Driven by a passion for leveraging machine learning to solve real-world challenges and advance the field of AI, I am a graduate research assistant at Mila and a master’s student at McGill University. Under the mentorship of Prof. Doina Precup, I focused on pioneering solutions in drug discovery. My work involved harnessing reinforcement learning and generative modeling techniques, particularly exploring the innovative Generative Flow Networks (GFlowNets) algorithms. These efforts resulted in the creation of diverse and high-quality candidates for drug discovery, showcased through publications at ICML and Neurips workshops in 2023.
Prior to my academic pursuits, I accumulated three years of industry experience at Shell as a software developer. During my tenure, I contributed to projects ranging from developing chatbots with natural language processing capabilities to designing document classifiers using bi-directional LSTMs.
My diverse skill set extends to various domains within machine learning, including developing classifiers using Visual Transformers (ViTs), crafting RL agents with policy gradient algorithms for task-solving, and building models for medical applications such as breast cancer severity detection and diabetic retinopathy detection in eyes. I’ve also spearheaded the development of real-time impact analysis dashboards using Power BI for chatbot performance evaluation.
Passionate about tackling real-world challenges through the application of ML/DL technologies, I am keen on exploring opportunities as an applied researcher/engineer in the ML/DL domain. I am open to learning and embracing emerging new technologies to drive meaningful impact.