Oracle — Senior Member of Technical Staff
Knowledge-grounded AI using GraphRAG, ontologies, property graphs, post-training, and agentic workflows.
Vritansh Kamal
I am from Kullu, a hill town in Himachal Pradesh. I completed my B.Tech. in Computer Science and Engineering at SRM Institute of Science and Technology and my M.S. in Data Science at Columbia University.
I work across machine learning, data science, and AI systems. My experience spans healthcare, telecom, search, graph analytics, and enterprise software.
VK
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Research interests
I am interested in reliable and useful machine-learning systems, particularly where learning, reasoning, and decision-making must work under real constraints.
- How can deep reinforcement learning improve decisions in structured, high-stakes environments?
- How can post-training make language models more capable, efficient, and dependable?
- How can neural models reason over knowledge graphs and symbolic constraints?
- How can we identify and mitigate bias while improving fairness and interpretability in ML systems?
- How can medical AI quantify uncertainty before supporting decisions in high-risk settings?
- How can applied mathematics, optimization, and graph algorithms improve modern AI systems?
Education & affiliations
Industry experience
Axheleon — Senior Search Data Scientist
Semantic retrieval, indexing, and search relevance for information systems.
CVS Health — Data Scientist
Forecasting, pricing and underwriting analytics, geospatial routing, and claims data.
Oracle — Member of Technical Staff
NLP search, graph visualization, ETL, and analytics systems for telecom data.
Verizon — Member of Technical Staff
ML classification, automation, and AI-ops analytics.
Deloitte & Tata Consultancy Services
Enterprise software, data visualization, and technology consulting.
Teaching experience
Teaching Assistant II, Quantitative Methods in the Social Sciences
Supported quantitative-methods instruction and student learning as a Teaching Assistant II.
Research experience
GE HealthCare — Student Data Scientist, Capstone Research
Researched Conditional DETR for lesion detection from volumetric PET/CT data and evaluated transformer-based models against YOLO baselines.
Graph-guided language-model systems
Designed ontology-guided manager–planner–executor architectures for grounded, traceable multi-step reasoning.
Large-scale graph visualization and analytics
Built visual and analytic workflows for large graphs using force-directed layouts, clustering, and graph algorithms.
Advanced Big Data and AI — Digital Humans
Explored research on digital humans and emerging generative methods, including 2D GANs, diffusion models, neural radiance fields (NeRFs), and Gaussian splatting. Studied monocular 3D reconstruction and facial-animation pipelines from monocular video, with particular attention to lip-synchronization objectives and lip-sync loss. The work culminated in a completed lip-sync generation project that generated synchronized facial video from speech.
HIPPO — Founding Member & Research Assistant
Contributed to collaborative research and applied AI exploration as a founding member and research assistant.