About me

Hi everyone! This is Hasan. I am a computer engineering PhD candidate in ECpE Department at Iowa State University. My research focuses on applications of machine learning, neural networks, and generative AI. In my recent research, I have worked on dynamic graph learning from evolving graph networks using Transformer and LLM based model [Paper] [code]. I have also experience in time series analysis and spatio-temporal patterns from large-scale data where I collaborated with chemists and introduced a [compression techniques] and clustering of trajectory patterns. I received Best Paper Award and recognized multiple times for my research work.

Contact

Email: hasan92niloy@gmail.com, mhanowar@iastate.edu
Office: Coover Hall, Ames, IA 50010

Md Hasan Anowar

Ph.D. Candidate, Computer Engineering

Iowa State Univeristy

Education

Ph.D. in Computer Engineering, 2021-2026, (GPA 3.94/4.00)
Iowa State University, Ames, Iowa

M.S. in Electrical Engineering, 2019 - 2020 (GPA 4.00/4.00)
The University of Texas Rio Grande Valley, Edinburg, Texas

B.Sc. in Electrical and Electronic Engineering, 2010 - 2015
Bangladesh University of Engineering and Technology (BUET)

Experiences

  • Machine Learning Engineer Intern, ViaSat, Carlsbad, CA (June 2024 - Sept 2024)

    • Developed ML forecasting models on AWS to predict the active network traffic patterns from large-scale time series data.
    • Designed ETL pipelines with SQL in Snowflake for models ranging from tree-based (XGBoost) to neural networks (MLP, ANN); achieved under 6 RMSE.

  • Machine Learning and AI Researcher, Iowa State University (Jan 2021 - Present)

    • Engineered dynamic graph structures through data augmentation of time-stamped spatial datasets.
    • Developed transformer encoder models for evolving graphs using PyTorch.
    • Performed link prediction, demonstrating proficiency on benchmark recommender datasets against static and dynamic GNN baselines.
    • Uncovered spatial-temporal patterns from large moving object data (>2,000 GBs).
    • Proposed a novel spatiotemporal cluster using various clustering algorithms.
    • Developed a compression algorithm for trajectory data, improving query processing efficiency by 4x and storage efficiency by 5x.
    • Collaborated with chemists to capture causal relationships based on domain semantics.

  • Project Mentor (Aug 2023 - May 2024)

    • Mentored a team of 4 undergraduate students in developing a software tool for visualizing co-moving patterns from trajectory data.
    • Supervised project implementation, guiding the students in coding, debugging, and optimization.
    • Open-source project available on GitHub.

  • Instructor for Large Scale Data Systems (Spring 2024, 2025)

    • Taught databases and distributed systems to a class of 70+ students.
    • Designed and delivered lectures, assignments, and hands-on projects.
    • Provided mentorship and support to students on database queries, big data processing, and distributed computing concepts.

Publications

Complete list of publications on Google Scholar. ‡ indicates equal contribution.

  • Detecting and Visualizing Bond-Forming Convoys in Atomic and Molecular Trajectories


    Md Hasan Anowar, Abdullah Shamail, Ayden J Albertsen, Benjamin Hall, Timothy J Thielen, Benjamin Riemersma, Goce Trajcevski
    ACM SIGSPATIAL 2024 (SIGSPATIAL), 2024
    [Paper] [Code]

  • Bond-Aware Moving Cluster of Atomic Trajectories with Relaxed Persistency


    Abdullah Shamail‡, Md Hasan Anowar‡, Goce Trajcevski, Sohail Murad, Cynthia J Jameson, Ashfaq Khokhar
    ACM SIGSPATIAL 2024 (SIGSPATIAL), 2024

  • Compressing generalized trajectories of molecular motion for efficient detection of chemical interactions


    Md Hasan Anowar, Abdullah Shamail, Xiaoyu Wang, Goce Trajcevski, Sohail Murad, Cynthia J Jameson, Ashfaq Khokhar
    Information Systems 2024 (Information Systems), 2024

  • Generalization Aware Compression of Molecular Trajectories


    Md Hasan Anowar, Abdullah Shamail, Xiaoyu Wang, Goce Trajcevski, Sohail Murad, Cynthia J Jameson, Ashfaq Khokhar
    ADBIS 2022 (ADBIS), 2022 Best paper Award!
    [Paper] [Code]

Projects

  • Sentiment Analysis

    • Designed a sentiment detection model for movie reviews using the sequential models.
    • Implemented an LSTM based model which outperformed vanilla RNN by 50% accuracy.

  • Image Style Transfer

    • Tasked to transfer style from an art image to a content image. Implemented convolutional neural network model (VGG19) to extract the feature map.
    • Introduced an additional convolution layer and fine-tuned feature weights resulting in obtaining the visually optimal image.

  • Database and Query Processing

    • Designed multidimensional cubes using SQL Server Data Tools and SQL Server Management Studio and performed OLAP using MDX on large volume of data from the data warehouse.
    • Worked with NoSQL database management system (Neo4j, MongoDB) and geospatial data using geographic system application (QGIS).

  • Integration of Chameleon Cloud with LLVM Compilation

    • Provisioned a bare-metal node and launched a bare-metal instance.
    • Built a functional docker container from image, compiled a C program into intermediate representation (IR) code using LLVM/Clang, and deployed it in the cloud.

Leadership & Achievements

  • Best Paper Award at ADBIS 2022: Received best paper award for my work on ‘Generalization Aware Compression’

  • GPSS Travel grant for SIGSPATIAL'24 conference

  • Organized the inaugural IBM Quantum Computing (Qiskit) workshop as Vice-President of Graduate Organization of Electrical and Computer Engineering (Oct 2024)

Last Updated: 03/05/2026