Danial Maleki

Enthusiastic and technologically proficient professional, with a diverse portfolio that illustrates a deep knowledge and broad experience in state-of-the-art design, creation, and optimization of Machine Learning (ML) models especially Self-Supervised Learning (SSL) Knowledge Distillation, Computer vision and NLP. My career is a testament to my prowess in data analysis and visualization, as well as database and web development. As an analytical problem solver, I have been at the forefront of crafting and implementing world-class learning models to drive efficiency and breathe life into innovation. I am recognized for my thought leadership and strong communication skills that have consistently steered teams toward success. Core competencies include:

Artificial Intelligence, Deep Learning, Natural Language Processing, Image Processing and Computer Vision, Exploratory Data Analysis, Data Visualization, Team Building, Strong Communication, Thought Leadership, Advanced Programming, Testing & Troubleshooting Scripting, Database Systems

My professional portfolio is well-rounded, spanning from machine learning to deep learning and computer vision. Leveraging proficiency in Python, R, and C++, I have effectively designed and implemented machine-learning solutions. Moreover, my hands-on experience with PyTorch, TensorFlow,

and Keras has allowed me to utilize cutting-edge frameworks to their fullest. Furthermore, my familiarity with LLMs and transformer-based architectures such as BERT and GPT, coupled with my adeptness in cloud computing platforms like AWS and Google Cloud, have enabled me to create scalable models.

Beyond these technical skills, I bring a robust academic foundation with a Master's degree in Machine

Learning from a top-tier university. Currently, I am pursuing a Ph.D., focusing on Machine Learning,

further honing my knowledge in this field. I have also supplemented my education with practical coursework in Deep Learning and Medical Imaging, Pattern Recognition, Machine Vision, Neural

Networks, Data Analytics, and Social Networks.

My current role as a Researcher at Mayo Clinic has offered me an opportunity to put my skills into practice. There, I developed and implemented a deep learning model for cross-modal information retrieval, significantly enhancing patient diagnostic outcomes. Collaborating closely with medical professionals, we have made substantial strides in improving retrieval accuracy and efficiency.

During my tenure as a Machine Learning Scientist Intern at 1QBit, I co-developed an AI-driven method to optimize delivery routes, marking a clear reduction in costs and improving delivery times. In another cross-functional project, we used advanced optimization techniques to increase efficiency and reduce costs significantly.

My experience as a Graduate Research Assistant at the KIMIA Lab, University of Waterloo, afforded me the opportunity to develop the LILE cross-modal retrieval method, yielding performance superior to the prevailing state-of-the-art results. I also co-developed KIMIANet, a medical image search engine published in the Medical Image Analysis Journal. My contributions significantly boosted the accuracy and efficiency of medical image retrieval.

PhD in Machine Learning, MSc in Computer Science focused on Machine Learning, BSc in Computer Engineering