I am a Ph.D. candidate in the Electrical Engineering and Computer Science program at the University of California, Merced. I am also a member of Dr. David C. Noelle’s Computational Cognitive Neuroscience Lab. I am expecting to defend my dissertation in spring of 2019 and graduate by the end of May 2019. I would like to pursue an academic career path. I am searching to find a Postdoctoral position starting from Summer or Fall 2019.
My research focus is on Optimization, Machine Learning and Reinforcement Learning. I am interested in studying, designing and implementing numerical algorithms for large-scale optimization problems. Currently, I am working on two main projects. (1) My Ph.D. thesis, "Learning Representations in Reinforcement Learning", under the supervision of Dr. David C. Noelle. (2) I am also collaborating with Dr. Roummel F. Marcia -- a faculty of Applied Mathematics at UC Merced -- on Quasi-Newton optimization methods for large-scale machine learning problems, such as deep learning and deep reinforcement learning.
 Jacob Rafati, and David C. Noelle (2019). Learning Representations in Model-Free Hierarchical Reinforcement Learning. In 33rd AAAI Conference on Artificial Intelligence (AAAI-19), Honolulu, Hawaii.[bibtex] [abstract] [long version preprint]
 Jacob Rafati, and Roummel F. Marcia (2018). Quasi-Newton Optimization in Deep Q-Learning for Playing ATARI Games. arXiv e-print (arXiv:1811.02693).[bibtex] [preprint]
 Jacob Rafati, and David C. Noelle (2018). Learning Representations in Model-Free Hierarchical Reinforcement Learning. arXiv e-print (arXiv:1810.10096).[bibtex] [preprint]
 Jacob Rafati, and Roummel F. Marcia (2018). Improving L-BFGS Initialization For Trust-Region Methods In Deep Learning. In 17th IEEE International Conference on Machine Learning and Applications (ICMLA 2018), Orlando, Florida.[bibtex] [paper] [sildes] [project website] [code]
 Jacob Rafati, Omar DeGuchy and Roummel F. Marcia (2018). Trust-Region Minimization Algorithm for Training Responses (TRMinATR): The Rise of Machine Learning Techniques. In 26th European Signal Processing Conference, Rome, Italy.[bibtex] [paper] [project website] [code]
 Jacob Rafati and David C. Noelle (2017). Sparse Coding of Learned State Representations in Reinforcement Learning. In Cognitive Computational Neuroscience Conference, Newyork City, NY.[bibtex] [abstract] [poster] [ link ] [project website] [code]
 Jacob Rafati and David C. Noelle (2015). Lateral Inhibition Overcomes Limits of Temporal Difference Learning. In 37th Annual Cognitive Science Society Meeting, Pasadena, CA, USA.[bibtex] [paper] [poster] [link] [project website] [code]
 Jacob Rafati, Mohsen Asghari and Sachin Goyal (2014). Effects of DNA Encapsulation On Buckling Instability of Carbon Nanotube based on Nonlocal Elasticity Theory. In Proceedings of the ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (IDETC/CIE 2014), Buffalo, New York, USA.[bibtex] [paper] [doi] [link]
 Mohsen Asghari, Jacob Rafati and Reza Naghdabadi (2013). Torsional instability of carbon nano-peapods based on the nonlocal elastic shell theory. Physica E: Low-dimensional Systems and Nanostructures, 47:316-323.[bibtex] [paper] [doi] [link]
 Mohsen Asghari, Reza Naghdabadi and Jacob Rafati (2011). Small scale effects on the stability of carbon nano-peapods under radial pressure. Physica E: Low-dimensional Systems and Nanostructures, 43(5):1050-1055.[bibtex] [paper] [doi] [link]
 Mohsen Asghari and Jacob Rafati (2010). Variational Principles for the Stability Analysis of Multi-Walled Carbon Nanotubes Based on a Nonlocal Elastic Shell Model. In 10th ASME Biennial Conference on Engineering Systems Design and Analysis, paper no. ESDA2010-24473, pages 591-598.[bibtex] [paper] [doi] [link]
January 2019. The paper, "Learning Representations in Model-Free Hierarchical Reinforcement Learning" co-authored with Dr. David C. Noelle has been selected for poster presentation in 33rd AAAI Conference on Artificial Intelligence (AAAI-19) in Honolulu, Hawaii for Student Abstract and Poster Program. Congratulations David!
December 2018. The paper, "Improving L-BFGS Initialization For Trust-Region Methods In Deep Learning" co-authored with Dr. Roummel F. Marcia has been accpeted to the 17th IEEE International Conference on Machine Learning and Applications conference (ICMLA 2018) in Orlando, Florida for oral presentation. Congratulations Roummel!
November 2018. I will give a talk in UC Merced EECS Graduate Seminar on November 2nd, 2018 in COB 265 from 12:00 to 1:15 PM. Title: Limited-Memory Quasi-Netwon Optimization Methods in Deep Learning.
August 2018. I am officially a Ph.D. Candidate. Dr. Marjorie S. Zatz, the vice provost and Dean of the Graduate Division congratulated me the advancement to the Ph.D. candidacy.
June 2018. I passed the qualification examination by defending my Ph.D. dissertation research proposal succesfully. The title of my thesis is "Learning Representation in Reinforcement Learning". The Ph.D. commitee members are Dr. David. C. Noelle, Dr. Roummel F. Marcia, Dr. Shawn Newsam, Dr. Marcelo Kallmann and Dr. Jeffrey Yoshimi.
May 2018. The paper, "Trust-Region Minimization Algorithm for Training Responses (TRMinATR): The Rise of Machine Learning Techniques" has been accepted to the 26th European Signal Processing Conference (EUSIPCO 2018), Italy, Rome. Co-authored with Omar DeGuchy and Dr. Roummel F. Marcia.
Apart from being a Ph.D. candidiate in computer science, reasercher, developer and teaching assistant, I am a serious artist too. The number of art projects in my art resume is more than the number of research projects in my academic resume. I am a performer, painter and installation artist. You can visit my art website at http://jacobrafati.com for more details.
I enjoy walking in nature. I love coffee and coffeeshops. I mostly lived in cities, Tabriz, Tehran and San Francisco. I like Poker and mind games. I love adventurous sports such as Bungee jumping.