- From Search Engine to Match Engine @Enigma
On Monday, December 10th, Jarrod Parker, Principal Data Scientist at Enigma, will be speaking on how Enigma's Knowledge Graph went from search-based queries to match-based queries, allowing for fragmented input data to be confidently matched and inspected at scale. Schedule 6:30 PM - 7:00 PM, food, refreshments, and networking 7:00 PM - 8:00 PM, main speaker event 8:00 PM - 8:30 PM, Q&A, more networking Talk Title: From Search Engine to Match Engine Talk Description: Data about real world entities are fragmented across disparate datasets all over the public and private internet. Hear about how Enigma's Knowledge Graph went from search-based queries to match-based queries, allowing for fragmented input data to be confidently matched and inspected at scale. Jarrod will dig into character, word and statistical based matching features and show how it can be used as features for context-specific match classification. Speaker Bio Jarrod Parker is Principal Data Scientist at Enigma. He studied network security at Rochester Institute of Technology with a passion in machine learning and ontology. After developing entity disambiguation systems for a defense contractor, he led engineering at a social video startup called VYou, which happened to share an office space with Enigma. When he's not linking all the public data, he's at home learning to play a new instrument or trying to regularize his models. Misc Food and drinks will be available at the event, courtesy of Enigma. We look forward to meeting you!
- An Evening with South China Morning Post on China, AI, and Technology
This month, we would like to cross promote an event hosted by South China Morning Post on China, AI, and technology. Please see the event description below and kindly RSVP so we have an estimated headcount. ***** Please NOTE: The event starts at 5:30 PM, not 5:00 PM, as an earlier version indicates. Hope this allows everyone more buffer travel time. Event Description: A presentation and talk with Ravi Hinridad (Editor-in-chief of Abacus) and Malcolm Ong (Head of Product at South China Morning Post) as they discuss the China tech industry, AI and news media, and the launch of Abacus. Schedule: 5:30 - 5:50 PM Background of SCMP and Abacus, with an insight to AI by Malcolm Ong 5:50 - 6:10 PM China tech industry overview by Ravi Hinridad 6:10 - 6:30 PM Q&A with Malcolm and Ravi 6:30 - 7:30 PM Cocktail Reception Topics to be discussed: 1. Background on South China Morning Post and the launch of Abacus, a brand new mobile news brand exclusively focusing on the booming China tech industry. 2. An overview of the growing global influence of China's tech giants and industry, and why it matters to the world, covered by Ravi Hinridad 3. AI and news media, covered by Malcolm Ong Speaker Biographies: Ravi Hinridad Ravi is Abacus's Editor-in-Chief and SCMP’s editorial leadership from CNN. He offers an insider’s look at the China tech revolution, highlighting the key players, emerging companies, hottest gadgets, and more through both editorial and multimedia storytelling. Malcolm Ong Malcolm is the Head of Product at South China Morning Post (SCMP). He was previously a Product & Growth Manager at Lyft, an Entrepreneur-in-Residence (EIR) at 500 Startups, Co-founder & CTO of Skillshare, Product Lead at OMGPOP (acq. Zynga), a Senior Developer at Razorfish, an E-commerce Specialist for IBM Global Services, and a graduate of IBM’s Extreme Blue program where he co-filed four patents.
- Data Science Study Session @Insight Data Science
You are invited to join us for a data science study session. The start of a new year often means looking for new job opportunities. But how do you land your first data science role if you don't have any relevant work experience? On Monday, February 5th, join us for a discussion on how to choose the right demo project to help you stand out during a data science interview. We will kick off the evening with a moderated Q&A session with Rockson Chang, Program Director at Insight Data Science, and Pallab Paul, A.I. Student Ambassador with Intel. During the second part of the evening, Pallab will present a fun recommendation system demo project he built using deep learning. We welcome you to bring your questions and project ideas to this event. In the meantime, we strongly recommend reading this article after you RSVP. https://blog.insightdatascience.com/understand-product-on-your-path-to-a-data-science-career-1b2a51a60a0e Space is limited and will be first-come first-serve. We hope to see you there! Schedule: 6:30 PM - 7:00 PM, networking and socializing 7:00 PM - 7:20 PM, Q&A with Rockson and Pallab 7:20 PM - 7:50 PM, deep learning project demo 8:00 PM - 8:30 PM, more socializing
- Data Science Study Session @AppEagle
You are invited to join us for a data science study session. On Thursday, December 14th, 2017, Manojit Nandi will be speaking at AppEagle's office (Exchange Place, Jersey City) on algorithm fairness and discrimination. Schedule 6:30 PM - 7:00 PM, networking, lightning talk from AppEagle 7:00 PM - 8:00 PM, Manojit's talk on algorithm fairness and discrimination 8:00 PM - 8:30 PM, more networking Speaker Biography: Manojit is a Big Data Developer at AppEagle where he architects machine learning pipelines for seller analytics. He has previously worked as a data scientist in cyber-security, civic tech, and telecom industry. He is interested in applying machine learning and data science to solve problems with high societal impact. He is also a mentor for Thinkful’s Data Science Bootcamp and regularly speaks about machine learning at technology conferences. Talk Description: As data-driven models are more commonly used in decision-making and public policy, we as data practitioners must be aware of the systematic biases present in our data, so we do not discriminate or reinforce vicious cycles against vulnerable groups. We explore the concept of algorithmic fairness and how it relates to the traditional view of machine learning classifiers. I will discuss ways to measure the extent to which a classifier discriminates against a particular minority group and showcase special algorithms for mitigating the level of disparate impact of a classifier. Misc Food and drinks will be available at the event, courtesy of AppEagle. Thank you! [UPDATE] 1. Please bring a photo ID to get pass the security in the main lobby. The AppEagle office is on the 22nd floor. 2. We will be recording this event!
- Data Science Study Session @Squarespace
You are invited to join us for a data science study session. On Tuesday, November 14th, 2017, Aileen Nielsen will be speaking at Squarespace on the what, why, and how of probabilistic graphical Models in Python. Schedule 6:00 PM - 6:30 PM, networking, lightning talk from Squarespace 6:30 PM - 7:30 PM, Aileen's talk, followed by Q&A 7:30 PM - 8:00 PM, more networking Talk Description: Probabilistic Graphical Models in Python: What, Why, and How in 45 minutes or less This talk will give a high level overview of probabilistic graphical models and a practical introduction to available options for implementing graphical models in Python. We'll discuss what kinds of problems you can tackle with such models, and where they are currently deployed in research and industry. Time allowing, we'll also take a quick detour into R to compare what is available in R vs. Python. We will conclude with a deep dive into some package source code and a discussion of the weaknesses and "to-do's" for current Python probabilistic graphical models. Speaker Bio Aileen has worked in corporate law, physics research labs, and, most recently, a variety of NYC tech startups. Her interests range from defensive software engineering to UX designs for reducing cognitive load to the interplay between law and technology. Aileen is currently working at an early-stage NYC startup that has something to do with time series data and neural networks. She also serves as chair of the New York City Bar Association’s Science and Law committee, which focuses on how the latest developments in science and computing should be regulated and how such developments should inform existing legal practices. In the recent past, Aileen worked at mobile health platform One Drop and on Hillary Clinton's presidential campaign. She is a frequent speaker at machine learning conferences on both technical and sociological subjects. Misc Food and drinks will be available at the event, courtesy of Squarespace. Thank you!
- Data Science Study Session @Metis
You are invited to join us for a data science study session. On Tuesday, October 10th, Sophie Searcy, Senior Data Scientist at Metis, will be speaking on building home-made, bespoke deep learning models from scratch, starting only with python, some data, and a bit of math. Schedule 6:30 PM - 7:00 PM, networking, introductions from our sponsors 7:00 PM - 8:00 PM, main speaker event 8:00 PM - 8:30 PM, Q&A, more networking Talk Description: It seems like deep learning is everywhere: beating humans at go, translating text, driving cars. Tools abound that come with pre-built deep learning models. Just like store-bought pies, these tools are great for quick results that will be adequate in many cases. However, if we wish to better understand deep learning networks and if we wish to be able to tailor our models to specific cases rather than the middle, then we might benefit from learning to build deep networks from scratch (in part or in whole). In this talk we will cover building home-made, bespoke deep learning models from scratch, starting only with python, some data, and a bit of math. Speaker Bio Sophie Searcy comes to Metis from Elektra, a wearable startup that is replacing haptics with electricity. At Elektra she was cofounder and CTO, designing everything from the electronics to the framework for analyzing data. Before that she worked in the CoDaS lab at Rutgers where she combined cognitive science and theoretical computer science to build models of how people and machines teach and learn. She holds masters degrees in Electrical and Computer Engineering and Psychology. She is passionate about teaching, both in theory and in practice, and about making sure that data science is primarily a tool that is used to improve people's lives. Misc Food and drinks will be available at the event, courtesy of BeMyApp. Venue and speaker, courtesy of Metis. Big thank you to our sponsors, and we look forward to meeting you all! Message from our sponsors: Intel also created a mini contest for all the participants. If you have a ML project and want to showcase it, share it, or collaborate it with others, submit it to DevMesh. Everyone who submits a project to DevMesh will get remote access to Machine Learning Servers. On top of that, best projects will be selected and each winner will receive a $50 gift card Instructions to join DevMesh: 1. Create a new account at devmesh.intel.com (http://devmesh.intel.com/) 2. Join your dedicated group - Artificial Intelligence East Coast: https://devmesh.intel.com/groups/448 3. To submit a project, click on “add a project” *when submitting your project, make sure to select “Artificial Intelligence East Coast (https://devmesh.intel.com/groups/448)” as your group. To receive invitations for Intel webinars, news and tools for Machine Learning and Deep Learning, register on this link (https://plan.seek.intel.com/us_en_software_registration-form_DeveloperMeetupFollow-up_html?registration_source=florida_meetup)
- Data Science Study Group @ThoughtWorks
You are invited to join us for a data science study session. On Monday, October 2, Dinesh Kulkarni (https://www.linkedin.com/in/dckulkarni/), who is based in Google's Seattle office, will be giving a virtual presentation on tool-assisted machine learning life cycle using Google Cloud Datalab and Cloud ML Engine. Schedule: 6:00 PM - 7:00 PM EST main presentation, Q&A 7:00 PM - 7:30 PM EST networking among the attendees Talk Summary: Tool-assisted Machine Learning Life Cycle Machine Learning is being applied to an increasing range of problems with different data sets. The expectations are high but the life cycle to meet the expectations is complex - starting from data exploration through preparation, pre-processing, training, model testing/evaluation, inference explanation and batch/online prediction. We will illustrate how these life cycle phases can be implemented in a notebook environment using the example of Google Cloud Datalab and Cloud ML Engine. Time permitting, we will also briefly cover some key considerations in turning the lifecycle into a repeatable pipeline. Biography: Dinesh Kulkarni (@Di_Ku (https://twitter.com/Di_Ku)) is a Product Manager in Google Cloud Platform team. He drives Cloud ML Engine (hosted, managed TensorFlow) and Cloud Datalab (Jupyter-based, hosted Python notebooks). He has previously worked on Microsoft developer tools (C#, .NET framework, Visual Studio), Azure and SQL Server. Miscellaneous Details: This event will not be live-streamed or recorded, so please attend in person if you would like to hear the presentation. Our speaker is based in Seattle and will be presenting via Google Hangouts. There will be a chance to ask questions after the presentation, but there will be technical constraints for networking with the speaker. We will not be providing food or drinks for this event, so please bring your own dinner if you are going to be hungry!
- Data Science Study Session @Lifion
You are invited to join us for a data science study session. On Monday, August 21, Bruno Gonçalves, Moore-Sloan Fellow at NYU's Center for Data Science, will explain the intuition behind word embeddings and the word2vec family of algorithms. The venue this month is provided by our friends at Lifion. We hope to see you there! Registration for this event will open on Monday, August 7. Word2vec in Theory and Practice Word embeddings have received a lot of attention ever since Google researchers published word2vec in 2013. Their work demonstrated that embeddings learned by neural networks after "reading" a large corpus of text are able to preserve semantic relationships between words. As a result, this type of embedding started being studied in more detail and applied to more serious NLP and IR tasks such as summarization, query expansion, etc. In this talk we will cover the intuitions and algorithms underlying the word2vec family of algoritms and analyze in detail Tensorflow's word2vec implementation. IMPORTANT: Please use your FULL REAL NAME to register and bring your ID. Speaker Biography: Bruno Gonçalves is currently a Moore-Sloan Fellow at NYU's Center for Data Science. With a background in Physics and Computer Science his career has revolved around the use of datasets from sources as diverse as Apache web logs, Wikipedia edits, Twitter posts epidemiological reports and Census data to analyze and model Human Behavior and Mobility. More recently he has focused on the application of machine learning and neural network techniques to analyze large geolocated datasets. He is the editor of "Social Phenomena: From Data Analysis to Models" (Springer, 2015) and a co-author of the forthcoming "Twitterology: The Social Science of Twitter" (Springer, 2018). About Our Sponsor Lifion, by ADP, invites you to visit our new home for big ideas, ambitious folks, and those who are truly committed to delivering finely made products at scale. Join us after-hours for provocative talks, engaging discussions and some great opportunities to connect with serious thinkers and doers. Visit us at http://www.lifion.com for a calendar of upcoming events and current job opportunities. We look forward to meeting you soon!
- Data Science Study Session @Enigma
You are invited to join us for a data science study session. On Tuesday, July 11th, Jarrod Parker, Principal Data Scientist at Enigma, will be speaking on the technical details involving linking data from a high variety of public data sources. Schedule 6:30 PM - 7:00 PM, networking 7:00 PM - 8:00 PM, main speaker event 8:00 PM - 8:30 PM, Q&A, more networking Talk Title: Linking Data In The Wild Wild West Talk Description: If you had access to every database in the world what would you do? At Enigma, it sometimes feels like we're inheriting the schema debt of the entire world. This talk will explore the technical details involving linking data from a high variety of public data sources. Topics include: ontology, data cleaning, machine learning, entity disambiguation. Speaker Bio Jarrod Parker is Principal Data Scientist at Enigma. He studied network security at Rochester Institute of Technology with a passion in machine learning and ontology. After developing entity disambiguation systems for a defense contractor, he led engineering at a social video startup called VYou, which happened to share an office space with Enigma. When he's not linking all the public data, he's at home learning to play a new instrument or trying to regularize his models. Misc Food and drinks will be available at the event, courtesy of Enigma. We look forward to meeting you!
- Data Science Study Session @Metis
You are invited to join us for a data science study session. On Wednesday, June 14th, Julia Lintern, senior data scientist at Metis, will lead a presentation on Deep Learning Explorations with Keras. We hope to see you there! Schedule 6:30 PM - 7:00 PM, networking 7:00 PM - 8:00 PM, main speaker event 8:00 PM - 8:30 PM, Q&A, more networking Topic Description Certainly, some of the most exciting research going on right now is in the area Deep-Learning. But how do we get started with hands-on practice and how do we gain a basic understanding of what is going on within all of those deep learning layers? This lesson will help the beginner-level deep-learner navigate this new landscape. Julia will explain both the design theory and the Keras implementation of some of today’s most widely used deep-learning algorithms including convolutional neural nets and recurrent neural nets. Julia will also discuss some of her own recent explorations via Keras including a spin-off of Style Transfer. Speaker Bio Julia currently works at Metis as a Senior Data Scientist where she co-teaches the data science bootcamp, develops curricula and focuses on various other special projects. Prior to Metis she worked as a data scientist at Jetblue, where she used quantitative analysis and machine learning methods to provide continuous assessment of the aircraft fleet. Julia began her career as a structures engineer, where she designed repairs for damaged aircraft. In 2011, she transferred into a quantitative role at JetBlue and began her M.A. in Applied Math at Hunter College, where she focused on visualizations of various numerical methods including collocation and finite element methods. She discovered a deep appreciation for the combination of mathematics and visualizations and found data science to be a natural extension. She continues to collaborate on various projects including the current development of a Trap music generator. During certain seasons of her career, she has also worked on creative side projects such as Lia Lintern, her own fashion label. Water and soft drinks will be available at the event. Big thanks to our host Metis!