Tanav Singh Bajaj

Data Scientist

Making decisions based on vibes and p-values.

Publications

[1] KA-GMN: Breaking Attention Bottlenecks with Adaptive State Spaces for Heterogeneous Graphs

Eishkaran Singh, Tanav Singh Bajaj
GCLR Workshop at AAAI 2026 (Accepted)

[2] Circuit Complexity Limits of In-Context Learning

Eishkaran Singh, Tanav Singh Bajaj
WCTD Workshop at NeurIPS 2025 (Accepted)

[3] REAMS: Reasoning Enhanced Algorithm for Maths Solving

Eishkaran Singh, Tanav Singh Bajaj, Siddharth Nayak

[Arxiv]  

[4] A Graph Embedding Approach for Deciphering the Longitudinal Associations of Global Mobility and COVID-19 Cases

Raghav Awasthi, Meet Modi, Hardik Dudeja, Tanav Bajaj, Shruti Rastogi, Tavpritesh Sethi

[Medrxiv]  

Education

University of British Columbia

Masters of Data Science

August 2025 - July 2026

IIIT Bhopal

BTech in Information Technology

July 2020 - July 2024

Professional Experience

University of British Columbia

Teaching Assistant

September 2025 - Present
TA for Introduction to AI (CPSC 322), guiding students through search, logic, and probabilistic reasoning via revision sessions and one-on-one support.
Algorithmic ReasoningCommunication

Slice (Bank)

Data Scientist

Jan 2024 - July 2025
Managed and created credit risk models influencing $96M+ monthly GTV, with key contributions to fraud detection, ML monitoring, and CX automation.
Credit RiskData ScienceGraph Networks

Thapar University

Guest Lecturer

July 2023
Taught Deep Learning and Natural Language Processing for Prof PS Rana's students.
Deep LearningNatural Language ProcessingAcademic Mentorship

Internship Experience

HackerEarth,

SQL Intern

June 2022 - December 2022
PostgreSQLMSSQLMySQL

Applied Analytics,

Machine Learning Content Writer

May 2022 - July 2022
Machine Learning

Research Experience

Massachusetts Institute of Technology,

Research Intern under Prof. (Dr.) Hamsa Balakrishnan

Apr 2024 - August 2024
Developed a reasoning-based LLM approach for university-level math, setting a new benchmark accuracy of 90.15% (prev. 81.1%)
LLMs

Artificial Intelligence Institute of South Carolina,

Research Intern under Prof. (Dr.) Amitava Das

Sept 2023 - July 2024
Explored hallucination mitigation in LLMs via Stable Diffusion and built RAG/textual entailment baselines for real-world LLM applications.
Stable DiffusionRAGBERT

University of British Columbia,

Research Intern under Prof (Dr.) Tao Huan

May 2023 - Jan 2024
Conducted metabolomics research using deep learning on LC/MS data, achieving 95%+ detection accuracy with Gaussian Mixture noise filtering.
Machine LearningMetabolomics

University of Toronto,

Research Intern under Prof (Dr.) Zahra Shakeri Hossein Abad

May 2023 - Present
Collaborated with health experts to detect diabetes-related stigma using LLMs (85% accuracy) and analyzed hospital reviews with unsupervised BERT-based sentiment analysis.
NLPPublic Health

Indraprastha Institute of Information Technology,

Research Intern under Prof (Dr.) Tavpritesh Sethi

May 2022 - July 2023
Applied graph analysis for COVID cluster detection and built Meta's data viz dashboard (Docker, ELK) for 12GB+ survey data; also developed a synthetic data pipeline using GANs and Bayesian models.
Graph NetworksData Visualiation