Babak Esmaeili is a PhD student in the Machine Learning program at Northeastern University, advised by Jan-Willem van de Meent. Babak’s research is focused on Bayesian methods for inference in probabilistic models, probabilistic programming, and efficient inference techniques. Prior to joining Northeastern, Babak earned his bachelor’s and master’s degrees from the University of Edinburgh.
I completed the Master’s of Data Science program at the University of Edinburgh. I also completed a four-year degree in Artificial Intelligence and Computer Science at the same university.
One problem I’m always interested in is how to faithfully evaluate deep generative models and make sure they are useful for downstream tasks. Evaluation in unsupervised learning is generally a difficult problem as we often don’t know the downstream tasks in advance. Deep generative models are typically trained in an unsupervised manner; therefore, it is important to think about the evaluation metrics we use and how well they match the final application we are considering.
There is a lot of data out there in many fields: Heath care, chemistry, biology, vision, … With the recent developments in machine learning, many interesting patterns and models can be learned from all this data that can help the researchers in their field to have a better understating of their problems. I find applying these techniques to real-life applications quite fascinating as it enables one to explicitly observe how an adjustment in parameters or model complexity can shift the result in the system’s performance or class prediction accuracy.
Ultimately, I would like to pursue an academic career and eventually establish myself as an independent researcher in the machine learning field.
Babak Esmaeili is a PhD student in the Machine Learning program at Northeastern University, advised by Jan-Willem van de Meent. Babak’s research is focused on Bayesian methods for inference in probabilistic models, probabilistic programming, and efficient inference techniques. Prior to joining Northeastern, Babak earned his bachelor’s and master’s degrees from the University of Edinburgh.
I completed the Master’s of Data Science program at the University of Edinburgh. I also completed a four-year degree in Artificial Intelligence and Computer Science at the same university.
One problem I’m always interested in is how to faithfully evaluate deep generative models and make sure they are useful for downstream tasks. Evaluation in unsupervised learning is generally a difficult problem as we often don’t know the downstream tasks in advance. Deep generative models are typically trained in an unsupervised manner; therefore, it is important to think about the evaluation metrics we use and how well they match the final application we are considering.
There is a lot of data out there in many fields: Heath care, chemistry, biology, vision, … With the recent developments in machine learning, many interesting patterns and models can be learned from all this data that can help the researchers in their field to have a better understating of their problems. I find applying these techniques to real-life applications quite fascinating as it enables one to explicitly observe how an adjustment in parameters or model complexity can shift the result in the system’s performance or class prediction accuracy.
Ultimately, I would like to pursue an academic career and eventually establish myself as an independent researcher in the machine learning field.
