Welcome to Mitra Baratchi's
website

My research interests lie in spatio-temporal, time-series, and mobility data modeling. More specifically, I aim at designing algorithms that extract patterns from such data in a fully automated manner. Such research is targeting applications in a broad range of urban, environmental, and industrial domains for which I have collaborations, notably, with the European Space Agency, Honda Research Institute, various municipalities, and researchers in other scientific disciplines.

I am an associate professor at the Leiden Institute of Advanced Computer Science (LIACS), Leiden University. I am the leader of the Spatio-temporal data Analysis and Reasoning (STAR) research group, the founder of the Special Interest Group on Spatio-Temporal Data Mining (SIG-SDTM) and co-leader (together with Holger Hoos and Jan van Rijn) of the Automated Design of Algorithms (ADA) research group. Previously, I worked as a post-doctoral researcher in the Design and Analysis of Communication Systems (DACS) group at the University of Twente in the Living Smart campus project.

Before joining DACS, I was the project leader of SaxShirt project in Ambient Intelligence group (AMI) at Saxion University of Applied Sciences. I received my PhD degree from the Pervasive Systems (PS) group at University of Twente on analysing mobility data. Before coming to the Netherlands, I completed both my Master’s and Bachelor’s studies in computer engineering in Iran.

Grants

  • NWO-Aspasia, 2024-2029, €120K

    • Awarded based on NWO-Vidi proposal "ParallelWorlds: The First Automated Intervention Recommendation System for Grand Global Challenges"

  • Horizon - Marie Skłodowska-Curie Actions, 2023-2026, €2,930,482

    • Learning Network for Advanced Behavioural Data Analysis (LABDA)

  • ESA - Open Science Innovation Platform (OSIP) Grants , 2021-2025, €90K

    • Physics-aware Automated Machine Learning (PA-AutoML) for Earth Observations

  • NWO-KLEIN 1, 2021-2025, €350K

    • Physics-aware Spatio-temporal Machine Learning for Earth Observation Data

  • Leiden-Delft-Erasmus Center for BOLD Cities, 2020-2024, €135K

    • Data-driven, Urban Policy-making for Social Inclusion of Young, Vulnerable People

  • Endeavour Awards, Australian Department of Education, 2014

    • Compressed Trajectory Data Sensing

  • EU Erasmus Mundus PhD Fellowships, 2011-2014

    • Mining Mobile Object's Behaviour

Find the full list of my publications in google scholar

2022

  • Automated machine learning for COVID-19 forecastings, J Tetteroo, M Baratchi, HH Hoos, in IEEE ACCESS 10, 2022.
  • A novel data-driven approach to examine children’s movements and social behaviour in schoolyard environments, M Nasri, YT Tsou, A Koutamanis, M Baratchi, S Giest, D Reidsma, C Rieffe, in Children 9 (8), 2022.
  • VPint: value propagation-based spatial interpolation, L Arp, M Baratchi, H Hoos, in Data Mining and Knowledge Discovery, 2022.
  • An end-to-end pipeline for uncertainty quantification and remaining useful life estimation: an application on aircraft engines, M Kefalas, B van Stein, M Baratchi, A Apostolidis, T Bäck, PHM Society European Conference, 2022, Turin, Italy.
  • Towards time-Series feature engineering in automated machine learning for multi-step-ahead forecasting, C Wang, M Baratchi, T Bäck, HH Hoos, S Limmer, M Olhofer, in Engineering Proceedings 18 (1), 2022.
  • A systematic analysis on the impact of contextual information on point-of-interest recommendation, HA Rahmani, M Aliannejadi, M Baratchi, F Crestani, in ACM Tansactions on Information Systems 40 (4), 2022.

2021

  • Automated machine learning for satellite data: integrating remote sensing pre-trained models into AutoML systems, N. R. P. Salinas, M. Baratchi, J. N. Rijn, A. Vollrath, in ECML-PKDD'21, online event.
  • MultiETSC: automated machine learning for early time series classification, G. Ottervanger, M. Baratchi, H. H. Hoos, in Data Mining and Knowledge Discovery, 2021.
  • Automated machine learning for remaining useful life estimation of aircraft engines, M. Kefalas, M. Baratchi, A. Apostolidis, D. van den Herik, T. Bäck, in IEEE-PHM 21, online event.
  • Exploring the impact of noise on hybrid inversion of PROSAIL RTM on Sentinel-2 data, N. C. de Sá, M. Baratchi, L. T. Hauser, P. van Bodegom, in Remote Sensing 13 (4), 2021.

2020

  • An intelligent tree planning approach using location-based social networks data, J. H. van Staalduinen, J. Tetteroo, D. Gawehns, M. Baratchi, in BNAIC-BeneLearn'20, Leiden, The Netherlands.
  • Unsupervised discretization by two-dimensional mdl-based histogram, L. Yan, M. Baratchi, M. van Leeuwen, Paris
  • Dynamic macro scale traffic flow optimisation using crowd-sourced urban movement data, L. Arp, D. van Vreumingen, D. Gawehns, M .Baratchi, in MDM'20, Paris, France.
  • Joint geographical and temporal modeling based on matrix factorization for point-of-interest recommendation, H. A. Rahmani, M. Aliannejadi, M. Baratchi, F. Crestani, in ECIR'20, Lisbon, Potugal.
  • Reconciling predictions in the regression setting: an application to bus travel time prediction, J. Mendes-Moreira, M. Baratchi, in IDA'20, Lake Constance, Germany.

2019

  • Automated Machine Learning for Short-term Electric Load Forecasting, C. Wang, S. Limmer and M. Baratchi and T. Baeck and H. Hoos and M. Olhofer, in IEEE SSCI'19, Xiamen, China.
  • A Multi-Objective Approach for Optimal Store Placement, J. Rook, B. Verpaalen, D. Gawehns and M. Baratchi, in NetMob'19 special session Future Cities Challenge, Oxford, UK.
  • Metaheuristic macro scale traffic flow optimisation from urban movement data, L. Arp, D. van Vreumingen, D. Gawehns and M. Baratchi, in NetMob'19 special session Future Cities Challenge, Oxford, UK.
  • An intelligent tree planning approach using location-based social networks data, J. van Staalduinen, J. Tetteroo, D. Gawehns and M. Baratchi, in NetMob'19 special session Future Cities Challenge, Oxford, UK.
  • Category-aware location embedding for point-of-interest recommendation, H. A. Rahmani, M. Aliannejadi, R. Mirzaei Zadeh, M. Baratchi, M. Afsharchi, F. Crestani, ACM ICTIR'19, Santa Clara, California, USA.
  • Design & analysis of a distributed routing algorithm towards Internet-wide geocast, B. Meijerink, M. Baratchi, G. Heijenk , Computer Communications 19 (149), pp. 201-2018.
  • A glossary for research on human crowd dynamics, J. Adrian, M. Amos, M. Baratchi, M. Beermann, et. al , Collective Dynamics 4 (A19).

2018

  • Identifying Stops and Moves in WiFi Tracking Data, C. Chilipirea, M. Baratchi, C. Dobre, M. van Steen, Sensors 18 (11).
  • A Distributed Routing Algorithm for Internet-wide Geocast, B. Meijerink, M. Baratchi, G. Heijenk, arXiv preprint arXiv:1805.01690 .
  • Identifying movements in noisy crowd analytics data, C. Chilipirea, C. Dobre, M. Baratchi, M. van Steen, IEEE MDM'18, Aalborg, Denmark.

2017

  • Inferring the social-connectedness of locations from mobility data, T. Brugman, M. Baratchi, G. Heijenk, M. van Steen, Socinfo'17, Oxford, UK.
  • Evaluation of geocast routing trees on random and actual networks, B. Meijerink, M. Baratchi, G. Heijenk, WWIC'17, St. Petersburg, Russia.
  • Spaceprint: a mobility-based fingerprinting scheme for public spaces, M. Baratchi, G. Heijenk, M. van Steen, SIGSPATIAL'17, California, USA.
  • Reliable vehicular broadcast using 5G device-to-device communication, M. Gholibeigi, N. Sarrionandi, M. Karimzadeh, M. Baratchi, H. van den Berg, G. Heijenk, WMNC'17, Valencia, Spain.
  • Wifi tracking of pedestrian behavior, C. Chilipiria, A. Petre, M. Baratchi, C. Dobre, M. van Steen, in Smart Sensor Networks: Communication Technologies and Intelligent Applications, Elsevier, 2017.

2016

  • A multi-channel multiple access scheme for wireless sensor networks using frequency offsets - modeling and analysis, S. Morshed, M. Baratchi, G. Heijenk, WiMob'16, New York, USA.
  • Towards reliable multi-Hop broadcast in VANETs: an analytical approach, M.Gholibeigi, M. Baratchi, H. van den Berg, G. Heijenk, VNC'16, Ohio, USA.
  • Towards decisive garments for heat stress risk detection, M. Baratchi, L. Teunissen, P. Ebben, W. Teeuw, J. Laarhuis, M. van Steen, UBICOMP'16, Adjunct, Heidelberg, Germany.
  • An efficient geographical addressing scheme for the Internet, B. Meijerink, M. Baratchi, G. Heijenk, WWIC'16, Thessaloniki, Greece. **Best paper**
  • Traffic-adaptive duty cycle adaptation in TR-MAC protocol for Wireless Sensor Networks, S. Morshed, M. Baratchi, G. Heijenk, WD'16, Toulouse, France.

2015

  • Lattitude, longitude, and beyond: mining mobile objects' behavior, M. Baratchi, 2015, PhD Thesis, University of Twente.

2014

  • A hierarchical hidden semi-Markov model for modeling mobility data, M. Baratchi, N. Meratnia, P.J.M. Havinga, A.K. Skidmore, A.K.G. Toxopeus, UBICOMP'14, Seattle, USA.

2013

  • Recognition of periodic behavioral patterns from streaming mobility data, M. Baratchi, N. Meratnia, P.J.M. Havinga, MOBIQUITOUS'13, Tokyo, Japan.
  • On the use of mobility data for discovery and description of social ties, M. Baratchi, N. Meratnia, P.J.M. Havinga, ASONAM'13, Niagara falls, Canada.
  • Finding frequently visited paths: dealing with the uncertainty of spatio-temporal mobility data, M. Baratchi, N. Meratnia, P.J.M. Havinga, ISSNIP'13, Melbourne, Australia.
  • Evaluation of incentives for body area network-based healthcare systems, S. Aflaki, N. Meratnia, M. Baratchi, PJM Havinga, ISSNIP'13, Melbourne, Australia.
  • Sensing solutions for collecting spatio-temporal data for wildlife monitoring applications: a review, M. Baratchi, N. Meratnia, P.J.M. Havinga, A.K. Skidmore, A.K.G Toxopeus, Sensors 13 (5).

2011

  • A distributed verification scheme for encrypted data aggregation in wireless sensor networks, Baratchi, K. Jamshidi, CNDS'11, Tehran, Iran.

PhD students

  • Khashayar Fathinejad, 2024-now, co-supervised with Wessel Kraaij, Saber Salehkaleybar
  • Selin Acan, 2023-now, co-supervised with Peter de loof, Erno Hermans, Martin Dresler
  • Wadie Skaf, 2023-now, co-supervised with Holger Hoos
  • Julia Wasala, 2022-now, co-supervised with Holger Hoos, Ilse Aben, Bram Maasakkers
  • Laurens Arp, 2021-now, co-supervised with Holger Hoos, Peter van Bodegom
  • Maedeh Nasri, 2020-now, co-supervised with Carolien Rieffe, Alexander Koutamanis, Sarah Giest
  • Zhou Zhou, 2019-2020, Visiting CSC PhD student
  • Marios Kefalas, 2019-2022, co-supervised with Thomas Bäck
  • Can Wang, 2019-2024, co-supervised with Holger Hoos and Thomas Bäck
  • Nuno Sa, 2018-now, co-supervised with Peter van Bodegom
  • Cristian Chilipirea , 2015-2019, co-supervised with Maarten van Steen and Ciprian Dobre

Current master students

  • Sietse Schroder
  • Oscar Bernal Nebara
  • Gerlise Chan
  • Gareth Kok

Previous master students

  • Tobias Oberkofler, Zhiwei Zhang, Julia Wasala, ZhiZhou Fang, Jaco Tetteroo, Victor Noteboom, Laurens Arp, Gilles Ottervanger, Nelly Palacios, Lincen Yang
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Current courses at Leiden University

Past courses at Leiden University

  • Research Methods in Computer Science (2020-2023), Study Guide
  • Software Engineering (2017-2018), Study Guide
  • Seminar Business Information Systems (2017-2018), Study Guide

Past courses at University of Twente

  • Data Visualization (2016)
  • Mobile and Wireless Networks (2015-2016)
  • Advanced Mobile and Wireless Networks (2015-2016)
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