Research Students

PhD students

  • Paulo De Oliveira Da Costa, Machine learning in maintenance logistics, co-promoter and daily supervisor, 2018 –
  • Reza Refaei Afshar, Programmatic advertising decision system, co-promoter and daily supervisor, 2018 –
  • Ya Song, Data-driven predictive maintenance, daily supervisor, 2020 –
  • Gonenc Tarakcioglu (external), co-promoter and daily supervisor, 2018 –
  • Jason Rhuggenaath, Data-aided adaptive decision support for online revenue optimization, co-promoter and daily supervisor, 2016 – 2020
  • Chetan Yadati (TU Delft), Coordinated Autonomous Planning and Scheduling,  co-promoter and daily supervisor, graduated in 2012
  • Qing Chuan (Charlie) Ye (Erasmus), Multi-objective Optimization Methods for Allocation and Prediction, co-promoter and daily supervisor,  graduated in May 2019

Postdoc researchers

  • Vahideh Reshadat, Lane Assessment and Route Planning, 2020-
  • Shahrzad Faghih Roohi, Data driven risk assessment for pharmaceutical logisitcs, 2018

Master students at TU/e and JADS:

  • Joris Bonders. with the Dutch police. 2020-2021
  • Ludo Nieuwelaar, with NS, 2020-2021
  • John Brouwers, 2020-2021
  • Nino Nussy: with CEVA logistics, 2020-2021
  • Wout Olde Hampsink: Machine learning for lane selection with thermal conditions in pharmaceutical transport, with Validaide, 2020-2021
  • Joanne Meeuwis: Real time prediction of crises with machne learning, with Safety Region Utrecht, 2020-2021
  • Martijn Beeks: The order batching problem: a scalable deep reinforcement learning approach, with Vanderlande, 2020-2021
  • Jelle Koks: Data driven maintenance, with Philips, 2020-2021
  • Antoine van Esch: Machine learning for modelling and decisions in city logistics, LCB, 2020-2021
  • Sam van Eekeren: Predicting service neediness, with Vanderlande, 2020-2021
  • Valentin Dmitrochenko: Deep Reinforcement Learning for spare parts allocation within ASML service supply chain, with ASML, 2019-2020
  • Akash Kalwar: Developing and Analysing Resource Sharing Mechanism for multiple-tenants operating under same Roof, 2019-2020
  • Vincent Fokker: Empty order carrier handling in a warehousing solution using machine learning, with Vanderlande, 2019-2020
  • Sneha Banoth: Embedding machine learning with decision making models for optimizing vehicle routing, 2019-2020
  • Robbert Reijnen: A Reinforcement Learning approach to the Multi-Agent Path Finding problem in a segment-based layout. with Vanderlande, 2019-2020, see a conference publication here
  • Dave Brandhoff: Optimizing conversion rates by applying data-driven lead-to-agent allocation. with Dreamcatch, 2019-2020
  • Jeroen Verheugd, Predicting water pipe failures: a neural Hawkes process approach  OML, 2019-2020, with Vitens & Deloitte, thesis available here
    (A conference paper was made based on his thesis, which is available to download here)
  • Myrte van Dongen, Designing a policy making tool to mitigate security of supply risks,  2019-2020, with Ministry of Economic Affairs and Climate & TNO, thesis available here
  • Jeroen Schoonderbeek, Predicting airline passengers with deep multi-task learning
    OML, 2019-2020, with KLM, thesis available here
  • Rico Wismans, Domain adaptation for prognostics in the aerospace industry, OML, 2019, with Fokker, available here
  • Bram Cals,The order batching problem:a deep reinforcement approach. OML,  2019, with Vanderlande. Download here (a journal paper under submission)
  • Han Qi Xia, Applications of time-to-event data analysis in root cause analysis of medical imaging systems, OML, 2019, with Philips. Download here
  • Clint Rooijakkers, Prioritizing Price Sensitivity Drivers Using Machine Learning Classification Algorithms, OML, 2019, with Download here
  • Ismay Cohen, A Multi-Agent Deep Reinforcement Learning Approach to Solving the Train Unit Shunting Problem, Jheronimus Academy of Data Science, 2019, with NS
  • Herbert van Leeuwen, Prediction of successful baggage transfers, Jheronimus Academy of Data Science, 2018-2019, with KLM. A conference paper was made based on his thesis, which is available to download here.
  • Rehan Saif, Equine lameness detection based on rein sensors, Jheronimus Academy of Data Science, 2018-2019
  • Jasper Paalman, Proposing a ranking function for very short texts, Jheronimus Academy of Data Science, 2018-2019. A conference paper was made based on his thesis, which is available to download here
  • Thomas Hagebols, Image based dietary assessment using artificial neural networks, 2018. BIS & DSE double degree, with Philips. Download here
  • Victor Stastra, Machine learning in cryptocurrency markets. Jheronimus Academy of Data Science, 2018
  • Evertjan Peer: The Train Unit Shunting Problem: a deep reinforcement learning approach, 2018. OML & CSE double degree (Best OML Thesis Award 2018), with NS.
    (A conference paper was made based on his thesis, which is available to download here)
  • Bart Terpstra, Ambient temperature prediction of pharmaceutical air freight, OML, 2018, with Validaide. Download here
  • Wouter van de Donk, Achieving more accurate truck warning lights by descriptive and predictive analytics, OML/MSE, 2018, with DAF. Download here
  • Arno van de Ven, A deep graph convolutional neural network aiding in finding feasible shunt plans, OML, 2018, with NS. Download here.
    (A conference paper was made based on his thesis, which is available to download here)
  • Vasanth Mohan: Development of a risk exposure method for global pharmaceutical logistics, OML, 2017, with Validaide. Download here
  • Edward Goudsmits: A fi rst step towards predictive maintenance: predicting the number of repairs of a truck, OML, 2017, with DAF. Download here
  • Jelle Dikker: Boosted tree learning for balanced item recommendation in online retail, BIS, 2017, with Building Blocks. Download here
  • Dylan Rijnen: Applied simulation optimization in the trailer management process, BIS, 2017, with ABI. Download here
    (A conference paper was made based on his thesis, which is available to download here)