January 31, 2022 Newsletter

January 31, 2022 Newsletter

January 31

Take a look at our January 31, 2022 Newsletter with updates from the Ken Kennedy Institute.

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In This Issue

  • 2022 Energy HPC Conference
  • Why Venture Capitalists are Investing in 'Software Beyond the Screen'
  • Community Highlights

Register for the 2022 EnergyHPC Conference

Registration for the 2022 EnergyHPC Conference is now open. Register today!

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2022 Energy HPC Conference

BRC at Rice University | March 1-3, 2022 | Houston, TX

Register for the 15th annual Energy High Performance Computing Conference! Check out the conference schedule here.

The agenda for the conference includes invited keynote speakers, technical program, birds of a feather sessions, exhibit hall, networking reception, student poster session, and post-conference workshops.
Register Here

Why Venture Capitalists are Investing in 'Software Beyond the Screen'

Sunil Nagaraj | March 9, 2022 | 4:00 - 5:00pm CST | Virtual

Software has had an amazing decade, as it has transitioned from desktop computers into the cloud and onto smartphones. In the process, entrepreneurs and venture capitalists have smartly capitalized on this trend. The next decade will focus on software making an even more important jump: moving beyond the screen and into the real world around us.

In this talk, Sunil Nagaraj of Ubiquity Ventures will explore how software is beginning to animate, understand and navigate the real world with profound implications. Using examples ranging from hospital robots to rocket engines to dairy cows, he will explain how powerful this shift will be to our daily lives and our global economies. He will touch on how the software engineering toolchain (APIs, scrum, object-oriented programming, over-the-air updates, etc.) has been perfected for computer software and how this powerful software development paradigm is being applied conceptually and tactically to enable smart hardware in the real world. He will also cover several applications of the machine learning branch of software and its ability to connect the dots and navigate our complex physical world.

Want to meet with Sunil Nagaraj? Virtual office hours will be on Wed, March 23 from 5-7 PM CST. Apply here.
Register Here

Thompson Distinguished Lecture Series

Adrian Raftery | January 31, 2022 | 4:00 - 5:00pm CST | Probabilistic Sea-Ice Forecasting | McMurtry Auditorium (Virtual)

In recent decades, climate change has caused sharp reductions in the volume of sea ice in the Arctic Ocean. This has created demand for accurate forecasts of Arctic sea ice, for decisions about resource management and shipping. Such forecasts have two main components: where sea ice will be present, and how thick the ice will be if it is present. Existing methods rely on ensembles of deterministic dynamic models, but we show that these can be both biased and poorly calibrated. We propose a probabilistic contour model to predict the area where sea ice will be present, which corrects the bias in existing physical models and assesses their uncertainty. We then develop a Gaussian random field model for ice thickness, conditional on the ice-covered region. We apply our method to forecast Arctic sea ice thickness, and find that point predictions and prediction intervals from our model offer improved accuracy and calibration compared with existing forecasts. We also show that our model can generate well-calibrated short-term forecasts of aggregate quantities such as overall sea ice volume. This is joint work with Hannah Director, Peter Gao, and Cecilia Bita.

Learn more here. Register here.

Quantum Machine Learning with Subspace States

Anupam Prakash | February 1, 2022 | 12:00 - 1:00pm CST | Virtual

Abstract: Unary amplitude encodings $\ket{x} = \sum_{i} x_{i} \ket{i} $ of vectors $x\in \R^{n}$ are commonly used in quantum machine learning for representing classical data. In this work, we introduce subspace states $\ket{Col(X)} = \sum_{S\subset [n], |S|=k} det(X_{S}) \ket{S}$ for matrices $X \in \R^{n \times k}$ with orthonormal columns as a quantum representation for $k$-dimensional subspaces of $\R^{n}$. We provide subspace state preparation algorithms with gate complexity $O(nk)$ and depth $O(k \log n)$.

We present three new quantum machine learning algorithms using subspace states. The first is a quantum determinant sampling algorithm using $O(nd)$ gates and with circuit depth $O(d\log n)$. The state of art classical algorithm for the task requires $O(d^{3})$ operations. The second quantum algorithm is a potentially exponential speedup for singular value estimation (SVE) on exponentially large compound matrices $\mathcal{A}^{k}$ with the same resources as SVE for A. The third algorithm reduces the depth of circuits used in quantum topological data analysis from $O(n)$ to $O(\log n)$, an exponential improvement in terms of circuit depth.

Bio: Anupam Prakash obtained his Ph.D. in Computer Science from UC Berkeley, his dissertation was on quantum algorithms for linear algebra and machine learning. His research during his post-doctoral work at Centre for Quantum Technologies, Singapore and IRIF, Paris, France continued to focus on quantum machine learning and includes works on quantum recommendation systems and quantum linear systems for low-rank matrices. More recently, he has been working on bringing quantum machine learning closer to implementation on near term quantum hardware at QCWare, a Silicon Valley based quantum computing startup.

Click here to join.

Robust U-I Collaborations: Insights for Researchers

First Session Airs on February 3, 2022 at 11:00am EST | Virtual

UIDP’s Insights for Researchers Webinar Series provides researchers from both companies and universities with guidance and tools to strengthen sponsored research collaboration. With discussion leaders representing perspectives from both sectors, the series covers essential topics from UIDP’s cornerstone researcher guidance documents, the Researcher Guidebook, and the Researcher Quick Guide.

From tips for establishing initial contacts to budgeting and managing intellectual property, these training sessions build a strong foundation for anyone interested in sponsored research collaboration.

Why every researcher on your team should attend:
  • Learn about the types of research ripe for industry-university collaboration and how to manage expectations when navigating partner priorities and needs.
  • For university researchers and those in academia who facilitate sponsored research relationships: Get insight into what industry seeks from academic research partners, ways to recruit industry sponsors, and practical tips for successful collaboration.
  • For industry researchers and their academic connectors: Learn about the motivations and constraints that arise when forging relationships with academic partners. Learn more.

Click here to register.

Registration is required. UIDP is offering this series at no charge to members. The fee for non-members is $100 per webinar or $900 entire series.

Atlas of AI: Mapping the Political Economies of Planetary Computation

Kate Crawford | February 9, 2022 | 4:00 - 5:00pm CST | Virtual

Machine learning systems are already playing a significant role in many of our social institutions, including healthcare, education, hiring and criminal justice. But despite the patina of objectivity and neutrality, recent years have shown these systems can reproduce and intensify forms of structural bias and discrimination. Rather than taking a narrow focus on code and algorithms, Crawford reveals the wider political economy of how AI is “made” — from lithium mines in Nevada, to the exploited workers behind “automated” systems, to the regimes of classification in training data. This talk outlines how AI functions as a registry of power, which has only amplified during the pandemic at a time of deepening inequality around the world.

Click here to register.
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