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My name is Akshay Aravamudan. I am currently a fourth year Ph.D. student in Computer Engineering at Florida Institute of Technology. My research primarily comprises of machine learning applications. Under that rather broad umbrella, I have and continue to work on stochastic temporal point processes, machine learning on the edge and machine learning for hydrology. I graduated with my Master’s Degree in Computer Engineering from Florida Institute of Technology on July of 2019. The following page is used to track my current research interest and to maintain a record of works, both academic and extra-curricular, that I have completed throughout my ongoing academic career. You can find my resume here. A collection of my published papers presentations can also be found on my Google scholar profile. Also do check out our research lab’s website here


  1. [November 27, 2022]: Our 2022 ICML paper was featured in Florida Tech's news website Massive congratulations to the paper lead Xi Zhang!
  2. [November 22, 2022]: Our paper "Anytime User Engagement Prediction in Information Cascades for Arbitrary Observation Periods" has been accepted to AAAI 2023! Super excited to be presenting our work in Washington, D.C, come February! More details to follow soon. Temporary location of the paper can be found [here](


I joined Florida Tech at 2014 to purse my Bachelor’s degree in computer engineering. I started with an interest in microcontrollers and microcomputers. During my undergraduate years, I developed a passion for machine learning, starting with a naive bayes classification tool. I got interested in the intricacies involved in developing complex models, which eventually led me down the path of neural networks. I ended up taking courses like Pattern Recognition and Neural Networks. I continued onto pursue my Master’s degree, working under adviesement of Dr. Georgios Anagnastopolous. My master’s thesis dealt with the study of information spread and more specifically utilizing it better understand the spread of software vulnerabilities in their domains.

Current Research Interests

  1. Information Diffusion
  2. Stochastic Temporal Point Processes
  3. Machine Learning for Hydrology
  4. Machine Learning on the Edge
  5. Influence Characterization in Social Media