October 2021

Upcoming Events


Introducing D3FEND, A Knowledge Graph of Cybersecurity Countermeasures

Presented by Peter Kaloroumakis
November 9 | 18:00 | Virtual

D3FEND is a knowledge base, but more specifically a knowledge graph, of cybersecurity countermeasure techniques. In the simplest sense, it is a catalog of defensive cybersecurity techniques and their relationships to offensive/adversary techniques. The primary goal of the initial D3FEND release is to help standardize the vocabulary used to describe defensive cybersecurity technology functionality. Join us in November to learn how D3FEND is helping security systems architecture experts and technical executives to make acquisition or investment decisions.

June 4, 2022 (Tentative)


We are putting the bow on our speaker list and securing a venue. Get in touch if you would like to submit a talk or sponsor the event.

Past Events

If you weren't able to see Ryan Carr's talk on Privacy Enhancing Technologies (PET) you can stream it on-demand. You can catch it and all of our past talks on our YouTube channel

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Data News and Articles

2021 Data/AI Salary SurveyIn June 2021, we asked the recipients of our Data & AI Newsletter to respond to a survey about compensation. The results gave us insight into what our subscribers are paid, where they’re located, what industries they work for, what their concerns are, and what sorts of career development opportunities they’re pursuing. Tags: AI, Management, Pay

Toward Fast and Accurate Neural Networks for Image RecognitionTAs neural network models and training data size grow, training efficiency is becoming an important focus for deep learning. In this post, we introduce two families of models for image recognition that leverage neural architecture search, and a principled design methodology based on model capacity and generalization. Tags: Visualization, AI

Red Hot: The 2021 Machine Learning, AI and Data (MAD) LandscapeA lot of the action has been happening behind the scenes on the data and ML infrastructure side, with entire new categories (data observability, reverse ETL, metrics stores, etc.) appearing and/or drastically accelerating. To keep track of this evolution, this is our eighth annual landscape and “state of the union” of the data and AI ecosystem – co-authored this year with my FirstMark colleague John Wu. Tags: ML, AI, Future

How Midsize Companies Can Compete in AIArtificial intelligence (AI) as an upcoming general-purpose technology is poised to create many new business opportunities and to disrupt entire industries. Startups and large corporations are seizing AI opportunities and strengthening their position. But what about midsize companies that often lack access to big data and AI talent? These midsize firms risk being left behind in the age of AI. As a remedy, these firms should consider pooling their data and talent in joint AI ventures. Tags: AI, Management

The First Rule of Machine Learning: Start without Machine LearningApplying machine learning effectively is tricky. You need data. You need a robust pipeline to support your data flows. And most of all, you need high-quality labels. As a result, most of the time, my first iteration doesn’t involve machine learning at all. Tags: ML

A New Link to an Old Model Could Crack the Mystery of Deep LearningTo help them explain the shocking success of deep neural networks, researchers are turning to older but better-understood models of machine learning. Tags: Deep Learning, ML, Neural Networks

Storage for Unstructured Big Data Should be Part of a Company's StrategyFor many IT organizations, data storage is an afterthought and not a strategic concern. However, when it comes to big data management, storage should occupy center stage. Tags: Storage, Unstructured Data

The Dawn of Quantum Natural Language ProcessingIn this paper, we discuss the initial attempts at boosting understanding human language based on deep-learning models with quantum computing. We successfully train a quantum-enhanced Long Short-Term Memory network to perform the parts-of-speech tagging task via numerical simulations. Moreover, a quantum-enhanced Transformer is proposed to perform the sentiment analysis based on the existing dataset. Tags: Quantum Computing, Modeling, ML

Data Tools and Resources  

BorbBorb is a pure python library to read, write and manipulate PDF documents. It represents a PDF document as a JSON-like datastructure of nested lists, dictionaries and primitives (numbers, string, booleans, etc). Tags: Python, PDF

SeedSeed allows you to develop the front-end with all the benefits of Rust, meaning speed, safety, and too many more things to count.The Seed templating system uses a macro syntax that makes Rustaceans feel right at home. This means linting, formatting, and commenting will work, and it's all in Rust. This is opposed to a JSX-like syntax that relies on IDE extensions to improve the developer experience. Tags: Rust, Elm, Web Apps

CoinBaseStart building your crypto project today with Coinbase Cloud APIs and blockchain infrastructure. Whether it’s crypto payment or trading APIs, data access, or staking infrastructure, Coinbase Cloud has it all. Tags: Cloud, CryptoPayment

How To's and Tutorials  


Reinforcement Learning Course MaterialsLecture notes, tutorial tasks including solutions as well as online videos for the reinforcement learning course hosted by Paderborn University. Source code for the entire course material is open and everyone is cordially invited to use it for self-learning (students) or to set up your own course (lecturers). Tags: Learning, Paderborn University

GNNNeural networks have been adapted to leverage the structure and properties of graphs. We explore the components needed for building a graph neural network - and motivate the design choices behind them. Tags: Neural Networks, Graphing

Clustering with Scikit-Learn in PythonThis tutorial demonstrates how to apply clustering algorithms with Python to a dataset with two concrete use cases. The first example uses clustering to identify meaningful groups of Greco-Roman authors based on their publications and their reception. The second use case applies clustering algorithms to textual data in order to discover thematic groups. After finishing this tutorial, you will be able to use clustering in Python with Scikit-learn applied to your own data, adding an invaluable method to your toolbox for exploratory data analysis. Tags: Python, Clustering



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Are you an expert with data and willing to mentor, or are you an up and coming hobbyist looking for a side project to work on? We have put together a group to focus on a few problems working with Baltimore City data and need your help. The current project focuses on data parsing and analysis for the Baltimore Board of Estimates. If interested, please send us an email or join us on Slack to discuss building a side project group.

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