Loading...
I received my Ph.D. in computer engineering from University of Florida in 2020 amid the Covid pandemic. I was a member of Mobile Networking Laboratory under supervision of Prof. Ahmed Helmy in Computer and Information Science and Engineering department of University of Florida. During my graduate program, I have been UF graduate school and UF Informatics Institute graduate fellows. Prior to joining UF, I received my B.Sc. in Computer Engineering - Information Technology from department of Computer Engineering at Sharif University of Technology.
My general research direction is mobility modeling with a focus on vehicular mobility (in particular applications of machine learning in mobility). I've also been a part of a series of collaborations on mobility modeling in pedestrian settings.
You can find my CV here.
Updated April 2021.
As part of MobiBench, we explore how people move in different settings and how it affects potential applications (i.e. networking, ride-sharing, etc.)
This includes systematic analysis, statistical and machine learning modeling for various goals including forecasting and prediction, from different spatio-temporal perspectives, simulation and scenario generation, and these kind of stuff!
Using estimates of traffic density extracted from traffic cameras around the globe, we propose a framework to estimate traffic demands in the form of an OD matrix. Then we examine different routing ideas to finally generate simulation scenarios using SUMO.
For more details including data and generated simulation scenarios (for London and Washington DC. for now) visit our En Route page.
Using data of kolntrace we explored the trips happening in city of Cologne from a similarity point of view. By proposing a spatio temporal measure of similarity we find clusters that are spatially and/or temporally distinguishable. Then we explore the applications of it in different formulations of ride or car sharing (carpooling, catch-a-ride, and car sharing or minimum path coverage in ride request graph).
For more details, see its repo on my github Playing with Matches page.
In this investigation we designed and evaluated various timeseries models including recurrent neural networks (deep learning) to forecast density of traffic (and/or count of cars) against the estimates from the traffic camera images.
We have also collected newer images, for a longer timespan and higher spatial resolution, to process through more advanced image processing methods to create a more accurate & recent basis for the study.
Huge traces of wireless lan access point logs (100s of gb) and network flows (terabytes) are the data foundation and we lay our work on. We have investigated the behavioral differences of highly mobile users vs those of stop to use nature, taking steps toward integrated modeling human mobility and network usage.
Work on predicting next building or access point where users are most likely to go in our campus setting. Building on processed data from our wireless lan traces and using machine/deep learning models in addition to theoretical predictors we investigated the matter in different spatio-temporal granularities.
Collaborating with colleagues in other departments on problems that require computer engineering expertise. So far we've worked on a blockchain prototype and a finite difference time domain method based 3d simulation of sound waves on cuda nvidia gpus.
Python's Pandas, Numpy and Scikit-learn, Hadoop MadReduce in Java. Tensorflow and Keras for deep learning. Some experience in R.
Mainly C/C++, Python. Comfortable with Java.
I have done some R, C#. Net, Php, and Matlab
Also have done some MIPS programming and Verilog HDL.
Not afraid of Unix and Linux! Have done some Bash scripting in my time.
Next to learn: What is functional programming :thinking: (Scala and Haskell I guess)
Have done some HTML, CSS, Javascript and JQuery, ASP .Net, Php Symfony and Python Django.
One day I'll learn NodeJs and AngularJs! (but then probably there are newer frameworks for both web and data science!).
[Theoretical] SQL, MS SQL Server (T-SQL and familar with Entity Framework), Oracle (PL/SQL) and MySql. Have worked with warehousing (i.e. Hive). Some familiarity with Apache ecosystem.
Next to learn: NOSQL and other types of persistent storage (document storage, key-value, graph, etc.)
SUMO for vehicular mobility, Cisco Packet Tracer, OMNet++, NS2 (Otcl), ONE simulator, and Arena (minor experience).
VS Code, MS Visual Studio, Android Studio (IntelliJ), PhpStorm, PyCharm, Eclipse (C++ and Java), VIM.
Adobe Photoshop, Adobe Lightroom, Autodesk Autocad (LoL!).
Amazon AWS services including: S3, EC2, EMR, EBS, Elastic Beanstalk (web app deployment)
Wireshark and packet inspection.
Hardware modelers such as XilinX ISE, Altera Quartus and Mentor Graphic's Modelsim
Below is a short description list of some of project that I can remember that I've done: