Morteza Haghir Chehreghani

Associate Professor

Data Science and AI Division

Department of Computer Science and Engineering

Chalmers University of Technology

Home

  Since 2018, I am an Associate Professor in Machine Learning and Artificial Intelligence at the Department of Computer Science and Engineering, Chalmers University of Technology. I received my Ph.D. from ETH Zurich (Switzerland) in Computer Science in 2014, where I worked at the Machine Learning Institute under the supervision of Prof. Joachim M. Buhmann. After the Ph.D., I spent about four years at Xerox Research Centre Europe (later called Naver Labs Europe) as a researcher (Staff Research Scientist I, and then Staff Research Scientist II).

Research

  Artificial Intelligence, Machine Learning and Data Science constitute my general research interests. In particular, I am interested in Under-Supervised Machine Learning and Decision Making, i.e., the AI-based decision-making problems that need to deal with the lack of sufficient available information (e.g., labelled data, decision parameters, uncertainty and beyond). I study these problems in the contexts of Sequential Decision Making (Interactive Machine Learning) and Unsupervised Learning:

§  Interactive Machine Learning and Decision Making

o   Active learning and active decision making

o   Online learning (Bandits)

o   (Deep) Reinforcement learning

o   Bayesian optimization

o   Deep learning for reinforcement/online/active learning and decision making

§  Unsupervised Learning

o   Cluster modelling

o   Unsupervised representation learning

o   Learning theory and generalization performance of unsupervised learning

In all the problems, aspects such as generality, computational efficiency, transparency and explainability are taken into account. My research has been applied to several real-world problems in domains such as transport, life science, energy, automation, recommendation, and decision support systems.

Currently, I work with the following PhD students and postdocs (as the main supervisor).

o  Niklas Åkerblom (industrial PhD student, Volvo Cars): Online learning of energy-efficient navigation for electric vehicles

o  Linus Aronsson (academic PhD student): Active learning for deep neural network models

o  Ebrahim Balouji (postdoc, in collaboration with E2, M2 and CEVT): Trip planning and prediction with deep reinforcement learning

o  Hampus Gummesson Svensson (industrial PhD student, AstraZeneca): Online/active learning and exploration for automated drug discovery

o  Fazeleh S. Hoseini, (academic PhD student): Online learning of minimax distance measures

o  Arman Rahbar (academic PhD student): Under-supervised representation learning (interactive and transfer learning for representation learning)

Teaching

·   Advanced Topics in Machine Learning (reinforcement learning, online learning, active learning, and relevant deep learning models), Spring 2022.

·   Algorithms for Machine Learning and Inference, Spring 2022.

·   Advanced Topics in Machine Learning (reinforcement learning, online learning, active learning, and relevant deep learning models), Spring 2021.

·   Algorithms for Machine Learning and Inference, Spring 2021.

·   Algorithms for Machine Learning and Inference, Spring 2020.

·   Statistical Methods for Data Science, with Magnus V. Persson, Fall 2019.

·   Theoretical Foundations of Machine Learning (PhD course), with Devdatt Dubhashi, Fredrik Johansson, Ashkan Panahi, Fall 2019.

·   An intense course on Machine Learning to the Chalmers CSE faculty, June 2019.

·   Algorithms for Machine Learning and Inference, Spring 2019.

·   Statistical Methods for Data Science, with Richard Johansson, Fall 2018.

·   Algorithms for Machine Learning and Inference, with Devdatt Dubhashi, Spring 2018.

·   ML-HW CoDesign (Efficient Deep Learning, PhD course), with Fredrik Dahlgren, Devdatt Dubhashi, Olof Mogren, Miquel Pericas, Pedro Petersen, Ioannis Sourdis and Per Stenström, Spring 2018.

Contact

     Chalmers University of Technology

     Department of Computer Science and Engineering

     SE-412 96 Göteborg

     Sweden

     https://www.chalmers.se/en/staff/Pages/haghir.aspx

Publications

   * Pre-prints and the under-review submissions can be found in the DBLP profile.

[1] Niklas Åkerblom, Fazeleh Sadat Hoseini, and Morteza Haghir Chehreghani, “Online Learning of Network Bottlenecks via Minimax Paths”, Machine Learning (MLJ), 2023.

[2] Jan Zrimec, Xiaozhi Fu, Azam SheikhMuhammad, Christos Skrekas, Vykintas Jauniskis, Nora K. Speicher, Christoph S. Börlin, Vilhelm Verendel, Morteza Haghir Chehreghani, Devdatt Dubhashi, Verena Siewers, Florian David, Jens Nielsen, and Aleksej Zelezniak, “Controlling gene expression with deep generative design of regulatory DNA”, Nature Communications, 2022.

[3] Carl Johnell, and Morteza Haghir Chehreghani, “Efficient Optimization of Dominant Set Clustering with Frank-Wolfe Algorithms”, 31st ACM International Conference on Information and Knowledge Management (CIKM), 2022. [Full research paper]

[4] Ashkan Panahi, Arman Rahbar, Chiranjib Bhattacharyya, Devdatt Dubhashi, and Morteza Haghir Chehreghani, “Analysis of Knowledge Transfer in Kernel Regime”, 31st ACM International Conference on Information and Knowledge Management (CIKM), 2022. [Full research paper]

[5] Balázs Varga, Balázs Kulcsár, and Morteza Haghir Chehreghani, “Deep Q-learning: a robust control approach”, International Journal of Robust and Nonlinear Control, 2022.

[6] Arman Rahbar, Emilio Jorge, Devdatt Dubhashi, and Morteza Haghir Chehreghani, "Do Kernel and Neural Embeddings Help in Training and Generalization?", Neural Processing Letters (NPL), 2022.

[7] Victor Eberstein, Jonas Sjöblom, Nikolce Murgovski, and Morteza Haghir Chehreghani, “A Unified Framework for Online Trip Destination Prediction”, Machine Learning (MLJ), 2022.

[8] Morteza Haghir Chehreghani, “Shift of Pairwise Similarities for Data Clustering”, Machine Learning (MLJ), 2022.

[9] Sanna Jarl, Linus Aronsson, Sadegh Rahrovani, and Morteza Haghir Chehreghani, “Active Learning of Driving Scenario Trajectories”, Engineering Applications of Artificial Intelligence, 2022.

[10] Simon Viet Johansson, Hampus Gummesson Svensson, Esben Bjerrum, Alexander Schliep, Morteza Haghir Chehreghani, Christian Tyrchan, and Ola Engkvist, “Using Active Learning to Develop Machine Learning Models for Reaction Yield Prediction”, Molecular Informatics, 2022.

[11] Fazeleh Sadat Hoseini, and Morteza Haghir Chehreghani, “Memory-Efficient Minimax Distance Measures”, 26th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2022.

[12] Yuxin Chen, and Morteza Haghir Chehreghani, “Trip Prediction by Leveraging Trip Histories from Neighboring Users”, 25th IEEE International Intelligent Transportation Systems Conference (IEEE ITSC), 2022.

[13] Federica Comuni, Christopher Meszaros, Niklas Åkerblom, and Morteza Haghir Chehreghani, “Passive and active learning of driver behavior from electric vehicles”, 25th IEEE International Intelligent Transportation Systems Conference (IEEE ITSC), 2022.

[14] Hazem Peter Samoaa, Antonio Longa, Mazen Mohamad, Morteza Haghir Chehreghani, and Philipp Leitner, “TEP-GNN: Accurate Execution Time Prediction of Functional Tests using Graph Neural Networks”, 23^rd International Conference on Product-Focused Software Process Improvement (PROFES), 2022. [Full paper]

[15] John Daniel Bossér, Erik Sörstadius, and Morteza Haghir Chehreghani, “Model-Centric and Data-Centric Aspects of Active Learning for Deep Neural Networks”, IEEE International Conference on Big Data (IEEE BigData), 2021.

[16] Masoud Malek, Mostafa H. Chehreghani, Ehsan Nazerfard, and Morteza Haghir Chehreghani, “Shallow Node Representation Learning using Centrality Indices”, IEEE International Conference on Big Data (IEEE BigData), 2021.

[17] Fazeleh Hoseini, Sadegh Rahrovani, and Morteza Haghir Chehreghani, “Vehicle Motion Trajectories Clustering Via Embedding Transitive Relations”, 24^rd IEEE International Intelligent Transportation Systems Conference (IEEE ITSC), 2021.

[18] Sanna Jarl, Julia Wennerblom, Maria Svedlund, Sadegh Rahrovani, and Morteza Haghir Chehreghani, “Annotation of Traffic Scenarios for AD Verification Using Active Learning”, 6th International Symposium on Future Active Safety Technology Toward zero traffic accidents (FAST-zero), 2021.

[19] Morteza Haghir Chehreghani, “Reliable Agglomerative Clustering”, International Joint Conference on Neural Networks (IJCNN), 2021.

[20] Morteza Haghir Chehreghani, “Unsupervised Representation Learning with Minimax Distance Measures”, Machine Learning (MLJ), 109 (11), 2063-2097, 2020.

[21] Morteza Haghir Chehreghani, Mostafa H. Chehreghani, “Learning Representations from Dendrograms”, Machine Learning (MLJ), 109 (9): 1779–1802, 2020.

[22] Ashkan Panahi, Morteza Haghir Chehreghani, Devdatt Dubhashi, “Accelerated Proximal Incremental Algorithm Schemes for Non-Strongly Convex Functions”, Theoretical Computer Science, 812: 203-213, 2020.

[23] Niklas Åkerblom, Yuxin Chen, and Morteza Haghir Chehreghani, “An Online Learning Framework for Energy-Efficient Navigation of Electric Vehicles”, 29th International Joint Conference on Artificial Intelligence (IJCAI), 2020.

[24] Andreas Demetriou, Henrik Alfsvåg, Sadegh Rahrovani, and Morteza Haghir Chehreghani, “Generation of Driving Scenario Trajectories with Generative Adversarial Networks”, 23^rd IEEE International Intelligent Transportation Systems Conference (IEEE ITSC), 2020.

[25] Erik Thiel, Morteza Haghir Chehreghani, and Devdatt Dubhashi, “A Non-convex Optimization Approach to Correlation Clustering”, Thirty-Third AAAI Conference on Artificial Intelligence (AAAI), 2019.

[26] Claes Strannegård, Herman Carlström, Niklas Engsner, Fredrik Mäkeläinen, Filip Slottner Seholm, and Morteza Haghir Chehreghani, “Lifelong Learning Starting from Zero”, The 12th annual conference on Artificial General Intelligence (AGI), 2019.

[27] Claes Strannegård, Herman Carlström, Niklas Engsner, Fredrik Mäkeläinen, Filip Slottner Seholm, and Morteza Haghir Chehreghani, “Dynamic Deep Learning”, The 31st annual workshop of the Swedish Artificial Intelligence Society (SAIS), 2019.

[28] Mostafa H. Chehreghani, and Morteza Haghir Chehreghani, “Efficient Context-Aware K-Nearest Neighbor Search”, 40th European Conference on Information Retrieval (ECIR), 2018. [long paper]

[29] Yuxin Chen, Jean-Michel Renders, Morteza Haghir Chehreghani, and Andreas Krause, “Efficient Online Learning for Optimizing Value of Information: Theory and Application to Interactive Troubleshooting”, Thirty-Third Conference on Uncertainty in Artificial Intelligence (UAI), 2017.

[30] Morteza Haghir Chehreghani, “Classification with Minimax Distance Measures”, Thirty-First AAAI Conference on Artificial Intelligence (AAAI), 2017.

[31] Morteza Haghir Chehreghani, “Clustering by Shift”, IEEE International Conference on Data Mining (ICDM), pp. 793-798, 2017.

[32] Morteza Haghir Chehreghani, “Efficient Computation of Pairwise Minimax Distance Measures”, IEEE International Conference on Data Mining (ICDM), pp. 799-804, 2017.

[33] Morteza Haghir Chehreghani, “Feature-Oriented Analysis of User Profile Completion Problem”, 39th European Conference on Information Retrieval (ECIR), 2017. [long paper]

[34] Morteza Haghir Chehreghani, “Adaptive Trajectory Analysis of Replicator Dynamics for Data Clustering”, Machine Learning (MLJ), 104 (2-3): 271-289, 2016.

[35] Morteza Haghir Chehreghani, Mostafa H. Chehreghani, “Modeling Transitivity in Complex Networks”, Thirty-Second Conference on Uncertainty in Artificial Intelligence (UAI), 2016.

[36] Mostafa H. Chehreghani, Morteza Haghir Chehreghani, “Transactional Tree Mining”, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery (ECML/PKDD), (1) 182-198, 2016.

[37] Morteza Haghir Chehreghani, “K-Nearest Neighbor Search and Outlier Detection via Minimax Distances”, SIAM International Conference on Data Mining (SDM), 2016.

[38] Ana Paula Oliveira, Sotiris Dimopoulos, Alberto Giovanni Busetto, Stefan Christen, Reinhard Dechant, Laura Falter, Morteza Haghir Chehreghani, Szymon Jozefczuk, Christina Ludwig, Florian Rudroff, Juliane Schulz, Alexandre Soulard, Daniele Stracka, Ruedi Aebersold, Joachim M. Buhmann, Michael N. Hall, Matthias Peter, Uwe Sauer, and Jörg Stelling, “Inferring causal metabolic signals that regulate the dynamic TORC1-dependent transcriptome”, Molecular Systems Biology,11(4), 2015.

[39] Morteza Haghir Chehreghani, “A Probabilistic Analysis of Interactive Graph Clustering”, Machine Learning and Statistical Analysis, 3(2), 2014.

[40] Ludwig M. Busse, Morteza Haghir Chehreghani, Joachim M. Buhmann, “Approximate Sorting”, 35th German Conference on Pattern Recognition (GCPR), 142-152, 2013.

[41] Morteza Haghir Chehreghani, Ludwig M. Busse, Joachim M. Buhmann, “Information Content in Clustering Algorithms”, The Deutsche ArbeitsGemeinschaft Statistik Conference (DAGStat), 2013.

[42] Morteza Haghir Chehreghani, Alberto G. Busetto, Joachim M. Buhmann, “Information Theoretic Model Validation for Spectral Clustering”, International Conference on Artificial Intelligence and Statistics (AISTATS), pp. 495-503, 2012.

[43] Morteza Haghir Chehreghani, Mostafa H. Chehreghani, and Hassan Abolhassani, “Probabilistic Heuristics for Hierarchical Web Data Clustering”, Computational Intelligence, 28(2): 209-233, 2012.

[44] Ludwig M. Busse, Morteza Haghir Chehreghani, Joachim M. Buhmann, “The Information Content in Sorting Algorithms”, International Symposium on Information Theory (ISIT), 2746-2750, 2012.

[45] Alberto G. Busetto, Morteza Haghir Chehreghani, Joachim M. Buhmann, “Approximate Set Coding for Information Theoretic Model Validation”, The Fifth Workshop on Information Theoretic Methods in Science and Engineering (WITMSE), pp. 15-18, 2012.

[46] Joachim M. Buhmann, Morteza Haghir Chehreghani, Mario Frank, and Andreas P. Streich, “Information Theoretic Model Selection for Pattern Analysis”, Journal of Machine Learning Research (JMLR), extended from ICML workshop on Unsupervised and Transfer Learning, 27:51–64, 2012.

[47] K. H. Brodersen, Z. Lin, A. Gupta, W.D. Penny, A.P. Leff, Morteza Haghir Chehreghani, Alberto G. Busetto, Joachim M. Buhmann, K.E. Stephan, “Model-based clustering using generative embedding”, Human Brain Mapping, 2012. [Oral presentation] [recipient of Trainee Abstract Award].

[48] Mostafa H. Chehreghani, Morteza Haghir Chehreghani, Caro Lucas, and Masoud Rahgozar, “OInduced: An Efficient Algorithm for Mining Induced Patterns from Rooted Ordered Trees”, IEEE Transactions on Systems, Man, and Cybernetics, Part A 41(5): 1013-1025, 2011.

[49] Ludwig M. Busse, Morteza Haghir Chehreghani, Joachim M. Buhmann, “Approximate Sorting of Preference Data”, NIPS workshop on Choice Models and Preference Learning, 2011. [selected for NIPS workshop contributed talk]

[50] Morteza Haghir Chehreghani, Alberto G. Busetto, Joachim M. Buhmann, “Cluster Model Validation by Maximizing Approximation Capacity”, NIPS workshop on New Frontiers in Model Order Selection, 2011. [received NIPS travel grant]

[51] Mario Frank, Morteza Haghir Chehreghani, and Joachim M. Buhmann, “The Minimum Transfer Cost Principle for Model-Order Selection”, European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD), (1) 423-438, 2011.

[52] Morteza Haghir Chehreghani, Hassan Abolhassani, and Mostafa H. Chehreghani, “Density link-based methods for clustering web pages”, Decision Support Systems, 47 (4): 374-382, 2009.

[53] Mostafa H. Chehreghani, Morteza Haghir Chehreghani, Masoud Rahgozar, Caro Lucas, and Euhanna Ghadimi, “Efficient Rule Based Structural Algorithms for Classification of Tree-Structured Data”, Intelligent Data Analysis, 13 (1): 165-188, 2009.

[54] Morteza Haghir Chehreghani, Hassan Abolhassani, and Mostafa H. Chehreghani, “Improving Density Based Methods for Hierarchical Clustering of the Web Pages”, Data and Knowledge Engineering, 67 (1): 30-50, 2008.

[55] Mehrdad Mahdavi, Morteza Haghir Chehreghani, Hassan Abolhassani, and Rana Forsati, “Novel Meta-Heuristic Algorithms for Clustering Web Documents”, Applied Mathematics and Computation, 201 (1-2):  441–451, 2008.

[56] Mohammad Rahimi, Morteza Haghir Chehreghani, Hassan Abolhassani, “Improving Tree Structures to Find Dense Clusters from Web Documents”, International Conference on Information and Knowledge Technology (IKT), 2008.

[57] Mostafa H. Chehreghani, Masoud Rahgozar, Caro Lucas, and Morteza Haghir Chehreghani, “A Heuristic Algorithm for Clustering Rooted Ordered Trees”, Intelligent Data Analysis, 11 (4): 355-376, 2007.

[58] Morteza Haghir Chehreghani, and Hassan Abolhassani, “Attaining Higher Quality for Density Based Algorithms”, International Conference on Web Reasoning and Rule Systems (RR), LNCS, 329–338, 2007.

[59] Mostafa H. Chehreghani, Masoud Rahgozar, Caro Lucas, Morteza Haghir Chehreghani, “Mining Maximal Embedded Unordered Tree Patterns”, IEEE Symposium on Computational Intelligence and Data Mining (CIDM), 437-443, 2007.

[60] Morteza Haghir Chehreghani, Hassan Abolhassani, “H-BayesClust: A new Hierarchical Clustering based on Bayesian Networks”, International Conference on Advanced Data Mining and Applications (ADMA), LNCS, pp. 616–624, 2007.

[61] Morteza Haghir Chehreghani, Mohammad Rahmati, “Design and Implementation of a 2-D Barcode based on Data Matrix Format”, 15th Iranian Conference on Electrical Engineering, 2006.

 Patents

[1]  Morteza Haghir Chehreghani, “System and method for performing k-nearest neighbor search based on minimax distance measure and efficient outlier detection”, Patent number: 10073887, Date of Patent: September 11, 2018, https://patents.justia.com/patent/10073887 [Type: Grant]

[2]  Morteza Haghir Chehreghani, “Efficient Calculation of All-Pair Path-based Distance Measures”, Patent number: 9805138, Date of Patent: October 31, 2017, https://patents.justia.com/patent/9805138 [Type: Grant]

[3]  Morteza Haghir Chehreghani and Yuxin Chen, “Method of Trip Prediction by Leveraging Trip Histories from Neighboring Users”, Publication number: 20180012141, Publication date: January 11, 2018, https://patents.justia.com/patent/20180012141 [Type: Application]

[4]  Morteza Haghir Chehreghani, “Adaptive Trajectory Analysis of Replicator Dynamics for Data Clustering”, Publication number: 20160179923, Publication date: June 23, 2016, https://patents.justia.com/patent/20160179923 [Type: Application]

Codes

·   An efficient method for correlation clustering with fixed and variable number of clusters

·   Efficient computation of Minimax distances