Zana Rashidi

I'm a Senior Machine Learning Engineer in the New Frontiers team at Chubb focusing on Large Language Models. Prior to Chubb, I was a Senior Data Scientist at Scotiabank working on applied NLP.

I graduated from my M.Sc. in Computer Science from York University where I was advised by Aijun An in the Data Mining Lab. I completed my B.Sc. in Computer Engineering at Sharif University of Technology, working with Mahdi Jafari Siavoshani in the Information, Network, and Learning Lab.

My research interests include ML fundamentals, natural lanaguage processsing, optimization, reinforcement learning, generative models and computer vision. On the applied side, I'm interested in ML problems at scale, modelling, training, deployment and evaluation.

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Publications

	GPU Resource Scheduling Using CNN-based Deep Reinforcement Learning Xingye Fan, Zana Rashidi, Bon Ryu, Hossein Pourmodheji, Aijun An, Junfeng Liu, Yonggang Hu. CSSE, 2021 paper DQN-based resource management system for a multi-GPU, multi-machine cluster.
	Towards Topology Aware Pre-Emptive Job Scheduling with Deep Reinforcement Learning Bon Ryu, Zana Rashidi, Aijun An, Junfeng Liu, Yonggang Hu. CSSE, 2020 paper Topology-aware Deep RL scheduler for distributed Deep Learning in a GPU cluster.
	AMoC: Adaptive Momentum Coefficient for Neural Network Optimization Zana Rashidi, Kasra Ahmadi, Aijun An, Xiaogang Wang, ECML, 2020 paper / video / github Novel momentum-based first-order optimization algorithm for neural networks.
	Paywall Policy Learning in Digital News Media Heidar Davoudi Zana Rashidi, Aijun An, Morteza Zihayat, Gordon Edall, TKDE, 2020 paper Dynamic digital media paywall policy based on Deep Q-Learning and Monte Carlo sampling.
	Decoupling the Layers in Residual Networks Ricky Fok Aijun An Zana Rashidi, Xiaogang Wang, ICLR, 2018 paper Novel deep learning architecture based on ResNet for faster training and inference.

Projects

Transcription Factor Binding
github

Transcription Factor (TF) binding preference prediction using deep neural networks.

Loopy Belief Propagation using CUDA
B.Sc. Project, 2017
github

Open-source implementation of Loopy Belief Propagation algorithm using CUDA for GPU.

Reviewing Experience

AAAI 2021, 2018
ECML 2020 (Program Committee), 2019
IJCAI 2020, 2018
KDD 2019
ICDM 2020, 2019, 2018

Workshops

Deep Learning & Reinforcement Learning Summer School 2018

Miscellaneous

Outside of my passion for ML, you will often find me climbing, biking, reading a book, or just hanging out with my cat (Yuki). I've dabbled in playing guitar, photography and boxing too!

Template borrowed from Jon Barron