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My name is Akshay Aravamudan. I am currently a 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. In the fall of 2023, I interned at Amazon as an applied scientist wherein I worked on a discrete event simulation for routing of support calls for the SPeXSci (Selling Partner Experience Science) team.

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. [December 8, 2023]: We will be presenting our work titled "Regional Seismic Discrimination using Machine Learning" at AGU 2023!
  2. [August 27, 2023]: I will be interning at Amazon, Seattle this Fall semester. Very excited to work with the team
  3. [November 27, 2022]: Our 2022 ICML paper was featured in Florida Tech's news website Massive congratulations to the paper lead Xi Zhang!
  4. [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 for Seismology
  5. Influence Characterization in Social Media






Upcoming works

Other projects of relevance

  1. Container Migration
  2. Discriminant Analysis classifier
  3. Population Prediction LSTM
  4. Branch and Bound: an OpenMP Implementation
  5. Torque3D
  6. File System for and FRAM

Contact info

I can be contacted via my email or via my Linkedin page