Dara Varam

PhD in EDEE @ École Polytechnique Fédérale de Lausanne, specifically at the Embedded Systems Laboratory (ESL). Previously I did my MS in Machine Learning and my undergrad in Computer Engineering and Mathematics @ AUS with a minor in Data Science. I'm Iranian and I'm 24 years old.

From May 2025 to October 2026, I was a visiting researcher at the Senseable City Lab @ MIT.

My research focuses mainly on model compression, optimization and quantization for neural networks and LLMs. I have also done studies in training optimization, computer vision, and eXplainable AI.

The picture on the right isn't what I look like now. I just wanted to put it up there so you know I've always been cool 👺

dara.varam [at] epfl [dot] ch varam [at] mit [dot] edu
Dara Varam as a child, wearing round sunglasses

Academic Qualifications

2018 – 2023: BSc. Computer Engineering, BSc. Mathematics  |  American University of Sharjah
2024 – 2026: MSc. Machine Learning  |  American University of Sharjah
Current: PhD in Electrical Engineering (EDEE)  |  École Polytechnique Fédérale de Lausanne

Machine Learning Thesis

Not All Objectives Are Born Equal: Priority-Constrained Descent for Hierarchical Multi-Objective Optimization (Advised by: M. I. AlHajri)

I worked on the development of a multi-objective optimization framework to allow for optimization problems where a natural hierarchy exists in the “importance” of the tasks for modern deep learning architectures. You can check the project page here.

PhD Thesis

Topic TBD (Advised by: David Atienza Alonso)

Publications

These are papers that have already been published, as reflected on my Google Scholar page (and other venues).

Figure from Not All Objectives Are Born Equal: Priority-Constrained Descent for Hierarchical Multi-Objective Optimization

Not All Objectives Are Born Equal: Priority-Constrained Descent for Hierarchical Multi-Objective Optimization video demo

D. Varam, M. I. AlHajri

Transactions on Machine Learning Research (TMLR), 2026

We present Priority-Constrained Descent, a drop-in gradient optimizer that follows your primary objective and bends away from it only as much as needed to keep every secondary constraint progressing.

Figure from ER-NeRV: Training Error-Resilient Neural Representations for Videos

ER-NeRV: Training Error-Resilient Neural Representations for Videos

A. J. N. Panopio, D. Varam, I. Amer , and M. Hafeeda

IEEE International Conference on Consumer Electronics (ICCE), 2026

We propose ER-NeRV (Error Resilient Neural Representations for Videos), a new training / finetuning framework that allows for neural representation models to be robust against hardware failures on consumer electronics.

Figure from Class Separation Dynamics in Vision Transformers: An Empirical Study

Class Separation Dynamics in Vision Transformers: An Empirical Study video demo

D. Varam, L. Khalil, M. Darwish, M. I. AlHajri

IEEE Access, 2025

We study the class separation dynamics of Vision Transformers (ViTs) as they train for classification tasks, empirically deriving two “laws” that are always followed as a function of training. These two laws have implications that can strongly guide the training strategy and model optimization for transformer-based learning.

Figure from Cognitive Radio Spectrum Sensing on the Edge: A Quantization-Aware Deep Learning Approach

Cognitive Radio Spectrum Sensing on the Edge: A Quantization-Aware Deep Learning Approach video demo

H. A. Abushahla, D. Varam, M. I. AlHajri

IEEE Communications Letters, 2025

We study the effect of quantization-aware-training (QAT) on two SOTA spectrum sensing models - DeepSense and ParallelCNN. Models are deployed on a Sony Spresense for hardware evaluation.

Figure from Image-Scaling Attack Prevention in Industrial Cyber Physical Systems

Image-Scaling Attack Prevention in Industrial Cyber Physical Systems

D. Varam, A. Bayoumy, D. A. Abuhani, M. Zulkernine

[IEEE] International Conference on Communication, Computing, Networking, and Control in Cyber-Phyisical Systems (CCNCPS'25), 2025

We propose a novel methodology for detecting and preventing image-scaling attacks for industrial cyber-physical systems using open data.

Figure from Estimating Nitrogen Dioxide Levels Using Open Data and Machine Learning: A Comparative Modeling Study

Estimating Nitrogen Dioxide Levels Using Open Data and Machine Learning: A Comparative Modeling Study

Dara Varam, Rohan Mitra, Furzeen Kamran, Diaa A. Abuhani, Hana Sulieman, Imran Zualkernan

International Society for Photogrammetry and Remote Sensing (ISPRS), 2025

We study trends in NO2 levels through publicly available geospatial data, specifically in the Lombardy region of Italy.

Figure from On-Edge Deployment of Vision Transformers for Medical Diagnostics Using the Kvasir-Capsule Dataset

On-Edge Deployment of Vision Transformers for Medical Diagnostics Using the Kvasir-Capsule Dataset

Dara Varam, Lujain Khalil, Tamer Shanableh

MDPI Applied Sciences, 2024

Applying quantization and optimization techniques to a publicly available wireless capsule endoscopy dataset. Paired with a mobile application for deployment.

Figure from Variable Selection in Data Analysis: A Synthetic Data Toolkit

Variable Selection in Data Analysis: A Synthetic Data Toolkit

Rohan Mitra, Eyad Ali, D. Varam, Hana Sulieman, Firuz Kamalov

MDPI Mathematics, 2024

Follow-up study on developing synthetic datasets algorithmically for variable / feature selection algorithms.

Figure from Wireless Capsule Endoscopy Image Classification: An Explainable AI Approach

Wireless Capsule Endoscopy Image Classification: An Explainable AI Approach

Dara Varam, Rohan Mitra, Meriam Mkadmi, Radi Aman Riyas, Diaa A. Abuhani, Salam Dhou, and Ayman Alzaatreh

IEEE Access, Vol. 11 (IEEE Engineering in Medicine and Biology Society Section), 2023

We explore eXplainable AI techniques on a Wireless Capsule Endoscopy dataset (publicly available) to better understand model decision-making and classification.

Figure from Construction of Multiplicative Groups of Polynomials with Non-Zero Identities in Z_p[x]

Construction of Multiplicative Groups of Polynomials with Non-Zero Identities in Z_p[x]

Dara Varam, Ayman Badawi

American University of Sharjah Mathematics Theses, 2023

Explorinig construction of multiplicative groups of polynomials with non-zero identities.

Figure from A Hybrid Rolling and Flying Robot for Pipeline Fault Detection Using Deep Learning

A Hybrid Rolling and Flying Robot for Pipeline Fault Detection Using Deep Learning

Megan Ghaly, Rohan Mitra, Abdullah Al Rayess, Assem Ahmed, D. Varam, Mohammad A. Jaradat, Michel Pasquier, Ahmed Khalil

[IEEE] Advances in Science and Engineering Technology International Conferences (ASET), 2023

Designed, built and programmed a hybrid drone designed for detecting faults on the bodies of oil and gas pipelines.

Figure from Lumerical Simulation of Surface-illuminated Silicon PIN Photodiodes for Datacenter Interconnects

Lumerical Simulation of Surface-illuminated Silicon PIN Photodiodes for Datacenter Interconnects

S. Ghandiparsi, D. B. Hamadou, D. Varam, A. Soufi, T. Landolsi, A. F. Elrefaie, A. S. Mayet, C. B. Perez, E. P. Devine, S. Y. Wang, T. Yamada, M. S. Islam

[IEEE] International Conference on Communications, Signal Processing, and their Applications (ICCSPA), 2022

Studying different characteristics of two novel silicon photodiode (SiPD) devices at small- and large-scale.

Figure from Development of Synthetic Data Benchmarks for Evaluating Feature Selection Algorithms

Development of Synthetic Data Benchmarks for Evaluating Feature Selection Algorithms

Rohan Mitra, Dara Varam, Eyad Ali, Hana Sulieman, Firuz Kamalov

[IEEE] International Seminar on Machine Learning, Optimization, and Data Science (ISMODE), 2022

Developing synthetic datasets for benchmarking the performance of feature selection algorithms. First of two works that explores the field.

Submitted Papers

These are papers that have been submitted for publication, but have not yet been released.

Figure from FedPCD: Priority-Constrained Federated Aggregation

FedPCD: Priority-Constrained Federated Aggregation video demo

Y. Irshaid, D. Varam, M. I. AlHajri

Transactions on Machine Learning Research (TMLR), 2026

We propose FedPCD, a priority-constrained federated optimization framework that keeps the FedAvg direction as the primary objective while applying the minimum correction necessary to meet client performance constraints, achieving the highest global accuracy across 18 heterogeneous FL benchmarks.

Figure from Content Matters: Characterizing Reconstruction Behavior in Neural Video Representations

Content Matters: Characterizing Reconstruction Behavior in Neural Video Representations

Anonymous Submission

ACM International Conference on Multimedia (ACMMM), 2026

We investigate the content characteristics of video sequences to understand failure modes in Neural Representations for Videos.

Figure from Towards Deployable Neural Representations for Videos Using Quantization-Aware Training

Towards Deployable Neural Representations for Videos Using Quantization-Aware Training

Anonymous Submission

IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2026

We propose QAT-NeRV, a quantization-aware training framework for neural video representations that integrates quantization effects directly into the training process.

Figure from Neural Network Quantization for Microcontrollers: A Comprehensive Survey of Methods, Platforms, and Applications

Neural Network Quantization for Microcontrollers: A Comprehensive Survey of Methods, Platforms, and Applications

H. A. Abushahla, D. Varam, A. J. N. Panopio, and M. I. AlHajri

Artificial Intelligence Review, 2025

We comprehensively review the quantization landscape, specifically for microcontroller-class (MCUs) devices. The survey covers advanced quantization techniques, hardware platforms as part of three families (ARM-based, RISC-V-based and NPU-based), and the software toolchains.

Research Projects / arXiv

This includes other projects that I am currently working on, including arXiv submissions.

Figure from Machine Learning: A Soft Introduction

Machine Learning: A Soft Introduction

D. Varam,M. I. AlHajri

Unpublished, 2025

I’m in the process of writing a book on Machine Learning, to be hosted on GitHub. Compiled as part of my role as the sole TA for the Advanced Machine Learning (MLR570) graduate course offered at the American University of Sharjah.

Figure from Low-Bit, High-Fidelity: Optimal Transport Quantization for Flow Matching

Low-Bit, High-Fidelity: Optimal Transport Quantization for Flow Matching

D. Varam, D. A. Abuhani, I. Zualkernan, R. AlDamani, and L. Khalil

arXiv, 2025

We study the effects of an adapted “Optimal Transport”-based quantization on Flow Matching models, showing better preservations of generating quality and latent disentanglement at extreme low-bits (sub-4 bits).

Presentations, Features

Includes presentations conducted online and in-person, and other miscellaneous academic things I've been featured in.

Figure from UAE: How AI is a 'game changer' in academics and industry; educators urge 'honest' use

UAE: How AI is a 'game changer' in academics and industry; educators urge 'honest' use

Dara Varam

Khaleej Times, 2025

I was featured on an article regarding AI and its uses in academia. Feel free to take a read based on the website provided.

Figure from eXplainable AI for Image Classification Tasks in Medicine

eXplainable AI for Image Classification Tasks in Medicine

Dara Varam

Google I/O Extended, 2024

Spoke at the Google I/O Extended UAE 2024 keynote, organized through Google Developer Groups Sharjah.

Miscellanea

Teaching Assistantships

Teaching assistantship positions I held at the American University of Sharjah.

  • Graduate-level: MLR570 (×2) - Advanced Machine Learning — 70+ graduate students
  • Software: CMP120 / CMP220 / CMP305 / CMP320 – Programming I & II, Data Structures & Algorithms, Database Systems
  • Hardware: COE242 / COE251 – Microcontrollers & Embedded Systems, Intro. to Computer Systems
  • Networks: COE271 – Computer Networks I
  • Mathematics: MTH103 / MTH104 / MTH203 / MTH205 / MTH221 – Calculus I, II & III, Differential Equations, Linear Algebra

Awards & Honors

  • Winner of the Master’s Level – Graduate Student Research, Scholarly, and Creative Work Excellence Award @ AUS
  • Winner of the 2026 CEN Graduate Academic Excellence Award @ AUS
  • Winner of the 2026 CEN Graduate Research Excellence Award (MS Level) @ AUS
  • 1st place at the AI-City Quest Hackathon.
  • 2nd place at the Planet-X Mars Rover Mission challenge.
  • Member of the IEEE-Eta Kappu Nu Honors Society.
  • Member of the AUS Engineering Honors Society.