Rethinking Memory System Design (and the Computing Platforms We Design Around It)
Onur Mutlu, ETH Zurich – Abstract: The memory system is a fundamental performance and energy bottleneck in almost all computing systems. Recent system design, application, and technology trends that require more capacity, bandwidth, efficiency, and predictability out of the memory system make it an even…
Certifying Computations: Algorithmics meets Software Engineering
Kurt Mehlhorn, Max-Planck-Institute for Informatics – Abstract: I am mostly interested in algorithms for difficult combinatorial and geometric problems: What is the fastest tour from A to B? How to optimally assign jobs to machines? How can a robot move from one location to another…
People-Centered Design. Why it matters?
Prof. Don Norman, University of California – Abstract: At the new Design Lab at UC San Diego, Design is a way of thinking, understanding people real, fundamental needs, and designing systems that fulfill those needs in an understandable, enjoyable manner. Does it matter? Yes. Medical…
VersionClimber: an algorithm and system for package evolution in data science
Prof. Dennis Shasha, Courant Institute of New York University – Abstract: Imagine you are a data scientist (as many of us are/have become). Systems you build typically require many data sources and many packages (machine learning/data mining, data management, and visualization) to run. Your working…
Computational Sustainability: Computing for a Better World
Prof. Carla Gomes, Cornell University – Abstract: Computational sustainability is a new interdisciplinary research field with the overarching goal of developing computational models, methods, and tools to help manage the balance between environmental, economic, and societal needs for a sustainable future. I will provide an…
Toward a Unified Approach to Sustainable and Resilient Electric Energy Systems – Modeling, Control and Testbeds
Prof. Marija Ilic, Carnegie Mellon University – Abstract: In this talk we present the changing objectives of the electric energy systems as complex dynamical systems. We briefly provide the basic landscape in the industry first. We take a broader look at the objectives of deploying…
The Five Tribes of Machine Learning, and What You Can Take from Each
Prof. Pedro Domingos, University of Washington – Abstract: There are five main schools of thought in machine learning, and each has its own master algorithm – a general-purpose learner that can in principle be applied to any domain. The symbolists have inverse deduction, the connectionists…
Tardis: Time Traveling Coherence Algorithm for Distributed Shared Memory
Prof. Srini Devadas, MIT – Abstract: (Work done with Xiangyao Yu) A new memory coherence protocol, Tardis, is presented. Tardis uses timestamp counters representing logical as opposed to physical time to order memory operations and enforce sequential consistency in any type of shared memory system….
Evolving Critical Systems
Prof. Mike Hinchey, Lero – the Irish Software Research Centre University of Limerick, Ireland – Abstract: Increasingly software can be considered to be critical, due to the business or other functionality which it supports. Upgrades or changes to such software are expensive and risky, primarily…
Next-generation data-parallel dataflow systems
Prof. Frank McSherry, ETH Zurich – Abstract: The Naiad project at Microsoft Research introduced a new model of dataflow computation, timely dataflow, which was designed to support low-latency computation in data-parallel dataflow graphs containing structured cycles. This model substantially enlarged the space of data-parallel computations…
Mathematics, Physics & Machine Learning Seminar Series (Online)
The Mathematics, Physics & Machine Learning seminar series takes place until July 16, between 17:30 and 18:30, via Zoom.
The seminars aims to bring together mathematicians and physicists interested in machine learning (ML) with ML and AI experts interested in mathematics and physics, with the goals of introducing innovative Mathematics and Physics-inspired techniques in Machine Learning and, reciprocally, applying Machine Learning to problems in Mathematics and Physics.
Attendance is free but registration is required.
More information and the next seminars available here.
LxMLS 2020 – 10th Lisbon Machine Learning School
The 10th edition of Lisbon Machine Learning School, LxMLS, will take place from July 21st to July 29th at Instituto Superior Técnico (IST).
This event is organized by IST, Instituto de Telecomunicações (IT), INESC-ID, Unbabel, and Priberam Labs.
Applications until March 15th. For more information and to apply, access here.