Computer science

Reproducibility in Computer Science - Part 1

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B9 L3 R3223

Roy Maxion will give three lectures focusing broadly on different aspects of an increasingly important topic: reproducibility. Reproducibility tests the reliability of an experimental result and is one of the foundations of the entire scientific enterprise. We often hear that certain foods are good for you, and a few years later we learn that they're not. A series of results in cancer research was examined to see if they were reproducible. A startling number of them - 47 out of 53 - were not. Matters of reproducibility are now cropping up in computer science, and given the importance of computing in the world, it's essential that our own results are reproducible -- perhaps especially the ones based on complex models or data sets, and artificial intelligence or machine learning. This lecture series will expose attendees to several issues in ensuring reproducibility, with the goal of teaching students (and others) some of the crucial aspects of making their own science reproducible. Hint: it goes much farther than merely making your data available to the public.

Reproducibility in Computer Science - Part 2

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B9 L3 R3223

Roy Maxion will give three lectures focusing broadly on different aspects of an increasingly important topic: reproducibility. Reproducibility tests the reliability of an experimental result and is one of the foundations of the entire scientific enterprise. We often hear that certain foods are good for you, and a few years later we learn that they're not. A series of results in cancer research was examined to see if they were reproducible. A startling number of them - 47 out of 53 - were not. Matters of reproducibility are now cropping up in computer science, and given the importance of computing in the world, it's essential that our own results are reproducible -- perhaps especially the ones based on complex models or data sets, and artificial intelligence or machine learning. This lecture series will expose attendees to several issues in ensuring reproducibility, with the goal of teaching students (and others) some of the crucial aspects of making their own science reproducible. Hint: it goes much farther than merely making your data available to the public.

Reproducibility in Computer Science - Part 3

-

B9 L3 R3223

Roy Maxion will give three lectures focusing broadly on different aspects of an increasingly important topic: reproducibility. Reproducibility tests the reliability of an experimental result and is one of the foundations of the entire scientific enterprise. We often hear that certain foods are good for you, and a few years later we learn that they're not. A series of results in cancer research was examined to see if they were reproducible. A startling number of them - 47 out of 53 - were not. Matters of reproducibility are now cropping up in computer science, and given the importance of computing in the world, it's essential that our own results are reproducible -- perhaps especially the ones based on complex models or data sets, and artificial intelligence or machine learning. This lecture series will expose attendees to several issues in ensuring reproducibility, with the goal of teaching students (and others) some of the crucial aspects of making their own science reproducible. Hint: it goes much farther than merely making your data available to the public.

Finding the Face in the Crowd

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An algorithm that maximizes the difference between data categories achieves high accuracy in classifying faces.

Unlocking complex interactions

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Functions of interacting molecular complexes are uncovered through a novel method of aligning the interaction interface of each complex.

Noisy cells produce bursts of protein

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A new mathematical model explains how random factors affect the production of proteins within the cells.

Faster prediction of wireless downtime

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An efficient simulation scheme that hones in on the rarest elements in a dataset can help predict capacity exceedances in wireless networks.

Sharing movement aids learning

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Automated learning of an individual’s movement patterns shared over mobile and social networks could help us to connect better.

Exploring protein form to find function

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Existing systems for predicting protein structure are outperformed by a newly developed method.

Exploring artificial biosynthesis

1 min read ·
Metabolic route explorer helps to optimize the pathways for artificial biosynthesis of valuable products.

Giving power to demand-driven electricity

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A new approach to analyzing household electricity: smart-meter data could improve the performance and efficiency of national power grids.

Sharing expert experimental knowledge to expedite design

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A repository of metabolic information provides a quick reference tool for designing useful synthetic biological systems.

The crest of waveforms for next-gen radar

1 min read ·
A new method for shaping the waveform generated by multi-antenna radar systems is inexpensive and practical.

Xiangliang Zhang joins CBRC

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Xiangliang Zhang, KAUST associate professor of computer science and principal investigator of the Machine Intelligence & Knowledge Engineering Lab, joined the Computational Bioscience Research Center (CBRC) on July 1, 2018. In CBRC, Xiangliang will work on the problems of modeling biological networks using machine learning models.

Simple statistics can be good enough

1 min read ·
Study of the mismatch between spatial environmental data and a commonly used statistical analysis suggests simpler statistics are sufficient in many cases.

Less chat leads to more work for machine learning

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Deep analysis of the way information is shared among parallel computations increases efficiency to accelerate machine learning at scale.

Machine learning models gather momentum

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New methods for training machine learning models are quicker and more accurate than current approaches, previously considered state-of-the-art.

AI reveals nature of RNA-protein interactions

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A deep learning tool could help in structure-based drug discovery.

Training agents to walk with purpose

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By training a search agent to make smarter exploratory decisions, relational data can be classified more accurately and efficiently.

Angling for underwater WiFi

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Scuba divers could send sea life shots in real time using an aquatic internet service.

Computers excel in chemistry class

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Machine learning models can rapidly and accurately estimate key chemical parameters related to molecular reactivity.

The IEEE CS Global Student Challenge seeks top KAUST CS talent

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The annual IEEE Computer Society Global Student Challenge provides an avenue for students worldwide to create innovative solutions to big data problems. This year’s competition, IEEE CS GSC 2021, encourages each individual or team (max three students) to present solutions to pre-determined problem/issue statement of relevance in CS today.

Training an AI eye on the Moon

1 min read ·
Machine learning accelerates the search for promising Moon sites for energy and mineral resources.

New database to "SpUR" on cancer research

1 min read ·
Xin Gao's team developed an interactive web portal where cancer scientists can interrogate how RNA splicing in noncoding parts of genes fuels the growth of different types of tumors.

AI helps diagnose post-COVID lung problems

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A new AI diagnostic tool developed by KAUST scientists allows doctors to visualize lung damage caused by COVID-19 in more detail.