• Navigating Data Science at a Startup
    ACM NY, Dataiku and General Assembly are hosting two talks focused on building successful data science efforts at startups. Please be sure to RSVP both on Meetups and the GA site here: https://generalassemb.ly/education/navigating-data-science-for-startups/new-york-city/62276 ---------------------- Tentative Schedule: 6:30pm: Pizza + Beer mingling 7:00pm: TBD with a Dataiku Data Scientist 7:30pm: Navigating Data Science for Startups with Lisa Burton, PhD Executive Director of HearstLab and Kishau Rogers, Founder and CEO of Time Study ---------------------- Talk Abstracts: Navigating Data Science for Startups: Startups are agile and can move quickly, but often don't have significant historical data or many users generating new data. So what does "data science" at an early-stage startup look like? How does it change as the startup grows? What should you ask before joining a startup as their first data scientist? Lisa Burton will discuss data science for startups, from the perspectives as both a founder and a data scientist. She'll then be joined by Kishau Rogers, the founder and CEO of Time Study, for a fireside chat to learn more about how Kishau integrated data into her product from day one and what she looks for when hiring her initial data science team. ---------------------- Speaker Bios: Lisa Burton, PhD is the Executive Director of HearstLab, a community of early stage, women-led startups innovating in media, data and technology. HearstLab provides assistance with building teams and refining products, along with office space in their New York City offices. At HearstLab, Lisa meets with prospective startups and supports the portfolio companies in residence, including advising on data and data science. Throughout her career, Lisa has built and led data science programs at startups and as a consultant across diverse industries -- from mobile payments to advertising to healthcare. Most recently, she cofounded a startup that leveraged data from social media to help brands understand and connect with their customers. Lisa came to data science from Mechanical Engineering, where she specialized in data-driven modeling and machine learning to predict the motion of swimming animals. She holds a PhD and SM from MIT and BSE from Duke. Are you the founder of a women-led startup interested in HearstLab? Visit hearstlab.com or contact Lisa at [masked] to apply. ---------------------- Kishau Rogers is the Founder and CEO of Time Study. Time Study's mission is to eliminate time sheets through machine learning and mobile technology to automatically identify how employees spend their time, starting with health systems. Time Study is live with over 15,000 end users at health systems like NewYork-Presbyterian and Stony Brook Medicine. Kishau is a serial entrepreneur with over 20 years of experience developing software for hospitals. She previously founded Websmith, Inc., creating software solutions for partners from health and wellness agencies to non-profits, and PeerLoc, a technology startup providing a location services platform for indoor and other GPS-denied environments. Time Study is hiring engineers and data scientists! Contact Kishau at [masked] to learn more about open positions.

    General Assembly NYC

    902 Broadway, Level 4 · New York, NY

  • Detecting Misconduct and Malfeasance within Financial Institutions
    For our September meetup, ACM NY is partnering with Dataiku and NYC Data Science Academy to host two talks. Abstracts: Detecting Misconduct and Malfeasance within Financial Institutions by Dr. Ipeirotis: Misbehavior in the online world manifests itself in several forms, and often depends on the domain at hand. In the financial domain, firms have the regulatory obligation to self-monitor the activities of their employees (e.g., emails, chats, phone calls), in order to detect any form of misconduct. Some forms of misconduct are illegal activities (e.g., insider trading, bribery) while others are various forms of policy violations (e.g., following improper security practices, or inappropriate language use). Traditionally, and due to ease of understanding and implementation, firms deployed relatively archaic, rule-based systems for employee surveillance. Such rule-based systems generate a large number of false positive alerts, and are hard to adapt in changing environments. More recent techniques aimed at solving the problem by simply transitioning from simple rule-based techniques to statistical machine learning approaches, trying to treat the problem of misconduct detection as a single-document classification problem. We discuss why approaches that try to identify misconduct within single documents are destined to fail, and we present a set of approaches that focus on actors, connections among actors, and on cases of misconduct. Furthermore, we highlight the importance of having a "human in the loop'' approach, where humans are both guided and guide the system at the same time, in order to detect malfeasance faster, and also adapt to changing environments; we also show how humans can play an important role for detecting shortcomings of existing machine-learning-based malfeasance-detection systems, and how humans can be incentivized to detect such shortcomings. Our multifaceted approach has been used in real environments within both big, multinational and smaller financial institutions; we discuss practical constraints and lessons learned by operating in such non-tech, highly regulated environments. ---------------------- Transformers in NLP- Building a Neural Machine Translator by Alexander Wolf: In the past, Natural Language Processing has been dominated by Recurrent Models, but now a new architecture called the Transformer has been shown to dominate NLP in many domains. The revolutionary new model uses no recurrence but attention only and develops state of the art accuracy in a fraction of the training time compared to other Deep Learning models. Alex has built a translator using this architecture and will give introduction plus deep dive of how it works along, will explain how it can overcome pitfalls of RNN/LSTM models and will present a history of NLP/ Translation systems. ---------------------- Bios: Panos Ipeirotis is a Professor and George A. Kellner Faculty Fellow at the Department of Information, Operations, and Management Sciences at Leonard N. Stern School of Business of New York University. He received his Ph.D. degree in Computer Science from Columbia University in 2004. He has received nine "Best Paper" awards and nominations, a CAREER award from the National Science Foundation, and is the recipient of the 2015 Lagrange Prize in Complex Systems, for his contributions in the field of social media, user-generated content, and crowdsourcing. Alex is a Data Scientist at Dataiku, working with clients around the world to organize their data infrastructures and deploy data-driven products into production. Prior, he worked on software and business development in the tech industry and studied Computer Science and Statistics at Dartmouth College. He's passionate about the latest developments in Deep Learning/Tech and enriches Dataiku's NLP features. ---------------------- Schedule: 6:00pm: Pizza + Beer networking 6:15pm: Dr. Panos Ipeirotis, Professor at NYU 7:15pm: Alexander Wolf, Data Scientist at Dataiku

    NYC Data Science Academy

    500 8th Ave, Suite 905 · New York, NY

    5 comments
  • NLP for Language Translation, Comprehension, & Generation
    For our July meetup, we are partnering with Dataiku for two talks about NLP for language translation, comprehension, & generation! We are excited to have Nasrin Mostafazadeh (http://www.cs.rochester.edu/~nasrinm/) join us to talk about language comprehension and language generation around events. Nasrin is currently working at Elemental Cognition in NYC. We also welcome Alex Wolf, who is a Data Scientist at Dataiku responsible for deploying data-driven products into production. Thank you to NYC Data Science Academy (www.nycdatascience.com) for hosting us. ---------------------------- Talk 1 : A Novel Neural Network Architecture for NLP Deep Learning in NLP has been dominated in the past years by recurrent and convolutional models. But other models emerge to improve translation quality and performance. Alex has developed a translator for his team and clients using a new neural network architecture called the Transformer. Unlike traditional translator models, this one solely focuses on attention instead of recurrence and develops powerful NLP models in a fraction of the training time. Alex will explain how he built the translator, give a live demo, and discuss how the Transformer is able to overcome pitfalls of RNN models. ---------------------------- Talk 1 Speaker: Alex is a Data Scientist at Dataiku, working with clients around the world to organize their data infrastructures and deploy data-driven products into production. Prior to that, he worked on software and business development in the tech industry and studied Computer Science and Statistics at Dartmouth College. He's passionate about the latest developments in Deep Learning/Tech and works at enriching Dataiku's NLP features. ---------------------------- Talk 2 : Language Comprehension & Language Generation in Eventful Contexts Building AI systems that can process user input, understand it, and generate an engaging and contextually-relevant output in response, has been one of the longest-running goals in AI. Humans use a variety of modalities, such as language and visual cues, to communicate. A major trigger to our meaningful communications are "events" and how they cause/enable future events. In this talk, I will present my research about language comprehension and language generation around events, with a major focus on commonsense reasoning, world knowledge, and context modeling. I will focus on multiple context modalities such as narrative, conversational, and visual. ---------------------------- Talk 2 Speaker: Nasrin Mostafazadeh is a senior AI research scientist at Elemental Cognition where she works on the next generation of AI systems that not only comprehend language, but also explain their reasoning and answer 'why'. She has previously held research positions at BenevolentAI, Microsoft, and Google, working on various language comprehension tasks. Nasrin got her PhD at the University of Rochester at the conversational interaction and dialogue research group, during which she worked on language understanding in the context of stories, mainly through the lens of events and their causal and temporal relations. She has developed models for tackling various research tasks that push AI toward deeper language understanding with applications ranging from story generation to vision & language. ---------------------------- Agenda: 6:00 PM: Pizza, beer, & networking 6:30 PM: Talk by Alex Wolf, Data Scientist at Dataiku 7:00 PM: Networking break 7:15 PM: Talk by Nasrin Mostafazadeh, Senior AI Research Scientist at Elemental Cognition

    NYC Data Science Academy

    500 8th Ave, Suite 905 · New York, NY

  • Peeking into the On-Demand Economy
    For our June meetup, we are excited to have Ming Yin (http://mingyin.org) join us to talk the On-Demand Economy. Ming is currently a postdoctoral researcher at Microsoft Research New York City, where she is a member of the Computational Social Science group. ---------------------------- Abstract: Today, an increasing number of digital and mobile technologies have emerged to match customers, in almost real time, with a potentially global pool of self-employed labor, leading to the rise of the on-demand economy, which has brought about dramatic changes in our society. It creates new business models and new dynamics of labor allocation. It enables new models of computation, that is, human-in-the-loop computing. And it leads to new forms of knowledge creation—people all over the world are contributing to scientific studies in dozens of fields, either by making scientific observations as amateur scientists or by participating in online experiments as subjects. Despite its already significant impacts, the on-demand economy has still been considered as a black-box approach to soliciting labor from a crowd of on-demand workers. Little is known about these workers and their aggregated behavior. In this talk, using the on-demand crowdsourcing platforms as an example, I present my attempts and findings on opening up this black box with a combination of experimental and computational approaches, with focuses on understanding who the on-demand workers are, how to model their unique working behavior, and how to improve their work experience. ---------------------------- Short Bio: Ming Yin is currently a postdoctoral researcher at Microsoft Research New York City. Starting in Fall 2018, she will join Purdue University as an Assistant Professor in the Department of Computer Science. Ming’s primary research interests lie in the interdisciplinary area of social computing and crowdsourcing. Her research has contributed to better understanding human behavior in social computing and crowdsourcing systems through large-scale online behavior experiments, as well as incorporating the empirical insights from the behavioral data into developing models, algorithms, and interfaces to facilitate the design towards better systems. More broadly, her research connects to the fields of applied artificial intelligence and machine learning, computational social science, human-computer interaction and behavioral economics. Ming’s work is published in top venues like WWW, CHI, AAAI and IJCAI. Ming is named as a Siebel Scholar (Class of 2017), and she has received Best Paper Honorable Mention at the ACM Conference on Human Factors in Computing Systems (CHI’16). Ming obtained her bachelor's degree from Tsinghua University, Beijing, China, in 2011, and completed her PhD at Harvard University in 2017. ---------------------------- Agenda: 6:30 PM: Pizza, beer, & networking 7:00 PM: "Best Practices for Driving Data Science at Scale" by Charlie Cohen, Deployment Strategist at Dataiku 7:10 PM: "Peeking into the On-Demand Economy" by Ming Yin, Postdoctoral Researcher at Microsoft Research + Q&A ---------------------------- We are organizing this event in collaboration with Dataiku (https://www.meetup.com/Analytics-Data-Science-by-Dataiku-NY/). and NYC Data Science Academy (https://www.meetup.com/NYC-Open-Data/). We thank NYC Data Science Academy for hosting this meetup and Dataiku in helping at various steps.

    NYC Data Science Academy

    500 8th Ave, Suite 905 · New York, NY

  • ML and Deep Learning Use Cases from the Trenches
    Join us for a deep dive into business applications of machine learning and deep learning (intermediate level) at Dataiku and WeWork! Talk #1: Building a Translator Using a New Deep Learning Model for NLP Deep Learning in NLP has been dominated in the past years by recurrent and convolutional models. But other models emerge to improve translation quality and performance. Alex has developed a translator for his team and clients using a new neural network architecture called the Transformer. Unlike traditional translator models, this one solely focuses on attention instead of recurrence and develops powerful NLP models in a fraction of the training time. Alex is a Data Scientist at Dataiku, working with clients around the world to organize their data infrastructures and deploy data-driven products into production. He's passionate about the latest developments in deep learning. Talk #2: Powered by Machine Learning: Recommendation Systems at WeWork Team Rex at WeWork deploys intelligent applications to help our members create their life's work. One of our first products is a personalized newsfeed designed to surface the most relevant user-generated content for each member. This application is powered by a suite of recommendation models embedded within a novel multi-armed bandit-based ML platform. This framework allows for faster experimentation and more granular optimizations compared to a standard A/B testing framework. Karry Lu is a data scientist at WeWork with interests in recommendation systems, NLP and Bayesian inference. Prior lives include relapsed statistician, community organizer and failed writer. Alex Choi is a software engineer at WeWork, as well as a thought-leader, truth-seeker, and patriot. Note: This event is jointly organized with Dataiku. https://www.meetup.com/Analytics-Data-Science-by-Dataiku-NY/events/249943007/

    NYC Data Science Academy

    500 8th Ave, Suite 905 · New York, NY

    2 comments
  • Using Theory and Data for Better Decisions
    Abstract: The internet and modern technology enables us to communicate and interact at lightening speed and across vast distances. The communities and markets created by this technology must make collective decisions, allocate scarce resources, and understand each other quickly, efficiently, and often in the presence of noisy communication channels, ever changing environments, and/or adversarial data. Many theoretical results in these areas are grounded on worst case assumptions about agent behavior or the availability of resources. Transitioning theoretical results into practice requires data driven analysis and experiment as well as novel theory with assumptions based on real world data. I'll discuss recent work that focus on creating novel algorithms for including a novel, strategyproof mechanism for selecting a small subset of winners amongst a group of peers and algorithms for resource allocation with applications ranging from reviewer matching to deceased organ allocation. These projects require novel algorithms and leverage data to perform detailed experiments as well as creating open source tools. Bio: Nicholas Mattei (http://www.nickmattei.net) is a Research Staff Member in the Cognitive Computing Group the IBM TJ Watson Research Laboratory. His research is in artificial intelligence (AI) and its applications; largely motivated by problems that require a blend of techniques to develop systems and algorithms that support decision making for autonomous agents and/or humans. Most of his projects and leverage theory, data, and experiment to create novel algorithms, mechanisms, and systems that enable and support individual and group decision making. He is the founder and maintainer of PrefLib: A Library for Preferences; the associated PrefLib:Tools available on Github; and is the founder/co-chair for the Exploring Beyond the Worst Case in Computational Social Choice (2014 - 2017) held at AAMAS. Nicholas was formerly a senior researcher working with Prof. Toby Walsh in the AI & Algorithmic Decision Theory Group at Data61 (formerly known as the Optimisation Group at NICTA). He was/is also an adjunct lecturer in the School of Computer Science and Engineering (CSE) and member of the Algorithms Group at the University of New South Wales. He previously worked as a programmer and embedded electronics designer for nano-satellites at NASA Ames Research Center. He received his Ph.D from the University of Kentucky under the supervision of Prof. Judy Goldsmith in 2012. ---------------------------- Agenda: • 6:00 pm -- Check in at Guest Registration Tablet at the Front Desk. Networking • 6:30 pm -- Introduction, Announcements • 7:00 pm -- Presentation and Discussion ---------------------------- Important Note: • We require full name in RSVP for building security access • Please bring ID proof to allow entry to venue • Due to limited space, we request you to change your RSVP to `No' if your plan changes

    This Meetup is past

    We Work (Fulton St), 19th Floor

    222 Broadway · 10007, NY

    8 comments
  • Tech Talk by Blue Sky Studios on CGI @ CUNY Queens College Tech Expo / Job Fair
    **UPDATE*** The Fall 2017 Business and Tech Expo Event will have representatives from: New York Life, Baker Tilly, Canon, Abbot, Streetlib, Hollister, Bloomberg, SCIP, BNY Mellon, Byteflow Dynamics, Queens EDC, and more. It will also be attended by a large number of Queens College Computer Science students as well as from other disciplines. It will be taking place at 12pm tomorrow on the Quad at Queens College. ***AT 1 PM*** in the Science Building located adjacent to South West corner of the quad in Room C-201 will be the tech talk by Maurice Van Swaaij from Blue Sky Studios. For a map of campus please see [ http://www.qc.cuny.edu/about/directions/2d/Pages/default.aspx ] . The Queens College Chapter of ACM is co-hosting a tech talk by Blue Sky Studios at the Fall 2017 Business-Tech Expo on Campus. The expo is essentially a stop-and-chat job fair between NYC employers and Students at Queens College. It's a great opportunity to network and find work for students in Computer Science and for companies to find entry level employees. Queens College produces more CS Bachelor's degrees than any other NYC school and just recently produced the winning team in the 2017 Governor's "Making College Possible" Coding Challenge. Maurice Van Swaaij is the R&D head of Blue Sky Studios. He will be giving a presentation on their proprietary graphics rendering tool CGIStudio. He received his Software Engineering degree in the Netherlands and a Masters in Scientific Computing from Courant Institute in New York.After starting his career in CG at TDI in Paris, he joined Blue Sky in 1994 where he developed a voxel based ray-tracing technology for rendering hair for the first Ice Age movie. Subsequently the technology has been used to render fur, feathers, foliage, flowers, grass, saw dust, snow and crowds of characters among other things. We hope anyone interested in this event will come and check out the growing ACM community in Queens! http://qc-acm.github.io http://www.qc.cuny.edu/Pages/home.aspx http://blueskystudios.com/

    Queens College

    65-30 Kissena Blvd · Queens, NY

    5 comments
  • How search engines work and how to make them work for you
    An introduction to information retrieval: how search engines work and how to make them work for you Abstract: Search engines are designed for remembering, but sometimes you need to forget. I built a custom search engine in Python to help me quit my nail art habit and my cuticles have never felt better. I'll introduce the data structures and algorithms that make speedy full-text retrieval possible in production search contexts and demonstrate how they can be modified to change the search engine's behavior. Using my nail art addiction as a motivating example, I'll show ranking techniques to hide dangerous reminders, text normalization as a safeguard against ill-advised nostalgia, and token blacklists to forget about the old you once and for all. Bio: Fiona Condon is an engineer on GIPHY's Search & Discovery team, where she tweaks queries to help you find the perfect reaction gif. Before that, she worked on search ranking and internationalization at Etsy. She co-hosts a weekly online radio show from a shipping container in her home of Brooklyn, NY, and her favorite gif is Pee-Wee Herman rescuing handfuls of snakes from a burning pet store.

    Needs a location

    12 comments
  • Applications of Deep Learning in Healthcare
    Abstract: Arguable the two domains where artificial intelligence is poised to have the biggest impact on humanity are autonomous vehicles and healthcare. In each of these application areas, deep learning is starting make significant in-roads and holds the promise of revolutionizing them. In this talk I will give an overview of the use of deep learning models in the space of healthcare, with a particular focus towards medical imaging. In addition, I will give a glimpse of the work being done at Imagen with an aim towards transforming healthcare. Bio: Sumit Chopra is the head of A.I. Research at Imagen Technologies: a well funded stealth startup working towards transforming healthcare using AI. He is interested in advancing AI research with a particular focus towards deep learning in the area of healthcare. Before Imagen, he was a research scientist at Facebook AI Research (FAIR), where he worked on understanding natural language. He graduated with a Ph.D., in computer science from New York University under the supervision of Prof. Yann LeCun. His thesis proposed a first of its kind neural network model for relational regression, and was a conceptual foundation for a startup for modeling residential real estate prices. Following his Ph.D., he was a research scientist at AT&T Labs – Research working on building novel deep learning models for speech recognition, natural language processing, computer vision, and other areas of machine learning, such as, recommender systems, computational advertisement, and ranking.

    Needs a location

    3 comments
  • Introduction to Deep Learning using Pytorch (by Soumith Chintala)
    Abstract: In this talk, we will cover PyTorch, a new deep learning framework that enables new-age A.I. research using dynamic computation graphs. We shall look at the architecture of PyTorch and discuss some of the reasons for key decisions in designing it and subsequently look at the resulting improvements in user experience and performance. Bio: Soumith Chintala is a Researcher at Facebook AI Research, where he works on deep learning, reinforcement learning, generative image models, agents for video games and large-scale high-performance deep learning. He holds a Masters in CS from NYU, and spent time in Yann LeCun’s NYU lab building deep learning models for pedestrian detection, natural image OCR, depth-images among others.

    Needs a location

    2 comments