Webinars

Event Info

icon_calendar.jpgSeptember 13, 2022
icon_clock.jpg12pm ET
icon_stopwatch.jpgDuration: 1.30 hour

Philadelphia & Pittsburgh Chapter Presents:
Women in Analytics

Abstract/Speaker

Abstract:

A panel discussion addressing the challenges and opportunities for women in the analytics space including panelists from academia and industry. Hosted by the Philadelphia and Pittsburgh INFORMS Chapters.


    Presenters Bios:
    thumbnail_image009.png Alison Cozad

    Dr. Alison Cozad holds a Ph.D. in Chemical Engineering from Carnegie Mellon University where she leveraged mixed-integer and semi-infinite optimization methods to improve machine learning algorithms. Prior to joining Gurobi, she held multiple roles at ExxonMobil, including as a Senior Data Science Lead and Real-time Optimization Engineer. In her free time, Alison loves making things from CNC woodworking to electronics to cheese making to sock puppetry.

    thumbnail_image009.png Lisa Maillart

    Dr. Lisa Maillart is Professor and Interim Chair of the Department of Industrial Engineering. She also co-directs the Stochastic Modeling, Analysis and Control (SMAC) Laboratory. Prior to joining the faculty at Pitt, she served on the faculty of the Department of Operations in the Weatherhead School of Management at Case Western Reserve University. She received her M.S. and B.S. in industrial and systems engineering from Virginia Tech, and her Ph.D. in industrial and operations engineering from the University of Michigan. She also spent a year as a Fulbright U.S. Scholar and visiting faculty at the Technical University of Eindhoven. Her primary research interest is in sequential decision making under uncertainty, with applications in medical decision-making, healthcare operations, healthcare policy, and maintenance optimization.

    thumbnail_image009.png Susan Davidson

    Susan B. Davidson received the B.A. degree in Mathematics from Cornell University, Ithaca, NY, in 1978, and the M.A. and Ph.D. degrees in Electrical Engineering and Computer Science from Princeton University, Princeton NJ, in 1980 and 1982. Dr. Davidson is the Weiss Professor of Computer and Information Science (CIS) at the University of Pennsylvania, where she has been since 1982. Her research interests include data management for data science, database and web-based systems, provenance, crowdsourcing, and data citation.

    thumbnail_image009.png Neha Khanna

    Neha Khanna is a Technical Product Manager at Wayfair and is based out of Boston, Massachusetts. She leads SEO initiatives which drive organic consumer traffic to the platform. She has a strong passion for data-based storytelling and for creating products and solutions. She has an undergraduate degree in Computer Science and a Masters in Business Analytics from Drexel University. Being an advocate of the responsible use of data, she led a research project on Data Privacy that has also been published in the Delaware Times. She has over 6 years of experience managing products across a wide spectrum of industries, such as ecommerce, retail, marketing, healthcare and travel.

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    Optimization of Promotion Resources

    Event Info

    icon_calendar.jpgJune 14, 2022
    icon_clock.jpg5pm ET
    icon_stopwatch.jpgDuration: 1 hour

    Abstract/Speaker

    Abstract:

    Allocation of scarce promotion resources among competing brands in a portfolio is a key strategic planning decision in pharmaceutical marketing. Typical business questions include:

    • How effective were specific promotion tactics in the past as measured by return-on-investment (ROI)?
    • Can the promotion budget for particular tactics for specific brands be modified going forward in order to efficiently increase revenue?

    Marketing mix (MMx) – a series of statistical, regression models - estimates the level of return associated with each level of promotion spend. The model generates response curves, for each tactic and brand across the portfolio. The response curves are the inputs into a non-linear programming (NLP) model that solves the portfolio optimization business problem. 

    At Bayer Healthcare, we integrated MMx statistical models with an NLP optimization model. Our solution approach identified opportunities to both grow top-line revenue and profitability by optimally investing in various promotion tactics. 


    Presenters Bio:
    thumbnail_image009.png Moshe Rosenwein

    Moshe Rosenwein is interested in the application of optimization models and methods to business problems across many application areas: pharmaceutical marketing and sales, customer relationship management, e-commerce, call center operations, telecommunications network design, and supply chain optimization. He spent his 34-year career at AT&T Bell Laboratories, Medco Health Solutions, Novartis, Eisai, and, currently, Bayer. In his current role, Rosenwein is responsible for optimizing the allocation of promotion resources – including sales force personnel and digital media – across the Bayer portfolio. 

    Rosenwein received his B.S.E. in Civil Engineering on 1980 and a Ph.D. in Decision Sciences from the University of Pennsylvania (Wharton) in 1986.


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    Event Info

    icon_calendar.jpgApril 6, 2022
    icon_clock.jpg5pm ET
    icon_stopwatch.jpgDuration: 1 hour

    Driving Growth with Data: Discussing the Intersection of Marketing & Analytics

    Speakers



    Will Crowley


    Megan Ingram


    Michael Kania

    This panel will bring together industry leaders in the intersection of analytics and marketing and provide an engaging discussion on how to leverage the power of analytics to develop innovative marketing solutions. Topics include a wide range of methods and applications such as using lead scoring via logistic regression, using visualizations to drive data awareness, and building insights for clients using CRM data and machine learning.

    Join here

    Passcode: phlinforms
    Meeting ID: 890 0955 3479

    Event Info

    icon_calendar.jpgFebruary 16, 2022
    icon_clock.jpg6pm ET
    icon_stopwatch.jpgDuration: 1 hour

    A New Flat Map of a Sphere via Stress Minimization

    Speaker

    vanderbei__2_.jpg

    Robert Vanderbei is a Professor in the Department of Operations Research and Financial Engineering at Princeton University. From 2005 to 2012, he was chair of the department. In addition, he holds courtesy appointments in the Departments of Mathematics, Astrophysics, Computer Science, and Mechanical and Aerospace Engineering. He is also a member of the Program in Applied and Computational Mathematics, is a founding member of the Bendheim Center for Finance, and a former Director of the Engineering and Management Systems Program.

    Beyond Princeton, he is a Fellow of the American Mathematical Society (AMS), the Society for Applied and Industrial Mathematics (SIAM) and the Institute for Operations Research and the Management Sciences (INFORMS). Within INFORMS, he has served as President of the Optimization Society and the Computing Society and is the 2017 winner of the Khachiyan Prize for his work in optimization. He also serves on the Advisory Board for the journal Mathematical Programming Computation.

    He has degrees in Chemistry (BS), Operations Research and Statistics (MS), and Applied Mathematics (MS, PhD). After receiving his PhD from Cornell (1981), he was an NSF postdoc at the Courant Institute for Mathematical Sciences (NYU) for one year, then a lecturer in the Mathematics Department at the University of Illinois-Urbana/Champaign for two years before joining Bell Labs in 1984. At Bell Labs he made fundamental contributions to the field of optimization and holds three patents for his inventions. In 1990, he left Bell Labs to join Princeton University where he has been since.

    In addition to hundreds of research papers, he has written three books: (i) a textbook entitled Linear Programming: Foundations and Extensions now in its fifth edition and published by Springer, (ii) Sizing Up The Universe, an introductory astronomy book written jointly with J. Richard Gott and published by National Geographic, and (iii) Real and Convex Analysis, a textbook written jointly with Erhan Cinlar and published by Springer.

    Earth is not flat---it's a sphere.  Maps as they appear in books or on computer screens are flat.  Hence, it's an interesting challenge to find a projection from the sphere to a flat surface that accurately preserves that structure and metrics one inherits from the sphere.   Recently Gott, Goldberg and myself, introduced a new map that is better than all previously studied maps.  In this talk, I will describe this map, why it's best, and a new way of thinking about this map based on a very intuitive physical description of the map making process.

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    Applying Satellite Data for Public Good

    Event Info

    icon_calendar.jpgJune 29, 2021
    icon_clock.jpg6pm EDT
    icon_stopwatch.jpgDuration: 1 hour

    Speaker

    Philadelphia_webinar_March_2021.jpg

    Robert Cheetham
    President and CEO of Azavea

    Falling launch costs have combined with the microelectronics, optics, and fast hardware iterations from smartphones to create a revolution in Earth observation satellites. A few bus-sized satellites are being replaced by constellations of dozens or hundreds. Further, optical and multispectral data are being supplemented with radar and hyperspectral instruments. The results include avalanches of data, dozens of well-funded startups, and a widening array of potential applications. While the satellite industry was built on the back of national defense and intelligence budgets, today's companies are looking beyond that to commercial and humanitarian use cases. This talk will outline some of the latest open source tools and standards for working with Earth observation imagery, describe several contemporary use cases, and suggest some important technical challenges ahead.

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    A Recipe for Analytics Success (What I Learned the Hard Way so You Don't Have to)

    Event Info

    icon_calendar.jpgSeptember 29, 2020
    icon_clock.jpg6pm EDT
    icon_stopwatch.jpgDuration: 1 hour

    Speaker

    Dr. Erick Wikum
    Independent Analytics Consultant,Wikalytics, LLC.

    Dr. Erick Wikum is an independent analytics consultant for his own firm, Wikalytics, LLC. Over a nearly 30 year career in the practice of operations research and analytics, Erick has served as an internal and external consultant for numerous organizations in a variety of industries including transportation and logistics, retail and agribusiness. Erick is an active INFORMS member, recently serving as president and Subdivisions Council representative of the Analytics Society. Erick is an associate editor of the INFORMS Journal on Applied Analytics and is slated to serve as general chair of the 2022 INFORMS Business Analytics Conference. Erick earned M.S. and Ph.D. degrees in operations research from Georgia Tech and a B.S. in mathematics and operations research from the U.S. Air Force Academy.

    The hype surrounding data analytics would have you believe that combining one part data with one part mathematics, stirring liberally and salting to taste will produce a dish sure to please, but the truth is far more complicated.  You see, when it comes to achieving success with analytics, it is not just what you do, but how you do it.  In this talk, Erick Wikum will share 10 hard-earned lessons for how you, as an individual contributor, can deliver real value with analytics.

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    Real-Time Routing for On-Demand Delivery

    Event Info

    icon_calendar.jpgSeptember 29, 2020
    icon_clock.jpg6pm EDT
    icon_stopwatch.jpgDuration: 1 hour

    Speakers

    Presented by Carolyn Mooney and Ryan O'Neil, Co-founders of Nextmv

    The on-demand economy is exploding. With it, consumers demand immediate access to everything from meals to electronics. We will discuss the challenges such as highly dynamic business models create, and new optimization and simulation technologies for addressing them.

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    Use of Machine Learning and AI at Comcast to Drive Business Objectives & The Use of Machine Learning to Predict Customer Out-of-Home Network Service Issue

    Event Info

    icon_calendar.jpgOctober 27, 2020
    icon_clock.jpg6pm EDT
    icon_stopwatch.jpgDuration: 1 hour

    Speakers

    Ranjit Jangam
    Executive Director of Data Science, Comcast

    Machine learning and data science plays significant role at cable & telecommunication industries. There is vast amount of data with 3600 view to develop models to drive revenue & growth, manage risk, improve customer experience, provide personalized experiences to consumers, developing innovative & reliable product experiences. Due to excessive competition in the industry, emphasis on data science driven machine learning algorithms including parametric or non-parametric, big data and AI tools has been severe and never been most important. Objective of this session is to review complete end to end cycle of developing machine learning models at scale including feature engineering, model development, model deployment, data & model governance and review real-world business applications of predictive models in the cable industry.

    Rama Mahajanam
    Director of Machine Learning, Comcast

    With the COVID-19 pandemic starting mid-March, 2020, the rapid development of an “outside network check” provided an opportunity for the ML/AI modeling team to gather better features and labels. With a keen desire to keep technicians and customers isolated, the team explored new ML/AI models. These new models are trained to use cloud-based RF measurements. These measurements include remote telemetry from DOCSIS, and other equipment logged by collection systems. Another set of measurements were taken at the tap and ground block, finally offering a way to segment the network and train the machines differently. We will review a few highlights of this fascinating exercise, which is currently underway.

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