Homepage of Aswin Raghavan

Our work introducing Eigentasks has been accepted at ICML 2020 Lifelong ML workshop. Good empirical results in Starcraft2 surpassing DeepMind's RL performance in one case. Same method also works for supervised continual learning!

I gave an invited talk at the American Statistics Association QPRC 2019 conference covering three of our papers on anomaly detection at the processor and controller level for cybersecurity applications (slides).

Our demo "Aesop: A Visual Storytelling Platform for Conversational AI" won the best demo award at IJCAI 2018.

I won an honorable mention to the Best Dissertation award at International Conference on Automated Planning and Scheduling (ICAPS) 2018.

Most recently, I was a Program Committee member for AAAI 2019, IJCAI 2018, AAAI 2018, ICLR 2018 and ICAPS 2019 and ICAPS 2018 (Main track and Planning and Learning), and reviewer for journals JAIR, Sensors and Image Processing.

I graduated with a PhD in Computer Science in Jan 2017 advised by Prof. Prasad Tadepalli at Oregon State University, Corvallis, USA. I joined the Computer Vision Technologies group at SRI International in Princeton.

Previously, I was a Student Associate (intern) at the Computer Vision Technologies group, SRI International in Princeton during Winter and Spring of 2016 on new Deep Learning algorithms.



Raghavan, A., Hostetler, J., Sur, I., Rahman, A., & Divakaran, A. Lifelong Learning using Eigentasks: Task Separation, Skill Acquisition, and Selective Transfer. Lifelong Machine Learning Workshop, International Conference on Machine Learning (ICML 2020). (workshop) (paper) (video)

He, Zecheng, Aswin Raghavan, Sek Chai, and Ruby Lee Power-Grid Controller Anomaly Detection with Enhanced Temporal Deep Learning TrustCom 2019 (18th IEEE International Conference on Trust, Security and Privacy in Computing and Communications) (arxiv)

Meo, Timothy J., Chris Kim, Aswin Raghavan, Alex Tozzo, David A. Salter, Amir Tamrakar, and Mohamed R. Amer. "Aesop: A visual storytelling platform for conversational AI and common sense grounding." AI Communications Preprint (2019): 1-18. (PDF)

Samyak Parajuli and Aswin Raghavan and Sek Chai, Generalized Ternary Connect: End-to-End Learning and Compression of Multiplication-Free Deep Neural Networks, (arxiv)

Durga Harish Dayapule, Aswin Raghavan, Prasad Tadepalli, Alan Fern, Emergency Response Optimization using Online Hybrid Planning, 27th International Joint Conference on Artificial Intelligence and the 23rd European Conference on Artificial Intelligence (IJCAI-ECAI 2018) (PDF)

Mohamed Amer, Tim Meo, Aswin Raghavan, Alex Tozzo, David Salter, Amir Tamrakar, Aesop: A Visual Storytelling Platform for Conversational AI, Demos Track of IJCAI-ECAI 2018. (PDF)

Tharindu Mathew, Aswin Raghavan, Sek Chai, Event Prediction in Processors using Deep Temporal Models, 1st Workshop on Energy Efficient Machine Learning And Cognitive Computing for Embedded Applications (EMC2), 23rd ACM Intl. Conf. on Architectural Support for Programming Languages and Operating Systems (ASPLOS 2018) (IEEE)

Aswin Raghavan, Scott Sanner, Roni Khardon, Prasad Tadepalli, Alan Fern, Hindsight Optimization for Hybrid State and Action MDPs, Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI-2017). (PDF)

Aswin Raghavan, Mohamed R. Amer, Timothy Shields, David Zhang, Sek Chai (SRI International), GPU Activity Prediction using Representation Learning, ML Systems Workshop, International Conference on Machine Learning (ICML), 2016. (PDF)

Sek Chai, Aswin Raghavan, David Zhang, Mohamed Amer, Tim Shields, Low Precision Neural Networks using Subband Decomposition, Presented at CogArch Workshop, Atlanta, GA, April 2016. (PDF)

Aswin Raghavan, Prasad Tadepalli, Alan Fern, Roni Khardon, Memory-Efficient Symbolic Online Planning for Factored MDPs, 31st Conference on Uncertainty in Artificial Intelligence (UAI), 2015. (PDF) (Poster)

A. Raghavan, A. Fern, P. Tadepalli, and R. Khardon, Symbolic Opportunistic Policy Iteration for Factored-Action MDPs, Proceedings of the International Conference on Neural Information Processing Systems (NIPS), 2013. (PDF)(BibTex)(Poster)

S. Joshi, R. Khardon, P. Tadepalli, A. Fern, A. Raghavan, Relational Markov Decision Processes: Promise and Prospects, StarAI Workshop help at the Twenty-Seventh AAAI National Conference on Artificial Intelligence (StarAI), 2013 (PDF).

S. Joshi, R. Khardon, P. Tadepalli, A. Raghavan, A. Fern, Solving Relational MDPs with Exogenous Events and Additive Rewards, The European Conference on Machine Learning (ECML/PKDD) , 2013 (PDF) (Arxiv).

Aswin Raghavan, Saket Joshi, Alan Fern, Prasad Tadepalli, Roni Khardon, Planning in Factored action spaces using Symbolic Dynamic Programming, Proceedings of the 26th Conference on Artificial Intelligence (AAAI-12) Toronto, Canada. (source)(pdf)(poster).

Aswin N. R., Manimaran S. S., Harini A., and Ravindran, B. (2010), Accurate Mobile Robot Localization in Indoor environments using Bluetooth. In the Proceedings of the 2010 IEEE International Conference on Robotics and Automation (ICRA 2010), pp. 4391-4396. IEEE Press. (PDF).

R.Malmathanraj, Aswin N Raghavan, V.Srivas and R.Gowtham Rangarajan, Mammogram tumor classification using Q learning based thresholding, BEATS 2010 : International Conference on Biomedical Engineering and Assistive Technologies, NIT JALANDHAR.