ISLAB/CAISR - User contributions [en]

AI R&D at King

2019-10-25T05:10:37Z

Seppas: Created page with "{{StudentProjectTemplate |Summary=Reinforcement learning |TimeFrame=Fall 2019 |Prerequisites=Artificial Intelligence, Learning Systems Course |Supervisor=Sepideh Pashami, |Le..."

{{StudentProjectTemplate
|Summary=Reinforcement learning
|TimeFrame=Fall 2019
|Prerequisites=Artificial Intelligence, Learning Systems Course
|Supervisor=Sepideh Pashami,
|Level=Master
|Status=Open
}}
AI R&D at King:
1- AI/ML Engineer thesis: Reinforcement learning-based bot - algorithmic improvement
2- Data Science thesis: Adoption of observational studies methodology to experiments
You can find more info and link to apply in our career page:
https://king.com/sv/jobs/master-thesis-internships-spring-2020-2651?breadcrumbs=/sv/jobs&location=stockholm

Value of BIG DATA for Large Building Owners

2019-10-23T07:17:55Z

Seppas:

{{StudentProjectTemplate
|Summary=It is an explanatory project with a company called Mutual Benefits Engineering AB
|TimeFrame=Fall 2019
|Prerequisites=AI course
|Supervisor=Sepideh Pashami
|Level=Master
|Status=Open
}}
MBE has developed, DEEP-Digitized Energy Efficiency Portal, together with AI and Cleantech optimize
energy efficiency by enabling measurement and control of all electrical and digital features in a
property, such as heating, cooling, ventilation, EV charge, etc. All aggregated data will then be used
for further analysis and optimization. Thus optimizing our customers' efficiency in energy
consumption and -cost, CO2 emissions and maintenance.

In a project with a big international property owner we are collecting a large volume of data. The
Master Thesis proposal includes analysis of which types of machine learning methods are best used
for analysing the data. It also involves the development of new functionality in digitization, machine
learning and energy efficiency for Large non-residential and multifamily buildings of sufficient size.
Measurement data is stored in DEEP’s cloud database, and addressed from API Server with NodeJS
app, and a REST API towards Angular front-end. The code for the site is performed in JavaScript, CSS,
HTML.

We think that the scope of the Master thesis would be for two people, about 500 hours each, and
cover questions regarding "Value of BIG DATA for Large Building Owners" such as:
1. What types of Machine learning methods are best for analyzing measurement data to
optimize energy consumption in large buildings?
a. #Pattern recognition – With the support of pattern recognition. Are you able to
identify patterns & abnormalities in a building’s energy consumption?
b. #Machine learning – With the support of machine learning. Are you able to identify
patterns & abnormalities in a building’s energy consumption?
c. #Reinforcement learning – Is it possible to learn which actions that works well (or
not well) to regulate a buildings energy consumption?
d. #Deep learning – Is it possible to use modern machine learning technologies (e.g.
Deep learning) to predict energy consumption over time?
e. #Big Data (perhaps also Internet of things)- Which data is important, valuable and
useful (alternatively redundant)? How to streamline data collection and
management?
f. #Sustainable development - How can research fields like 'big data', 'deep learning',
'machine learning' and 'reinforcement learning' be used to reduce carbon emissions
and create smart societies?
2. Predicting a property's energy consumption using the property's previous energy data (kWh /
m2 / year) through information about number of visitors to the property at certain times,
weather forecasts, other external 3rd party information about the building?

MBE owns and develops AI-algorithms, database, back-end and front-end for DEEP.

Did you know?
CO2 emission contribution from buildings incl construction are twice as high (40%) compared to
vehicles fuel combustion (23%) (IEA, Digitalization & Energy, 2017).

The master thesis scope will be described and adapted to the students during a personal meeting.
Contact: Niclas Jarhäll, Managing Director, Mutual Benefits Engineering AB (MBE),
niclas.jarhall@mutualbenefits.se

Value of BIG DATA for Large Building Owners

2019-10-23T07:12:32Z

Seppas:

{{StudentProjectTemplate
|Summary=It is an explanatory project with a company called Mutual Benefits Engineering AB
|Prerequisites=AI course
|Supervisor=Sepideh Pashami
|Level=Master
|Status=Open
}}
MBE has developed, DEEP-Digitized Energy Efficiency Portal, together with AI and Cleantech optimize
energy efficiency by enabling measurement and control of all electrical and digital features in a
property, such as heating, cooling, ventilation, EV charge, etc. All aggregated data will then be used
for further analysis and optimization. Thus optimizing our customers' efficiency in energy
consumption and -cost, CO2 emissions and maintenance.

In a project with a big international property owner we are collecting a large volume of data. The
Master Thesis proposal includes analysis of which types of machine learning methods are best used
for analysing the data. It also involves the development of new functionality in digitization, machine
learning and energy efficiency for Large non-residential and multifamily buildings of sufficient size.
Measurement data is stored in DEEP’s cloud database, and addressed from API Server with NodeJS
app, and a REST API towards Angular front-end. The code for the site is performed in JavaScript, CSS,
HTML.

We think that the scope of the Master thesis would be for two people, about 500 hours each, and
cover questions regarding "Value of BIG DATA for Large Building Owners" such as:
1. What types of Machine learning methods are best for analyzing measurement data to
optimize energy consumption in large buildings?
a. #Pattern recognition – With the support of pattern recognition. Are you able to
identify patterns & abnormalities in a building’s energy consumption?
b. #Machine learning – With the support of machine learning. Are you able to identify
patterns & abnormalities in a building’s energy consumption?
c. #Reinforcement learning – Is it possible to learn which actions that works well (or
not well) to regulate a buildings energy consumption?
d. #Deep learning – Is it possible to use modern machine learning technologies (e.g.
Deep learning) to predict energy consumption over time?
e. #Big Data (perhaps also Internet of things)- Which data is important, valuable and
useful (alternatively redundant)? How to streamline data collection and
management?
f. #Sustainable development - How can research fields like 'big data', 'deep learning',
'machine learning' and 'reinforcement learning' be used to reduce carbon emissions
and create smart societies?
2. Predicting a property's energy consumption using the property's previous energy data (kWh /
m2 / year) through information about number of visitors to the property at certain times,
weather forecasts, other external 3rd party information about the building?

MBE owns and develops AI-algorithms, database, back-end and front-end for DEEP.

Did you know?
CO2 emission contribution from buildings incl construction are twice as high (40%) compared to
vehicles fuel combustion (23%) (IEA, Digitalization & Energy, 2017).

The master thesis scope will be described and adapted to the students during a personal meeting.
Contact: Niclas Jarhäll, Managing Director, Mutual Benefits Engineering AB (MBE),
niclas.jarhall@mutualbenefits.se

Value of BIG DATA for Large Building Owners

2019-10-23T07:10:59Z

Seppas: Created page with "{{StudentProjectTemplate |Summary=It is an explanatory project with a company called Mutual Benefits Engineering AB |Prerequisites=AI course |Supervisor=Sepideh |Level=Master..."

{{StudentProjectTemplate
|Summary=It is an explanatory project with a company called Mutual Benefits Engineering AB
|Prerequisites=AI course
|Supervisor=Sepideh
|Level=Master
|Status=Open
}}
MBE has developed, DEEP-Digitized Energy Efficiency Portal, together with AI and Cleantech optimize
energy efficiency by enabling measurement and control of all electrical and digital features in a
property, such as heating, cooling, ventilation, EV charge, etc. All aggregated data will then be used
for further analysis and optimization. Thus optimizing our customers' efficiency in energy
consumption and -cost, CO2 emissions and maintenance.

In a project with a big international property owner we are collecting a large volume of data. The
Master Thesis proposal includes analysis of which types of machine learning methods are best used
for analysing the data. It also involves the development of new functionality in digitization, machine
learning and energy efficiency for Large non-residential and multifamily buildings of sufficient size.
Measurement data is stored in DEEP’s cloud database, and addressed from API Server with NodeJS
app, and a REST API towards Angular front-end. The code for the site is performed in JavaScript, CSS,
HTML.

We think that the scope of the Master thesis would be for two people, about 500 hours each, and
cover questions regarding "Value of BIG DATA for Large Building Owners" such as:
1. What types of Machine learning methods are best for analyzing measurement data to
optimize energy consumption in large buildings?
a. #Pattern recognition – With the support of pattern recognition. Are you able to
identify patterns & abnormalities in a building’s energy consumption?
b. #Machine learning – With the support of machine learning. Are you able to identify
patterns & abnormalities in a building’s energy consumption?
c. #Reinforcement learning – Is it possible to learn which actions that works well (or
not well) to regulate a buildings energy consumption?
d. #Deep learning – Is it possible to use modern machine learning technologies (e.g.
Deep learning) to predict energy consumption over time?
e. #Big Data (perhaps also Internet of things)- Which data is important, valuable and
useful (alternatively redundant)? How to streamline data collection and
management?
f. #Sustainable development - How can research fields like 'big data', 'deep learning',
'machine learning' and 'reinforcement learning' be used to reduce carbon emissions
and create smart societies?
2. Predicting a property's energy consumption using the property's previous energy data (kWh /
m2 / year) through information about number of visitors to the property at certain times,
weather forecasts, other external 3rd party information about the building?

MBE owns and develops AI-algorithms, database, back-end and front-end for DEEP.

Did you know?
CO2 emission contribution from buildings incl construction are twice as high (40%) compared to
vehicles fuel combustion (23%) (IEA, Digitalization & Energy, 2017).

The master thesis scope will be described and adapted to the students during a personal meeting.
Contact: Niclas Jarhäll, Managing Director, Mutual Benefits Engineering AB (MBE),
niclas.jarhall@mutualbenefits.se

Sepideh Pashami

2019-01-29T09:59:35Z

Seppas:

{{Person
|Family Name=Pashami
|Given Name=Sepideh
|Title=PhD
|Phone=+4635167151
|Cell Phone=+46737552016
|Position=Assistant Professor
|Email=sepideh.pashami@hh.se
|Image=Sepideh2crop.jpg
|Office=E511
}}
{{AssignProjects
|project=BIDAF A Big Data Analytics Framework for a Smart Society
}}
{{AssignProjects
|project=HEALTH
}}
{{AssignProjects
|project=ARISE
}}
{{AssignSubjectAreas
|SubjectArea=Machine Learning
}}
{{AssignSubjectAreas
|SubjectArea=Data Mining
}}
{{AssignApplicationAreas
|ApplicationArea=Intelligent Vehicles
}}
{{AssignTeacher
|Teacher=Perspective on Data Science
}}
[[Category:Staff]]


{{ShowPerson}}
{{InsertProjects}}
=='''Publications'''==
See my Google Scholar publication list [https://scholar.google.se/citations?user=dP8O7_AAAAAJ&hl=en by clicking here]

Safe deployment of Deep Neural Networks in automotive engineering

2017-10-13T18:40:33Z

Seppas: Created page with "{{StudentProjectTemplate |Summary=Evaluation of DNN factors with respect to robustness in safety critical systems. |References=[1] K. He, X. Zhang, S. Ren, and J. Sun, “Dee..."

{{StudentProjectTemplate
|Summary=Evaluation of DNN factors with respect to robustness in safety critical systems.
|References=[1] K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” CoRR, vol. abs/1512.03385, 2015. [Online]. Available: http://arxiv.org/abs/1512.03385

[2] C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. E. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” CoRR, vol. abs/1409.4842, 2014. [Online]. Available: http://arxiv.org/abs/1409.4842

[3] A. H. Abdelaziz, S. Watanabe, J. R. Hershey, E. Vincent, and D. Kolossa, “Uncertainty propagation through deep neural networks,” in Interspeech 2015, Dresden, Germany, Sep. 2015. [Online]. Available: https://hal.inria.fr/hal-01162550

|Prerequisites=AI and learning system course
|Supervisor=Cristofer Englund, Sepideh Pashami,
|Level=Master
|Status=Open
}}
The overall goal of this project is to create a framework for deploying deep neural networks (DNN) in safety critical systems. Indeed DNN are popular and efficient, used with great success to solve pattern recognition tasks in various environments [1, 2]. However, to deploy such technology in safety critical systems, such as vehicles, deeper understanding on how and when they work as expected is needed. Deeper understanding makes it possible to detect weaknesses and take accurate precautions for improved reliability and safety.

Robustness of the results produced by DNN is important for safety critical systems. This project will investigate the robustness of the results from a DNN by adding noise to the input data set, removing some data points or inserting inaccurate data and calculating the propagation of the uncertainty through the network [3].

The thesis will focus on evaluation of DNN factors w.r.t. robustness.

Possible data that can be used for the project is either Deeptraffic (http://selfdrivingcars.mit.edu/deeptraffic/) or Torcs (https://sourceforge.net/projects/torcs/).

Detecting changes in causal relations

2017-09-27T17:06:04Z

Seppas:

{{StudentProjectTemplate
|Summary=Monitoring the operation of bus fleet by tracking the changes in causal network
|Keywords=Causal network, change detection, stream mining
|TimeFrame=Winter 2016 - Spring 2017
|References=Structural causal discovery techniques: https://arxiv.org/pdf/1211.3295.pdf

Change detection in Granger causality: http://cowles.yale.edu/sites/default/files/files/pub/d20/d2059.pdf
|Prerequisites=Artificial Intelligence and Learning Systems courses
|Supervisor=Sepideh Pashami, Sławomir Nowaczyk,
|Level=Master
|Status=Open
}}
Causal relation between variable does not stay constant through time. Changes in the relations between variables can be evidence of structural change. For example, detecting changes during the operation of the buses in a city can identify different modes that these buses are operating on.

The challenge is that the results of causal network discovery methods based on observational data are not robust to noise. Therefore, simply comparing the causal networks producing by these methods will not work due to robustness issue. Further, time segment for comparison of the causal graphs is needed. Granger causality has been used previously for finding causal changes through time. However, detecting changes in structural causal discovery methods through time has not been investigated before.

The first step is to find the robust causal graph which can be achieved by bootstrapping techniques or Bayesian aggregation. The next step is to create causal networks through time segments and to measure the difference between the causal networks. The final step is to identify changes in the signal.

Detecting changes in causal relations

2017-09-27T17:04:42Z

Seppas: Created page with "{{StudentProjectTemplate |Summary=Monitoring the operation of bus fleet by tracking the changes in causal network |Keywords=Causal network, change detection, stream mining |R..."

{{StudentProjectTemplate
|Summary=Monitoring the operation of bus fleet by tracking the changes in causal network
|Keywords=Causal network, change detection, stream mining
|References=Structural causal discovery techniques: https://arxiv.org/pdf/1211.3295.pdf

Change detection in Granger causality: http://cowles.yale.edu/sites/default/files/files/pub/d20/d2059.pdf

|Prerequisites=Artificial Intelligence and Learning Systems courses
|Supervisor=Sepideh Pashami, Sławomir Nowaczyk,
|Level=Master
|Status=Open
}}
Causal relation between variable does not stay constant through time. Changes in the relations between variables can be evidence of structural change. For example, detecting changes during the operation of the buses in a city can identify different modes that these buses are operating on.

The challenge is that the results of causal network discovery methods based on observational data are not robust to noise. Therefore, simply comparing the causal networks producing by these methods will not work due to robustness issue. Further, time segment for comparison of the causal graphs is needed. Granger causality has been used previously for finding causal changes through time. However, detecting changes in structural causal discovery methods through time has not been investigated before.

The first step is to find the robust causal graph which can be achieved by bootstrapping techniques or Bayesian aggregation. The next step is to create causal networks through time segments and to measure the difference between the causal networks. The final step is to identify changes in the signal.

Automatic Machine Learn (AUTO-AUTO-ENCODER!)

2017-09-27T14:14:41Z

Seppas: Seppas moved page Automatic Machine Learn (AUTO-AUTO-ENCODER!) to Automatic Machine Learning (AUTO-AUTO-ENCODER!)

#REDIRECT [[Automatic Machine Learning (AUTO-AUTO-ENCODER!)]]

Automatic Machine Learning (AUTO-AUTO-ENCODER!)

2017-09-27T14:14:41Z

Seppas: Seppas moved page Automatic Machine Learn (AUTO-AUTO-ENCODER!) to Automatic Machine Learning (AUTO-AUTO-ENCODER!)

{{StudentProjectTemplate
|Summary=Automatic configuration algorithm for autoencoders
|Keywords=Deep learning, autoencoder, meta learning, AutoML
|TimeFrame=Winter 2016 - Spring 2017
|References=The following paper summarises the algorithm configuration in the different domain :
http://aad.informatik.uni-freiburg.de/papers/16-AUTOML-AutoNet.pdf

This paper presents the initial idea behind Bayesian optimization for estimating parameter:
https://www.cs.ubc.ca/~hutter/papers/10-TR-SMAC.pdf

Previous master thesis on applying autoencoder for histogram data:
Robin Ng, “Efficient Implementation of Histogram Dimension Reduction using Deep Learning”, 2017.

|Prerequisites=Artificial Intelligence and Learning Systems courses,
|Supervisor=Sławomir Nowaczyk, Sepideh Pashami,
|Level=Master
|Status=Open
}}
For anyone who is tired of machine learning algorithm’s configuration.

Algorithm configuration plays an important role in the performance of machine learning methods. In addition, data scientists every day spend a lot of time with a little guidance to choose the parameters of the algorithm. Further, learning the parameter of the algorithm as automatic as possible enables the use of machine learning for a wider range of science and technology.

Usually, state of the art methods target supervised classification machine learning tasks. This project focuses on parameter configurations of autoencoders for variously available datasets. Autoencoder is unsupervised feature extraction technique based on the neural network which trains in a supervised fashion. Following explains the necessary steps toward achieving an automatic autoencoder during this project.
- Studying recent advances in meta-learning, transfer learning, algorithm selection, and algorithm configuration.
- Studying and implementing autoencoder
- Adapting existing algorithm configurations for autoencoder and comparing their performance

Name of the new projectAutomatic Machine Learn (AUTO-AUTO-ENCODER!)

2017-09-27T14:14:17Z

Seppas: Seppas moved page Name of the new projectAutomatic Machine Learn (AUTO-AUTO-ENCODER!) to Automatic Machine Learn (AUTO-AUTO-ENCODER!): renamed

#REDIRECT [[Automatic Machine Learn (AUTO-AUTO-ENCODER!)]]

Automatic Machine Learning (AUTO-AUTO-ENCODER!)

2017-09-27T14:14:17Z

Seppas: Seppas moved page Name of the new projectAutomatic Machine Learn (AUTO-AUTO-ENCODER!) to Automatic Machine Learn (AUTO-AUTO-ENCODER!): renamed

Automatic Machine Learning (AUTO-AUTO-ENCODER!)

2017-09-27T14:08:18Z

Seppas: Created page with "{{StudentProjectTemplate |Summary=Automatic configuration algorithm for autoencoders |Keywords=Deep learning, autoencoder, meta learning, AutoML |TimeFrame=Winter 2016 - Sprin..."

Sepideh Pashami

2016-02-03T15:31:52Z

Seppas:

{{Person
|Family Name=Pashami
|Given Name=Sepideh
|Title=PhD
|Phone=+4635167151
|Cell Phone=+46737552016
|Position=Postdoctoral researcher
|Email=sepideh.pashami@hh.se
|Image=Sepideh2crop.jpg
|Office=E511
}}
{{AssignProjects
|project=BIDAF A Big Data Analytics Framework for a Smart Society
}}
{{AssignSubjectAreas
|SubjectArea=Machine Learning
}}
{{AssignSubjectAreas
|SubjectArea=Data Mining
}}
{{AssignApplicationAreas
|ApplicationArea=Intelligent Vehicles
}}
[[Category:Staff]]


{{ShowPerson}}
{{InsertProjects}}
=='''Publications'''==
See my Google Scholar publication list [https://scholar.google.se/citations?user=dP8O7_AAAAAJ&hl=en by clicking here]

Sepideh Pashami

2016-02-03T15:30:17Z

Seppas:

{{Person
|Family Name=Pashami
|Given Name=Sepideh
|Title=PhD
|Cell Phone=0737552016
|Position=Postdoctoral researcher
|Email=sepideh.pashami@hh.se
|Image=Sepideh2crop.jpg
|Office=E511
|Phone=035167151
}}
{{AssignProjects
|project=BIDAF A Big Data Analytics Framework for a Smart Society
}}
{{AssignSubjectAreas
|SubjectArea=Machine Learning
}}
{{AssignSubjectAreas
|SubjectArea=Data Mining
}}
{{AssignApplicationAreas
|ApplicationArea=Intelligent Vehicles
}}
[[Category:Staff]]


{{ShowPerson}}
{{InsertProjects}}
=='''Publications'''==
See my Google Scholar publication list [https://scholar.google.se/citations?user=dP8O7_AAAAAJ&hl=en by clicking here]

Sepideh Pashami

2016-02-03T15:28:23Z

Seppas:

{{Person
|Family Name=Pashami
|Given Name=Sepideh
|Title=Ph.D.
|Cell Phone=0737552016
|Position=Postdoctoral researcher
|Email=sepideh.pashami@hh.se
|Image=Sepideh2crop.jpg
|Office=E511
|Phone=035167151
}}
{{AssignProjects
|project=BIDAF A Big Data Analytics Framework for a Smart Society
}}
{{AssignSubjectAreas
|SubjectArea=Machine Learning
}}
{{AssignSubjectAreas
|SubjectArea=Data Mining
}}
{{AssignApplicationAreas
|ApplicationArea=Intelligent Vehicles
}}
[[Category:Staff]]


{{ShowPerson}}
{{InsertProjects}}
=='''Publications'''==
See my Google Scholar publication list [https://scholar.google.se/citations?user=dP8O7_AAAAAJ&hl=en by clicking here]

Sepideh Pashami

2016-02-03T15:27:31Z

Seppas:

{{Person
|Family Name=Pashami
|Given Name=Sepideh
|Title=Ph.D.
|Phone=035167151
|Cell Phone=0737552016
|Position=Postdoctoral researcher
|Email=sepideh.pashami@hh.se
|Image=Sepideh2crop.jpg
|Office=E511
}}
{{AssignProjects
|project=BIDAF A Big Data Analytics Framework for a Smart Society
}}
{{AssignSubjectAreas
|SubjectArea=Machine Learning
}}
{{AssignSubjectAreas
|SubjectArea=Data Mining
}}
{{AssignApplicationAreas
|ApplicationArea=Intelligent Vehicles
}}
[[Category:Staff]]


{{ShowPerson}}
{{InsertProjects}}
=='''Publications'''==
See my Google Scholar publication list [https://scholar.google.se/citations?user=dP8O7_AAAAAJ&hl=en by clicking here]

Sepideh Pashami

2016-02-03T15:25:37Z

Seppas:

Sepideh Pashami

2016-02-03T15:24:03Z

Seppas:

{{Person
|Family Name=Pashami
|Given Name=Sepideh
|Title=PhD
|Phone=035167151
|Cell Phone=0737552016
|Position=Postdoctoral researcher
|Email=sepideh.pashami@hh.se
|Image=Sepideh2crop.jpg
|Office=E511
}}
{{AssignProjects
|project=BIDAF A Big Data Analytics Framework for a Smart Society
}}
{{AssignSubjectAreas
|SubjectArea=Machine Learning
}}
{{AssignSubjectAreas
|SubjectArea=Data Mining
}}
{{AssignApplicationAreas
|ApplicationArea=Intelligent Vehicles
}}
[[Category:Staff]]


{{ShowPerson}}
{{InsertProjects}}
=='''Publications'''==
See my Google Scholar publication list [https://scholar.google.se/citations?user=dP8O7_AAAAAJ&hl=en by clicking here]

Sepideh Pashami

2016-02-03T15:22:11Z

Seppas:

{{Person
|Family Name=Pashami
|Given Name=Sepideh
|Title=PhD
|Cell Phone=0737552016
|Position=Postdoctoral researcher
|Email=sepideh.pashami@hh.se
|Image=Sepideh2crop.jpg
|Office=E511
}}
{{AssignProjects
|project=BIDAF A Big Data Analytics Framework for a Smart Society
}}
{{AssignSubjectAreas
|SubjectArea=Machine Learning
}}
{{AssignSubjectAreas
|SubjectArea=Data Mining
}}
{{AssignApplicationAreas
|ApplicationArea=Intelligent Vehicles
}}
[[Category:Staff]]


{{ShowPerson}}
{{InsertProjects}}
=='''Publications'''==
See my Google Scholar publication list [https://scholar.google.se/citations?user=dP8O7_AAAAAJ&hl=en by clicking here]

Sepideh Pashami

2016-02-03T15:18:50Z

Seppas:

Sepideh Pashami

2016-02-03T15:17:57Z

Seppas:

{{Person
|Family Name=Pashami
|Given Name=Sepideh
|Title=PhD
|Phone=+46 (0) 35 16 71 51
|Cell Phone=+46 (0) 73 755 20 16
|Position=Postdoctoral researcher
|Email=sepideh.pashami@hh.se
|Image=Sepideh2crop.jpg
|Office=E511
}}
{{AssignProjects
|project=BIDAF A Big Data Analytics Framework for a Smart Society
}}
{{AssignSubjectAreas
|SubjectArea=Machine Learning
}}
{{AssignSubjectAreas
|SubjectArea=Data Mining
}}
{{AssignApplicationAreas
|ApplicationArea=Intelligent Vehicles
}}
[[Category:Staff]]


{{ShowPerson}}
{{InsertProjects}}
=='''Publications'''==
See my Google Scholar publication list [https://scholar.google.se/citations?user=dP8O7_AAAAAJ&hl=en by clicking here]

Sepideh Pashami

2016-02-03T15:17:08Z

Seppas:

{{Person
|Family Name=Pashami
|Given Name=Sepideh
|Title=PhD.
Phone=+46 (0) 35 16 71 51
|Cell Phone=+46 (0) 73 755 20 16
|Position=Postdoctoral researcher
|Email=sepideh.pashami@hh.se
|Image=Sepideh2crop.jpg
|Office=E511
}}
{{AssignProjects
|project=BIDAF A Big Data Analytics Framework for a Smart Society
}}
{{AssignSubjectAreas
|SubjectArea=Machine Learning
}}
{{AssignSubjectAreas
|SubjectArea=Data Mining
}}
{{AssignApplicationAreas
|ApplicationArea=Intelligent Vehicles
}}
[[Category:Staff]]


{{ShowPerson}}
{{InsertProjects}}
=='''Publications'''==
See my Google Scholar publication list [https://scholar.google.se/citations?user=dP8O7_AAAAAJ&hl=en by clicking here]

Sepideh Pashami

2016-02-03T15:15:19Z

Seppas:

{{Person
|Family Name=Pashami
|Given Name=Sepideh
|Title=PhD.
|Cell Phone=+46737552016
|Position=Postdoctoral researcher
|Email=sepideh.pashami@hh.se
|Image=Sepideh2crop.jpg
|Office=E511
}}
{{AssignProjects
|project=BIDAF A Big Data Analytics Framework for a Smart Society
}}
{{AssignSubjectAreas
|SubjectArea=Machine Learning
}}
{{AssignSubjectAreas
|SubjectArea=Data Mining
}}
{{AssignApplicationAreas
|ApplicationArea=Intelligent Vehicles
}}
[[Category:Staff]]


{{ShowPerson}}
{{InsertProjects}}
=='''Publications'''==
See my Google Scholar publication list [https://scholar.google.se/citations?user=dP8O7_AAAAAJ&hl=en by clicking here]

Sepideh Pashami

2015-09-15T08:24:50Z

Seppas:

{{Person
|Family Name=Pashami
|Given Name=Sepideh
|Title=M.Sc.
|Cell Phone=+46737552016
|Position=Research Engineer
|Email=sepideh.pashami@hh.se
|Image=Sepideh2crop.jpg
|Office=E511
}}
{{AssignProjects
|project=BIDAF A Big Data Analytics Framework for a Smart Society
}}
{{AssignSubjectAreas
|SubjectArea=Machine Learning
}}
{{AssignSubjectAreas
|SubjectArea=Data Mining
}}
{{AssignApplicationAreas
|ApplicationArea=Intelligent Vehicles
}}
[[Category:Staff]]


{{ShowPerson}}
{{InsertProjects}}
=='''Publications'''==
See my Google Scholar publication list [https://scholar.google.se/citations?user=dP8O7_AAAAAJ&hl=en by clicking here]

Sepideh Pashami

2015-09-15T08:22:33Z

Seppas:

{{Person
|Family Name=Pashami
|Given Name=Sepideh
|Title=M.Sc.
|Cell Phone=+46737552016
|Position=Research Engineer
|Email=sepideh.pashami@oru.se
|Image=Sepideh2crop.jpg
|Office=E511
}}
{{AssignProjects
|project=BIDAF A Big Data Analytics Framework for a Smart Society
}}
{{AssignSubjectAreas
|SubjectArea=Machine Learning
}}
{{AssignSubjectAreas
|SubjectArea=Data Mining
}}
{{AssignApplicationAreas
|ApplicationArea=Intelligent Vehicles
}}
[[Category:Staff]]


{{ShowPerson}}
{{InsertProjects}}
=='''Publications'''==
See my Google Scholar publication list [https://scholar.google.se/citations?user=dP8O7_AAAAAJ&hl=en by clicking here]

Sepideh Pashami

2015-09-15T08:20:52Z

Seppas:

{{Person
|Family Name=Pashami
|Given Name=Sepideh
|Title=M.Sc.
|Cell Phone=+46737552016
|Position=Research Engineer
|Email=sepideh.pashami@oru.se
|Office=E511
}}
{{AssignProjects
|project=BIDAF A Big Data Analytics Framework for a Smart Society
}}
{{AssignSubjectAreas
|SubjectArea=Machine Learning
}}
{{AssignSubjectAreas
|SubjectArea=Data Mining
}}
{{AssignApplicationAreas
|ApplicationArea=Intelligent Vehicles
}}
[[Category:Staff]]


{{ShowPerson}}
{{InsertProjects}}
=='''Publications'''==
See my Google Scholar publication list [https://scholar.google.se/citations?user=dP8O7_AAAAAJ&hl=en by clicking here]

File:Sepideh2crop.jpg

2015-09-15T08:18:54Z

Seppas:

Sepideh Pashami

2015-09-15T08:18:09Z

Seppas: Created page with "{{Person |Family Name=Pashami |Given Name=Sepideh |Title=M.Sc. |Cell Phone=+46737552016 |Position=Research Engineer |Email=sepideh.pashami@oru.se |Image=file:///Users/seppas/D..."

{{Person
|Family Name=Pashami
|Given Name=Sepideh
|Title=M.Sc.
|Cell Phone=+46737552016
|Position=Research Engineer
|Email=sepideh.pashami@oru.se
|Image=file:///Users/seppas/Desktop/personal/Sepideh2crop.jpg
|Office=E511
}}
{{AssignProjects
|project=BIDAF A Big Data Analytics Framework for a Smart Society
}}
{{AssignSubjectAreas
|SubjectArea=Machine Learning
}}
{{AssignSubjectAreas
|SubjectArea=Data Mining
}}
{{AssignApplicationAreas
|ApplicationArea=Intelligent Vehicles
}}
[[Category:Staff]]


{{ShowPerson}}
{{InsertProjects}}
=='''Publications'''==
See my Google Scholar publication list [https://scholar.google.se/citations?user=dP8O7_AAAAAJ&hl=en by clicking here]