About myself

As a dedicated academic and researcher, I've immersed myself in the fields of Artificial Intelligence (AI), Machine Learning (ML), and Natural Language Processing (NLP) starting in 2002. My career has spanned both academia and industry, allowing me to cultivate a well-rounded view that combines deep theoretical insights with real-world applications.

My academic career is fueled by an unwavering passion for both teaching and research. I've had the privilege of mentoring and inspiring students, playing a pivotal role in their intellectual development. This commitment has also opened doors for me to collaborate closely with industry experts. Over the past 15 years, I've worked with some of the brightest minds in developing AI-driven solutions.

Currently, my research has taken a fascinating turn towards Large Language Models (LLMs) and Generative AI. In this journey, I approach these models with a mix of awe and caution, recognizing their enormous potential, inherent limitations, and the subtle risks they pose in generating transformative products. This journey deepens my commitment to the responsible and mindful integration of AI.

My academic contributions encompass a range of research papers and presentations that bridge the gap between theoretical knowledge and its practical implications. It's a privilege to share my insights with a diverse audience, aiming to enhance the collective understanding of AI, ML, and NLP's transformative capabilities.

As my exploration in AI continues, my curiosity propels me to further push the boundaries of our understanding and application of AI. I am dedicated to the dissemination of knowledge and warmly invite you to get familiar with my work and join the conversation on AI's evolving role in our society.

To gain more insight into my research and explorations, I encourage you to visit my website and engage with the ideas and perspectives I offer.

My LinkedIn page

My medium page

Education

• • Ph.D., Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada, Sept. 2002-Aug. 2007

  • Master of Science (M.S.), Computer Engineering, Shiraz University, Shiraz, Iran, Feb 1992 - Oct. 1994

  • Bachelor of Science (B.Sc.), Electrical Engineering, Iran University of Science and Technology, Tehran, Iran, Feb. 1986 - Feb. 1991

Graduate Students

• • Iuliia Chepurna (MASc), 2015. Thesis: User-centric analytics for expert crowds

  • Mehrin Gilani (MASc), 2015. Thesis: Machine learning classifiers for critical cardiac conditions (Co-supervision with Dr. M. Eklund)

  • Jonathan Gillett (MASc), 2016. Thesis: Predicting Bitcoin (Co-Supervision with Dr. S. Rahnamayan)

  • Bahar Aghababaei (Ph.D.), 2017. Thesis: A Temporal topic model social trend prediction

  • Neil Seward (MASc), 2018. Thesis: Building an On-Ball screen dataset using supervised and unsupervised learning

  • Eren Gultepe (Ph.D.), 2018. Thesis: Pushing the limits of traditional unsupervised learning

  • Mehran Kamkarhaghighi (PhD), 2019. Research: Global-local word embedding

  • Fateme Azimlou (MASc), Sept. 2019-present. Research: Predicting the Real-estate market

  • Afsaneh Towhidi (MASc), Sept. 2019-present. Research: Unsupervised entity extraction 

  • Tara Ahmadalinezhad (Ph.D.), 2020-present. Research: Link sign prediction in signed social networks

  • Amit Maraj (Ph.D. Candidate). Research: Document Segmentation (Co-supervision with Dr. M. Martin)

  • Hamid Reza Rouzegar (MASc): LLM-Powered Active Learning

  • Farnaz Kheiri (Ph.D.): Bias in Machine Learning (Co-Supervision with Dr. S. Rahnamayan)

  • Chen Xing (MASc): Graph Newural Network for Sign Prediction. 

  • Vaishali Siddeshwar (MASc): Generative AI application in Software Engineering (Co-Supervision with Dr. Sanaa Alwidian)

Selected Publications 

Journal Papers

• • M. Ahmadalinezhad, M. Makrehchi. Edge-centric multi-view network representation for link mining in signed social networks. Expert Syst. Appl. 170: 114552 (2021).

  • M. Ahmadalinezhad, M. Makrehchi. Basketball lineup performance prediction using edge-centric multi-view network analysis. Soc. Netw. Anal. Min. 10(1): 72 (2020).

  • E. Gultepe, M. Kamkarhaghighi, M. Makrehchi. Document classification using convolutional neural networks with small window sizes and latent semantic analysis. Web Intell. 18(3): 239-248 (2020).

  • Mehran Kamkarhaghighi, Eren Gultepe, Masoud Makrehchi. Deep Learning for Document Representation. Handbook of Deep Learning Applications, pp. 101-110 (2019).

  • S. Aghababaei, M. Makrehchi. Interpolative self-training approach for link prediction. Intell. Data Anal. 23(6): 1379-1395 (2019).

  • E. Gultepe,  M. Makrehchi. Improving clustering performance using independent component analysis and unsupervised feature learning, Human-centric Computing and Information Sciences,  Vol. 8, No. 25, pp. 1-19, 2018.

  • A. M. Karimi-Majd, M. Fathian, M. Makrehchi, Consensus-based methodology for detection communities in multilayered networks, Physica A, Vol. 494, pp. 547-558, 2018.

  • S. Aghababaei,  M. Makrehchi. Mining Twitter Data for Crime Trend Prediction, Intelligent Data Analysis, Vol. 22, No. 1, pp. 117-141, 2018.

  • E. Gultepe, T. Conturo, M. Makrehchi, Predicting and grouping digitized paintings by style using unsupervised feature learning, Journal of Cultural Heritage,  Vol. 31, pp. 13-23, 2017.

  • M. Kamkarhaghighi,  M. Makrehchi. Content Tree Word Embedding for Document Representation, Expert Systems With Applications, pp. 241-249, 2017.

  • S. Aghababaei,  M. Makrehchi. Activity-based Twitter Sampling for Content-based and User-centric Prediction Models, Human-centric Computing and Information Sciences, Vol. 7, No. 3, pp. 1-20, 2017.

  • M. Makrehchi, M. S. Kamel. Extracting Domain-Specific Stopwords for Text Classifiers, Intelligent Data Analysis Journal. Vol. 21, No. 1, pp. 39-62, 2017.

  • M. Makrehchi. Predicting Political Conflicts from Polarized Social Media, Web Intelligence Journal, Vol. 14, no. 2, pp. 85-97, 2016.

  • M. Makrehchi. Evaluating Feature Ranking Methods in Text Classifiers, Intelligent Data Analysis Journal, Vol. 19, pp. 1151-1170, 2015.

  • M. Makrehchi, M. S. Kamel. Feature Ranking Fusion for Text Classification, Intelligent Data Analysis Journal, Vol. 16, pp. 879-896, 2012.

  • M. Makrehchi. Taxonomy-based Document Clustering, Journal of Digital Information Management, Vol. 9, No. 2, pp. 79-86, 2011.

  • M. Makrehchi, M. S.  Kamel.  Impact of Term Dependency and Class Imbalance on the Performance of Feature Ranking Methods for Text Classifiers, International Journal of Pattern Recognition and Artificial Intelligence. Vol. 25, No. 7, pp. 953-983, 2011. 

  • M. Makrehchi, M. S. Kamel. An Information Theoretic Approach to Generating Fuzzy Hypercubes for If-Then Classifiers, Journal of Intelligent and Fuzzy Systems, Vol. 22, pp. 33-52, 2011. BibTex  Abstract and Publisher website

  • R. A. Dara, M. Makrehchi, M. S. Kamel. Data Partitioning in Multiple Classifier Systems, IEEE Transactions on Knowledge and Data Engineering. Vol. 22, No. 4, pp. 508-522, 2010. BibTex   Abstract and Publisher website

  • M. Makrehchi, M. S. Kamel. Aggressive Feature Selection by Feature Ranking. In Computational Methods of Feature Selection, Huan Liu and Hiroshi Motoda, Eds.; Chapman and Hall/CRC Press, 2007. BibTex  [Book chapter]

Conference Papers

• • H. Rouzegar and M. Makrehchi. Enhancing text classification through LLM-driven active learning and human annotation. Proceedings of The 18th Linguistic Annotation Workshop (LAW-XVIII), pages 98–111, St. Julians, Malta, Mar. 2024. 

  • V. Gharavian, S. Rahnamayan, A. Asilian Bidgoli, M. Makrehchi. A Pairwise Surrogate Model using GNN for Evolutionary Optimization. SMC 2023: 3996-4002.

  • R. Khosrowshahli, S. Rahnamayan, A. Ibrahim, M. Makrehchi. Ranking Center-based NSGA-II. SMC 2023: 4162-4169 

  • R. Khosrowshahli, S. Rahnamayan, A. Asilian Bidgoli, M. Makrehchi. Self-Supervised Learning Using Noisy-Latent Augmentation. SMC 2023: 4170-4175.

  • A, Avan, Farnaz Kheiri, Q. H. Mahmoud, A. Azim, M. Makrehchi, S. Rahnamayan. A Task Scheduler for Mobile Edge Computing Using Priority-based Reinforcement Learning. SSCI 2023: 539-546.

  • V. Gharavian, R. Khosrowshahli, Q. H. Mahmoud, M. Makrehchi, S. Rahnamayan. Intrusion Detection for Wireless Sensor Network Using Graph Neural Networks. SSCI 2023: 807-813.

  • H. Rouzegar, S. Rahnamayan, A/ Asilian Bidgoli, M. Makrehchi. Enhancing Content-Based Histopathology Image Retrieval Using QR Code Representation. SSCI 2023: 1120-1125.

  • F. Azimlu, S. Rahnamayan, M. Makrehchi. House price prediction using clustering and genetic programming along with conducting a comparative study. GECCO Companion 2021: 1809-1816.

  • A. Maraj, M. V. Martin, M. Makrehchi. A More Effective Sentence-Wise Text Segmentation Approach Using BERT. ICDAR (4) 2021: 236-250, 2020.

  • J. Gillett, S. Rahnamayan, M. Makrehchi, A. A. Bidgoli. A pareto front-based metric to identify major bitcoin networks influencers. GECCO Companion 2020: 1395-1401

  • M. Ahmadalinezhad, M. Makrehchi, Neil Seward. Basketball lineup performance prediction using network analysis. ASONAM 2019: 519-524

  • F. Azimlu, S. Rahnamayan, M. Makrehchi, Pedram Karimipour-Fard. Designing Solar Chimney Power Plant Using Meta-modeling, Multi-objective Optimization, and Innovization. EMO 2019: 731-742

  • F. Azimlu, S. Rahnamayan, M. Makrehchi, Naveen Kalra. Comparing Genetic Programming with Other Data Mining Techniques on Prediction Models. ICCSE 2019: 785-791

  • M. Ahmadalinezhad, M. Makrehchi. Detecting Agreement and Disagreement in Political Debates, SBP-2018, Washington, DC, USA, pp. 54-60, July 10-13, 2018.

  • M. Ahmadalinezhad, M. Makrehchi. Sign Prediction in Sign Social Networks Using Inverse Squared Metric, SBP-2018, Washington, DC, USA, pp. 220-227, July 10-13, 2018.

  • E. Gultepe, M. Kamkarhaghighi, M. Makrehchi. Latent Semantic Analysis Boosted Convolutional Neural Networks for Document Classification. BESC 2018, pp. 93-98, 2018.

  • S. Aghababaei,  M. Makrehchi. Interpolative Self-Training Approach for Sentiment Analysis. 3rd International Conference on Behavioral, Economic, and Socio-Cultural Computing (BESC2016), Durham, NC, USA, November 11-13, 2016.

  • S. Aghababaei,  I. Chepurna, E. Gultepe, M. Makrehchi. Activity-Based Sampling of Twitter Users for Temporal Prediction Models, the 3rd International Conference on Behavioral, Economic, and Socio-Cultural Computing (BESC2016), Durham, NC, USA, pp. 23-28, pp. 29-34, November 11-13, 2016.

  • M. Makrehchi. Hierarchical Agglomerative Clustering based on Common Neighbors Similarity, 2016 IEEE/WIC/ACM International Conference on Web Intelligence, Omaha, NE, USA, pp. 546-551,  October 13-16, 2016.

  • S. Aghababaei, M. Makrehchi. Mining Social Media Content for Crime Prediction, 2016 IEEE/WIC/ACM International Conference on Web Intelligence, Omaha, NE, USA, pp. 526-531,  October 13-16, 2016.

  • I. Chepurna, M. Makrehchi. We Didn't Miss You: Interpolating Missing Opinions. 2016 IEEE/WIC/ACM International Conference on Web Intelligence, Omaha, NE, USA, pp. 552-557,  October 13-16, 2016.

  • I. Chepurna, M. Makrehchi. Social Filtering: User-Centric Approach to Social Trend Prediction. 2016 IEEE/WIC/ACM International Conference on Web Intelligence, Omaha, NE, USA, pp. 650-655,  October 13-16, 2016.

  • M. Kamkarhaghighi, I. Chepurna, S. Aghababaei, M. Makrehchi. Discovering Credible Twitter Users in Stock Market Domain, 2016 IEEE/WIC/ACM International Conference on Web Intelligence, Omaha, NE, USA, pp. 66-72,  October 13-16, 2016.

  • M. Gilani, J. M. Eklund, M. Makrehchi, Automated Detection of Atrial Fibrillation Episode Using Novel Heart Rate Variability Features, The 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’16), Orlando, FL, USA, August 16-20, pp. 3461-3464, 2016.

  • S. Aghababaei,  M. Makrehchi. Temporal Topic Inference for Trend Prediction, ICDM Workshops 2015, Atlantic City, NJ, USA, pp. 877-884, November 14-17, 2015.

  • I. Chepurna,  M. Makrehchi. Exploiting Class Bias for Discovery of Topical Experts in Social Media, ICDM Workshops 2015, Atlantic City, NJ, USA, pp. 64-71, November 14-17, 2015.

  • I. Chepurna, S. Aghababaei, M. Makrehchi. How to Predict Social Trends by Mining User Sentiments, the International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction (SBP-2015), Washington DC, USA, pp. 270-275, March 31- April 3, 2015.

  • M. Makrehchi. The correlation between Language Shift and Social Conflicts in Polarized Social Media, the IEEE/WIC/ACM International Conference on Web Intelligence (WI-14), Warsaw, Poland, pp. 166-171, Aug. 11-14, 2014. 

  • W. Liao, S. Shah, M. Makrehchi. Winning by Following the Winners: Mining the Behavior of Stock Market Experts in Social Media, the International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction (SBP-2014), Washington DC, USA, pp. 103-110, April 2-4, 2014. [Best paper award]

  • M. Makrehchi, S. Shah, W. Liao. Stock Prediction Using Event-based Sentiment Analysis, the IEEE/WIC/ACM International Conference on Web Intelligence (WI-13), Atlanta, GA,  pp. 337-342, Nov. 17-20, 2013. [acceptance rate: 20%]

  • M. Makrehchi. Social Links Recommendation by Learning Hidden Topics", ACM Conference on Recommender Systems (RecSys--2011), Chicago, IL, pp. 189--195,  Oct. 23-27, 2011.

  • M. Makrehchi. Query-relevant Document Representation for Text Clustering, Fifth International Conference on Digital Information Management (ICDIM 2010), Thunder bay, ON, Canada, pp. 132-138, 5--8 July, 2010.  BinTex  Abstract and Publisher website

  • M. Makrehchi. Text Clustering Using Taxonomy, the 2008 IEEE/WIC/ACM International Conference on Web Intelligence, Dec 9-12, 2008, Sydney, Australia. [acceptance rate: 19%]

  • M. Makrehchi, M. S. Kamel. Automatic Extraction of Domain-Specific Stopwords from Labeled Documents, the 2008 European Conference on Information Retrieval (ECIR2008), Glasgow, UK, pp. 222-233, 30th Mar.-3rd Apr, 2008. [acceptance rate: 23%]  BibTex    Abstract and Publisher website

  • M. Makrehchi, M. S. Kamel. A Text Classification Framework with a Local Feature Ranking for Learning Social Networks, Accepted in the 2007 IEEE International Conference on Data Mining, (ICDM 2007), Omaha, NE, USA, pp. 589-594, 28-31 Oct. 2007.[acceptance rate: 19%]  BibTex   Abstract and Publisher website

  • M. Makrehchi, M. S. Kamel. Automatic Taxonomy Extraction Using Google and Term Dependency, the 2007 IEEE/WIC/ACM International Conference on Web Intelligence (WI’07), Silicon Valley, CA, USA, pp. 321-325, 2-5 Nov. 2007.[acceptance rate: 17%]  BibTex  Abstract and Publisher website

  • M. Makrehchi, M. S. Kamel. Learning Social Networks from Web Documents Using Support Vector Classifiers, the 2006 IEEE/WIC/ACM International Conference on Web Intelligence, Hong Kong, pp. 88-94, 18-22 Dec. 2006. [acceptance rate: 18%]  BibTex  Abstract and Publisher website

  • M. Makrehchi, M. S. Kamel. Learning Term Dependency Links Using Information Theoretic Inclusion Measure, the 2007 ICDM Workshop on Mining Graphs and Complex Structures (MGCS2007), Omaha, NE, USA, pp. 423-428, 28-31 Oct. 2007.  BibTex  Abstract and Publisher website

  • M. Makrehchi, M. S. Kamel. Combining Feature Ranking for Text Classification, IEEE International Conference on Systems, Man and Cybernetics, Montreal, Canada, 7-10 Oct. 2007.   BibTex  Abstract and Publisher website

  • M. Makrehchi, M. S. Kamel. Learning Social Networks Using Multiple Resampling Method, IEEE International Conference on Systems, Man and Cybernetics, Montreal, Canada, 7-10 Oct. 2007.   BibTex  Abstract and Publisher website

  • M. Makrehchi, M. S. Kamel. Building Social Networks from Web Documents: A Text Mining Approach, The 2nd LORNET Scentific Conference, Vancouver, BC, Canada, Nov. 14-18, 2005.   BibTex 

  • M. Makrehchi, M. S. Kamel. Text Classification Using Small Number of Features, Machine Learning and Data Mining Conference (MLDM2005), Leipzig, Germany, pp. 580-589, July 9-11, 2005.   BibTex  Abstract and Publisher website

  • R. A. Dara, M. Makrehchi, M. S. Kamel. Data Partitioning Evaluation Measures for Classifier Ensembles, the 6th International Workshop on Multiple Classifier Systems, Seaside, CA, USA, pp. 306-315, June 13-15, 2005.   BibTex  Abstract and Publisher website

  • R. A. Dara, M. Makrehchi, M. S. Kamel. An Information-Theoretic Measure to Evaluate Data Partitions in Multiple Classifiers, IEEE SMC-2004, The Hague, The Netherlands, pp. 4826-4831, Oct. 10-13, 2004.   BibTex  Abstract and Publisher website