Summer Intern 2017 positions

We are recruiting a substantial number of summer interns to work on interesting research topics during the summer 2017. See full list of the topics below. You are supposed to express your preferences among the topics by specifying 1-3 preferred topics using the topic numbers available below.

The deadline for applications is Feb 7, 2017 and the applications must be submitted through Aalto University’s recruitment system. Please, see the application instructions here.

Summer Intern topics

1 Topic: Using Semi-definite Program (SDP) in Finding Network Structures

The problem of finding cliques is central in discrete optimization. In this project, the student will study a technique successfully used in finding relatively large cliques (Halperin, SICOMP 2002), and explore its applications in finding other important structures in the network (e.g. induced matching and bicliques). Strong background in algorithms and mathematical maturity are required.

Supervisor: Parinya Chalermsook

Email for more information: parinya.chalermsook [at] aalto [dot] fi

2 Topic: Fast univariate polynomial toolbox on GPUs

This is your opportunity to demonstrate your algorithms/mathematics skills and/or your expertise with GPUs (CUDA/PTX) to clean up and optimize an in-house-sketched programming toolbox for computing with univariate polynomials modulo a prime on anything ranging from a single GPU to a cluster of tens of multi-GPU nodes. Possibilities exist to continue the project all the way to a Master's thesis and beyond. In case you are wondering why we are interested in gigabyte-size univariate polynomials, take a look here: http://dx.doi.org/10.1145/2933057.2933101

Supervisor: Petteri Kaski

Email for more information: petteri.kaski [at] aalto [dot] fi

3 Topic: Courseware development for CS-A1120 Programming 2

Have you taken CS-A1120 Programming 2 and liked the course? This is your opportunity to practice your programming skills and get a backstage view how programming courseware is developed.

Supervisor: Petteri Kaski

Email for more information: petteri.kaski [at] aalto [dot] fi

4 Topic: Programming Assignment System Developer for CS-A1140 Data Structures and Algorithms

The course was almost completely renewed in 2016 and a next step in it is to further improve the programming assignment system. Potential tasks include (i) developing new assignments [including assignment description, student code package and grading code], (ii) porting the assignment grading system and assignments to other programming languages such as C++ [this includes both the code delivered to students as well as the grading code], and (iii) developing a plugin system that allows students to (voluntarily) participate in a competition for the fastest solutions for the assignments in the course. The applicant should have good experience in the Scala and C++ programming languages, experience in Python is an additional merit.

Supervisor: Tommi Junttila

Email for more information: Tommi.Junttila [at] aalto [dot] fi

5 Topic: Constraint-Based Programming and Optimization

Constraints lend themselves to the declarative specification of solutions to a wide variety of problems arising in computer science and artificial intelligence. Actual solutions to a problem can be computed using existing general-purpose solver technology and even optimized given suitable objective function. Constraints can be expressed, e.g., in terms of Boolean logic and its extensions as well as rules exploited in logic programming. In this summer project, you are supposed to get acquainted with a particular constraint language or languages, to be agreed with the supervisor, and your goal is to investigate the use of related solver technology to solve given application problems. The actual tasks may involve the implementation of required tools for constraint processing, performing actual modeling with constraints, and benchmarking in order to compare the performance of different tool chains. Excellent programming skills are necessary and previous experience with constraint programming is an asset.

Supervisor: Tomi Janhunen

Email for more information: Tomi.Janhunen [at] aalto [dot] fi

6 Topic: Computational techniques in the design of distributed algorithms

This is a meta-computational job: your task is to design algorithms that design algorithms; you will use computational techniques to better understand what computational tasks can be solved efficiently with distributed algorithms. You will need to have a good knowledge of theoretical computer science, algorithms, discrete mathematics, and graph theory, and good practical programming skills. Prior knowledge of e.g. theory of distributed computing and constraint satisfaction solvers is helpful but not necessary.

Supervisor: Jukka Suomela

Email for more information: jukka.suomela [at] aalto [dot] fi

7 Topic: String searching with SIMD

The aim is to code and test new string searching algorithms with SIMD computation. Novel algorithms may be designed based on the results. Mastering of C and Linux is necessary. The job suits to a M.Sc. thesis project.

Supervisor: Jorma Tarhio

Email for more information: jorma.tarhio [at] aalto [dot] fi

8 Topic: Enhancing Cryptocurrency Wallets Using Trusted Hardware

Although cryptocurrencies are popular, they have several drawbacks including long transaction times and linkability of payment transactions. This project attempts to address these concerns by making use of hardware security mechanisms widely available in modern computing platforms. The selected intern will be working with senior researchers to implement techniques to support fast payment transactions by leveraging mechanisms like Intel SGX and ARM TrustZone. Required skills: web technologies like javascript; Nice-to-have: experience with cryptocurrencies and/or hardware security mechanisms.

Supervisor: N. Asokan and Andrew Paverd

Email for more information: n.asokan [at] aalto [dot] fi

9 Topic: Security/privacy challenges device-to-device communication in 5G networks

The selected intern will be working closely with senior researchers on selected security/privacy issues involved in D2D communication on emerging 5G network architectures. The work is expected to involve both design/analysis of schemes as well as prototyping them. Required skills: basic concepts in networking and network security; software development skills in scripting (e.g., python), low-level languages (C or C++) and databases (e.g., SQL)

Supervisor: N. Asokan

Email for more information: n.asokan [at] aalto [dot] fi

10 Topic: Applying machine learning techniques to solve security/privacy problems

The selected intern will work with senior researchers in implementing various applications of machine learning techniques in the domain of security and privacy. Example tasks include detecting anomalies and making security/privacy mechanisms easy-to-use. Required skills: knowledge of various machine learning techniques and experience in using them (not necessarily for security/privacy applications)

Supervisor: N. Asokan

Email for more information: n.asokan [at] aalto [dot] fi

11 Topic: Mapping of home IoT devices and their Internet connections

New Internet of Things (IoT) devices cannot always be trusted. Think of a new IP camera you just bought from the shop. Where is it sending photos? How would you detect such misbehavior? And do you even know how many wireless and network connected devices there are in your home, especially if they connect to open access points? In this project the task would be to implement a wireless spectrum scanner that can create a map of all the wireless devices in your house. Alternatively, you can also use a Linux router that monitors all connections to and from devices and warn the user when his/her device is trying to connect to a malicious server on the Internet.

Supervisor: Tuomas Aura (co-supervised by Mohit Sethi)

Email for more information: tuomas.aura [at] aalto [dot] fi

12 Topic: Integrating popular web services (Netflix, Spotify, Youtube) into a single web-based remote control platform

We need a more convincing selection of applications for remote device management demos.  Different Internet-connected devices are currently controlled with a variety of different user interfaces. For a TV, you have buttons and a remote control. For a speaker, you generally have buttons.  It is also quite common for new Internet-connected devices to come with a complimentary mobile app through which they are controlled. The task in this project would be to make a single web application from which several types of Internet connected devices can be controlled. Initially, the web application can, for example, allow you to stream Netflix, Youtube or play a Spotify playlist on remote screens and speakers. We will use this application as a use case for device configuration, remote management, and failure recovery research.

Supervisor: Tuomas Aura (co-supervised by Mohit Sethi)

Email for more information: tuomas.aura [at] aalto [dot] fi

13 Topic: Mobile Cloud Computing 2.0

The student involved in this summer internship will help prepare the the course material and the assignments for the CS-E4100 (Mobile Cloud Computing) course to be held in Fall 2017. The work includes: definition and proof-of-concept implementation of a project work on mobile cloud computing; evaluation of different cloud computing providers and their suitability for carrying out project work.

Supervisor: Mario Di Francesco

Email for more information: mario.di.francesco [at] aalto [dot] fi

14 Topic: Mobile Computing and the Internet of Things

The student involved in this summer internship will be involved in current research on mobile computing and the Internet of Things. Possible topics include: visible-light communications; edge and fog computing; architectures and protocols for the Internet of Things (IoT); performance of IoT applications.

Supervisor: Mario Di Francesco

Email for more information: mario.di.francesco [at] aalto [dot] fi

15 Topic: Development of Automated Home Assignments for Concurrent Programming Course

The aim of this summer job is to develop new home assignments for the Concurrent Programming course that allow for automated or computer assisted grading of home assignments. The job requires good programming skills in Java and/or Scala and you will learn state-of-the-art techniques for automated testing during the course of the summer.

Supervisor: Assoc. Prof. Keijo Heljanko

Email for more information: keijo.heljanko [at] aalto [dot] fi

16 Topic: Development of Big Data Processing Pipeline Development

The aim of this summer job is to help the research group develop further our Big Data processing pipelines, mostly focusing on next generation sequencing data analysis for genomics. The pipeline is built on top of the Apache Spark framework and thus programming skills in Java/Scala and Apache Spark knowledge are a definite plus. You will learn to be an expert in Apache Spark based Big Data processing pipeline development over the course of summer.

Supervisor: Assoc. Prof. Keijo Heljanko

Email for more information: keijo.heljanko [at] aalto [dot] fi

17 Topic: Latency in Mobile Augmented Reality applications

Mobile Augmented Reality (AR) applications can have strict delay requirements for the processing pipeline depending on the use case. In this topic, the summer intern studies the delays and delay requirements of mobile AR applications as a research assistant. Previous experience on mobile programming (Android) and/or computer vision is beneficial.

Supervisor: Teemu Kämäräinen and Antti Ylä-Jääski

Email for more information: teemu.kamarainen [at] aalto [dot] fi

18 Topic: 360 video and VR

This is a research assistant position. The student will conduct experiments and develop optimizations in research pertaining to video quality adaptation in the context of 360 video and VR applications.

Supervisor: Matti Siekkinen and Antti Ylä-Jääski

Email for more information: matti.siekkinen [at] aalto [dot] fi

19 Topic: Data-driven business platform

Requirements study of a platform for cross-enterprise data sharing ("sharetribe for industry")

Supervisor: Kari Hiekkanen

Email for more information: Martti.Mantyla [at] aalto [dot] fi

20 Topic: Evaluation and survey of esuomi.fi infrastructure

esuomi.fi is a communication channel for national architecture for digital services. Its services are available to any organization operating in Finland. The purpose of the job is to make a map of its services, users and possible extensions in the near future. The task includes making a survey for esuomi.fi users and documenting the results. Finnish-speaking skill is a benefit in this job. The task can be included in a bachelor or a master thesis. The job is a part of an Academy of Finland project DINE, Digital Infrastructures and Enterprise Integration.

Supervisor: Kari Smolander/Jesse Yli-Huumo

Email for more information: kari.smolander [at] aalto [dot] fi

21 Topic: Empirical experiences in the use of the Scaled Agile Framework (SaFE)

Many large organizations are adopting agile software development, and recently several frameworks for scaling agile have been developed and actively promoted by consultants. Currently, the framework with the most traction is the Scaled Agile Framework (SAFe). Destpite its popularity, little empirical evidence on how to scale agile exists in the literature. In this project, the intern will conduct research on SAFe usage in the Finnish software industry by analysing existing interview data from companies who have taken the framework into use and, time permitting, possibly collect additional data.

Supervisor: Casper Lassenius, Maria Paasivaara

Emails for more information: Casper.Lassenius [at] aalto [dot] fi, Maria.Paasivaara [at] aalto [dot] fi

22 Topic: Agile methods in public IT projects

The project consists of conducting a literature study and possibly small scale empirical (interview) study of agile method usage in Finnish public sector IT projects. The position requires familiarity with agile methods and an interest in public sector projects. Experience in qualitative analysis is beneficial, but not a strict requirement.

Supervisor: Casper Lassenius, Maria Paasivaara

Emails for more information: Casper.Lassenius [at] aalto [dot] fi, Maria.Paasivaara [at] aalto [dot] fi

23 Topic: Interactive graph mining

The project is on developing, implementing, and testing new algorithms for interactive graph mining. The main research question is how to design efficient algorithms for mining and exploring large graphs in a setting where users can interact with the data and make different types of queries. Several directions are possible, depending on the interests of the student, for example, focus on theoretical aspects, or study specific applications in social-network analysis and social media. The ideal candidate should be mathematically inclined and should be proficient with programming and managing large datasets.

Supervisor: Aristides Gionis

Email for more information: aristides.gionis [at] aalto [dot] fi

24 Topic: Development of automated service process modelling

Taking part in the development of automated service process modelling tools. Conducting case studies. Reporting and analysing of research results.

Supervisor: Eljas Soisalon-Soininen

Emails for more information: ye.zhang [at] aalto [dot] fi, eljas.soisalon-soininen [at] aalto [dot] fi

25 Topic: Network Information Theory of Graphical Models

Job description (brief but informative): study fundamental limits for inference based on graphical models

Supervisor: Alex Jung

Email for more information: alexander.jung(at)aalto.fi

26 Topic: Computer-based game theory experiments set-up

The intern should write code, debug, run and analyse online game theory experiments, using existing software platforms e.g. oTree. Experiments and models in this field are diverse, and a broad survey of different models and state-of-the-art related literature should be done by the intern. As part of IBSEN project, our group is working on new models and experiments for studying human behaviour, and the deliverables done by the intern would be applied in experiments involving groups with  a large number people (of the order of thousands). Requirements: Python, Django, Php/Mysql, web databases and servers

http://ibsen-h2020.eu/

http://www.otree.org/

Supervisor: Kimmo Kaski

Emails for more information: kimmo.kaski [at] aalto [dot] fi, daniel.monsivaisvelazquez [at] aalto [dot] fi, kunal.bhattacharya [at] aalto [dot] fi

27 Topic: Machine learning (ML)/deep learning (DL) for data-driven healthcare

Carry out research in collaboration with K. K. and J. K. on one of the two following topics:

    i) Development of ML/DL models and methods for retinal-image analysis for computer-aided detection of diabetic retinopathy.   

    ii) Development of ML/DL models and methods for computer-aided detection of chronic obstructive pulmonary disease (COPD).

Successful internship enables the extension of the project onto a Master's thesis project.

Supervisor: Prof. Kimmo Kaski (K. K.), Dr. Jyri Kivinen (J. K.)

Emails for more information: kimmo.kaski [at] aalto [dot] fi, jyri.kivinen [at] aalto [dot] fi

28 Topic: Approximate Bayesian Computation: inference on intractable models

We recently released ELFI, an engine for likelihood-free inference, with which it is possible to efficiently solve the hard task of fitting simulator-based models to data. At the moment we are both continuing our basic research on the inference techniques, and applying the methods to new problems. We are looking for a summer trainee to join our team to do both basic and applied research in probabilistic machine learning. Students having a strong background in mathematics and interest in modelling and inference are especially encouraged to apply.

http://www.aalto.fi/en/current/news/2017-01-04-002/

http://research.cs.aalto.fi/pml/

Supervisor: Dr. Henri Vuollekoski, Prof. Samuel Kaski

Emails for more information: first.last [at] aalto [dot] fi

29 Topic: Interactive Learning for Personalized Medicine

In this summer project, the goal is to improve as much as possible the prediction accuracy of drug effects, by interacting and obtaining feedback from an expert (doctor). Assuming a limited number of interactions with the expert, active learning techniques need to be used to identify and obtain the most relevant feedback for the expert. In other words, we develop new user interaction principles which combine machine learning with HCI for prior elicitation, that is, how can we extract the user’s prior knowledge to be included in probabilistic models with Bayesian inference. We are looking for a summer intern who has a strong background in machine learning and has basic programming skills. During the summer internship, you can contribute to model development and implementation of the models.

http://research.cs.aalto.fi/pml/

Supervisor: M.Sc Iiris Sundin, Prof. Samuel Kaski

Emails for more information: first.last [at] aalto [dot] fi

30 Topic: Probabilistic machine learning: next-generation meta-analysis

We are developing new inference techniques that are necessary in three seemingly different research problems: 1. How to find experimental data sets, in which similar findings have been made as in a new data set? 2. How to do accurate inference by combining results from a massive number of sub-models, parallelized for speed?, and 3. What is the best we can say about the result of our modelling problem, given everything others have said on more or less related problems before? The answer is next generation of meta-analysis. Come develop that with us. Requirements: strong background in math, decent skills in programming, and/or a very steep gradient in the learning curve.

http://research.cs.aalto.fi/pml/

Supervisor: Dr. Paul Blomstedt, Dr. Xiangju Qin, Prof. Samuel Kaski

Emails for more information: first.last [at] aalto [dot] fi

31 Topic: Privacy-preserving machine learning

We develop methods for learning from data given the constraint that privacy of the data needs to be preserved. This problem can be formulated in terms of a recent breakthrough called differential privacy, and we have introduced ways of doing the learning such that performance actually improves, in contrast to in alternative methods. A couple of “minor” problems still remain; come to solve them with us! Requirements: strong background in math, decent skills in programming, and/or a very steep gradient in the learning curve.

http://research.cs.aalto.fi/pml/

Supervisor: Dr. Teppo Niinimäki, Prof. Samuel Kaski

Emails for more information: teppo.niinimäki [at] aalto [dot] fi, samuel.kaski [at] aalto [dot] fi

32 Topic: Machine Learning for High-Dimensional and Relational Data, applied to Information Retrieval, Personalized Medicine and Plant Breeding

In this summer project, you will work in methods development in a Finnish-Japanese collaboration to address problems that arise jointly in the three applications of Information Retrieval, Personalized Medicine and Plant Breeding. Although the problems may appear superficially different, for example, the same matrix/tensor factorization methods can be used to answer questions that arise in each of them. The project is funded by TEKES and a spectrum of companies working with the applications and company commitment is strong. This project requires good programming skills.

http://research.cs.aalto.fi/pml/

Supervisor: Prof. Hiroshi Mamitsuka, Prof. Samuel Kaski

Emails for more information: first.last [at] aalto [dot] fi

33 Topic: Adaptive User Interfaces and Information Visualizations in Human-Computer Interaction

Adaptive user interfaces (UIs) hold incredible potential for increasing human capabilities in computerized activities. However, despite extensive research on the topic, the problem of how to adapt a UI to a task and a user is open, as no method exists that can reliably improve a user’s capabilities without massive data, extensive trials, or explicit input. In this project, we choose the optimum adaptation for an individual by anticipating how the individual will react and by predicting the consequences of an adaptation to an individual. We are looking for a summer trainee who wants to contribute to this new and promising research area, combining computational user interface design, information visualization, and machine learning. Knowledge in information visualization, human-computer interaction and programming skills are required. Additional information: http://userinterfaces.aalto.fi, http://research.cs.aalto.fi/pml, http://www.helsinki.fi/bsg

Supervisor: Prof Antti Oulasvirta, Prof Samuel Kaski, Prof Jukka Corander, Dr Luana Micallef, Dr Ulpu Remes

Email for more information: luana.micallef [at] hiit [dot] fi

34 Topic: Learning cognitive model parameters with approximate Bayesian computation

Research on human-computer interaction (HCI) and adaptive user interfaces (UI) utilises cognitive models to understand user interaction and predict how users respond to adaptation. An important problem in this research area is how to determine the cognitive model parameters based on behavioural data. The conventional methods used to conclude model parameters based on observed data are not applicable due to the complex simulator-based approaches that are needed to model user interaction. In the proposed work, user interaction models are learned based on behavioural data with approximate Bayesian computation (ABC). We are looking for a summer trainee who wants to contribute to this new and promising research area, combining machine learning, information visualisation, and user interface design. Knowledge in machine learning and programming skills are required. Basic understanding of Bayesian statistics is recommended. Additional information:  http://www.helsinki.fi/bsg, http://research.cs.aalto.fi/pml, http://userinterfaces.aalto.fi

Supervisor: Prof Antti Oulasvirta, Prof Samuel Kaski, Prof Jukka Corander, Dr Ulpu Remes, Dr Luana Micallef

Email for more information:  ulpu.remes [at] aalto [dot] fi

35 Topic: On-off Gaussian processes with spike-and-slab priors

Spike-and-slab (SnS) prior is a probabilistic sparse prior that drives model parameters towards zero, and thus performs feature learning, while a structured SnS can drive parameters to zero as groups (Andersen, Winther & Hansen, 2014, NIPS). Gaussian processes (GPs) are powerful models which usually can't model zero signals, hence in this project a spike-and-slab prior will be extended to GPs using approximative inference techniques. This will allow new kinds of GP models which can model important ‘on-off’ signals, step functions and switching between signals. This work will require good background in mathematics, statistics and programming, while Gaussian process, Bayesian modelling and approximative inference knowledge are useful. For more information, see research group web page http://research.cs.aalto.fi/pml/

Supervisor: Dr. Markus Heinonen, Msc. Sami Remes, Prof. Samuel Kaski

Emails for more information: markus.o.heinonen [at] aalto [dot] fi, sami.remes [at] aalto [dot] fi, samuel.kaski [at] aalto [dot] fi

36 Topic: Statistical methods to analyze single cell RNA sequencing data

During the past few years, the development of single cell sequencing techniques has revolutionized the systems biology field by allowing quantification of RNA and DNA content in all individual cells. This new technology has important applications in revealing novel insights into basic molecular biology and various diseases, including cancer genomics and others. However, this novel data type also poses many new bioinformatics challenges when the data are processed and analyzed by means of statistical techniques. Your task in this project is to first familiarise yourself with single cell data analysis methods and then participate in developing new statistical methods for 1) genome-wide gene co-expression analysis and 2) gene regulatory network inference from single cell RNA-seq data in our multidisciplinary team. The project requires good knowledge of mathematics, statistics, and programming (e.g. Matlab) as well as interest in molecular biology. For more information, see research group web page http://research.ics.aalto.fi/csb/

Supervisor: Dr. Jukka Intosalmi/Henrik Mannerström, Assist. Prof. Harri Lähdesmäki

Emails for more information: harri.lahdesmaki [at] aalto [dot] fi, jukka.intosalmi [at] aalto [dot] fi, henrik.mannerstrom [at] aalto [dot] fi

37 Topic: Bioinformatics methods for chromatin accessibility data analysis

In one human cell two meters of DNA are packed tightly within a five-micron nucleus while active DNA regions, such as promoters, enchancers and other regulatory elements need to be unpacked. These active regions are bound by transcription factors that regulate gene transcription. High throughput measurement techniques, such as DNase-seq and ATAC-seq, combined with advanced computational methods provide us genome-wide information on DNA accessibility and transcription factor binding. On the other hand, R-loops, associated with open chromatin at promoters, are three-stranded nucleic acid structures. They are formed when a nascent RNA binds to template DNA leaving the non-template DNA single-stranded. R-loops are important regulators of gene expression but they can also promote DNA damage and are involved in neurological diseases and cancer. Our aim in this project is to get a better understanding in R-loop formation by combining different types of sequencing data. Your task in this project is to familiarise yourself with the topic, especially high-throughput sequence data analysis, and participate in analyzing ATAC-seq data and developing novel statistical bioinformatics methods to predict R-loops and TF binding. The project requires good knowledge of statistics and programming (e.g. R/Python/Matlab) as well as interest in molecular biology. For more information, see research group web page http://research.ics.aalto.fi/csb/

Supervisor: Sini Rautio, Assist. Prof. Harri Lähdesmäki

Emails for more information: harri.lahdesmaki [at] aalto [dot] fi, sini.rautio [at] aalto [dot] fi

38 Topic: Non-stationary Gaussian processes for large-scale datasets

Gaussian processes are probabilistic machine learning models which are highly useful due to their ability to properly account for the prediction uncertainty. Non-stationary GPs are extended models necessary for phenomena that have e.g. temporally or spatially evolving dynamics. Recently the CSB group developed a non-stationary base GP model (Heinonen et al 2016, with github implementation), and in this project it will be extended into a large-scale model scaling up to millions of data points, using existing approximative inference techniques. This allows application on spatial datasets on the global scale, or e.g. to construct a map of property values from hundreds of thousands of sales records. This work will require good knowledge of mathematics, statistics and programming. Knowledge of Gaussian processes, Bayesian modeling and/or approximative inference are advantageous for this project. For more information, see research group web page.

Supervisor: Dr. Markus Heinonen, Assist. Prof. Harri Lähdesmäki

Emails for more information: markus.o.heinonen [at] aalto [dot] fi, harri.lahdesmaki [at] aalto [dot] fi

39 Topic: Gaussian process ODE modelling

ODE models are the workhorse of numerous fields. Very recently ODE techniques have emerged where the previously parameterised fixed differential function is instead learned automatically from data using machine learning. This leap forward allows ODE modelling to be applied on new kinds of problems, where the ODE equations does not need to be manually written down. In this project an implementation of a Gaussian process ODE system will be done, based on models previously developed by the CSB group. A kernel-based implementation of the model already exists, which should be translated into a similar Gaussian process model, and further developed if time permits. Knowledge of Bayesian modelling, and either Gaussian processes or kernel methods is necessary, while good knowledge of mathematics, statistics and programming are highly useful. For more information, see research group web page http://research.ics.aalto.fi/csb/

Supervisor: Dr. Markus Heinonen/Jukka Intosalmi, Assist. Prof. Harri Lähdesmäki

Emails for more information: markus.o.heinonen [at] aalto [dot] fi, jukka.intosalmi [at] aalto [dot] fi, harri.lahdesmaki [at] aalto [dot] fi

40 Topic: Statistical and machine learning methods for biomarker discovery and personalized medicine

Personalised medicine paradigm aims at collecting massive amounts of longitudinal data from each person in order to monitor, predict, prevent and treat diseases, individually for each person. Our research group works in a close collaboration with biological and clinical researchers in order to identify biomarkers for type 1 diabetes, asthma and alzheimer’s disease, among others. A number of longitudinal -omics data (including e.g. transcriptome, proteome, metabolome, epigenome, metagenome) has been collected from patients and their matched controls.  In order to identify predictive biomarkers and to gain insight into disease pathogenesis, the goal of this project is to develop novel statistical modeling and machine learning methods using Gaussian processes and apply them on these unique longitudinal data sets. The project requires a good knowledge of statistical and machine learning methods, programming, and interest in molecular biology and biomedicine. For more information, see research group web page http://research.ics.aalto.fi/csb/

Supervisor: Juhi Somani, Dr Lu Cheng, Assist. Prof. Harri Lähdesmäki

Emails for more information: juhi.somani [at] aalto [dot] fi, lu.cheng [at] aalto [dot] fi, harri.lahdesmaki [at] aalto [dot] fi

41 Topic: Bioinformatics methods to quantify and analyze somatic mutations

So-called somatic mutations are genetic aberrations that are acquired during the lifetime. Somatic mutations are thought to be a hallmark of a cancer as they can alter key cellular functions and result in a malignant transformation. Recently, somatic mutations have also been observed in other contexts, including non-malignant disorders. Bioinformatic methods are needed to quantify somatic mutations from next-generation sequencing data and statistical significance of the detected mutations can be assessed using a number of tools. In this summer project you will familiarize yourself with the computational and statistical methods used to detect and analyze acquired genetic aberrations and participate in a fascinating interdisciplinary project together with Prof. Satu Mustjoki’s group from Univ. of Helsinki. The project requires a good knowledge of statistical methods, bioinformatics, programming skills and interest in molecular biology and biomedicine. For more information, see research group web page.

Supervisor: Kari Nousiainen, Assist. Prof. Harri Lähdesmäki

Emails for more information: kari.nousiainen [at] aalto [dot] fi, harri.lahdesmaki [at] aalto [dot] fi

42 Topic: Automatic assessment of expressions of relational algebra

One of the topics covered in course CS-A1150 (the old code CSE-A1200) Databases is relational algebra. The students write database queries as expressions of relational algebra. Nowadays, these exercises are graded by hand by the course staff. The purpose of the job is to examine which kind of automatic assessment tools are availabe and implement one of them in A+ system. The applicant should be fluent in programming and should have completed the courses CS-A1150 / CSE-A1200 Databases and CS-C3170 / CSE - C3210 Web Software Development or have the equivalent knowledge. Knowledge about regular expressions, finite automata and completion of the course Introduction to Compiling are considered a plus.

Supervisor: Kerttu Pollari-Malmi

Email for more information: kerttu.pollari-malmi [at] aalto [dot] fi

43 Topic: Web designer for the Aalto Online Learning - A!OLE project

The task for the summer job (3 months) is to support A!OLE pilots on online course material development tasks. The applicant should be fluent in basic web techniques: HTML5, Javascript, Django, NoSQL, Linked Data. Experience in data visualization libraries (D3.js, highcharts) is considered a plus.  The applicant is supposed to have completed relevant CS courses (like Databases, Web Software Development) by the Summer.

Supervisor: Tomi Kauppinen

Email for more information: Tomi.Kauppinen [at] aalto [dot] fi

44 Topic: Interaction designer for the Aalto Online Learning - A!OLE project

The task for the summer job (3 months) is to support A!OLE pilots on online course material development tasks, especially concerning interaction. The interaction designer will help A!OLE pilot projects to create interactive online learning materials. The applicant should be fluent in design thinking and basic web techniques: HTML5, Javascript, Django, NoSQL, Linked Data. Experience in data visualization libraries (D3.js, highcharts) is considered a plus.  The applicant is supposed to have completed some relevant courses, especially from Information Networks (like Design of WWW Services, User Interface Construction, Explorative Information Visualization) and other CS courses (like Web Software Development) by the Summer.

Supervisor: Tomi Kauppinen

Email for more information: Tomi.Kauppinen [at] aalto [dot] fi

45 Topic: Bayesian methods for disease risk prediction and personalised medicine

The goal is to develop probabilistic modeling, Bayesian inference and machine learning methods for epidemiology, disease risk prediction, and personalised medicine. Ever increasing computing performance makes it possible to use more complex models to model phenomena which are inherently complex containing nonlinearities and interactions. Bayesian approach provides consistent and flexible way to combine available structural information and uncertain observations.  The summer project can be taking part of the methodological development or more applied analysis depending on your interests. Strong background in mathematics and some experience in programming is beneficial.

Supervisor: Prof. Aki Vehtari

Email for more information: aki.vehtari [at] aalto [dot] fi

46 Topic: Computer Vision

Research Project in Computer Vision: We are a relatively new research group working broadly in the field of computer vision. We are pursuing research problems both in geometric computer vision (including topics such as visual SLAM, visual-inertial odometry, and 3D scene reconstruction) 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. Previous experience in computer vision is not required. The precise topics of the research will be chosen together with the students to match their personal interests. For more information about our research, please visit http://users.aalto.fi/~kannalj1/

Supervisor: Juho Kannala

Email for more information: juho.kannala [at] aalto [dot] fi

47 Topic: Everyday Cultural Heritage

Digital cultural heritage is common in both museums and outdoor sites. Open-air museums and industrial areas are often augmented with digital technologies (e.g. 'apps', or tangible devices). Such systems often assume that using the cultural heritage is the user's primary task. We are interested in how these technologies might fit into other everyday activities (e.g. just going for a walk, or during regular day to day practices), where accessing cultural heritage is something the user is interested in, but not their primary goal. We think this approach can work with much sparser public data sets, where the augmented heritage is not significant enough to be an interactive experience itself, but rather would be nice to know if the user walks nearby.

In this project the intern will develop this idea in some way. There are a number of approaches, and they depend on the student skills and interest. We would expect some indication of the potential direction the student would like to go in the application. For example, building an app that supports such sparse datasets and evaluating it with people. Applying tangible approaches to engage users outside with digital heritage and which integrates with an existing app on the users smartphone. Developing and evaluating techniques that combine tangible outdoor interaction, with an existing smartphone app (e.g. installing an old telephone box with archive audio recordings triggered as a user with the smartphone walks by. There is significant scope here, and opportunity to co-develop the idea. Applicants should have strong development skills (Arduino, Android, Swift, iOS, Raspberry PI), physical computing skills, experience in HCI and an interest in cultural heritage. It is advised to make clear your skills in relation to the direction you would want to go in.

Supervisor: David McGookin

Email for more information: david.mcgookin [at] aalto [dot] fi

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Following topics require proficiency in Finnish language

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48 Topic: Semanttiset verkkojulkaisut -- tekstit linkitettynä datapalveluna

Työssä tutkitaan tekstiaineistojen julkaisemista ja rikastamista linkitettynä datapalveluna semanttisessa webissä. Työtä tehdään liittyen SeCo-ryhmän Severi-projektiin liittyen, jossa tutkitaan lakiaineistojen, uutisten,  rakennusnormien ja elämäkertojen julkaisemista datana. Yhteistyötä tehdään Helsingin yliopiston digitaalisten ihmistieteiden keskuksen HELDIG kanssa, ja siihen liittyy myös kansainvälistä yhteistyötä. Työssä louhitaan ja rakenteistetaan eri lähteistä saatavaa teksti- ja muuta dataa semanttiseen RDF-muotoon ja otetaan sitä käyttöön SPARQL-palvelupisteen kautta erilaisissa demonstraattoreissa Linked Data Finland -alustaa hyödyntäen. Kesätyön laajentaminen DI-työksi voi tilanteesta ja hakijasta riippuen olla mahdollista.

SeCo-ryhmän kotisivu, HELDIG-keskus, Linked Data Finland

Supervisor: Prof. Eero Hyvönen

Email for more information: eero.hyvonen [at] aalto [dot] fi

49 Topic: Ohjelmistokehittäjää kursseille Tietotekniikka sovelluksissa ja Tietorakenteet ja algoritmit Y

Haemme ohjelmistokehittäjää Aallon kursseille CS-A1130 Tietotekniikka sovelluksissa ja CS-A1141 Tietorakenteet ja algoritmit Y. Kesällä tehtäviin kuuluvat kurssien harjoituksiin ja oppimisympäristöön liittyvät ohjelmistokehitystyöt. Työ voi jatkua myös kesän jälkeen. Lukukausien aikana työtehtävät sisältävät myös kurssin oppimisympäristön (A+) ylläpitotehtäviä sekä opiskelijoiden töiden arviointia.Työ alkaa kesällä kokopäiväisenä ja mahdollisesti jatkuu syksyllä ja keväällä osa-aikaisena (lukukausien aikainen työmäärä voidaan neuvotella erikseen).

Edellytämme hakijalta hyvää suomen ja englannin kielen taitoa, ohjelmointitaitoa ja kokemusta ainakin joitain täsmäkielistä (joko kurssin CS-A1130 tai CS-A1141 suoritus hyvällä arvosanalla tai muutoin tuntemusta mm. Pythonista, SQL:stä, MATLAB:stä, LabVIEW:stä, jne.). Eduksi ovat myös hyvä opintomenestys sekä aiempi opetuskokemus.

Supervisor: Vanhempi yliopistonlehtori Ari Korhonen

Email for more information: ari.korhonen [at] aalto [dot] fi

50 Topic: Pääassistentteja kursseille Ohjelmointi 1 ja Ohjelmointistudio 1

Haemme kahta henkilöä pääassistenteiksi Aallon kursseille CS-A1110 Ohjelmointi 1 ja CS-C2110 Ohjelmointistudio 1: mediaohjelmointi syksyksi 2017 ja ehkäpä siitä pidemmällekin. Kumpikin työ alkaa jo kesällä 2017 kurssin harjoitusten uudistuksiin ja/tai oppimisympäristön A+ jatkokehitykseen (ohjelmointiin) liittyvillä tehtävillä. Pääassistentti toimii kurssin vastaavan opettajan apuna mm. muiden assistenttien koordinoimisessa ja kurssin oppimisympäristön (A+) ylläpidossa sekä osallistuu opiskelijoiden neuvomiseen kurssin aikana. Kesän valmistelevista tehtävistä sovitaan valitun henkilön osaamisen ja kiinnostuksen mukaan. Tehtävät sopivat hyvin Aallon kandi- tai maisteriopiskelijalle.

Työ on kesäkuukausina täyspäiväinen, syyslukukaudella esim. 15-20h viikossa (osin neuvoteltavissa). Mahdollisista muista tehtävistä kevätlukukaudeksi voidaan sopia erikseen myöhemmin. Pääassistentin toimi tarjoaa mahdollisuuksia edetä tekemään opinnäytteitä, kenties jatko-opintojakin, Learning + Technology -tutkimusryhmän yhteydessä (http://research.cs.aalto.fi/LeTech/).

Hakijalta edellytetään täsmällisyyttä ja luotettavuutta, erinomaista kirjallista ja suullista ilmaisutaitoa suomeksi, kiinnostusta opetukseen ja sen kehittämiseen sekä hyvää perusohjelmointitaitoa. Eduksi katsotaan muun muassa hyvä yleinen opintomenestys, kokemus opetustehtävistä, kiinnostus pidempiaikaiseen työskentelyyn opetuksen parissa ja/tai Learning + Technology -ryhmän tutkimusaiheisiin, kokemus web-ohjelmoinnista sekä muu kielitaito.

Supervisor: Juha Sorva / Otto Seppälä

Email for more information: juha.sorva [at] aalto [dot] fi / otto.seppala [at] aalto [dot] fi

51 Topic: Pääassistentti/ohjelmistokehittäjä kurssille Web Software Development

Haemme henkilöä pääassistentin ja ohjelmistokehittäjän tehtäviin Aallon kurssille CS-C3170 Web Software Development, joka järjestetään talvella 2017-2018. Tehtävät alkavat jo kesällä kurssin harjoituksiin ja oppimisympäristöön liittyvillä ohjelmistokehitystöillä. Kurssin aikana pääassistentti toimii kurssin vastaavan opettajan apuna mm. kurssin oppimisympäristön (A+) ylläpidossa ja opiskelijoiden töiden arvioinnissa. Työ on kesällä täyspäiväinen; lukukausien aikainen työmäärä voidaan neuvotella erikseen.

Edellytämme hakijalta hyvää ohjelmointitaitoa ja kokemusta web-ohjelmoinnista (vähintään kurssin CS-C3170 suoritus hyvällä arvosanalla tai vastaavat tiedot) sekä hyvää ilmaisutaitoa sekä suomeksi että englanniksi. Eduksi ovat myös hyvä opintomenestys, aiempi opetuskokemus ja muu kielitaito.

Supervisor: Otto Seppälä

Email for more information: otto.seppala [at] aalto [dot] fi

Page content by: communications-cs [at] aalto [dot] fi (Department of Computer Science) | Last updated: 31.01.2017.