Senior AI Scientist @ CIBC
ML Researcher @ Vector Institute
Education
Master of Applied Science (M.A.Sc)
University of Toronto
Specialization: Deep Learning, Natural Language Processing, Healthcare
Advisors: Prof. Bo Wang, Prof. Deepa Kundur, Prof. Yuri Lawryshyn
Research Interests
- Large Language Models
- Deep Learning
- Natural Language Processing
- Deep Reinforcement Learning
- Privacy Preserved ML
- Recommender Systems
Hi, I'm
About Me
I am a Senior AI Scientist at CIBC, CTO of Slate, and CEO & Managing Director of NeuroSage. Previously, I worked as a ML Researcher at WangLab affiliated with Vector Institute and University Health Network, proudly advised by Prof. Bo Wang.
I completed my Master's degree in ECE, specialized in Machine Learning at the University of Toronto advised by Prof. Deepa Kundur and by Prof. Yuri Lawryshyn. In my free time I work on projects at the intersection of Deep Learning, Natural Language Processing and Healthcare. Apart from that, I am also interested in Large Language Models and Deep Reinforcement Learning.
My ultimate goal is to build robust, privacy-preserved, and interpretable algorithms with human like ability to generalize in real-world environments by using data as its own supervision.
I am a Stream Owner and Discussion Group Lead of the "Machine Learning in Healthcare" stream at AISC (Aggregate Intellect), where we discuss one paper at a time every week from basics to state-of-the-art ML papers in HealthCare.
Throughout my life, I have approached every challenge with enthusiasm, creativity, and a ceaseless desire to achieve success. This passion and drive have paved the way to countless opportunities, unique experiences, and excellent relationships, both personally and professionally. I enjoy working with people and discussing ideas.
Experience
Senior AI Scientist
CIBC
- Technical Lead driving AI/ML strategy with full autonomy: presented architectural decisions to 4 Senior Directors, led 3 POCs end-to-end to production, mentored 4 Data Scientists/Engineers, managed 5 co-ops (2 converted to FT). Designed ML interview frameworks and led hiring panels.
- Owned end-to-end design, build, and deployment of enterprise-scale GenAI Chatbot using RAG, vector indexing, and prompt engineering, implementing LLM-as-a-Judge evaluation framework, now serving 50,000+ employees enterprise-wide.
- Led and architected a Multi-Agent AI System for Anti-Money Laundering (AML) investigations, orchestrating 7 specialized agents powered by Google Gemini 2.5 Pro/Flash, integrating external knowledge bases and real-time web grounding (Google Search API) to generate Suspicious Transaction Report (STR) recommendations, reducing analyst research time by 70%, with end-to-end observability via Langfuse.
- Spearheaded cloud migration of ML infrastructure to GCP with NVIDIA GPU clusters, owning architecture decisions for training, inference, and ETL pipelines, achieving 92% reduction in model training time (3 days to 6 hours).
- Independently designed and shipped production AI Agents using OpenAI SDK: Talent Acquisition Agent, HR Document Generation Agent (saving 900+ hours annually), Meeting Minutes Agent (saving 12 hours/week per team member).
- Owned end-to-end HR chatbot powered by GPT-4o, from architecture to deployment, reducing response time by 99% (15-20 minutes to seconds).
- Led Conversational AI initiative, building system with ASR and Neural Agent Assistance, reducing call-center volume by 25%. Published research at EMNLP 2022 Industry Track.
- Architected Strategic Workforce Management tool using Gaussian Deep Learning and Graph Neural Networks, enabling 3-5 year workforce forecasting with uncertainty quantification, reducing HR decision time by 40%.
- Owned RAG systems architecture using vector databases, improving information retrieval accuracy by 35%.
- Established LLMOps practices using MLFlow and Databricks ML, reducing model deployment time by 25%.
- Led development of NLP-based Search tools for AML investigations, reducing investigation time by 35%.
- Owned end-to-end self-served BERT pipeline for client feedback analysis, reducing 80% manual labor.
- Led LLM fine-tuning initiatives (GPT-3.5, Llama, Falcon) with prompt engineering, achieving 30% improvement in query accuracy.
- Architected and deployed high-performance Semantic Search Engine using Transformer models with data and model parallelism. Deployed using Docker and Kubernetes.
- Built DistilGPT-2 generative model for synthetic financial data generation, augmenting training datasets.
- Built Semantic Feedback Analysis Pipeline using NLP and embeddings for CIBC and ME Chatbot, enabling data-driven model improvements.
ML Researcher
WangLab - Vector Institute
- WangLab is affiliated with Vector Institute and University Health Network - Advised by Bo Wang
- Worked on a project at the intersection of Computational Biology, Deep Learning and Natural Language Processing.
- Applied Self Supervised Learning, Weak Supervision and Data Programming on MIMIC III database.
- Built Transformer based architecture model to improve the accuracy of massively multi-task classification problem.
Graduate Machine Learning Researcher
University of Toronto
- Responsible for developing and building cutting edge state of the art deep learning based recommendation system.
- Built a Deep Learning-based Recommendation System for Wolseley's e-commerce website from scratch to production.
- Dataset is massive involving more than 200,000 unique customers and 500,000 unique SKUs.
- Achieved a personalized NDCG score of 72.4% and improved the One-Product Hit Ratio to 100%.
Data Science/Machine Learning Intern
Cybersecurity Research
- Worked with cybersecurity professionals and built a defense mechanism using deep learning and unsupervised learning techniques to prevent cyberattacks.
- Built Unsupervised Auto Tagging algorithm and Automatic Rule Synthesis for Octavius.
- Investigated an Automatic Rule synthesis algorithm for Octavius using Deep Reinforcement Learning to improve overall defense mechanism.
- Devised a Deep Learning model that detects and prevents the cyberattack before it happens (ProjectX).
- Used NLP, Neural Networks, Knowledge Graphs for Keyword Extraction, etc.
Projects
LLM & Agentic AI
Large Language Models, RAG Systems, and AI Agents
Coming Soon
Currently building projects exploring LLM applications, Agentic AI systems, and RAG architectures. Stay tuned for exciting new work in this space!
Research & Past Work
Deep Reinforcement Learning, Computer Vision, and ML Projects
Research Publications
Bringing the State-of-the-Art to Customers: A Neural Agent Assistant Framework for Customer Service Support
Building Agent Assistants that can help improve customer service support requires inputs from industry users and their customers, as well as knowledge about state-of-the-art Natural Language Processing (NLP) technology. We combine expertise from academia and industry to bridge the gap and build task/domain-specific Neural Agent Assistants (NAA) with three high-level components for: (1) Intent Identification, (2) Context Retrieval, and (3) Response Generation. In this paper, we outline the pipeline of the NAA core system and also present three case studies in which three industry partners successfully adapt the framework to find solutions to their unique challenges. Our findings suggest that a collaborative process is instrumental in spurring the development of emerging NLP models for Conversational AI tasks in industry. The full reference implementation code and results are available at https://github.com/VectorInstitute/NAA.
arXiv
@inproceedings{obadinma-etal-2022-bringing,
title = "Bringing the State-of-the-Art to Customers: A Neural Agent Assistant Framework for Customer Service Support",
author = "Obadinma, Stephen and
Khan Khattak, Faiza and
Wang, Shirley and
Sidhorn, Tania and
Lau, Elaine and
Robertson, Sean and
Niu, Jingcheng and
Au, Winnie and
Munim, Alif and
Kalaiselvi Bhaskar, Karthik Raja",
editor = "Li, Yunyao and
Lazaridou, Angeliki",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = dec,
year = "2022",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-industry.44",
doi = "10.18653/v1/2022.emnlp-industry.44",
pages = "440--450",
abstract = "Building Agent Assistants that can help improve customer service support requires inputs from industry users and their customers, as well as knowledge about state-of-the-art Natural Language Processing (NLP) technology. We combine expertise from academia and industry to bridge the gap and build task/domain-specific Neural Agent Assistants (NAA) with three high-level components for: (1) Intent Identification, (2) Context Retrieval, and (3) Response Generation. In this paper, we outline the pipeline of the NAA{'}s core system and also present three case studies in which three industry partners successfully adapt the framework to find solutions to their unique challenges. Our findings suggest that a collaborative process is instrumental in spurring the development of emerging NLP models for Conversational AI tasks in industry. The full reference implementation code and results are available at \url{https://github.com/VectorInstitute/NAA}.",
}
Implicit Feedback Deep Collaborative Filtering Product Recommendation System
In this paper, several Collaborative Filtering (CF) approaches with latent variable methods were studied using user-item interactions to capture important hidden variations of the sparse customer purchasing behaviours. The latent factors are used to generalize the purchasing pattern of the customers and to provide product recommendations. CF with Neural Collaborative Filtering(NCF) was shown to produce the highest Normalized Discounted Cumulative Gain (NDCG) performance on the real-world proprietary dataset provided by a large parts supply company. Different hyperparameters were tested using Bayesian Optimization (BO) for applicability in the CF framework. External data sources like click-data and metrics like Clickthrough Rate (CTR) were reviewed for potential extensions to the work presented. The work shown in this paper provides techniques the Company can use to provide product recommendations to enhance revenues, attract new customers, and gain advantages over competitors.
arXiv
@article{raja2020implicit,
title={Implicit Feedback Deep Collaborative Filtering Product Recommendation System},
author={Raja Kalaiselvi Bhaskar, Karthik and Kundur, Deepa and Lawryshyn, Yuri},
journal={arXiv e-prints},
pages={arXiv--2009},
year={2020}
}
Technical Skills
Machine Learning
PyTorch
TensorFlow
Keras
Scikit-learn
MLflow
LLMs & NLP
LangChain
LlamaIndex
HuggingFace
Spacy
Programming
Python
SQL
Git
Linux
Spark
Data Processing & Analysis
Numpy
Pandas
SciPy
PySpark
Visualization
Plotly
Seaborn
Matplotlib
Tableau
Web Development
Streamlit
Flask
AngularJS
NodeJS
Bootstrap
Cloud & DevOps
Azure
AWS
GCP
Docker
Kubernetes
Database & Vector Search
Pinecone
Ray
MLOps & LLMOps
Model Lifecycle
Experiment Tracking
Observability
Monitoring
Research Areas
Deep Learning
LLMs
Generative AI
Reinforcement Learning
Recommendation Systems
Conference Talks & Presentations
Deep Learning in HealthCare and its Practical Limitations
Deep learning uses statistical techniques to give computer systems the ability to learn with incoming data and to identify patterns and make decisions with minimal human direction. Armed with such targeted analytics, doctors may be better able to assess risk, make correct diagnoses, and offer patients more effective treatments. Deep Learning has a lot of potential in Healthcare. But why don't these techniques are adopted in hospitals yet? What are the gaps between academic research and production level code in Deep Learning and Healthcare? How can we mitigate this production level gap in Deep Learning and Healthcare, and what are some of the tools and techniques we can deploy?
History of Deep Learning
Why DeepLearing for Healthcare?
Practical Limitations
Research vs Production
Data Augmentation
Data Synthesis
Pretraining
Deep Learning Engineering
ML Lifecycle
Watch Talk
Let's Connect
I'm always interested in discussing AI research, collaboration opportunities, or just connecting with fellow enthusiasts in the field.