Thesis offers

This is precisely why companies such as Google have invested a lot of resources on exploring applications of GNN, some of which are as mediatic as Alpha Fold (recently solving the Protein-Folding problem), or used by Goolge Maps to make predictions of the expected traffic of a road. In this regard, the goal of this thesis is to continue investigating about the potential of GNNs when applied to the field of Computer Networks. Our goal is to provide GNN-based solutions that make computer networks run more autonomously and in a more efficient way.

The Accelerators and Communications for HPC Group leads cutting-edge research and development around accelerators/coprocessors in HPC and high-performance networking. We collaborate closely with the major vendors in the topic for HPC: NVIDIA and Intel. We organize locally international events such as the PUMPS Summer School, PATC Courses on CUDA/OpenACC, and annual hackathons, and collaborate in the organization of related international conferences and workshops such as SC, IEEE Cluster, or AsHES.

The group has been recently awarded one of the prestigious ERC Consolidator Grants. We are looking to cover a PhD Student positions to work on novel system architectural proposals applied to Homomorphically Encrypted Deep Learning Inference. Prospective candidates may take a look at the following paper to get an initial idea of our focus:

https://arxiv.org/abs/2103.16139

Group Web Page: https://www.bsc.es/discover-bsc/organisation/scientific-structure/accelerators-and-communications-hpc
Key Duties

• Drive research in the above-mentioned topic, under the supervision of the Group Manager and the mentorship of a postdoctoral researcher

Application

  Contact Antonio Peñaor apply directly:

https://webapps.bsc.es/recruitment/job/2444

The result of a dimensionality reduction algorithm is typically plotted as a scatterplot. However, evaluating the effectiveness, and more concretely, comparing the results of different algorithms is difficult under this scenario. The goal of the project is the creation of an interactive tool that facilitates the visual comparison of 2D and 3D datasets resulting from the application of different dimensionality reduction algorithms. The idea is to use a 3D rendering library, such as deckGL to create an interactive application for the visualization of complex datasets, and the implementation of interactive tools for cross selecting elements in multiple views. 

The Accelerators and Communications for HPC Group leads cutting-edge research and development around accelerators/coprocessors in HPC and high-performance networking. We collaborate closely with the major vendors in the topic for HPC: NVIDIA and Intel. We organize locally international events such as the PUMPS Summer School, PATC Courses on CUDA/OpenACC, and annual hackathons, and collaborate in the organization of related international conferences and workshops such as SC, IEEE Cluster, or AsHES.

The group has been recently awarded one of the prestigious ERC Consolidator Grants. We are looking to cover a PhD Student positions to work on novel system architectural proposals applied to Homomorphically Encrypted Deep Learning Inference. Prospective candidates may take a look at the following paper to get an initial idea of our focus:

https://arxiv.org/abs/2103.16139

Group Web Page: https://www.bsc.es/discover-bsc/organisation/scientific-structure/accelerators-and-communications-hpc

Key Duties

• Drive research in the above-mentioned topic, under the supervision of the Group Manager and the mentorship of a postdoctoral researcher

Application

  Contact Antonio Peña or apply directly:

https://webapps.bsc.es/recruitment/job/2443

The Accelerators and Communications for HPC Group leads cutting-edge research and development around accelerators/coprocessors in HPC and high-performance networking. We collaborate closely with the major vendors in the topic for HPC: NVIDIA and Intel. We organize locally international events such as the PUMPS Summer School, PATC Courses on CUDA/OpenACC, and annual hackathons, and collaborate in the organization of related international conferences and workshops such as SC, IEEE Cluster, or AsHES.

The group has been recently awarded one of the prestigious ERC Consolidator Grants. We are looking to cover a PhD Student positions to work on novel system architectural proposals applied to Homomorphically Encrypted Deep Learning Inference. Prospective candidates may take a look at the following paper to get an initial idea of our focus:

https://arxiv.org/abs/2103.16139

Group Web Page: https://www.bsc.es/discover-bsc/organisation/scientific-structure/accelerators-and-communications-hpc

Key Duties

• Drive research in the above-mentioned topic, under the supervision of the Group Manager and the mentorship of a postdoctoral researcher

Application

  Contact Antonio Peña or apply directly:

https://webapps.bsc.es/recruitment/job/2443

 The goal of this thesis is to enable the application of Deep Reinforcement Learning techniques for optimization problems in very large and complex scenarios, in this case Data Center Networks. Inspired by the last related work from OpenAI (https://openai.com/blog/evolution-strategies/), we will explore time-efficient alternatives to train a Graph Neural Network based DRL agent on large optimization scenarios in a reasonable amount of time.

Quantum computers promise exponential improvements over conventional ones due to the extraordinary properties of qubits. However, a quantum computer faces many challenges relative to the movement of qubits which is completely different from the movement of classical data. This thesis delves into these challenges and proposes solutions to create scalable quantum computers enabled by quantum communications, following the current European projects at N3Cat (www.n3cat.upc.edu) on scalable quantum computing.

The interested candidate will work in a group of several PhD students and in collaboration with Universitat Politècnica de València, working in ONE of the following areas:

• Designing classical (bit) and quantum (qubit) communication protocols and network architectures for communications inside a quantum computer.
• Assisting in the modeling of quantum short-range communications within our simulation framework, which is based on NetSquid.
• Developing qubit mapping, partitioning, and routing for scalable quantum computers.
• Study the use of AI to model quantum computers or develop qubit mapping and routing schemes.
• Study the potential scaling and adaptation of the existing quantum computing architectures to specific quantum machine learning algorithms (QAOA, VQE, others)

Computing systems are ubiquitous in our daily life and have transformed the way we learn, work, or communicate with each other, to the point that progress is intimately tied to the improvements brought by new generations of the processors that lie at the heart of these systems. 

A common trait of current computing systems is that their internal data communication has become a fundamental bottleneck. Traditional interconnects are insufficient and threatening to halt progress unless fast and versatile communication alternatives are developed.

In the NaNoNetworking Center in Catalunya (N3Cat, www.n3cat.upc.edu), we envisage a revolution in computer architecture enabled by the use of wireless communications (and other unconventional interconnect technologies) inside computer chips, both classical and quantum. Of course, this vision comes with its own set of challenges since we need to figure out the best way to architect these systems with the new capabilities of the interconnects. In this direction, the candidate will work on one of the following areas:

• Analysis of the communications workload in nowadays computer chips and development of network-on-chip architectures to serve such traffic optimally.
• Development of chiplet-based architectures in the classical domain in cache-coherent based systems, delving into the cache coherence protocol and the interface with the wireless interconnect.
• Development of a supercomputer-in-a-package, a chiplet-based architecture based on message passing, delving into how to speed up the MPI primitives with wireless communications.
• Study of scalable quantum computing systems enabled by wireless classical communication across the chips of the quantum computer and inside a cryogenic refrigerator.

Seeing that not all neural networks fit to all problems, and that relational data is present in a wide variety of aspects of our daily life, the main focus of this thesis in N3Cat (www.n3cat.upc.edu) and BNN-UPC (www.bnn.upc.edu) is to explore the possibilities of the Graph Neural Networks (GNNs), whose aim is to learn and model graph-structured relational data. We are looking for students willing to study the uses, architectures, and algorithms of GNNs. To this end, the candidate will work on ONE of the following areas:

• Uses: Applying GNNs in emerging application areas, including but not limited to (1) electroencephalogram (EEG) analysis for Alzheimer's disease detection, epilepsy classification, motor imagery; (2) acceleration of the compilation of quantum computing algorithms
• Architectures: Investigating ways to accelerate the processing of GNNs in multiple computing platforms (CPU, GPU, accelerators).
• Algorithms: Developing meta-learning data-driven models to estimate the accuracy of a GNN for a given graph, without training, through synthetic graph generation and correlation analysis.

Recent advancements in nanotechnology have enabled the development of means for sensing and wireless communications with unprecedented miniaturization and capabilities, to the point that they can be introduced into the gastrointestinal tract inside a pill or into the bloodstream in the form of passively flowing nanomachines. 

This opens the door to the idea of intra-body communication networks, this is, a swarm of nanosensors inside the human body that use communications to coordinate their actions to sense and localize specific events (lack of oxygen, biomarkers, etc). This can lead to the development of applications such as continuous monitoring of diabetes, detection and localization of cancer micro-tumors, or early detection of blood clots. These possibilities are currently investigated by our team at the N3Cat (www.n3cat.upc.edu).

In this context, We are looking for excellent and self-motivated individuals who are eager to develop ideas that will have a positive societal or socio-economic impact. In particular, the students will work on ONE of the following areas:

• Developing means of zero-power communications between nanosensors inside the human body.
• Designing protocols for communications from nanomachines inside the human body and implants or antennas in the skin surface.
• Studying mobility inside the gastrointestinal tract and human bloodstream towards creating mobility models for accurate simulation of intra-body communications.
• Dimensioning the system (inferring the required nanomachine density) to achieve a particular sensing goal/application.
• Developing AI schemes for the localization of events inside of the human body.

The syntactic structure of a sentence can be represented as a tree where vertices are words and arcs indicate syntactic dependencies between words (Kübler et al 2012). In languages, these structures are typically planar, namely no two arcs cross when drawn above the sentence (Ferrer-i-Cancho et al 2018). Another property of these structures is that the distance between syntactically related words is much smaller than expected in a shuffling the sentence (Ferrer-i-Cancho et al 2022), a fact that lead to the formulation of the syntactic dependency distance minimization principle (Ferrer-i-Cancho 2004).

Syntactic dependency parsing is the branch of computational linguistic concerned with the extraction of syntactic dependency structures from raw text (Kübler et al 2012). This research proposal is focused on unsupervised syntactic dependency parsing, namely methods to extract syntactic dependency structures from unlabelled data (Han et al 2020). Although these methods can be extremely complex, here we aim to start from simple by taking Yuret's (1998) proposal as starting point to build from it more complex methods. Yuret's (1998) unsupervised parser consists of three steps:
1) training, i.e. learning the statistics of word co-occurrences in texts
2) generating a weighted graph where all vertices are the words that have appeared during training and weights are the mutual information between word pairs.
3) parsing a new sentence and thus obtaining its syntactic dependency structure. This structure is the maximum spanning tree from the subgraph of the weighted graph in Step 2 that is induced by the words of the sentence.

In Yuret's proposal, the maximum spanning tree in Step 3 is restricted to be planar. Interestingly, it has been argued that planarity may be a side-effect of pressure to reduce the distance between syntactically related word rather than a direct pressure of sentence to be planar (Gómez-Rodríguez & Ferrer-i-Cancho 2017, Gómez-Rodriguez, Christiansen & Ferrer-i-Cancho 2022). Therefore, the main goal of this project is to explore alternative unsupervised parsing methods:
a) an unsupervised parser that does not impose planarity. That is, Step 3 consists of computing a maximum spanning tree using classic algorithms for calculating maximum spanning trees.
b) an unsupervised parser that does not impose planarity but incorporates syntactic dependency distance minimization and thus tends to produce planar syntactic dependency structures naturally.

The bulk of the project consists of implementing simple parsers of the sort of a-b) and evaluating their performance. The parsers will be applied to human languages and also to sequences that other species produce (great apes and cetaceans).

This project will be developed in the environment of the LQMC research group https://lqmc.upc.edu/.

REFERENCES

Ferrer-i-Cancho, R (2004). Euclidean distance between syntactically linked words. Physical Review E 70, 056135.
Gómez-Rodríguez, C. & Ferrer-i-Cancho, R. (2017). Scarcity of crossing dependencies: a direct outcome of a specific constraint? Physical Review E 96, 062304.
Gómez-Rodríguez, C., Christiansen, M. & Ferrer-i-Cancho, R. (2022). Memory limitations are hidden in grammar. Glottometrics 52, 39-64.
Ferrer-i-Cancho, R. Gómez-Rodríguez, R., Esteban, J.L. (2018). Are crossing dependencies really scarce? Physica A: Statistical Mechanics and its Applications 493, 311-329.
Ferrer-i-Cancho, R., Gómez-Rodríguez & Esteban, J.L. & Alemany-Puig, L. (2022). Optimality of syntactic dependency distances. Physical Review E 105, 014308.
Han, W., Jiang, Y., Tou Ng, H. & Tu, K. (2020). A survey of unsupervised dependency parsing. In Proceedings of the 28th International Conference on Computational Linguistics, pages 2522¿2533, Barcelona, Spain. International Committee on Computational Linguistics.
Kübler, S., McDonald, R. & Nivre, J. (2009). Dependency parsing. Synthesis Lectures on Human Language Technologies 1, 1-127.
Yuret, D. (1998). Discovery of linguistic relations using lexical attraction. PhD Thesis, MIT.

PETGEM serves a pivotal role in geophysics by solving Maxwell's equations using a high-order vector finite element method. However, to meet the evolving demands of geophysics research, this project aims to enhance PETGEM's functionality by integrating new features and adopting PETSc-DMPlex programming principles. The key focus areas include code refactoring and the integration of high-performance computing (HPC) principles.

Key Objectives:

1. PETSc-DMPlex Integration:
2. 1. Incorporate PETSc's data management API for unstructured meshes (DMPlex) into the PETGEM codebase.
   2. Enable PETGEM to leverage PETSc's interfaces for various common mesh formats, enhancing interoperability.
   3. Remove legacy dependencies to improve code modularity and maintainability.
3. Enhanced Portability:
4. 1. Enable PETGEM to seamlessly adapt to diverse computing environments by leveraging PETSc's capabilities.
   2. Improve portability by allowing PETGEM to work with PETSc's standardized mesh representations.
5. Performance Optimization:
6. 1. Utilize DMPlex's runtime mesh decomposition capabilities to enhance PETGEM's computational efficiency.
   2. Alleviate I/O bottlenecks by eliminating the need to read mesh decompositions from disk.
7. Documentation and Workflow Improvement:
8. 1. Provide comprehensive documentation for the enhanced PETGEM version, ensuring ease of use and understanding for researchers.
   2. Streamline the workflow for electromagnetic modeling in geophysics within PETGEM, enhancing user experience.
9. Contribution to Scientific Computing:
10. 1. Position the enhanced PETGEM as a benchmark in scientific computing and software engineering.
    2. Demonstrate the broader applicability of the project's outcomes beyond geophysics, showcasing best practices in code development.
11. Real-world Significance and Future Opportunities:
12. 1. Strengthen the collaboration with leading research groups to ensure the real-world relevance of the project.
    2. Open avenues for future research and development by establishing PETGEM as a foundation for advanced electromagnetic modeling.

The culmination of this master's project will be a refined, well-documented version of PETGEM, enriching the workflow for electromagnetic modeling in geophysics while contributing valuable insights to the broader scientific computing and software engineering communities. The collaboration with esteemed research groups enhances the project's real-world impact and opens doors to continued advancements in the field.

Mobile networks are continuously experiencing a profound transformation, driven by the emergence of groundbreaking applications and communication paradigms, such as the eagerly expected metaverse. In the coming years networks will face daunting challenges to meet the demands of these future applications. In this (r)evolution, Deep Learning is among the most promising techniques for achieving unprecedented levels of connectivity and user experience that can meet such demands.

In this project, you will have the opportunity to work on AI and machine learning applied to real-world problems around 5G and beyond networks. We will focus on hard open challenges, such as AI-based mobility management optimization and planning in future 6G networks, or creating cutting-edge network automation technology to facilitate the day-to-day operations of network engineers around the world.

Quantum computing is an emerging field poised to revolutionize information processing. Unlike classical computers that use bits to represent information as 0 or 1, quantum computers employ qubits, which can exist in superposed and entangled states. This enables more efficient solutions to complex problems, such as large number factorization or quantum system simulation.

However, quantum computing still faces significant challenges, including algorithm optimization. Many quantum processes require finding optimal solutions for optimization problems, such as minimizing system energy or determining molecular structures. This is where Deep Learning comes into play.

The goal of this research project is to explore how Deep Learning (in particular Deep Reinforcement Learning) techniques can enhance optimization in quantum computing. By applying deep neural networks, we can learn intricate patterns in quantum data and develop more efficient algorithms for solving optimization problems. These advancements could accelerate practical applications in areas like quantum cryptography, material simulation, and industrial process optimization.

You'll join an interdisciplinary team, since the work will be carried in collaboration with the Quantum Information Group at IMDEA Networks in Madrid. They will offer the expertise in Quantum  Physics while we work on the Deep Learning part..

 Additional details can be found in these papers:

 https://arxiv.org/abs/2310.01113

https://arxiv.org/abs/1910.07421

As the field of deep learning continues to evolve, researchers are exploring novel ways to process and extract knowledge from complex data structures. One promising avenue is topological deep learning (TDL), which leverages topological domains such as simplicial complexes, cell complexes, and hypergraphs. TDL provides a comprehensive framework for analyzing data with intricate relationships, transcending traditional graph-based approaches. More information about TLD can be found here:

[2206.00606] Topological Deep Learning: Going Beyond Graph Data (arxiv.org)

On the other hand, IGNNITION is a powerful TensorFlow-based framework designed for fast prototyping of Graph Neural Networks (GNNs). It caters to network engineers, enabling them to create custom GNN models without delving into low-level TensorFlow code. With IGNNITION, scientists and engineers in the networking field can develop GNNs tailored to their specific problems within a matter of hours. IGNNITION is a VERY popular framework and accounts of thousands of downloads per month:

IGNNITION - Fast prototyping of Graph Neural Networks

ignnition · PyPI

Our research project aims to extend the IGNNITION framework to incorporate topological deep learning capabilities. Here are our specific objectives:

Combinatorial Complexes Integration: Enhance IGNNITION to handle combinatorial complexes, bridging the gap between graphs, hypergraphs, and cell complexes.

Message-Passing Combinatorial Complex Neural Networks (CCNNs): Develop a class of CCNNs within IGNNITION, emphasizing attention-based architectures.

Evaluation and Benchmarking: Assess the performance of CCNNs on mesh shape analysis and graph learning tasks, comparing them to existing state-of-the-art models.

Immersive analytics is a new area of research where data visualization techniques are used in VR environments. 

The objective of this project is to develop a concrete module that provides several charts of the outcome of a previous training session. The outcome must provide a visual comparison tool that allows the interactive exploration of different indicators, as well as temporal comparison of the evolution of indicators and comparison among users.

Internet services are known to collect large amounts of personal information. As a result, more than half of EU citizens are concerned about their online privacy. In this context, data brokers are companies devoted to collecting and selling personal information to other companies. Data brokers are often implemented as third-party trackers, which allow them to gain visibility across the Internet. Recent works show this personal information is not only used for targeted advertising, but also for more obscure practices, such as price discrimination, credit scoring, phishing or identity theft. The project addresses these growing privacy concerns by enhancing ePrivo, a new online privacy observatory. ePrivo will continuously scan the Internet to unveil third-party trackers, the tracking methods they use and the data brokers behind them. A proof-of-concept prototype of ePrivo is already available at https://eprivo.eu. In this project, we will deploy the ePrivo service in production, extend its capabilities for web tracking and phishing detection, and release it under an open-source license. The results will be useful to the RIPE community, including ISPs, network operators, Internet users, policy makers and researchers.

Cybersecurity is becoming an increasingly important challenge for all companies and individuals alike. While big names used to be the main targets in the past, as people's lives move online, anyone is nowadays a potential target for any kind of cyber-attack, ranging from phishing to ransomware or serious privacy issues. In order to fight against those ever-evolving threats, Machine Learning is increasingly being used behind the scenes to design better systems that are able of self-learning to boost detection rates and boost overall resilience to unknown attacks. As AI-based solutions penetrate products across the industry, a new kind of threat that is often overlooked is becoming more and more prominent and dangerous: adversarial machine learning (AML).

AML focuses on designing specific inputs to deceive a previously trained Machine Learning models into misclassifying them for a specific purpose. One of the main flaws of any state-of-the-art Machine Learning or Deep Learning algorithms is that they assume that the nature of the data they receive is systematically benign, which is generally the case but does not hold true when an adversarial input is received. The motivation behind altering a ML model into thinking that, for example, a new sample is benign when in fact is malicious can range from pure research to more serious real-life issues such as an autonomous car wrongly classifying a stop sign (and thus provoking a fatal accident) or a wrongly diagnosed disease because of a slightly manipulated magnetic resonance image.

This problem is no exception for Cybersecurity where companies wrongly assume that once the last AI-based product is deployed in their network, their employees are safe...

Segmenting 3D medical models is an important task that can be only automatized with learning algorithms for concrete cases. In general, costly user intervention is needed. Most systems work in a 2D fashion, letting the user handle 2D images one at a time. However, using only 2D slices makes the user not getting use of the 3D immersion in the model.

Currently, we have an application that enables the user-guided segmentation. The goal of the project is to extend the project by creating different interaction metaphors that facilitate the manipulation of the model.