March 30th – April 1st, 2021

AI for Cybersecurity

Learn the essentials to become a skilled expert in Machine Learning for Cybersecurity
Register now

Leverage Your Programming Skills

Become a

Machine Learning Specialist in Cybersecurity

With the rapid growth of Artificial intelligence comes rising demand for Machine Learning specialists in Cybersecurity. To analyze massive amounts of data to detect and protect against the latest malware, ransom, trojan horses and other threats requires serious engineering. These projects of the future promise to be some of the most exciting and in demand jobs in software engineering today.


An ML specialist in Cybersecurity helps organizations evolve and transform their cybersecurity attitudes through intelligent code analysis, configuration analysis, and activity monitoring. At ODSC, build on your programming skills to implement cybersecurity solutions to protect organizations from existing cyber threats and identify new types of malware. You will learn from leading experts everything from data mining and organization to essential use of AI neural networks and automatic learning model recognition.

Some Current AI for Cybersecurity Speakers

deep learning with TensorFlow
Chief Data ScientistUntapt

Dr. Jon Krohn

Jon Krohn is Chief Data Scientist at the machine learning company untapt. He authored the book Deep Learning Illustrated, which was released by Addison-Wesley in 2019 and became an instant #1 bestseller that was translated into six languages. Jon is renowned for his compelling lectures, which he offers in-person at Columbia University, New York University, and the NYC Data Science Academy, as well as online via O’Reilly, YouTube, and his A4N podcast on A.I. news. Jon holds a doctorate in neuroscience from Oxford and has been publishing on machine learning in leading academic journals since 2010.

Linear Algebra, Calculus, and Probability: The Math ML Experts Master(Tutorial)

Director, Data SciencePandata

Hannah Arnson, PhD

Hannah Arnson serves as Director of Data Science with Pandata – a Cleveland-based AI consulting firm. There, she leverages her 10+ years of experience to lead AI solution design and development, with a focus on ethical and approachable AI. Hannah began her career as a neuroscientist, receiving a Ph.D. in neuroscience from Washington University in St. Louis, then continuing on to do postdoctoral research. During this time, she developed statistical and mathematical models to better understand topics ranging from the sense of smell to navigation in pigeons. As a data scientist, Hannah’s passions lie in finding patterns within complex datasets and educating to make these technical concepts accessible to all.

Building a Holistic Risk Profile: Near Real-Time Approach to Insider Threat Detection(Talk)

Staff Associate - Machine LearningColumbia University in the City of New York

Thomas Fan

Thomas J. Fan is a Staff Associate at the Data Science Institute at Columbia University. He is one of the core developers of scikit-learn, an open source machine learning library written in Python. Thomas holds a Masters in Mathematics from NYU and Masters in Physics from Stony Brook University. He also maintains skorch, a scikit-learn compatible neural network library that wraps PyTorch. He believes that developing open source software is one of the best ways to maximize one’s impact.

Introduction to Scikit-learn: Machine Learning in Python(Half-Day Training)

Intermediate Machine Learning with Scikit-learn: Cross-validation, Parameter Tuning, Pandas Interoperability, and Missing Values(Half-Day Training)

Intermediate Machine Learning with Scikit-learn: Evaluation, Calibration, and Inspection(Half-Day Training)

Advanced Machine Learning with Scikit-learn: Text Data, Imbalanced Data, and Poisson Regression(Half-Day Training)

VP Data & Analytics, Cybersecurity Technology and ControlsJPMorgan Chase & Co

Charles Givre

Charles Givre recently joined JP Morgan Chase works as a data scientist and technical product manager in the cybersecurity and technology controls group. Prior to joining JP Morgan, Mr. Givre worked as a lead data scientist for Deutsche Bank. Mr. Givre worked as a Senior Lead Data Scientist for Booz Allen Hamilton for seven years where he worked in the intersection of cyber security and data science. At Booz Allen, Mr. Givre worked on one of Booz Allen’s largest analytic programs where he led data science efforts and worked to expand the role of data science in the program. Mr. Givre is passionate about teaching others data science and analytic skills and has taught data science classes all over the world at conferences, universities and for clients. Mr. Givre taught data science classes at BlackHat, the O’Reilly Security Conference, the Center for Research in Applied Cryptography and Cyber Security at Bar Ilan University. He is a sought-after speaker and has delivered presentations at major industry conferences such as Strata-Hadoop World, Open Data Science Conference and others. One of Mr. Givre’s research interests is increasing the productivity of data science and analytic teams, and towards that end, he has been working extensively to promote the use of Apache Drill in security applications and is a committer and PMC Member for the Drill project. Mr. Givre teaches online classes for O’Reilly about Drill and Security Data Science and is a coauthor for the O’Reilly book Learning Apache Drill. Prior to joining Booz Allen, Mr. Givre, worked as a counterterrorism analyst at the Central Intelligence Agency for five years. Mr. Givre holds a Masters Degree in Middle Eastern Studies from Brandeis University, as well as a Bachelors of Science in Computer Science and a Bachelor’s of Music both from the University of Arizona. Mr. Givre blogs at thedataist.com and tweets @cgivre.

FirstEnergy

Danielle Aring

Danielle Aring is an IT Security Data Engineer IV with the Transmission Security Operations Center (TSOC) at FirstEnergy. In her role, she is responsible for the design, development, implementation and maintenance of IT security equipment and software. Danielle holds a master’s in Computer Information Science from Cleveland State University. With an extensive background in software engineering and expertise in machine learning, Danielle is guiding the transition of the TSOC away from reactionary, rules-based threat detection to preventative, predictive, threat-hunting approaches. She built her organizations’ security data lake in Hadoop from the ground up. Developed several large-scale data pipelines for near real-time security log ingest along with alerting, monitoring and metrics. Danielle is passionate about cybersecurity educational awareness and innovative applications of AI/ML to the changing threat landscape.

Building a Holistic Risk Profile: Near Real-Time Approach to Insider Threat Detection(Talk)

Kirk borne
Principal Data ScientistBooz Allen Hamilton

Dr. Kirk Borne

Dr. Kirk Borne is the Principal Data Scientist and an Executive Advisor at global technology and consulting firm Booz Allen Hamilton. In those roles, he focuses on applications of data science, data management, machine learning, A.I., and modeling across a wide variety of disciplines. He also provides training and mentoring to executives and data scientists within numerous external organizations, industries, agencies, and partners in the use of large data repositories and machine learning for discovery, decision support, and innovation. Previously, he was Professor of Astrophysics and Computational Science at George Mason University for 12 years where he did research, taught, and advised students in data science. Prior to that, Kirk spent nearly 20 years supporting data systems activities on NASA space science programs, which included a period as NASA’s Data Archive Project Scientist for the Hubble Space Telescope. Dr. Borne has a B.S. degree in Physics from LSU, and a Ph.D. in Astronomy from Caltech. In 2016 he was elected Fellow of the International Astrostatistics Association for his lifelong contributions to big data research in astronomy. As a global speaker, he has given hundreds of invited talks worldwide, including conference keynote presentations at many dozens of data science, A.I. and big data analytics events globally. He is an active contributor on social media, where he has been named consistently among the top worldwide influencers in big data and data science since 2013. He was recently identified as the #1 digital influencer worldwide for 2018-2019. You can follow him on Twitter at @KirkDBorne.

Solving the Data Scientist’s Cold-Start Problem with Machine Learning Examples(Half-Day Training)

Atypical Applications of Typical Machine Learning Algorithms(Half-Day Training)


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You Will Meet

  • Some of the world’s leading AI experts

  • Some of the best minds and authors behind today’s most popular AI platforms

  • Artificial Ingelligence and data science innovators

  • Data science & analytics specialists

  • Developers, engineers and programmers looking to build AI enabled software

  • Hundreds of attendees focused on AI engineering

  • CTOs and Chief Data Scientists from startups and Fortune 500 companies

  • Data scientists, data engineers, and AI platform experts

  • Peers from startups to Fortune 500 companies wrestling with large sets of consumer data

  • Representatives from Government agencies, universities, and other large institutions

What You'll Learn

Talks + Workshops + Special Events on these topics:

UpSkill Topics

  • Enhanced Machine Learning for Cybersecurity

  • Building a Holistic Risk Profile: Near Real-Time Approach to Insider Threat Detection

  • Linear Algebra, Calculus, and Probability: The Math ML Experts Master

  • Solving the Data Scientist’s Cold-Start Problem with Machine Learning Examples

  • Atypical Applications of Typical Machine Learning Algorithms

  • Introduction to Scikit-learn: Machine learning in Python

  • Intermediate Machine Learning with Scikit-learn: Cross-validation, Parameter Tuning, Pandas Interoperability, and Missing Values

  • Intermediate Machine Learning with Scikit-learn: Evaluation, Calibration, and Inspection

  • Advanced Machine Learning with Scikit-learn: Text Data, Imbalanced Data, and Poisson Regression

  • and more…

Languages & Frameworks

  • Tensorflow 2, PyTorch, Keras, Caffe 2.0, CNTK

  • Python scikit-learn, SciPy, Pandas, PyMC3,

  • R Programming, Keras, CARET

  • spaCy, AllenNLP, Stanford NLP

  • Spark, MLlib, Storm, Hadoop, Mahout

  • Kubernetes, Kafka, Zeppelin, Ignite

  • Apache Airflow, KubFlow, MLFlow

  • NLP Transformers, BERT, ULMFit, ElMo

  • Julia, Java, Jupyter Notebooks, NoSql, Neo4J

Why Attend?

Immerse yourself in talks and workshops on AI Engineering frameworks, topics, and languages

Learn about AI Engineering from leading AI experts who authored and built many of the platforms in use today

Network and connect with like-minded attendees to discover your next job, service, product or startup

Get speaker insights and training in AI frameworks such as TensorFlow, MXNet, PyTorch, Spark, Storm, Drill, Keras, and other AI platforms

ODSC EAST | March 30th – April 1st, 2021

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