| Method and system for summarizing user activities of tasks into a single activity score using machine learning to predict probabilities of completeness of the tasks L Tang, MA Torkamani, M Subedi, K Leafstrand US Patent App. 15/688,758, 2019 | 48 | 2019 |
| Convex Adversarial Collective Classification MA Torkamani, D Lowd ICML 2013, 2013 | 33 | 2013 |
| Chaos theory and application in foreign exchange rates vs. IRR (Iranian Rial) MA Torkamani, S Mahmoodzadeh, S Pourroostaei, C Lucas International Journal of Human and Social Sciences 1 (3), 130-134, 2007 | 20 | 2007 |
| On Robustness and Regularization of Structural Support Vector Machines MA Torkamani, D Lowd Proceedings of the Thirty-First International Conference on Machine Learning …, 2014 | 15 | 2014 |
| Evaluate projects by using multiple criteria decision making techniques S Mahmoodzadeh, M Pariazar, MS Zaeri, MA Torkamani 2007 IEEE International Conference on Industrial Engineering and Engineering …, 2008 | 5 | 2008 |
| Estimating strange attractor's dimension in very noisy data, application to FOREX time series MA Torkamani, J Asgari, C Lucas 2006 2nd International Conference on Information & Communication …, 2006 | 5 | 2006 |
| Personalized Symptom Checker Using Medical Claims HK Sabin Kafle, Penny Pan, Ali Torkamani, Stevi Halley, John Powers Health Recommender Systems 2216, 13-17, 2018 | 4* | 2018 |
| Estimating Correlation Dimension on Japanese Candlestick, Application to FOREX Time Series S Mahmoodzadeh, J Shahrabi, MA Torkamani, JS Ghomi | 3 | 2007 |
| Learning Compact Neural Networks Using Ordinary Differential Equations as Activation Functions MA Torkamani, P Wallis, S Shankar, A Rooshenas ICML Workshop on On-Device ML and Compact Deep Neural Networks (ODML-CDNNR2019), 2019 | 2 | 2019 |
| A Hybrid Health Journey Recommender System Using Electronic Medical Records HK Soheil Jamshidi, Mohamad Ali Torkamani, Jynelle Mellen, Malhar Jhaveri ... Health Recommender Systems 2216, 57-62, 2018 | 2* | 2018 |
| Robust Large Margin Approaches for Machine Learning in Adversarial Settings MA Torkamani University of Oregon, 2016 | 2 | 2016 |
| Differential equations network MA Torkamani, P Wallis US Patent App. 16/160,933, 2019 | 1 | 2019 |
| Engagement Scoring for Care-gap Intervention Optimization HK Mohamad Ali Torkamani, Malhar Jhaveri, Jynelle Mellen, Michael Brown ... Health Recommender Systems 2216, 53-56, 2018 | 1* | 2018 |
| Adversarial Structured Output Prediction MA Torkamani https://www.cs.uoregon.edu/Reports/ORAL-201406-Torkamani.pdf, 2014 | 1 | 2014 |
| Differential Equation Units: Learning Functional Forms of Activation Functions from Data MA Torkamani, S Shankar, A Rooshenas, P Wallis 34th AAAI Conference on AI (AAAI 2020), 2020 | | 2020 |
| Differential Equation Networks MA Torkamani, P Wallis | | 2018 |
| Approximating Hypergeometric Functions for Neural Network MA Torkamani, P Wallis US Patent App. 62/572,110, 2017 | | 2017 |
| Method and system for summarizing user activities of tasks into a single activity score using machine learning to predict probabilities of completeness of the tasks L Tang, MA Torkamani, K LEAFSTRAND, M SUBEDI US Patent App. US15/688,758, 2017 | | 2017 |
| Applying Dropout Regularization to Support Vector Machines MA Torkamani, D Lowd NIPS Workshop on Perturbations, Optimization, and Statistics (POS-NIPS), 2014 | | 2014 |
| On Robustness and Regularization of Structural Support Vector Machines: Supplementary Material MA Torkamani, D Lowd | | 2014 |
Daniel LowdAssociate Professor of Computer Science, University of OregonVerified email at cs.uoregon.edu
