Passionate about leveraging AI and software development to solve complex challenges. Experienced in building predictive models, interactive web applications, and data-driven solutions.
I contribute to a research initiative that explores how emotions impact decision-making, with a particular focus on long-term memory. My work centers on analyzing language data to understand the relationship between emotional word norms and their usage in text. I develop scripts that examine how emotional characteristics of words relate to their log frequency and position within a corpus. As part of this project, I design and implement regression models that correlate emotional norms with linguistic features. This analysis helps uncover how emotionally charged language influences memory and decision-making processes, offering insights into the cognitive mechanisms behind emotional interpretation in language.
At Medium AI, I was actively involved in the full-stack development of a medical scribe platform, leveraging React and JavaScript to optimize user experience and scalability. In collaboration with the AI research team, I contributed to the design and implementation of a DirectRAG model that transformed transcripts into precise, medical-grade documentation. This role allowed me to merge advanced AI technology with intuitive design, addressing real-world challenges in medical documentation
At Northbridge Insurance, I focused on developing AI-driven solutions, particularly in the realm of chatbot and data automation. I built an intelligent chatbot designed to translate natural language into SQL queries, using open-source large language models (LLMs) and advanced prompt engineering techniques. By integrating Retrieval-Augmented Generation (RAG), the chatbot could clarify user inputs and offer adaptive responses, enabling seamless interaction with complex databases. Leveraging the Cortex API, I enhanced the chatbot's precision by 40%, ensuring it provided contextually accurate, data-driven answers. This innovation improved internal data accessibility, empowering users to make informed decisions efficiently. Additionally, I deployed these advancements through intuitive web applications with Streamlit and React, optimizing user experience and workflow productivity
As an Internal Outreach Director, I lead initiatives to engage and inspire students within our organization, fostering a collaborative and inclusive environment. I coordinate and promote events that bridge interdisciplinary interests in computer science, AI, and ethics, helping students connect with opportunities, gain insights, and develop a network for career and academic growth. My role involves liaising with team members to organize impactful events, streamline communications, and build lasting connections within the community.
I am a Computer Science, Cognitive Science and Mathematics student at UofT. I have a strong foundation in programming, data science, and machine learning, combined with a passion for AI applications. With hands-on experience in data engineering, web development, and cognitive modeling, I excel at creating solutions that bridge technology and practical needs. I am currently working as a Student Research in the field of AI and Cognitive Science.
Proficient in React and Streamlit, building interactive, responsive UIs. Improved user experience by 25% through intuitive design and functionality integration.
Skilled in REST APIs, SQL, and Azure Data Factory (ADF), streamlining data pipelines and enhancing database performance with Snowflake.
Experienced in PyTorch, TensorFlow, scikit-learn, and advanced data visualization with Power BI, specializing in predictive modeling and data analysis.
Expertise in open-source LLMs, prompt engineering, and Retrieval-Augmented Generation (RAG), achieving a 40% improvement in chatbot answer precision.
Enhanced report clarity and engagement using Power BI and visual analytics, delivering actionable insights through custom dashboards for decision-making.
Experience with system design for projects like a solar-powered heating system, optimizing water heating and purification, and designing scalable back-end systems.
Proficient in Python, Java, C++, SQL, and MATLAB, with experience in leveraging these languages for machine learning, data analysis, and software development projects.
Experienced with computational modeling, analyzing cognitive processes, optimizing model parameters, and validating findings through simulations, with experience in interdisciplinary research and creating clear visualizations.
Python • PyClarion • Cognitive Modeling • SNN
Developed a cognitive model for concept learning using the Clarion architecture, explaining human concept processing and categorization by integrating sensory, and linguistic representations through an associative merging mechanism.
Python • LSTM • Neural Networks • Cognitive Science • Sleep Research
Designed and implemented a Python-based simulation model utilizing LSTM neural networks to analyze the effects of sleep on DRM false memory formation, successfully replicating empirical study findings.
Python • RNN • LSTM • Machine Learning • Data Analysis
Developed a machine learning model using RNN and LSTM to predict F1 driver lap times and positions, achieving 80% accuracy and predictions within 3 seconds of target lap times.
Python • Machine Learning • Statistical Analysis
Enhanced an IRT model for individualized learning assessments, achieving a 90% improvement in accuracy by optimizing model parameters and algorithms.
C++ • Web Development • Image Processing • Server Architecture
Built a C++ server application enabling users to upload photos and apply filters via a user-friendly web interface, optimizing the image processing workflow for enhanced performance.
Java • REST APIs • Collaborative Development • User Interface Design
Developed a recipe search application in Java with a collaborative team, integrating APIs and user-driven contributions to boost daily active users by 40%.