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Farhan Asif Chowdhury

Graduate Research Assistant

The University of New Mexico

Biography

I am Farhan, a Computer Science PhD student at The University of New Mexico working with Dr. Abdullah Mueen. I am interested in (1) studying the temporal dynamics of user behaviour on social platforms with a focus on content recommendation and coordinated & anomalous activity detection, (2) temporal data mining with a focus on classification, predictive modeling and pattern recognition.

Previously, I have worked as Research Intern at Snap Inc, NEC Labs America and colloborated in research projects with Sandia National Labs, Air Force Research Lab and ExxonMobil.

Interests

  • Temporal Data Mining
  • Social Media Mining
  • User Behavior Modeling
  • Event and Anomaly Detection

Education

  • PhD in Computer Science, 2022 (Expected)

    The University of New Mexico

  • MS in Computer Science, 2019

    The University of New Mexico

  • BSc in Electrical and Electronic Engineering, 2016

    Bangladesh University of Engineering and Technology (BUET)

Publications

FASER: Seismic Phase Detection for Autmated Monitoring

In KDD 2021

Efficient Unsupervised Drift Detector for Fast and High-dimensional Data Streams

In KAIS

CEAM: The Effectiveness of Cyclic Ephemeral Attention Models of User Behavior on Social Platforms

In ICWSM 2021

AdverTiming Matters: Examining User Ad Consumption for Effective Ad Allocations on Social Media

In CHI 2021

Unsupervised Drift Detection on High-Speed Data Streams

In IEEE BigData 2020

On Twitter Purge: A Retrospective Analysis of Suspended Users

In WWW 2020.

Examining Factors Associated with Twitter Account Suspension Following the 2020 US Presidential Election

Submitted to ASONAM 2021

DiffuScope:Inferring Post-specific Diffusion Network

Submitted to ASONAM 2021

Predicting User States with Anonymous Activity Streams on Online Platforms

Under Submission

Experience

 
 
 
 
 

Research Intern

NEC Labs America

Jan 2021 – Aug 2021 Princeton, NJ

Few-shot Video Action Recognition System
Mentors: Biplob Debnath, Oliver Po, Srimat Chakradhar, Asim Kadav, Farley Lai

  • Worked on developing a few-shot video action recognition system that leverages temporal information to improve recognition performance for longer, sequential activity types.
  • One paper under submission and one patent application pending.
 
 
 
 
 

Research Intern

Snap Research

May 2020 – Aug 2020 Santa Monica, CA

Characterizing and Modeling Temporal Dynamics of User Behavior on Social Platforms
Mentors: Maarten W. Bos, Yozen Liu, Neil Shah, Leonardo Neves

  • Worked in the Computational Social Science research team to study and understand how user behavior on social platforms is driven by regularities and momentary cognition & feelings. The work is aimed towards better and privacy-preserving modeling of user behavior for dynamic and personalized content recommendation.
  • Published one paper in ICWSM, one paper in CHI and two patent applications pending.
 
 
 
 
 

Graduate Research Assistant

Dept of Computer Science
The University of New Mexico

Jan 2018 – Present Albuquerque, NM

Characterization and Detection of Malicious User Behavior in Social Media

  • Developed a Twitter data crawler; streamlined data collection, filtering, and storage process.

  • Crawled 560M Twitter user info & 300M Tweets and performed analysis (tweet content, user info & activity pattern) to characterize and model malicious vs. non-malicious users.(manuscript under review)

  • Currently developing a real-time, adaptive and scalable algorithm for malicious users and coordinated malicious activity detection on social media.

A Real-time Twitter Analytics Dashboard

  • Functionality: Hashtag & User Activity Tracking, Identifying frequent Word/Hashtag/URL & Influential users, Tweet filtering & classification (i.e. sentiment, intent, spam), Info-graphic visualization.

  • Tools & Framework: Flask, Django, PostgreSQL, Elasticsearch, Docker, Heroku, AWS, Chart.js.

Seismic Phase Classification for Automated Monitoring

  • Developed a deep-learning model for fine-grained phase classification from single-station seismic time-series. The model utilizes a Convolutional Neural Network and Long-Short Term Memory Network in combinationto exploit both short- and long-term temporal patterns in seismic data to improve classification accuracy.

Event and Anomaly Detection in Pressure Sensor Data

  • Developed a semi-supervised algorithm using Singular Spectrum Analysis for Structured Noise detection in Oil/Gas well pressure data to automate Pressure Transient Analysis; created a user interface.
 
 
 
 
 

Lecturer

Dept of Computer Science
Daffodil International University

Sep 2016 – Aug 2017 Dhaka, Bangladesh

Conducted Theory and Lab Courses of Undergraduate Computer Science students

 
 
 
 
 

Undergraduate Research Assistant

DSP Research Lab, Dept of EEE

Mar 2015 – Jun 2016 BUET

Developed a novel algorithm for automatic breast lesion segmentation from B-mode ultrasound images

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