Hi! 👋 I’m a 4th year computer science student at UC Davis with a broad interest in computer vision & machine learning and how they can be applied to solve important societal problems.

I am an undergraduate research assistant at UC Davis focused on self-supervised representation Learning and adversarial robustness. Previously I worked on applications of ML in healthcare at Stanford. I also spent some time at Twitch/Amazon as an Applied Scientist where I worked on unsupervised and continual visual representation learning on streaming video data.

Experience

Twitch/Amazon 3X Internship 📺

• 2022 (Applied Science): Developed unsupervised, continual learning framework on Twitch Streaming data. Framework allows for Machine Learning models developed by Twitch to be continuously and intelligently updated on new streams such that a model always stays up to date, learning new representations without forgetting old ones.

• 2021 Fall (Software Engineer): Designed & developed an end-to-end, real-time Media-Analysis backend service integrated with both other AWS services and my trained Image Embedding to moderate Twitch’s 9.36M+ streamers’ content. Service is in production!

• 2021 Summer (Applied Science): Developed a Twitch content representation Image Embedding trained using self-supervised learning. Improved prior method’s performance on downstream tasks such as Game Stream Classification by ~10%.

Undergraduate Research Intern 💻

• Research @ UC Davis under Dr. Hamed Pirsiavash

  • Project 1: SlowFormer: Universal Adversarial Patch for Attack on Compute and Energy Efficiency of Inference Efficient Vision Transformers.
    [In Subission in ICCV 2023].

• Research @ Stanford University under Dr. Dennis Wall and Dr. Peter Washington.

  • Project 1 (First Author): Deep Learning-Based Autism Spectrum Disorder (ASD) Detection Using Emotion Features From Video Recordings.
    [Published in JMIR 2022].
  • Project 2: Novel Facial Emotion Recognition Dataset: “TikTok for Good: Creating a Diverse Emotion Expression Database”.
    [Published at CVPRW 2022].
  • Project 3: Mitigating Negative Transfer in Multi-Task Learning with Exponential Moving Average Loss Weighting Strategies.
    [Published at AAAI Student Abstract 2023]

Publications

SlowFormer: Universal Adversarial Patch for Attack on Compute and Energy Efficiency of Inference Efficient Vision Transformers
In Submission: ICCV 2023. [Processing: Email me for access to pdf.]

Deep Learning-Based Autism Spectrum Disorder Detection Using Emotion Features From Video Recordings
Essam Sleiman; Onur Cezmi Mutlu; Saimourya Surabhi; Arman Husic; Aaron Kline; Peter Washington; Dennis P. Wall
JMIR 2022. [pdf]

TikTok for Good: Creating a Diverse Emotion Expression Database
Saimourya Surabhi,…,Essam Sleiman, Dennis P. Wall
Computer Vision and Pattern Recognition (CVPR) Workshop, 2022. [pdf]

Mitigating Negative Transfer in Multi-Task Learning with Exponential Moving Average Loss Weighting Strategies.
Anish Lakkapragada, Essam Sleiman, Mourya Surabhi, Dennis P. Wall
AAAI 2023 (Student Abstract)

Continually Learning Self-Supervised Image Embeddings
Essam Sleiman, Xiangbo Li, Saad Ali, Lukas Tencer
2022 [Processing: Email me for access to pdf.]