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We're trying to find in-person venues in Manhattan but in the meantime this talk will be virtual. It's possible that we can find a venue at the last minute so please provide an ID.

Our speaker is William Strickland, PhD who will be talking about error correction.

Title: Protecting qubits from bit and phase flips at the same time.

Abstract: Cooper pair transport in Josephson elements provides the key nonlinearity to construct quantum devices with superconducting circuits. While state of the art superconducting qubits are almost exclusively based on Al/AlOx/Al tunnel junctions, an alternative approach using hybrid superconductor-semiconductor Josephson junctions (JJs) can enable a superconducting qubit architecture with full electric field control. In this talk, I will present progress on using superconductor-semiconductor JJs as the key element in voltage-tunable qubits and couplers where our team has demonstrated a gate voltage tunable frequency (several GHz), impedance, and nonlinearity, as well as coherent control of the qubit state. Crucial to its application in qubit technologies, I will present a comprehensive analysis of the losses in the semiconductor and superconductor materials. Finally, I will present a new qubit architecture, named gatemonium, which may provide a path forward not only for improved coherence times in superconducting circuits based on S-Sm hybrid materials, but also provide a path forward for Josephson junction arrays with enhanced plasma frequencies.

Bio: William Strickland is a postdoctoral researcher in the Center for Quantum Information Physics at New York University. His research focuses on making novel qubits with hybrid superconductor-semiconductor materials. He received his PhD from NYU in 2025 and his B.S. in Physics at UC Santa Barbara in 2018. He received a Quantum Computing Graduate Research fellowship from the Laboratory for Physical Sciences in 2021 and a Graduate Student Award from the Materials Research Society in 2022.

The Pandas Workshop offers a detailed journey into the world of data analysis using Python and the pandas library. Throughout the book, you'll build skills in accessing, transforming, visualizing, and modeling data, all while focusing on real-world data science challenges. You will gain the knowledge and confidence needed to dissect and derive insights from complex datasets. What this Book will help me do Understand how to access and load data from various formats including databases and web-based sources. Manipulate and transform data for analysis using efficient pandas techniques. Create insightful visualizations using Matplotlib integrated with pandas for clearer data presentation. Build predictive and descriptive data models and glean data-driven insights. Handle and analyze time-series data to uncover trends and seasonal effects in data patterns. Author(s) Blaine Bateman, Saikat Basak, Thomas Joseph, and William So collectively bring diverse expertise in data analysis, programming, and teaching. Their goal is to make cutting-edge data science techniques accessible through clear explanations and practical exercises, helping learners from varied backgrounds master the pandas library. Who is it for? This book is best suited for novice to intermediate programmers and data enthusiasts who are already familiar with Python but are new to the pandas library. Ideal readers are those interested in honing their skills in data analysis and visualization, as well as leveraging data for informed decision-making. Whether you're an analyst, aspiring data scientist, or business professional seeking to strengthen your analytical toolkit, this book provides beneficial insights and techniques.

Why design matters in data products is a question that, at first glance, may not be easily answered for some until they see users try to use ML models and analytics to make decisions. For Bill Báez, a data scientist and VP of Strategy at Ascend Innovations, realizing that design and UX matters in this context was a realization that grew over the course of a few years. Bill’s origins in the Air Force, and his transition to Ascend Innovations, instilled lessons about the importance of using design thinking with both clients and users. 

After observing solutions built in total isolation with zero empathy and knowledge of how they were being perceived in the wild, Bill realized the critical need to bring developers “upstairs” to actually observe the people using the solutions that were being built. 

Currently, Ascend Innovation’s consulting is primarily rooted in healthcare and community services, and in this episode, Bill provides some real-world examples where their machine learning and analytics solutions were informed by approaching the problems from a human-centered design perspective. Bill also dives in to where he is on his journey to integrate his UX and data science teams at Ascend so they can create better value for their clients and their client’s constituents. 

Highlights in this episode include:

What caused Bill to notice design for the first time and its importance in data products (03:12) Bridging the gap between data science, UX, and the client’s needs at Ascend (08:07) How to deal with the “presenting problem” and working with feedback (16:00) Bill’s advice for getting designers, UX, and clients on the same page based on his experience to date (23:56) How Bill provides unity for his UX and data science teams   (32:40) The effects of UX in medicine (41:00)

Quotes from Today’s Episode “My journey into Design Thinking started in earnest when I started at Ascend, but I didn’t really have the terminology to use. For example, Design Thinking and UX were actually terms I was not personally aware of until last summer. But now that I know and have been exposed to it and have learned more about it, I realize I’ve been doing a lot of that type of work in earnest since 2018. - Bill (03:37)

“Ascend Innovations has always been product-focused, although again, services is our main line of business. As we started hiring a more dedicated UX team, people who’ve been doing this for their whole career, it really helped me to understand what I had experienced prior to coming to Ascend. Part of the time I was here at Ascend that UX framework and that Design Thinking lens, it really brings a lot more firepower to what data science is trying to achieve at the end of the day.” - Bill (08:29) “Clients were surprised that we were asking such rudimentary questions.  They’ll say ‘Well, we’ve already talked about that,’ or, ‘It should be obvious.’ or ‘Well, why are you asking me such a simple question?’ And we had to explain to them that we wanted to start at the bottom to move to the top. We don’t want to start somewhere midway and get the top. We want to make sure that we are all in alignment with what we’re trying to do, so we want to establish that baseline of understanding. So, we’re going to start off asking very simple questions and work our way up from there...” - Bill (21:09)

“We’re building a thing, but the thing only has value if it creates a change in the world. The world being, in the mind of the stakeholder, in the minds of the users, maybe some third parties that are affected by that stuff, but it’s the change that matters. So what is the better state we want in the future for our client or for our customers and users? That’s the thing we’re trying to create. Not the thing; the change from the thing is what we want, and getting to that is the hard part.” - Brian (@rhythmspice) (26:33)

“This is a gift that you’re giving to [stakeholders] to save time, to save money, to avoid building something that will never get used and will not provide value to them. You do need to push back against this and if they say no, that’s fine. Paint the picture of the risk, though, by not doing design. It’s very easy for us to build a ML model. It’s hard for us to build a model that someone will actually use to make the world better. And in this case, it’s healthcare or support, intervention support for addicts. “Do you really want a model, or do you want an improvement in the lives of these addicts? That’s ultimately where we’re going with this, and if we don’t do this, the risk of us pushing out an output that doesn’t get used is high. So, design is a gift, not a tax...” - Brian (@rhythmspice) (34:34)

“I’d say to anybody out there right now who’s currently working on data science efforts: the sooner you get your people comfortable with the idea of doing Design Thinking, get them implemented into the projects that are currently going on. [...] I think that will be a real game-changer for your data scientists and your organization as a whole...” - Bill  (42:19)

This third volume of the best-selling "Data Model Resource Book" series revolutionizes the data modeling discipline by answering the question "How can you save significant time while improving the quality of any type of data modeling effort?" In contrast to the first two volumes, this new volume focuses on the fundamental, underlying patterns that affect over 50 percent of most data modeling efforts. These patterns can be used to considerably reduce modeling time and cost, to jump-start data modeling efforts, as standards and guidelines to increase data model consistency and quality, and as an objective source against which an enterprise can evaluate data models. Praise for The Data Model Resource Book, Volume 3 "Len and Paul look beneath the superficial issues of data modeling and have produced a work that is a must for every serious designer and manager of an IT project." " The Data Model Resource Book, Volume 3: Universal Patterns for Data Modeling is a great source for reusable patterns you can use to save a tremendous amount of time, effort, and cost on any data modeling effort. Len Silverston and Paul Agnewhave provided an indispensable reference of very high-quality patterns for the most foundational types of datamodel structures. This book represents a revolutionary leap in moving the data modeling profession forward." — Ron Powell, Cofounder and Editorial Director of the Business Intelligence Network "After we model a Customer, Product, or Order, there is still more about each of these that remains to be captured, such as roles they play, classifications in which they belong, or states in which they change. The Data Model Resource Book, Volume 3: Universal Patterns for Data Modeling clearly illustrates these common structures. Len Silverston and Paul Agnew have created a valuable addition to our field, allowing us to improve the consistency and quality of our models by leveraging the many common structures within this text." — Steve Hoberman, Best-Selling Author of Data Modeling Made Simple "The large national health insurance company I work at has actively used these data patterns and the (Universal Data Models) UDM, ahead of this book, through Len Silverston’s UDM Jump Start engagement. The patterns have found their way into the core of our Enterprise Information Model, our data warehouse designs, and progressively into key business function databases. We are getting to reuse the patterns across projects and are reaping benefits in understanding, flexibility, and time-to-market. Thanks so much." — David Chasteen, Enterprise Information Architect "Reusing proven data modeling design patterns means exactly that. Data models become stable, but remain very flexible to accommodate changes. We have had the fortune of having Len and Paul share the patterns that are described in this book via our engagements with Universal Data Models, LLC. These data modeling design patterns have helped us to focus on the essential business issues because we have leveraged these reusable building blocks for many of the standard design problems. These design patterns have also helped us to evaluate the quality of data models for their intended purpose. Many times there are a lot of enhancements required. Too often the very specialized business-oriented data model is also implemented physically. This may have significant drawbacks to flexibility. I’m looking forward to increasing the data modeling design pattern competence within Nokia with the help of this book." — Teemu Mattelmaki, Chief Information Architect, Nokia "Once again, Len Silverston, this time together with Paul Agnew, has made a valuable contribution to the body of knowledge about datamodels, and the act of building sound data models. As a professional data modeler, and teacher of data modeling for almost three decades, I have always been aware that I had developed some familiar mental "patterns" which I acquired very early in my data modeling experience. When teaching data modeling, we use relatively simple workshops, but they are carefully designed so the students will see and acquire a lot of these basic "patterns" — templates that they will recognize and can use to interpret different subject matter into data model form quickly and easily. I’ve always used these patterns in the course of facilitating data modeling sessions; I was able to recognize "Ah, this is just like . . .," and quickly apply a pattern that I’d seen before. But, in all this time, I’ve never sat down and clearly categorized and documented what each of these “patterns’’ actually was in such a way that they could be easily and clearly communicated to others; Len and Paul have done exactly that. As in the other Data Model Resource Books, the thinking and writing is extraordinarily clear and understandable. I personally would have been very proud to have authored this book, and I sincerely applaud Len and Paul for another great contribution to the art and science of data modeling. It will be of great value to any data modeler." — William G. Smith, President, William G. Smith & Associates, www.williamgsmith.com "Len Silverston and Paul Agnew’s book, Universal Patterns for Data Modeling, is essential reading for anyone undertaking commercial datamodeling. With this latest volume that compiles and insightfully describes fundamental, universal data patterns, The Data Model Resource Book series represents the most important contribution to the data modeling discipline in the last decade." — Dr. Graeme Simsion, Author of Data Modeling Essentials and Data Modeling Theory and Practice "Volume 3 of this trilogy is a most welcome addition to Len Silverston’s two previous books in this area. Guidance has existed for some time for those who desire to use pattern-based analysis to jump-start their data modeling efforts. Guidance exists for those who want to use generalized and industry-specific data constructs to leverage their efforts. What has been missing is guidance to those of us needing guidance to complete the roughly one-third of data models that are not generalized or industry-specific. This is where the magic of individual organizational strategies must manifest itself, and Len and Paul have done so clearly and articulately in a manner that complements the first two volumes of The Data Model Resource Book. By adding this book to Volumes 1 and 2 you will be gaining access to some of the most integrated data modeling guidance available on the planet." — Dr. Peter Aiken, Author of XML in Data Management and data management industry leader VCU/Data Blueprint

“The authors, the best minds on the topic, are breaking new ground. They show how every organization can realize the benefits of a system that can search and present complex ideas or data from what has been a mostly untapped source of raw data.” --Randy Chalfant, CTO, Sun Microsystems The Definitive Guide to Unstructured Data Management and Analysis--From the World’s Leading Information Management Expert A wealth of invaluable information exists in unstructured textual form, but organizations have found it difficult or impossible to access and utilize it. This is changing rapidly: new approaches finally make it possible to glean useful knowledge from virtually any collection of unstructured data. William H. Inmon--the father of data warehousing--and Anthony Nesavich introduce the next data revolution: unstructured data management. Inmon and Nesavich cover all you need to know to make unstructured data work for your organization. You’ll learn how to bring it into your existing structured data environment, leverage existing analytical infrastructure, and implement textual analytic processing technologies to solve new problems and uncover new opportunities. Inmon and Nesavich introduce breakthrough techniques covered in no other book--including the powerful role of textual integration, new ways to integrate textual data into data warehouses, and new SQL techniques for reading and analyzing text. They also present five chapter-length, real-world case studies--demonstrating unstructured data at work in medical research, insurance, chemical manufacturing, contracting, and beyond. This book will be indispensable to every business and technical professional trying to make sense of a large body of unstructured text: managers, database designers, data modelers, DBAs, researchers, and end users alike. Coverage includes What unstructured data is, and how it differs from structured data First generation technology for handling unstructured data, from search engines to ECM--and its limitations Integrating text so it can be analyzed with a common, colloquial vocabulary: integration engines, ontologies, glossaries, and taxonomies Processing semistructured data: uncovering patterns, words, identifiers, and conflicts Novel processing opportunities that arise when text is freed from context Architecture and unstructured data: Data Warehousing 2.0 Building unstructured relational databases and linking them to structured data Visualizations and Self-Organizing Maps (SOMs), including Compudigm and Raptor solutions Capturing knowledge from spreadsheet data and email Implementing and managing metadata: data models, data quality, and more William H. Inmon is founder, president, and CTO of Inmon Data Systems. He is the father of the data warehouse concept, the corporate information factory, and the government information factory. Inmon has written 47 books on data warehouse, database, and information technology management; as well as more than 750 articles for trade journals such as Data Management Review, Byte, Datamation, and ComputerWorld. His b-eye-network.com newsletter currently reaches 55,000 people. Anthony Nesavich worked at Inmon Data Systems, where he developed multiple reports that successfully query unstructured data. Preface xvii 1 Unstructured Textual Data in the Organization 1 2 The Environments of Structured Data and Unstructured Data 15 3 First Generation Textual Analytics 33 4 Integrating Unstructured Text into the Structured Environment 47 5 Semistructured Data 73 6 Architecture and Textual Analytics 83 7 The Unstructured Database 95 8 Analyzing a Combination of Unstructured Data and Structured Data 113 9 Analyzing Text Through Visualization 127 10 Spreadsheets and Email 135 11 Metadata in Unstructured Data 147 12 A Methodology for Textual Analytics 163 13 Merging Unstructured Databases into the Data Warehouse 175 14 Using SQL to Analyze Text 185 15 Case Study--Textual Analytics in Medical Research 195 16 Case Study--A Database for Harmful Chemicals 203 17 Case Study--Managing Contracts Through an Unstructured Database 209 18 Case Study--Creating a Corporate Taxonomy (Glossary) 215 19 Case Study--Insurance Claims 219 Glossary 227 Index 233