Graduate Research Internship in Artificial Intelligence at IBM

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In this presentation, I will provide an overview of my research internship at the IBM Artificial Intelligence lab in Cambridge, Massachusetts. The projects I will present are related to Data Visualization and Generative Adversarial Networks (GAN)

Engineering

Luca Stornaiuolo
luca.stornaiuolo@polimi.it
18 Jan 2019

Luca Stornaiuolo
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THE END

Fudo (aka Luca Stornaiuolo)
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- Jul. 2017
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THE END

Fudo (aka Luca Stornaiuolo)
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- Sep. 2015
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- Mar. 2016
- May 2017 NGC’17@SF
- Jul. 2017
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- Nov. 2017

Mauro Daniel Steve Evan Hendrik
Owen
Luca

36
Artificial Intelligence and Creativity

Generative Adversarial Networks
Training Data

Generative Adversarial Networks
Training Data
Discriminator

Generative Adversarial Networks
Training Data
DiscriminatorGenerator

Train
Images
Generative Adversarial Networks

Generator
Generative Adversarial Networks

Generator
z
Random
Noise
Generative Adversarial Networks

Generative Adversarial Networks
Generator
z
Random
Noise Generated
Image

Discriminator
Generative Adversarial Networks

Generative Adversarial Networks
Discriminator
FAKE
REAL
?

Generative Adversarial Networks
Discriminator
REAL
Train
Image

Generated
Image
Generative Adversarial Networks
Discriminator
FAKE

Generative Adversarial Networks
Discriminator
Train
Image
REAL

REAL
Generative Adversarial Networks
Discriminator
Error
Generated
Image

Generator
z
Random
Noise Generated
Images
Discriminator
Train
Images
Generative Adversarial Networks

Generator
z
Random
Noise Generated
Images
Discriminator
Train
Images
FAKE
REAL
?
Generative Adversarial Networks

Generator
z
Random
Noise Generated
Images
Discriminator
Train
Images
D-Error
Generative Adversarial Networks

Generator
z
Random
Noise Generated
Images
Discriminator
Train
Images
minimize D-Error
D-Error
Generative Adversarial Networks

Generator
z
Random
Noise Generated
Images
Discriminator
Train
Images
minimize D-Error
maximize D-Error
D-Error
Generative Adversarial Networks

Generator
z
Random
Noise Generated
Image
Discriminator
Train
Images
minimize D-Error
maximize D-Error
D-Error
Generative Adversarial Networks

Train
Images
Generated
Images
Generative Adversarial Networks

https://github.com/hindupuravinash/the-gan-zoo
…
GAN Zoo

Generator
Encoder Decoder
z
Condition
Image
Generated
Image
Pix2Pix

Discriminator
FAKE couple
REAL couple
?
Pix2Pix

Discriminator
Condition
Image
Train
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REAL couple
Train Couple
Pix2Pix

Discriminator
Condition
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Generated
Image
FAKE couple
Generate Couple
Pix2Pix

Generator Generated Image
Discriminator
Train Couples
minimize D-Error
maximize D-Error
D-Error
Pix2Pix

3D Generator Generated Couples
3D Discriminator
Train Couples
minimize D-Error
maximize D-Error
D-Error
Vox2Vox

Luca Stornaiuolo
luca.stornaiuolo@polimi.it
Thank you!

- Silvia Brembati, Product Designer - Benedetta Bolis, Engineering Physics Student Due to the recent COVID-19 outbreak, everybody had to quickly rearrange their lifestyle and learn how to get through isolation. Keeping in touch has never been more compelling and challenging at the same time. A recent survey conducted in Italy, states that 80% of the population felt like they needed psychological support to get through quarantine. We believe that if people had a way to feel surrounded by their friends and had been able to share activities, this number would be significantly lower. This is where our new app TreeHouse comes in handy as it guides the user in contributing to the life of the community: a virtual tree will come to life and thrive thanks to both real-life and online interactions. Sharing content, chatting with friends, or drinking a cup of tea together will make a leaf or a branch grow, but if the user is missing for too long, the tree will suffer from their absence, in complete symbiosis. Nevertheless, checking how the tree develops helps the members feel the actual presence of the community, and makes them able to support each other, letting the tree flourish again.

TiReX: Tiled Regular eXpressionsmatching architecture

NECST Lab @ Politecnico di Milano

- Filippo Carloni, M.Sc. student in Computer Science and Engineering Expressions (REs) are widely used to find patterns among data, like in genomic markers research for DNA analysis, signature-based detection for network intrusion detection systems, or search engines. TiReX is a novel and efficient RE matching architecture for FPGAs, based on the concept of matching core. RE passes into the compilation and optimization phase to be efficiently translated into sequences of basic matching instructions that a matching core runs on input data, and can be replaced to change the RE to be matched.

Embedding based knowledge graph link prediction for drug repurposing

NECST Lab @ Politecnico di Milano

- Edoardo Ramalli, M.Sc. student in Computer Science and Engineering Drug Repurposing is the investigation of existing drugs on the pharmaceutical market for new therapeutic purposes; drug repurposing reduces the time and cost of clinical trial steps, saving years, and billions of dollars in R&D. Identifying new diseases on which a drug can be effective is a complex problem: our approach leverages knowledge graphs (KG), networks composed of many types of entities and relations, on which embedding and graph completion techniques can be applied to infer insights and analyses. Our KG is built from well-known databases such as DrugBank, UniProt, and CTD and contains over one million relationships between more than 70K biological and pharmaceutical entities like diseases, genes, proteins and drugs. In this work, we research the applicability of knowledge graph completion techniques, such as link prediction (and triple classification) using a various number of different embedding models from different families: matrix factorization, geometric and Deep learning. Using these models is possible to infer new drug-disease relationships on our KG, and identify novel drug repurposing candidates. Preliminary experimental results are encouraging and show how state-of-the-art machine learning models, combined with the ever-growing amount of biological data freely available to the research community, could significantly improve the field of drug repurposing.

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NECST Lab @ Politecnico di Milano

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NECST Lab @ Politecnico di Milano

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BlastFunction: How to combine Serverless and FPGAs

NECST Lab @ Politecnico di Milano

- Samuele Barbieri, B.Sc. student in Computer Science and Engineering The last decade saw cloud computing more and more involved as the primary technology to develop, deploy and maintain complex infrastructures and services at scale. This happened because cloud computing allows to consume resources on-demand and to dynamically scale performance. Some compute-intensive workloads require computing power that current CPUs are not able to provide and, for this reason, heterogeneous computing with FPGAs is becoming an interesting solution to continue to meet SLAs. However, requests to cloud services can come at unpredictable rates and, for this reason, resources may be underutilized for significant portions of time. To increase resource utilization, we propose BlastFunction, which is a system that allows to accelerate compute-intensive kernels with shared FPGAs handled in a serverless fashion, while reaching near-native execution latency. In this talk we will present the main aspects of BlastFunction, showing its capabilities to time-share FPGAs across multiple function instances to optimize devices utilization. We will also show how we implemented the sharing and orchestration mechanism on a Kubernetes cluster based on the Amazon Web Services (AWS) EC2 F1 instances.

Maeve - Fast genome analysis leveraging exact string matching

NECST Lab @ Politecnico di Milano

- Sofia Breschi, B.Sc. student in Biomedical Engineering - Beatrice Branchini, B.Sc. student in Biomedical Engineering In the last few years, the use of Next Generation Sequencing technology in medicine has become more and more common, in particular for the diagnosis of genetic diseases and the production of personalized drugs. In this context, the identification of characteristic patterns in the human genome plays an important role. Exact pattern matching algorithms are an efficient way to identify those sequences. However, this process represents a bottleneck in the genomic field as it is very computationally intensive and time-consuming. Moreover, general-purpose architectures are not optimized to handle the huge amount of data and operations used in a genomics context. Due to these considerations, we propose an implementation of the Knuth-Morris-Pratt (KMP) algorithm on FPGA, a particular family of integrated circuits capable of reconfiguration for an infinite number of times. The KMP algorithm results in being very fast and efficient, by reducing unnecessary comparisons of characters that have already been matched. Furthermore, to achieve an overall speedup of the alignment process, the implementation on FPGA will bring on an even faster and more efficient solution, thus providing the patient with a quick response.

EMoCy - Emotions Monitoring via wearable Computing System

NECST Lab @ Politecnico di Milano

- Ana Bogdanovic, M. Sc. student in Biomedical Engineering - Lorenzo Gecchelin, M. Sc. student in Design & Engineering - Anisia Lauditi, M. Sc. student in Biomedical Engineering - Noemi Gozzi, M. Sc. student in Biomedical Engineering - Armando Bellante, M. Sc. student in Computer Science & Engineering - Letizia Bergamasco, M. Sc. student in ICT for Smart Societies - Moaad Khamlich, M. Sc. student in Computational Engineering Stress is a psycho-physical response to very different loads, of an emotional, cognitive or social nature, which is perceived as excessive thus having severe implications on wellbeing both in the short and in the long term. Different physiological manifestations occur during stressful events which, if detected promptly, can help in managing the situation. Therefore, the objective of this project is to develop a small portable device for psychological stress detection. This includes design of machine learning framework for stress detection and a prototype of a low-cost portable device for recording the physiological data. The ML framework is including the model together with the heuristic and knowledge based feature engineering from physiological time series. As a result EMoCy system is achieving accuracy of 97.2 ± 2% on stress/baseline binary classification task.

Approximate Personalized PageRank on FPGA .

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