Publications Google Scholar

  1. Judging by the Cover: Cleaning LLM Truthfulness Benchmarks to Avoid Surface-Level Feature Leakage
    Judging by the Cover: Cleaning LLM Truthfulness Benchmarks to Avoid Surface-Level Feature Leakage
    Foad Namjoo, Remy Ogasawara , Amirali Abdullah , Cullen Anderson , Narmeen Fatimah Oozeer , and Jeff M. Phillips
    arXiv 2026, under review at ICLR 2027
    Summary: A six-feature surface probe that never reads the question separates TruthfulQA’s correct from incorrect answers (AUC 0.715); Audit-Prune removes the leaking pairs, and TruthfulQA-476 keeps the benchmark’s model ranking with the shortcut gone.
    Paper Project Code Data
  2. Sampling for Region-Aggregated Spatial Scan Statistics
    Sampling for Region-Aggregated Spatial Scan Statistics
    Foad Namjoo, Drew McClelland , Michael Matheny , and Jeff M. Phillips
    In Proceedings of the 34th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL), 2026
    Accepted as a full paper (58/248, 23.4%).
    Summary: Replacing each region with 20–50 sampled points improves the statistical power that centroid-collapsing loses — a drop-in fix for point-based spatial scan statistics on aggregated data.
    Paper Project Code
  3. Orthogonal and Parametrized Multi-Attribute Steering of LLMs
    Remy Ogasawara , Cullen Anderson , Narmeen Oozeer , Foad Namjoo, Amirali Abdullah , Jens Kristian Refsgaard Schou , and Jeff M. Phillips
    Under review at ICLR 2027
    Summary: Steering vectors for related traits point in similar directions, so pushing one drags the others; rescaling each vector to its most steerable range, setting a trait’s level instead of pushing it, and orthogonalizing the directions steer several traits at once without over- or under-steering.
    Project Code
  4. Understanding and Mitigating Dataset Corruption in LLM Steering
    Understanding and Mitigating Dataset Corruption in LLM Steering
    Cullen Anderson , Narmeen Oozeer , Foad Namjoo, Remy Ogasawara , Amirali Abdullah , and Jeff M. Phillips
    arXiv 2026, under review at ACL Rolling Review
    Summary: How noisy and adversarial corruption in steering datasets degrades LLM control — and robust estimation that mitigates it.
    Paper Project
  5. Designing a Secure and Resilient Distributed Smartphone Participant Data Collection System
    Designing a Secure and Resilient Distributed Smartphone Participant Data Collection System
    Foad Namjoo, Neng Wan , Devan Mallory , Yuyi Chang , Nithin Sugavanam , Long Yin Lee , Ning Xiong , Emre Ertin , and Jeff M. Phillips
    In Proceedings of the EAI International Conference on Security and Privacy in Cyber-Physical Systems and Smart Vehicles (SmartSP), 2025
    Summary: The system design behind MotionPI: secure, resilient, privacy-first mobile data collection at scale.
    Paper arXiv Project
  6. Addressing Data Limitations to Explore Management Strategies and Adaptations Using Stylized Agent-Based Modeling: A Case Study of Socio-Environmental Systems
    Addressing Data Limitations to Explore Management Strategies and Adaptations Using Stylized Agent-Based Modeling: A Case Study of Socio-Environmental Systems
    Mehrsa Pouladi , Parsa Pouladi , Saba Naderian Jahromi , and Foad Namjoo
    Ecological Modelling, 2025
    Summary: Stylized agent-based modeling to explore management strategies for socio-environmental systems under limited data.
    Paper
  7. Efficient and Stable Multi-Dimensional Kolmogorov–Smirnov Distance
    Efficient and Stable Multi-Dimensional Kolmogorov–Smirnov Distance
    Peter Matthew Jacobs , Foad Namjoo, and Jeff M. Phillips
    Submitted to Foundations of Data Science
    Authors listed alphabetically, following the convention in theory.
    Summary: A multi-dimensional Kolmogorov–Smirnov distance that’s a true metric, with a stable finite-sample two-sample test, near-linear in 2–4D.
    Paper Project HTML Code
  8. A Hyperspectral Change Detection (HCD-Net) Framework Based on Double Stream Convolutional Neural Networks and an Attention Module
    A Hyperspectral Change Detection (HCD-Net) Framework Based on Double Stream Convolutional Neural Networks and an Attention Module
    Seyd Teymoor Seydi , Mahboubeh Boueshagh , Foad Namjoo, Seyed Mohammad Minouei , Zahir Nikraftar , and Meisam Amani
    Remote Sensing, 2024
    Summary: A dual-stream CNN with an attention module for change detection in hyperspectral imagery.
    Paper
  9. A Machine Learning Approach for Harmful Algal Bloom (Red Tide) Forecasting Using MODIS Level 3 Ocean Colour Products from Google Earth Engine
    Moein Izadi , Foad Namjoo, and Zahir Nikraftar
    In AGU Fall Meeting Abstracts, 2021
    Summary: Forecasting harmful algal blooms (red tide) from MODIS ocean-colour products on Google Earth Engine.
    Link
  10. Remote Sensing & Statistical Learning Approach to Harmful Algal Bloom Forecasting Using MODIS Ocean Colour Parameters
    Moein Izadi , Mohamed Sultan , Racha Kadiri , Amin Ghannadi , Zahir Nikraftar , and Foad Namjoo
    In AGU Fall Meeting Abstracts, 2020
    Summary: A statistical-learning approach to harmful algal bloom forecasting from MODIS ocean-colour parameters.
    Link
  11. Australian 2020 Bushfire Propagation, Network Science Approach
    Foad Namjoo, Ali Kamandi , and Seyed Mohammad Minouei
    In National Conference on Vision of the Country, 2020
    Summary: Modeling the 2020 Australian bushfire propagation with a network-science approach.
    Link