HICT Supervisors & PhD projects in Autumn Call 2018

Research projects and supervisors participating in the HICT Autumn Call 2018

If you wish to be considered as a potential new doctoral student in HICT you can:

(1) apply generally for a PhD position in the research group of any HICT supervisor(s) of your choice. The doctoral supervisors participating in the HICT Autumn Call 2018, associated to each research track, are listed below. (Note that some of the supervisors have dual affiliations across tracks.)

(2) apply directly to one or a number of specific PhD projects. Research projects included in the present HICT Autumn Call 2018 are listed below after the supervisor list.

An applicant can choose up to 10 HICT supervisors and/or projects in the application form (mention the supervisors’ full names and project numbers in the application form). Express your motivation towards the supervisors and/or projects in your motivation letter (compulsory attachment).

Detailed information and instructions on the HICT Autumn Call 2018 can be found at: https://www.hict.fi/autumn_2018

 

  1. Algorithms and Machine Learning (AML)

 

  1. Life Science Informatics (LSI)

 

  1. Networks, Networked Systems and Services (NNSS)
    • Prof. Keijo Heljanko (Aalto/CS) distributed systems, cloud computing, big data
    • Prof. Sasu Tarkoma (UH/CS) Internet of Things, mobile sensing, analytics and security
    • Prof. N. Asokan (Aalto/CS) security and privacy
    • Prof. Tuomas Aura (Aalto/CS)
    • Prof. Chris Brzuska (Aalto/CS) cryptography, IT security, verification, theory of computation, discrete mathematics
    • Prof. Mario Di Francesco (Aalto/CS) wireless networking, mobile and ubiquitous computing, Internet of Things
    • Prof. Heikki Hämmäinen (Aalto/COMNET) techno-economics of new Internet architectures and protocols, big data analysis of mobile Internet usage
    • Prof. Pan Hui (UH/CS) mobile computing, data science, social networks, augmented reality
    • Prof. Jussi Kangasharju (UH/CS) opportunistic networks, information-centric networking, cloud computing
    • Prof. Raimo Kantola (Aalto/COMNET) customer edge switching (generalization of NATs), trust management for the Internet including privacy preservation, software defined networking applications for mobile networks
    • Prof. Mikko Kivelä (Aalto/CS) complex systems, network science, graph data and models, social networks, multilayer networks
    • Prof. Jukka Manner (Aalto/COMNET) mobile network and device data analytics, security of SDN-based networks
    • Prof. Valtteri Niemi (UH/CS) information security, cybersecurity, privacy
    • Prof. Pekka Nikander (Aalto/COMNET) blockchains, IoT, Industrial Internet
    • Prof. Stephan Sigg (Aalto/COMNET) pervasive computing, activity recognition, sentiment sensing
    • Prof. Tarik Taleb (Aalto/COMNET) 5G, network function virtualization (NFV), and mobile cloud
    • Prof. Stavros Tripakis (Aalto/CS) formal methods, computer-aided verification and synthesis, cyber-physical systems
    • Prof. Petri Vuorimaa (Aalto/CS) web services, web applications, web of things
    • Prof. Yu Xiao (Aalto/COMNET) mobile cloud computing, crowdsensing, smart cities
    • Prof. Antti Ylä-Jääski (Aalto/CS) mobile cloud computing, mobile crowdsensing, mobile multimedia systems

 

  1. Software and Service Engineering and Systems (SSES)

 

  1. User Centered and Creative Technologies (UCCT) 

 

Projects in HICT Autumn Call 2018

The present call includes a number of specific PhD projects by several HICT professors listed below.

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Project 1. Adversarial Machine Learning

Supervisor: Prof. Rohit Babbar, Department of Computer Science, Aalto University

Despite the success in various domains, deep learning models remain highly vulnerable to adversarial perturbations to data for both visual recognition and NLP tasks. It has been demonstrated that many recently proposed defence mechanisms have largely been unsuccessful (https://www.robust-ml.org/defenses/). The goal of the PhD project is investigate this challenge, and develop theory and algorithms towards addressing this task. Another objective is to explore connections to generalization in deep networks and generative modeling with deep networks such as GANs and its variants. The candidates are expected to have completed a Masters degree and be familiar with machine learning, optimization and strong programming skills.

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Project 2. Algorithms for content analysis and distribution in social media

Supervisor: Prof. Aristides Gionis, Department of Computer Science, Aalto University

We are looking for highly-qualified and motivated doctoral students to work on algorithms for content analysis and distribution in social media. Topics of interest include analysis of social-media content, controversy and polarization in social networks, echo chambers, dissemination of news in social media, opinion-formation models, and algorithms for online content recommendation. The PhD position is in the Data Mining group of Aalto University. Successful applicants are expected to have completed successfully a Masters degree from a reputable international university, and have familiarity with graph mining, machine learning, and/or combinatorial optimization.

Data Mining group website: http://research.cs.aalto.fi/dmg/

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Project 3. Algorithms for the Analysis of Large-Scale Social Interactions

Supervisor: Assistant Prof. Michael Mathioudakis Department of Computer Science, University of Helsinki

Digital traces of activity on the Web, mobile applications, and other digital technologies offer the opportunity to study interactions at societal scale - and thus extract insights about human social behavior. For example, user activity on popular discussion fora (e.g., Wikipedia, Reddit, and Twitter) is publicly available to a large extent -- and various aspects of it are studied intensively by scientists in a large range of fields (from computer scientists to social and political scientists to humanities and media scholars).

Despite the intense research interest, basic algorithmic tasks at the heart of many analyses (e.g., user-attribute prediction, user-network inference, or low-dimensional user representation for visualization) scale badly for large datasets or do not extend gracefully to cover temporal dynamics, noisy data or interactive settings.

Addressing such issues is the subject of this doctoral project - with the goal to build a system for the analysis and visualization of large-scale social interactions. Towards this goal, the project will combine (i) development of algorithms for the aforementioned tasks, (ii) development of a prototype system and accompanying software, (iii) applied cases of analysis.

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Project 4. Deep learning methods for speech and language modeling 

Supervisor: Prof. Mikko Kurimo, Department of Signal Processing and Acoustics, Aalto University

Short description:  We are developing video and online captioning methods for hearing impaired and language learners. The automatic captions are very useful also for everybody who wants to search and browse large audiovisual archives or data streams. Our focus is also on multimodal and multilingual transcription. Knowledge of the latest deep learning methods for speech and language processing and machine learning as well as all programming skills are valuable.  Because this is a large project and we already have experts in different areas, it will be possible to adjust the content of the work according to the candidate's interests and skills.

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Project 5. Doctoral student position in computer vision and machine learning

Supervisor: Prof. Juho Kannala,  Department of Computer Science, Aalto University

Abstract: Computer vision is a rapidly developing field that is at the forefront of recent advances in artificial intelligence. Our group has broad research interests within computer vision. We are pursuing problems both in geometric computer vision (including topics such as visual SLAM, visual-inertial odometry, optical flow, image-based 3D modeling and localization) and in semantic computer vision (including topics such as object detection and recognition, and deep learning). We are looking for students interested in both basic research and applications of computer vision. Students with good programming skills and strong background in mathematics are especially encouraged to apply. The precise topics of the research will be chosen together with the students to match their personal interests. Examples of our recent papers include https://aaltovision.github.io/PIVO/, https://arxiv.org/abs/1802.03237, and https://arxiv.org/abs/1705.03386. For more papers and further information visit: https://users.aalto.fi/~kannalj1/

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Project 6. Holistic query optimization on multi-model databases

Supervisor: Prof. Jiaheng Lu, Department of Computer Science, University of Helsinki

Abstract: Databases play an important role in our world today. But one of the greatest challenges in current database systems is the "Variety" of the Big Data. This project will study novel solutions for pressing problems on multi-model data management.

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Project 7. Human-guided data analysis

Supervisor: Prof. Kai Puolamäki, Department of Computer Science, Aalto University

Short abstract: This project will study human guided data-analysis using randomisation and simulation methods of real data, with the objective of finding various interesting features of the data and studying the model space of black-box algorithms such as classifier and regression. The application areas may include occupational accidents, particle physics, and data sets related to ecology and atmospheric sciences.

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Project 8. Intelligent Crop Production

Supervisors: Prof. Samuel Kaski, Prof. Pekka Marttinen and Prof. Hiroshi Mamitsuka, Department of Computer Science, Aalto University

Due to the rising population, global crop production needs to double by 2050. Now boosting crop yields is important, for which we believe artificial intelligence is key. Through this project we will develop machine learning techniques and combine them with climate and ecological simulation models, in order to advance intelligent crop production. These technologies are indispensable to address the challenge of feeding the rapidly growing global population in the face of the changing climate.

Keywords: Machine learning: Bayesian modeling, multi-view modeling, multi-task learning, likelihood-free inference, simulator -based inference, integrative multi-data learning

Application: Biodiversity, global sustainability, crop/plant breeding, climate simulator, agricultural model

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Project 9. Machine Learning for Health

Supervisor: Prof. Pekka Marttinen, Department of Computer Science, Aalto University

Abstract: Recent years have witnessed accumulation of large quantities of health related data, making it possible to address questions as different as the prediction of future costs of health care or the evolution of antibiotic resistance. Answering such questions requires new machine learning methodology to be developed. We have several interdisciplinary research projects ongoing, where the goal is to create new machine learning methods for applications in health and welfare, together with leading experts in the respective fields from both Finland and abroad. Examples of topics include: analysis of electronic health records, mobile health, genomics, antibiotic resistance, epidemiology. Machine learning methods required: probabilistic machine learning, deep learning, bioinformatics, biostatistics, causality, likelihood-free inference.

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Project 10. Machine learning methods for analyzing biospheric change

Supervisor: Assistant Prof. Indrė Žliobaitė, Department of Computer Science, University of Helsinki

Understanding the biospheric change processes and their causal mechanisms is one of the fundamental questions in science, also attracting massive public attention. The goal of this project is to develop new predictive modeling techniques for global scale analysis of environmental change based on characteristics animal communities preserved as fossils over the last 20 million years and beyond. This presents two interesting and generic machine learning challenges: data are not in a flat format but form multiple instances, and data distribution is severely changing over evolutionary time scales. The PhD student will develop tailored advanced machine learning solutions in this context.

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Project 11. Mathematical Foundations of Modern Machine Learning

Supervisor: Assistant Prof. Alexander Jung, Department of Computer Science, Aalto University

Short description: Despite the massive empirical success of machine learning methods, e.g. based on deep neural networks, there is currently no comprehensive understanding of when and how a particular machine learning approach will be successful. The aim of this project is to apply a mix of tools, ranging from statistical physics to information theory, to develop a theoretical understanding of modern machine learning successes and failures.

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Project 12. Non-parametric probabilistic machine learning

Supervisor: Assoc. Prof. Harri Lähdesmäki, Department of Computer Science, Aalto University

We are looking for a postdoc and a PhD student to develop novel non-parametric and deep machine learning methods for time-series and structured data, including data-driven non-parametric ordinary and stochastic differential equations and non-stationary/deep Gaussian processes with sparse approximations and inference methods. Applicants are expected to have strong background in probabilistic modeling, machine learning, programming, and have previous experience with (or desire to learn) auto-differentiation/Stan/TensorFlow. For more information and relevant recent publications, see (http://research.cs.aalto.fi/csb/publications) or contact Harri Lähdesmäki (harri.lahdesmaki@aalto.fi).

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Project 13. Probabilistic machine learning and bioinformatics (1 PhD student)

Supervisor: Assoc. Prof. Harri Lähdesmäki, Department of Computer Science, Aalto University

Our research group develops statistical and machine learning methods with applications to bioinformatics and biomedicine. We are looking for a PhD student to develop advanced probabilistic machine learning methods, including Gaussian processes, deep generative models and non-parametric longitudinal methods, to analyze time-series and next generation sequencing data sets. Primary applications include single-cell and multi-omics data sets for accurate diagnosis and personalized treatment decisions in cancer-immunology and other personalised medicine projects. Student can choose his/her balance between statistical method development and biomedicine applications. Applicants are expected to have knowledge about probabilistic modeling, machine learning, programming, and bioinformatics. For more information and relevant recent publications, see (http://research.cs.aalto.fi/csb/publications) or contact Harri Lähdesmäki (harri.lahdesmaki@aalto.fi).

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Project 14. Probabilistic machine learning

Supervisor: Prof. Samuel Kaski, Department of Computer Science, Aalto University

I am looking for a student eager to join the Aalto Probabilistic Machine Learning Group to develop new probabilistic models and inference techniques. Particularly promising thesis topics at the moment are (1) Approximate Bayesian Computation (ABC) techniques for inference in simulator-based models, and (2) flexible models (read: deep Bayesian learning and Bayesian deep learning). We have also excellent opportunities for applying the techniques across a range of exciting applications, for instance in interactive machine learning and personalized medicine. More information: http://research.cs.aalto.fi/pml/

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Project 15. Probabilistic machine learning for personalized medicine

Supervisor: Prof. Samuel Kaski, Department of Computer Science, Aalto University

I am looking for a student who wants to participate in developing the new probabilistic modelling and machine learning methods needed for genomics-based precision medicine and predictive modelling based on clinical data. Suitable candidates have either a strong background in machine learning and a keen interest to work with top-level medical collaborators to solve these profound medical problems, or strong background in computational biology and medicine, and a keen interest to develop new solutions by working with the probabilistic modelling researchers of the group. More information: http://research.cs.aalto.fi/pml/

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Project 16. Probabilistic user modelling in interactive human-in-the-loop machine learning

Supervisor: Prof. Samuel Kaski, Department of Computer Science, Aalto University

Interactive human-in-the-loop machine learning combines the skills and knowledge of humans with the computational and processing strengths of machines. We are developing new approaches and applications for interactive human-in-the-loop machine learning using probabilistic modelling methods, with the aim of increasing the performance and efficiency of the systems and for improving the user experience. This project lies at the intersection of machine learning, human-computer interaction, and cognitive science. More information: http://research.cs.aalto.fi/pml/

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Project 17. Quality of Service Optimisation for Video over 5G

Supervisor: Prof. Antti Ylä-Jääski, Department of Computer Science, Aalto University

5G mobile networks are coming. They are expected to substantially increase the data rates and reduce latency and to offer connectivity to more and more diverse applications and services. From the applications and services side, there is a trend towards higher degree of distribution in service deployment. In this project, we focus on mobile multimedia services. Some of them are distributed by nature, such as video streaming (client and server). Others, such as gaming and VR, may benefit from distributing the computational work because client devices do not have sufficiently powerful hardware or battery. High-level objective of this research projects is to develop methods and tools for realization of optimally distributed mobile multimedia systems by taking advantage of multi-tier computing resources (MEC, Fog) and flexible 5G network architectures. We want the resulting systems to maximize the utility of the physical infrastructure while providing a desired tradeoff with service KPIs (e.g., latency, bitrate, and quality of analytics). The main expected scientific impact of the project is to develop new algorithmic results on optimal distribution and adaptation of multimedia services for the next generation mobile networks having flexible resource slicing mechanisms and edge computing capabilities. In addition, we expect to demonstrate the algorithms with real prototype system implementations with the sample applications.

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Project 18. Real-time machine learning for streaming data

Supervisor: Prof. Arno Solin, Department of Computer Science, Aalto University

We are looking for exceptional and highly motivated doctoral students to work on algorithms and applications for real-time machine learning in large or unbounded (stream) data. Applications of interests are in sensor fusion for positioning and tracking systems, and online decision-making and control. The PhD candidate will work in close collaboration with the supervisor and other members of the team at Aalto University. Successful candidates are expected to have successfully completed a Masters degree in a reputable university, and have familiarity with machine learning and statistics.

Supervisor's home page: http://arno.solin.fi

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Project 19. Robust and Assisted Bayesian Modeling Workflows

Supervisor: Prof. Aki Vehtari, Department of Computer Science, Aalto University

Abstract: We will develop theory and methods for assessing the quality of Monte Carlo and variatonal inference methods, and develop tools for a principled and robust Bayesian modeling workflow. To guarantee wide applicability of the project results in data science industry and academic research, the novel methods will be evaluated on a range of practical machine learning models and implemented as part of the leading open-source probabilistic programming systems.

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PhD projects at FCAI (Finnish Center for Artificial Intelligence, http://fcai.fi/research/):

Project 20: Agile probabilistic AI  (Finnish Center for Artificial Intelligence)

The Finnish Center for Artificial Intelligence, a joint initiative of Aalto University and the University of Helsinki, is looking for doctoral students to tackle complex and exciting problems in the field of machine learning, and in particular in its research program: Agile probabilistic AI. The keywords for these positions include Probabilistic programming; Robust and automated Bayesian machine learning. Several top-notch professors from both Aalto University and the University of Helsinki participate in the Agile probabilistic AI research program. Please indicate clearly your interest for this project in your application form, and we will help with matchmaking. For more information on Finnish Center for Artificial Intelligence, please see http://fcai.fi/research/

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Project 21: Simulator-based inference (Finnish Center for Artificial Intelligence)

The Finnish Center for Artificial Intelligence, a joint initiative of Aalto University and the University of Helsinki, is looking for doctoral students to tackle complex and exciting problems in the field of machine learning, and in particular in its research program: Simulator-based inference.

Keywords for these positions include Approximate Bayesian Computation ABC; likelihood-free inference; Generative adversarial networks (GAN); applications in many fields including medicine, materials design, visualization, business, …  Several top-notch professors from both Aalto University and the University of Helsinki participate in the Simulator-based inference research program. Please indicate clearly your interest for this project in your application form, and we will help with matchmaking. For more information on Finnish Center for Artificial Intelligence, please see http://fcai.fi/research/

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Project 22: Next generation data-efficient deep learning  (Finnish Center for Artificial Intelligence)

The Finnish Center for Artificial Intelligence, a joint initiative of Aalto University and the University of Helsinki, is looking for doctoral students to tackle complex and exciting problems in the field of machine learning, and in particular in its research program: Next generation data-efficient deep learning. Keywords include deep reinforcement learning. Several top-notch professors from both Aalto University and the University of Helsinki participate in the Next generation data-efficient deep learning research program. Please indicate clearly your interest for this project in your application form, and we will help with matchmaking. For more information on Finnish Center for Artificial Intelligence, please see http://fcai.fi/research/

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Project 23:  Privacy-preserving and secure AI  (Finnish Center for Artificial Intelligence)

The Finnish Center for Artificial Intelligence, a joint initiative of Aalto University and the University of Helsinki, is looking for doctoral students to tackle complex and exciting problems in the field of machine learning, and in particular in its research program: Privacy-preserving and secure AI. The keywords for these positions include Privacy-preserving machine learning; differential privacy; adversarial machine learning. Several top-notch professors from both Aalto University and the University of Helsinki participate in the Privacy-preserving and secure AI research program. Please indicate clearly your interest for this project in your application form, and we will help with matchmaking. For more information on Finnish Center for Artificial Intelligence, please see http://fcai.fi/research/

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Project 24: Interactive AI  (Finnish Center for Artificial Intelligence)

The Finnish Center for Artificial Intelligence, a joint initiative of Aalto University and the University of Helsinki, is looking for doctoral students to tackle complex and exciting problems in the field of machine learning, and in particular in its research program: Interactive machine learning. Keywords for these positions include Interactive machine learning;  probabilistic inference of cognitive models from data; probabilistic programming for behavioral sciences. Several top-notch professors from both Aalto University and the University of Helsinki participate in the Interactive AI research program. Please indicate clearly your interest for this project in your application form, and we will help with matchmaking. For more information on Finnish Center for Artificial Intelligence, please see http://fcai.fi/research/

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Project 25: Deep reinforcement learning

Supervisor: Prof. Alexander Ilin (Department of Computer Science, Aalto University)

Artificial intelligence (AI) has recently shown great progress because of deep learning. Due to improved perceptual capabilities, AI can solve such complex tasks as playing computer games from pixels and playing challenging board games like Go. The goal of this project is to develop AI that is able to solve more challenging tasks which require, for example, continuous actions and hierarchical planning. The focus of the project is on model-based reinforcement learning in which AI builds an explicit model of the environment in order to do efficient planning.