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  Professor in Machine Learning at the Department of Computer Science and Engineering, Chalmers University of Technology. I received my Ph.D. from ETH Zurich (Switzerland) in Computer Science (Machine Learning) in 2014, where I worked at the Institute for Machine Learning under the supervision of Prof. Dr. 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) working in the machine learning and optimization team.

Research

  Artificial Intelligence, Data Science, Machine Learning and Deep Learning are my primary research interests. Currently, my research is focused on the following topics.

§  Interactive Machine Learning and Sequential Decision Making

o   Active learning and active decision making

o   Online learning (multi-armed bandits)

o   (Deep) Reinforcement learning

o   Human-in-the-loop machine learning

o   Multi-agent/federated learning

§  Unsupervised Learning

o   Generative AI & Large Language Models

o   Unsupervised representation learning

o   Cluster modelling

o   Learning with graphs and networks

o   Learning theory and generalization performance of unsupervised learning

§  Efficient Deep Learning

o   Computation-Efficient Deep Learning

o   Data-Efficient Deep Learning

o   Uncertainty-Aware Deep Learning

My research has been applied to real-world problems in domains such as transport, energy, life science, autonomous systems, recommendation, and decision support systems. I actively supervise master’s and PhD students (academic and industrial) as well as postdoctoral researchers in these topics and the applications.

My research is supported by Swedish Research Council (VR), Vinnova, and WASP (Wallenberg AI, Autonomous Systems and Software Program).

Teaching

   I am the examiner and teacher of the following courses:

o Advanced Topics in Machine Learning, DAT441/DIT471 (since 2021): The course is focused on advanced theory, methods and mathematical understanding of machine learning in particular in the context of sequential decision making. I cover the following topics in this course: reinforcement learning, multi-armed bandits, active learning, and the relevant deep learning methods, e.g., deep reinforcement learning.

o Algorithms for Machine Learning and Inference, TDA233/DIT382 (since 2018): The course is focused on supervised and unsupervised learning methods in machine learning including regression models, various classification algorithms, kernel methods, evaluation of machine learning methods, MCMC methods, Laplace approximation, Bayesian learning, deep learning (e.g., standard neural networks, convolutional neural networks – CNNs, and recurrent neural networks – RNNs), clustering, and Gaussian mixture models. The course aims to provide a deep rigorous understanding of these topics.

   Other teaching activities:

o Advanced Topics in Multi-Armed Bandits (PhD course), 2023 (examiner).

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

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

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

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

o 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 Gothenburg

     Sweden

     https://www.chalmers.se/en/persons/haghir/

     Email: morteza.chehreghani@chalmers.se

Publications

   * Pre-prints can be found in the arXiv and DBLP profiles.

[1] Morteza Haghir Chehreghani, and Mostafa H. Chehreghani, “Hierarchical Correlation Clustering and Tree Preserving Embedding”, The Thirty-Fourth IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024.

[2] Linus Aronsson, and Morteza Haghir Chehreghani, “Correlation Clustering with Active Learning of Pairwise Similarities”, Transactions on Machine Learning Research (TMLR), 2024.

[3] Hampus Gummesson Svensson, Christian Tyrchan, Ola Engkvist, and Morteza Haghir Chehreghani, "Utilizing Reinforcement Learning for de novo Drug Design", Machine Learning (MLJ), 113: 4811–4843, 2024.

[4] Tobias Lindroth, Axel Svensson, Niklas Åkerblom, Mitra Pourabdollah, and Morteza Haghir Chehreghani, “Online Learning Models for Vehicle Usage Prediction During COVID-19”, IEEE Transactions on Intelligent Transportation Systems, 25(8), pp. 9387-9396, 2024.

[5] Simon Johansson, Morteza Haghir Chehreghani, Ola Engkvist, and Alexander Schliep, “de novo generated combinatorial chemical libraries”, Digital Discovery, 3(1), pp. 122-135, 2024.

[6] Fazeleh Hoseini, Niklas Åkerblom, and Morteza Haghir Chehreghani, “A contextual combinatorial semi-bandit approach to network bottleneck identification”, 33rd ACM International Conference on Information and Knowledge Management (CIKM), 2024. [Short research paper]

[7] Arman Rahbar, Ashkan Panahi, Morteza Haghir Chehreghani, Devdatt Dubhashi, and Hamid Krim, “Recovery Bounds on Class-Based Optimal Transport: A Sum-of-Norms Regularization Framework”, 40th International Conference on Machine Learning (ICML), 2023.

[8] Arman Rahbar, Ziyu Ye, Yuxin Chen, and Morteza Haghir Chehreghani, “Efficient Online Decision Tree Learning with Active Feature Acquisition”, 32nd International Joint Conference on Artificial Intelligence (IJCAI), 2023.

[9] Niklas Åkerblom, Yuxin Chen, and Morteza Haghir Chehreghani, “Online Learning of Energy Consumption for Navigation of Electric Vehicles”, Artificial Intelligence (AIJ), 317, 2023.

[10] Niklas Åkerblom, and Morteza Haghir Chehreghani, “A Combinatorial Semi-Bandit Approach to Charging Station Selection for Electric Vehicles”, Transactions on Machine Learning Research (TMLR), 2023.

[11] Niklas Åkerblom, Fazeleh Sadat Hoseini, and Morteza Haghir Chehreghani, “Online Learning of Network Bottlenecks via Minimax Paths”, Machine Learning (MLJ), 112: 131–150, 2023.

[12] Morteza Haghir Chehreghani, “Shift of Pairwise Similarities for Data Clustering”, Machine Learning (MLJ), 112, 2025–2051, 2023.

[13] Balázs Varga, Balázs Kulcsár, and Morteza Haghir Chehreghani, “Deep Q-learning: a robust control approach”, International Journal of Robust and Nonlinear Control, 33(1), pp. 526-544, 2023.

[14] Arman Rahbar, Emilio Jorge, Devdatt Dubhashi, and Morteza Haghir Chehreghani, “Do Kernel and Neural Embeddings Help in Training and Generalization?”, Neural Processing Letters (NPL), 55, 1681–1695, 2023.

[15] Niklas Åkerblom, and Morteza Haghir Chehreghani, “Long-Distance Electric Vehicle Navigation using a Combinatorial Semi-Bandit Approach”, The 16th European Workshop on Reinforcement Learning (EWRL), 2023.

[16] Peter Samoaa, Linus Aronsson, Philipp Leitner, and Morteza Haghir Chehreghani, "Batch Mode Deep Active Learning for Regression on Graph Data", IEEE International Conference on Big Data (IEEE BigData), 2023.

[17] Simon Johansson, Ola Engkvist, Morteza Haghir Chehreghani, and Alexander Schliep, "Diverse Data Expansion with Semi-Supervised k-Determinantal Point Processes", IEEE International Conference on Big Data (IEEE BigData), 2023.

[18] Zahra Moteshaker Arani, Mostafa H. Chehreghani, and Morteza Haghir Chehreghani, "Non-Uniform Sampling Methods for Large Itemset Mining", IEEE International Conference on Big Data (IEEE BigData), 2023.

[19] Deepthi Pathare, Leo Laine, and Morteza Haghir Chehreghani, "Improved Tactical Decision Making and Control Architecture for Autonomous Truck in SUMO Using Reinforcement Learning", IEEE International Conference on Big Data (IEEE BigData), 2023.

[20] Andreas Demetriou, Henrik Alfsvåg, Sadegh Rahrovani, and Morteza Haghir Chehreghani, “A Deep Learning Framework for Generation and Analysis of Driving Scenario Trajectories”, SN Computer Science, 4, 251, 2023.

[21] Ali Samadzadeh, Fatemeh Sadat Tabatabaei Far, Ali Javadi, Ahmad Nickabadi, and Morteza Haghir Chehreghani, “Convolutional Spiking Neural Networks for Spatio-Temporal Feature Extraction”, Neural Processing Letters (NPL), 2023.

[22] 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, 13, 5099, 2022.

[23] 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), pp. 915–924, 2022. [Full research paper]

[24] 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), pp. 1615–1624, 2022. [Full research paper]

[25] Victor Eberstein, Jonas Sjöblom, Nikolce Murgovski, and Morteza Haghir Chehreghani, “A Unified Framework for Online Trip Destination Prediction”, Machine Learning (MLJ), 111, 3839–3865, 2022.

[26] Sanna Jarl, Linus Aronsson, Sadegh Rahrovani, and Morteza Haghir Chehreghani, “Active Learning of Driving Scenario Trajectories”, Engineering Applications of Artificial Intelligence, 113: 104972, 2022.

[27] 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, 41, 2200043, 2022.

[28] Sara Asghari, Mostafa H. Chehreghani, and Morteza Haghir Chehreghani, “On Using Node Indices and Their Correlations for Fake Account Detection”, IEEE International Conference on Big Data (IEEE BigData), 2022.

[29] Hampus Gummesson Svensson, Esben Bjerrum, Christian Tyrchan, Ola Engkvist, and Morteza Haghir Chehreghani, “Autonomous Drug Design with Multi-Armed Bandits”, IEEE International Conference on Big Data (IEEE BigData), 2022.

[30] Mahdi Ghanbari, Mostafa Haghir Chehreghani and Morteza Haghir Chehreghani, “Graph Clustering Using Node Embeddings: An Empirical Study”, IEEE International Conference on Big Data (IEEE BigData), 2022.

[31] Fazeleh Sadat Hoseini, and Morteza Haghir Chehreghani, “Memory-Efficient Minimax Distance Measures”, 26th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 13280, pp. 419-431, 2022.

[32] Yuxin Chen, and Morteza Haghir Chehreghani, “Trip Prediction by Leveraging Trip Histories from Neighboring Users”, IEEE 25th International Intelligent Transportation Systems Conference (IEEE ITSC), pp. 967-973, 2022.

[33] Federica Comuni, Christopher Meszaros, Niklas Åkerblom, and Morteza Haghir Chehreghani, “Passive and Active Learning of Driver Behavior from Electric Vehicles”, IEEE 25th International Intelligent Transportation Systems Conference (IEEE ITSC), pp. 929-936, 2022.

[34] 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), vol 13709, pp. 464–479, 2022. [Full paper]

[35] 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), pp. 5053-5062, 2021.

[36] 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), pp. 5209-5214, 2021.

[37] 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), pp. 1314-1321, 2021.

[38] 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), pp. 1-6, 2021.

[39] Morteza Haghir Chehreghani, “Reliable Agglomerative Clustering”, International Joint Conference on Neural Networks (IJCNN), pp. 1-8, 2021.

[40] Morteza Haghir Chehreghani, “Unsupervised representation learning with Minimax distance measures”, Machine Learning (MLJ), 109 (11), 2063-2097, 2020.

[41] Morteza Haghir Chehreghani, and Mostafa H. Chehreghani, “Learning representations from dendrograms”, Machine Learning (MLJ), 109 (9): 1779–1802, 2020.

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

[43] 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), pp. 2051-2057, 2020.

[44] 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.

[45] Erik Thiel, Morteza Haghir Chehreghani, and Devdatt Dubhashi, “A Non-convex Optimization Approach to Correlation Clustering”, Thirty-Third AAAI Conference on Artificial Intelligence (AAAI), 33(01), 5159-5166, 2019.

[46] 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), pp. 188–197, 2019.

[47] 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.

[48] Mostafa H. Chehreghani, and Morteza Haghir Chehreghani, “Efficient Context-Aware K-Nearest Neighbor Search”, 40th European Conference on Information Retrieval (ECIR), pp 466–478, 2018. [long paper]

[49] 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. (sup. here)

[50] Morteza Haghir Chehreghani, “Classification with Minimax Distance Measures”, Thirty-First AAAI Conference on Artificial Intelligence (AAAI), 31(1), 2017.

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

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

[53] Morteza Haghir Chehreghani, “Feature-Oriented Analysis of User Profile Completion Problem”, 39th European Conference on Information Retrieval (ECIR), pp. 304–316, 2017. [long paper]

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

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

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

[57] Morteza Haghir Chehreghani, “K-Nearest Neighbor Search and Outlier Detection via Minimax Distances”, SIAM International Conference on Data Mining (SDM), pp. 405–413, 2016.

[58] 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): 802, 2015.

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

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

[61] 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.

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

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

[64] 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.

[65] 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) Workshop and Conference Proceedings, extended from ICML workshop on Unsupervised and Transfer Learning, 27:51–64, 2012.

[66] 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].

[67] 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.

[68] 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]

[69] 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]

[70] 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.

[71] 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.

[72] 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.

[73] 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.

[74] 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.

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

[76] 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.

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

[78] 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.

[79] 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.

[80] 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 (in Persian).

Patents

[1]   Morteza 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. [Type: Grant]

[2]   Morteza Haghir Chehreghani, “Efficient Calculation of All-Pair Path-based Distance Measures”, Patent number: 9805138, Date of Patent: October 31, 2017. [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. [Type: Application]

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

Code

o Reinforcement learning for de novo drug design

o Correlation clustering with active learning of pairwise similarities

o Combinatorial multi-armed bandits for charging station selection for electric vehicles

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