THE FUZZY FRONT END OF PRODUCT INNOVATION

PDMA Learning and Networking Event

Thursday, November 20, 2014

6:00 - 8:00pm

Jon Marshall, President & Chief Innovateur, Innovation Frameworks

The Fuzzy Front End (FFE) is the messy "getting started" period of new product development processes. It includes opportunity analysis, concept generation, and pre-technical evaluation. As important as they are, these activities are often chaotic, unpredictable, and unstructured. Jon will provide an overview of this challenging period and then describe 2-3 tools or techniques that you can apply to start to bring clarity and order to this critical but least understood phase of the product life cycle. We will provide time for Q&A and an interactive discussion with the audience.

Jon is one of the area’s leading authorities on the FFE and innovation processes. For over 10 years Jon has been a fervent student and practitioner of many aspects of innovation, such as frameworks and models, creative and critical thinking, and metrics. His background in engineering and project management helps Jon merge innovation principles with the practical realities of developing products.

The Product Development and Management Association (PDMA) is the premier global advocate for product development and management professionals. The Oregon chapter's mission is to help local professionals and organizations to identify, develop, and launch more innovative and profitable products and services through cross-industry collaboration, thought leadership, and the sharing of best practices and practical knowledge. For more information about the Oregon Chapter of the PDMA, please contact: David Nash, Chapter President, at [email protected].

We encourage everyone in Oregon who is interested in the Product Development and Management Association to become a member of the National PDMA. For a great explanation on the benefits of membership in the PDMA, click here

Schedule:

6:00 – 6:30pm: Gathering / Networking / Refreshments**

6:30 – 6:45pm: Announcements (upcoming events, who’s hiring, etc.)

6:45 – 7:30pm: Panel Discussion

7:30 – 7:45pm: Q&A / Open discussion

7:45 – 8:00pm: Networking

Cost:

$10 on-line registration prior to the event **

$15 at the door

A discount is available for PDMA members

To register online, click the website above

** Note: This is a no-host event. The complete Lucky Lab food and drink menu is available. Grab a cold ale & bite to eat - and bring a friend or colleague to add to the discussion!

Check back for a rescheduled time.

Sunil Rao will be presenting his past research on SNOTEL data using the time series prediction.

Drought is a serious problem in much of the U.S., with the worst conditions across the southern and western parts of the nation. Much of irrigation and recreation facilities depend on proper forecasting of streamflow. The water supply for irrigation largely comes from rivers and creeks, whose streamflow originates from the springtime melting of winter snow. A water supply forecast is a prediction of streamflow volume that will flow past a point on a stream during a specified season, typically in the spring and summer. One of the primary sources for the data is through NRCS SNOTEL( Snow Telemetry) data (available to public as part of tax dollars at work). In this demo, we showcase one such tool (Timeseries ARIMAX model) to forecast Streamflow volume for Deschutes River Basin, OR and later compare with actual data to see how it performed.

If you would like to join the discussion check us out on Zoom https://zoom.us/j/7891236789.

Do you want to learn and share your passion in a supportive community? Knowledge Mavens is an ethos of sharing, creativity, and inspiration.

Our Meetup provides an opportunity to "Show and Tell" followed by feedback and Q&A. You'll have the opportunity to share with our channels such as Meetup, GitHub, YouTube, and Facebook to connect with more passionate people.

The second half of our session we'll collaborate on new topics. The winner wins an award for the most interesting topic and the opportunity to share in an upcoming session.

If there is anything that is universally true in machine learning, it is that more data is always better. That’s why data scientists always respond to the “How much data do you need?” question with “How much data can you get?”

But sometimes you don’t have much data, because: a) It’s genuinely hard to get; or b) You haven’t been gathering it for very long and it takes time. This is a much more common circumstance than you might think. And you are not alone if you're experiencing this issue. Luckily, and not surprisingly, the machine learning community has come up with numerous approaches for dealing with it. Come sit in on an exclusive conversation with Dr. Tim Oates, professor and practitioner, who will share what appeals to intuition as to why the methods work and provide concrete examples of their application to simple problems.

We will discuss transfer learning, open source datasets and synthetic data, few shot learning, active learning, and semi-supervised learning. By the end of the evening, you should understand what all of these methods are, when they are applicable, and what kinds of results you can expect from using them.

Dr. Tim Oates is Chief Data Scientist at Synaptiq and an Oros Family Professor of Computer Science and Technology in the Department of Computer Science and Electrical Engineering at the University of Maryland Baltimore County. He received a Ph.D. degree from the University of Massachusetts, Amherst in 2001 with a focus on Artificial Intelligence and Machine Learning, and spent a year as a postdoc in the MIT AI Lab. He is an author or co-author of more than 150 peer reviewed papers in AI, ML, and data mining. Dr. Oates served as Chief Scientist for a big data startup in the contact management space, and has consulted in a wide variety of industries, including healthcare, construction, amusement parks, publishing, and social media.

If there is anything that is universally true in machine learning, it is that more data is always better. That’s why data scientists always respond to the “How much data do you need?” question with “How much data can you get?”

But sometimes you don’t have much data, because: a) It’s genuinely hard to get; or b) You haven’t been gathering it for very long and it takes time. This is a much more common circumstance than you might think. And you are not alone if you're experiencing this issue. Luckily, and not surprisingly, the machine learning community has come up with numerous approaches for dealing with it. Come sit in on an exclusive conversation with Dr. Tim Oates, professor and practitioner, who will share what appeals to intuition as to why the methods work and provide concrete examples of their application to simple problems.

We will discuss transfer learning, open source datasets and synthetic data, few shot learning, active learning, and semi-supervised learning. By the end of the evening, you should understand what all of these methods are, when they are applicable, and what kinds of results you can expect from using them.

Dr. Tim Oates is Chief Data Scientist at Synaptiq and an Oros Family Professor of Computer Science and Technology in the Department of Computer Science and Electrical Engineering at the University of Maryland Baltimore County. He received a Ph.D. degree from the University of Massachusetts, Amherst in 2001 with a focus on Artificial Intelligence and Machine Learning, and spent a year as a postdoc in the MIT AI Lab. He is an author or co-author of more than 150 peer reviewed papers in AI, ML, and data mining. Dr. Oates served as Chief Scientist for a big data startup in the contact management space, and has consulted in a wide variety of industries, including healthcare, construction, amusement parks, publishing, and social media.