STUDY DIRECTORY // SELECT A SHELFI. ResearchSEARCH & RESCUE · ML ↓II. PublicationsPAPERS & ARXIV ↓III. ProjectsTERAS · KERAS 3 · OSS ↓IV. Experience & CVFULBRIGHT · AWS · CV ↓U.S. DEPT. OF STATE4.0 / 4.0 M.S. AI · RIT1ST RANK B.S. CS ’21FULBRIGHT SCHOLARAI/ML SDE · SUMMER ’25★ Full-Time Return OfferEAST PALO ALTO · SPARKAWS AI/ML INTERNPH.D. GRA · ROCHESTERSearch & Rescue · ADS-CTERAS · KERAS 3 · MARLKAS LAB · OPEN MLCV.PDFOPEN ↗KHAWAJA ABAID ULLAHPH.D. GRA · RIT KAS LABPull brass chain to switch study lighting

Ph.D. Researcher in ECE · Rochester Institute of Technology

Khawaja Abaid Ullah

I research cooperative multi-agent autonomy—dynamic resource allocation, constrained communication, and aerial recharging for disaster search-and-rescue robot teams—alongside inference-time defenses that protect deployed machine learning models from unauthorized extraction.

Previously an AI/ML Software Development Engineer Intern at Amazon Web Services (full-time return offer) and a U.S. Fulbright Scholar at RIT (M.S. in AI, 4.0 GPA). Alongside research, I build open-source ML tools including Teras and contribute to Keras 3.

01 / Research

Research Focus

Cooperative search-and-rescue autonomy and inference-time ML security at RIT’s KAS Lab.

Research overview
  1. Cooperative Multi-Agent Autonomy & Search-and-Rescue

    Primary Ph.D. Focus · KAS Lab

    Developing multi-agent reinforcement learning and coordination methods for disaster search-and-rescue teams—spanning dynamic resource allocation, communication relays under bandwidth limits, aerial UAV battery recharging (Fontus), and containment of compromised agents.

    • Multi-Agent RL
    • Search & Rescue
    • Resource Allocation
    • ROS 2
    • PettingZoo / RLlib
  2. ML Security & Inference-Time Antidistillation

    ADS-C · Matched-Cost Benchmark

    Designing closed-form per-input confidence-margin perturbations (ADS-C) and matched-cost evaluation wrappers that preserve 100% of served top-1 predictions while substantially degrading surrogate student distillation.

    • Model Extraction Defense
    • Antidistillation
    • Margin Bounds
    • PyTorch
  3. LLM Distillation, Alignment & Optimization

    JAX / Flax NNX · 34-LLM Study

    Exploring two-stage knowledge distillation for compact local code-generation models in JAX/Flax NNX (codriver, 260M parameters) alongside theory-grounded behavioral alignment studies across 34 LLMs and 198 human participants.

    • Knowledge Distillation
    • JAX / Flax NNX
    • LLM Evaluation
    • Combinatorial Optimization

02 / Papers

Publications

Concise summaries with direct links to arXiv preprints, PDFs, code, and BibTeX.

Standalone list

arXiv:2610.41044 · Preprint · Under review · 2026

Consensus Within, Competition Between: Decentralized LLM Teams for Perimeter Defense

Khawaja Abaid Ullah, Ananya Acharya, Mohammad Javad Khojasteh

Demonstrated that decentralized LLM teams using an external auction-based conflict-resolution protocol (CBAA) achieve lower breach rates than centralized or independently acting teams, and built a reproducible testbed evaluating coordination under communication constraints, decision authority, and multimodal visual deception.

BibTeX for Consensus Within, Competition Between: Decentralized LLM Teams for Perimeter Defense
@misc{ullah2026consensus,
  title = {Consensus Within, Competition Between: Decentralized LLM Teams for Perimeter Defense},
  author = {Khawaja Abaid Ullah and Ananya Acharya and Mohammad Javad Khojasteh},
  year = {2026},
  eprint = {2610.41044},
  archivePrefix = {arXiv},
  primaryClass = {cs.MA},
  url = {https://arxiv.org/abs/2610.41044}
}

arXiv:2607.15467 · Under review · IEEE OJCS · 2026

ADS-C: Antidistillation Sampling for Classification

Khawaja Abaid Ullah, Mohammad Javad Khojasteh

Margin-aware inference-time defense against soft-label model extraction that preserves 100% of served top-1 predictions while reducing distilled-student accuracy by 29.6 pp on CIFAR-10, 17.4 pp on CIFAR-100, and 13.3 pp on Tiny-ImageNet.

BibTeX for ADS-C: Antidistillation Sampling for Classification
@misc{ullah2026adsc,
  title = {ADS-C: Antidistillation Sampling for Classification},
  author = {Khawaja Abaid Ullah and Mohammad Javad Khojasteh},
  year = {2026},
  eprint = {2607.15467},
  archivePrefix = {arXiv},
  primaryClass = {cs.LG},
  url = {https://arxiv.org/abs/2607.15467}
}

Manuscript under review · 2026

AntidistillBench: A Unified Benchmark of Test-Time Antidistillation Defenses for Classification

Khawaja Abaid Ullah, Mohammad Javad Khojasteh

First-of-its-kind unified benchmark and matched-cost evaluation protocol for test-time antidistillation defenses in classification, comparing existing defenses under a common threat model for suppressing student distillation.

BibTeX for AntidistillBench: A Unified Benchmark of Test-Time Antidistillation Defenses for Classification
@unpublished{ullah2026antidistillbench,
  title = {AntidistillBench: A Unified Benchmark of Test-Time Antidistillation Defenses for Classification},
  author = {Khawaja Abaid Ullah and Mohammad Javad Khojasteh},
  year = {2026},
  note = {Manuscript under review}
}

arXiv:2608.09717 · Under review · 2026

How Do Large Language Models Judge Social Attraction? Evidence from Theory-Grounded Persona Ratings Across Multiple LLMs and Humans

Hasan Mahmud, Khawaja Abaid Ullah, Mohammad Javad Khojasteh, Jamison Heard, Prabu David

Evaluated social-judgment reliability and human-LLM alignment across 34 large language models and 198 human participants using theory-grounded persona ratings and controlled analyses of gender presentation.

BibTeX for How Do Large Language Models Judge Social Attraction? Evidence from Theory-Grounded Persona Ratings Across Multiple LLMs and Humans
@misc{mahmud2026socialattraction,
  title = {How Do Large Language Models Judge Social Attraction? Evidence from Theory-Grounded Persona Ratings Across Multiple LLMs and Humans},
  author = {Hasan Mahmud and Khawaja Abaid Ullah and Mohammad Javad Khojasteh and Jamison Heard and Prabu David},
  year = {2026},
  eprint = {2608.09717},
  archivePrefix = {arXiv},
  primaryClass = {cs.CL},
  url = {https://arxiv.org/abs/2608.09717}
}

03 / Projects

Projects & Code

Open-source libraries, official Keras 3 contributions, distilled models, and RL environments.

All projects

Created a Keras 3-based tabular deep-learning library supporting JAX, PyTorch, and TensorFlow backends. Implemented state-of-the-art research architectures (SAINT, TabNet, TabTransformer, FT-Transformer, CTGAN, TVAE, GAIN, PCGAIN) for classification, regression, data imputation, and synthetic-data generation.

  • Keras 3
  • JAX
  • PyTorch
  • TensorFlow
  • Tabular Deep Learning

Contributed Beta and Binomial random-sampling functionality (keras.random.beta, keras.random.binomial) and multi-backend consistency fixes to the official Keras 3 API across JAX, PyTorch, and TensorFlow.

  • Keras 3
  • JAX
  • PyTorch
  • TensorFlow
  • Random Sampling APIs

Custom PettingZoo Parallel multi-agent environment with vectorized NumPy dynamics and proximity-triggered aerial battery charging; trained parameter-shared IPPO/PPO policies in PyTorch and Ray RLlib and diagnosed a reward-hacking local optimum against an analytical baseline.

  • PettingZoo Parallel
  • PyTorch
  • Ray RLlib
  • IPPO / PPO
  • Vectorized NumPy

codriver (260M Distilled Code LLM)

Open-Source ML

Inspired by GitHub Copilot, built a custom 260M-parameter contextual code-generation model based on Google DeepMind’s Gemma LLMs. Ported Gemma 2 from Flax Linen to the NNX API and applied staged knowledge distillation—first using Gemma 2 2B on text (FineWebEdu) and then CodeGemma 2B on fill-in-the-middle code (CodeContests) across 8× H100 GPUs.

  • JAX
  • Flax (Linen → NNX)
  • Gemma 2 / CodeGemma
  • Staged Knowledge Distillation
  • 8× H100 GPUs

Implemented Andrej Karpathy’s micrograd from scratch in pure C: a scalar reverse-mode autograd engine and neural-network library exposing a PyTorch-like API.

  • C
  • Autograd Engine
  • Neural Networks
  • PyTorch-like API
Earlier student & personal projects (5)
  • Prewordict — A word-cloud guessing game built from clusters of words in Medium articles. GitHub
  • Gamer Assistant — A CS50 project combining game recommendations, PC suggestions, and FPS prediction.
  • PROLEAs — Undergraduate work on crowd detection and behavior analysis using computer vision.
  • FPS Predictor — An early machine-learning project estimating game performance from hardware data.
  • Tweets Cloud Bot — A Twitter bot that turned recent tweets into customizable word clouds. GitHub

04 / Record

Experience & CV

Appointments across RIT’s KAS Lab and AWS AI/ML, academic degrees, and technical stack.

Full CV dossier

Appointments & Industry

  1. Graduate Research Assistant (Ph.D. in ECE)

    Full GRA Fellowship

    Khojasteh Autonomous Systems Laboratory (KAS Lab), RIT · Rochester, NY

    • Advised by Dr. Mohammad Javad Khojasteh (expected graduation: May 2029).
    • Researching cooperative multi-agent decision-making, dynamic resource allocation, constrained communication, and compromised-agent containment for disaster search-and-rescue robot teams, alongside test-time antidistillation defenses.
  2. Software Development Engineer Intern, AI/ML

    Full-Time Return Offer

    Amazon Web Services (AWS) · East Palo Alto, CA

    • Engineered core Apache Spark testing and evaluation modules for a next-generation internal benchmarking platform, closing feature-parity gaps with its legacy predecessor.
    • Independently identified and delivered additional benchmarking capabilities beyond original project scope, earning team leadership recognition and a full-time return offer.
  3. Teaching Assistant — Principles of Robotics

    ROS 1 / ROS 2 / Docker

    Rochester Institute of Technology · Rochester, NY

    • Develop lecture and lab material for a combined undergraduate/graduate robotics course; designed reproducible ROS Docker containers and VM images replacing a brittle third-party setup.
  4. Manuscript Reviewer

    IEEE Signal Processing Letters · Academic Service

    • Peer-review manuscripts in machine learning and signal processing for technical soundness, novelty, and experimental reproducibility.
  5. Junior Python Developer (Remote)

    Comundo · Copenhagen, Denmark

    • Built semi-supervised data-curation pipelines for building energy-consumption records used in CO2 estimation and downstream forecasting.

Education

  1. Ph.D. in Electrical and Computer Engineering

    Fully Funded GRA · KAS Lab

    Rochester Institute of Technology · Rochester, NY

    Advised by Dr. Mohammad Javad Khojasteh in the Khojasteh Autonomous Systems Laboratory.

  2. M.S. in Artificial Intelligence

    U.S. Fulbright Scholar · 4.0 / 4.0 GPA

    Rochester Institute of Technology · Rochester, NY

    Funded by the U.S. Department of State Fulbright Foreign Student Program. Thesis: “Antidistillation Sampling for Classification.”

  3. B.S. in Computer Science

    Ranked 1st in Class of 2021

    University of Narowal · Narowal, Pakistan

    Graduated first (Valedictorian) in the Computer Science class of 2021.

Technical Stack

Languages
Python · C · C++ · Bash
ML & Deep Learning
JAX · Flax (Linen & NNX) · Keras 3 · PyTorch · TensorFlow · Ray RLlib · PettingZoo · NumPy · Apache Spark
Robotics & Systems
ROS 1 · ROS 2 · Docker · Linux · Git · Arduino
Research Focus
Multi-Agent Reinforcement Learning · Cooperative Search & Rescue · Antidistillation & ML Security · Knowledge Distillation · LLM Evaluation & Optimization