I’m an aspiring ML Engineer and Data Scientist, currently an intern at Cognichip and also a member of the Neurocognitive Computing lab at UWaterloo where we delve into the inspiring field of computational neuroscience. With over five years of experience in machine learning and software engineering, I’m currently pursuing a Bachelor's in Mathematics at the University of Waterloo, graduating Spring 2026! I'm passionate about Ethical AI and bridging AI with fields like cognitive science(my minor). Currently, I’m working on projects involving agentic AI, RAG pipelines and their evaluation, leveraging my skills in Python and machine learning frameworks.

Agentic Systems, RAG, Agent Evaluation, Ethical AI, Explainable AI, interdisciplinary applications of AI in neuroscience and cognitive sciences, biomedical engineering, as well as other fields of engineering, LLMs, Gen AI, machine learning, software development, &data science.

Hi, My name is Olivia. I'm currently seeking 2026 New Grad role in Software Development/Engineering. 🎓 Pursuing a Bachelor of Computer Science (BCS) degree from the University of Waterloo (Graduating April 2026). 🏠 Previously worked as a Software Developer Intern at Amazon, General Motors, Dayforce, Opentext. Experience working with large teams within Agile environment. 💻 Proficient in several programming languages, including C, C++, Java, Python, JavaScript, HTML, and CSS. 📚 Ability to easily pick up new technologies, frameworks, and tools with current experience in Spring, React.js, Flask, Node.js, PostgreSQL, Git, Docker, Postman, JIRA, and Confluence. 🌟 Top strengths include strong critical thinking, communication, curiosity, creativity, and continuous learning.

ML engineering, prompt engineerjng

trying to add new vocabularies to a LLM.

Hi, my name is Priyadarshini Saha (you can call me Priya). I'm a recent graduate from of Computer Science with an AI specialization at the University of Waterloo. Currently, I work as a research assistant at the NeuroCog lab at the University of Waterloo. My research revolves around computational neuroscience, wherein, I work on building computational models of the basal ganglia (a part of the brain responsible for action execution). Previously, I have worked as an AI/ML engineer at AiStrike Inc., building LLM RAG agents for a cybersecurity platform, as a Software Engineer at Manulife for the Legal & Compliance team, Software Developer at TD, RBC, and LTIMindtree.

AI/ML, Cryptography, Quantum Computing, Computational Neuroscience

Shruti Srivatsan is a Data Analyst at StarTech.com with a Master’s degree in Data Science from the University of Waterloo. She specializes in machine learning, data science, and Retrieval-Augmented Generation (RAG), and is passionate about building innovative AI-driven solutions. Her work spans projects in multimodal AI, medical AI, and applied AI use cases, focusing on developing systems that combine RAG and Agentic AI to deliver intelligent, real-time insights. Beyond her technical expertise, Shruti is interested in product management and enjoys generating creative ideas that bridge technology with business impact.

Data Science, Generative AI, Applied AI, RAG, AI Agents, Automation

I am currently researching a variety of topics, including BMI detection using Generative AI and the usage of AI in the MBTI personality test. Some of my earlier projects are AI Product Description Mapping, Golden Contacts Retrieval, FabAgent(Fabric Optimization), Women Health Risk Calculators, Multimodal Dementia Detection, Credit Card Attrition with GraphML.

Taiqian Zhang (he/him) is a double major in Combinatorics & Optimization and Pure Mathematics with a Computing Minor at the University of Waterloo. His interests include algorithm design, graph algorithms, and combinatorial optimization, with applications in theoretical computer science. Through AI internships at Coastal Carbon, he developed U-Net models for image segmentation, leveraged the Segment Anything Model to generate additional high-quality training data for kelp recognition, automated data pipelines and evaluated models such as ReShift and DDPM, analyzing trade-offs between inference speed and complexity. With strong skills in Python, C/C++, and PyTorch, Taiqian combines theoretical rigor with practical problem-solving.

I am interested in theoretical computer science, combinatorial optimization, optimization algorithms, and AI applications in research.

Working on contextual inverse optimization, combining optimization and AI methods to handle non-smooth and complex scenarios in data-driven applications