Abdhesh Dash

Associate Consultant

Delhi, India0 mo experience

Key Highlights

Developed efficient algorithms using CUDA-C++.
Experience in quantitative finance and risk management.
Assisted in teaching advanced mathematics courses.

Stackforce AI infers this person is a Quantitative Analyst with expertise in data processing and financial modeling.

Contact

Skills

Core Skills

University TeachingQuantitative FinanceParallel Computing

Other Skills

Python (Programming Language)CUDAPostgreSQLRecommender SystemsStock Market AnalysisAcademic PublishingNFHS-5graph clusterCausal InferenceNumerical OptimizationHedgingX-Value Adjustment (XVA)Index OptionsOptionsStochastic Calculus

Experience

Indian institute of technology, delhi

Undergraduate Teaching Assistant

Jul 2025 – Present · 8 mos · New Delhi, Delhi, India · On-site

MTL107 (Numerical methods) and MTL1002 (Linear Algebra and Differential equations) courses. Working directly under the professors and helping them with the smooth execution of their courses. Starting from taking tutorial classes to directly helping students solve questions, resolving their doubts, checking and grading of quiz and exam answer copies, and helping professors with the smooth conduct of exams in the course.

University Teaching

Wells fargo

Quantitative Analyst

May 2025 – Jul 2025 · 2 mos · Bengaluru, Karnataka, India · On-site

QAP intern analyst at Wells Fargo. I have joined the Traded Products Model Development, which is responsible for developing models for pricing, hedging, and official risk management. These models are used by various Wells Fargo trading desks.

Python (Programming Language)Quantitative Finance

The hong kong university of science and technology

Research Internship

May 2024 – Jul 2024 · 2 mos · Hong Kong, Hong Kong SAR · On-site

An efficient parallel Primary key-to-Primary key (Pk-Pk) join algorithm using CUDA-C++. Leveraging the CUB and Thrust libraries, the algorithm incorporates techniques such as BSP (Bulk Synchronous Parallel) sorting and Prefix sum. The performance of the implementation is evaluated against the Crystal library by Anil Shanbhag using identical datasets, demonstrating notable improvements in efficiency and execution time.

CUDAPostgreSQLParallel Computing