rating:4.5/5, excellent

  • Machine Learning for Cybersecurity Cookbook

    A handbook for the practitioner of machine learning in the field of cybersecurity, authored with publisher Packt.

    The most up-to-date most cutting-edge most practical guide out there for applying ML/data science to security!

    It teaches:

    1. Smart malware detection and evasion techniques using ML, deep learning for catching zero-day threats, overcoming obfuscation/packing, automating malware analysis...

    2. Social engineering using ML including voice impersonation, Deepfake, and fake review generation and *phishing on steroids* via ML...

    3. Next-gen pentesting including NN-based fuzzing, Metasploit made DANGEROUS with an RL agent, software vulnerability detection with AI and Tor deanonymizing!

    4. Upgraded intrusion detection with AI including Insider Threat detection, network anomaly detection, catching DDoS and financial fraud.

    5. Securing and attacking data with ML including Deep Learning for password cracking (#scary), steganalysis via AI, ML attacks on hardware, and encryption with NNs.

    6. Secure and Private AI to preserve customer privacy and confidentiality, including federated learning, differential privacy, and even adversarial robustness.

  • Machine Learning for Red Team Learning Path, InfoSec Institute

    I've developed the Machine Learning for Red Team learning path in collaboration with the InfoSec Institute.

    Here's the copy:

    Everyone knows that AI and machine learning are the future of penetration testing. Large cybersecurity enterprises talk about hackers automating and smartening their tools; The newspapers report on cybercriminals utilizing voice transfer technology to impersonate CEOs; The media warns us about the implications of DeepFakes in politics and beyond...

    This course finally teaches you how to do and defend against all these things.

    This course will be teaching you, in a hands-on and practical manner, how to use machine learning to perform penetration testing attacks, and how to perform penetration testing attacks on machine learning systems.

    You will learn
    • How to supercharge your vulnerability fuzzing using machine learning.
    • How to evade machine learning malware classifiers.
    • How to perform adversarial attacks on commercially-available machine learning as a service model.
    • How to bypass CAPTCHAs using machine learning.
    • How to create deep fakes.
    • How to poison, backdoor and steal machine learning models.

    And you will solidify your slick new skills in fun hands-on assignments.

    I wish this course was for everyone but unfortunately, it just ain’t so. You should enroll only if you are really passionate about computer security and want to be the best at what you do. This course will challenge you and introduce you to new ideas. It will offer you fun hands-on assignments that will require you to bypass CAPTCHA challenges, get your hands dirty and modify malware, fuzz a secretly-vulnerable program, trick a commercially-available machine learning as a service and create a realistic fake video. If this sounds exciting for you, then click the enroll button to get started!

  • Cybersecurity Data Science Learning Path, InfoSec Institute

    Authored online cybersecurity data science training available at InfoSec Institute.

    Here's the copy:

    This hands-on, comprehensive skill path covers everything from the fundamentals of cybersecurity data science to the state of the art. Among many other practical lessons, you will be setting up a cybersecurity lab, constructing classifiers to detect malware, utilizing deep learning technology and even hacking security systems with the help of machine learning — all taught by an award-winning expert in the field of cybersecurity data science.

  • Sound Logic and Monotonic AI Models (Publication)
    For those working with AI, the future is certainly exciting. At the same time, there is a general sense that AI suffers from one pesky flaw: AI in its current state can be unpredictably unreliable.


  • Languages

    Python, Python 3, SQL, C++, R, JavaScript
  • Libraries/APIs

    Keras, Scikit-learn, TensorFlow, PyTorch
  • Paradigms

    Data Science, Agile Software Development
  • Other

    Machine Learning, Malware Analysis, Software Development, Artificial Intelligence (AI), Natural Language Processing (NLP)
  • Frameworks

    RStudio Shiny, Boost, Hadoop, Spark
  • Tools

    Splunk, PyCharm, Jupyter, Git, MATLAB, Microsoft Power BI, Tableau
  • Industry Expertise

    IT Security
  • Platforms

    MacOS, Amazon Web Services (AWS), Docker


  • Ph.D. in Applied Mathematics
    2013 - 2017
    UC Berkeley - Berkeley, CA
  • Master's Degree in Computational and Mathematical Engineering (CME)
    2012 - 2013
    Stanford University - Palo Alto, CA
  • Bachelor's Degree with Honors in Mathematics
    2009 - 2013
    Stanford University - Palo Alto, CA


  • Deep Learning Specialization
    MARCH 2018 - PRESENT
  • IT Fundamentals

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