Akash Venkateshwaran Portfolio

I am Akash Venkateshwaran

Name: Akash Venkateshwaran

Email: akash.venkateshwaran@gmail.com
akashv22@student.ubc.ca

About me

Hello! I am passionate about bridging engineering and machine learning (ML) to solve complex, real-world challenges. With a focus on optimizing complex systems, my work involves applying ML models and simulating dynamic, adaptive systems that interact with and respond to changing environmental conditions.

Currently, I am advancing decision support systems for ship navigation, leveraging data-driven modeling of the oceanic environment to promote sustainability and efficiency. My experience spans designing and implementing deep learning solutions alongside physics-based simulations, enabling seamless integration of ML into engineering applications.

I thrive on exploring new methodologies and am continually driven to expand my skill set, aiming to create innovative and impactful solutions. If you're interested in the intersection of ML and engineering or data-driven problem-solving, let’s connect!

Education

University of British Columbia

2022 – Present

Vancouver, Canada

M.A.Sc. in Mechanical Engineering, Grades: 90.2%

Thesis: Adaptive ship optimization framework using machine learning: Sustainable and eco-friendly marine operations for marine mammals
Coursework: Computational Optimization, Machine Learning, Industrial Robotics and Deep Learning with Graph
Honors and Awards: Mitacs Globalink Graduate Fellowship, International Tuition Award

Vellore Institute of Technology

2018 – 2022

Chennai, India

B.Tech in Mechanical Engineering, Grades: 9.62/10

Honors and Awards: Achieved 5th rank in the mechanical engineering department

Experience

Graduate Research Assistant

September 2022 – Present

Computational Multiphysics Laboratory (CML) - University of British Columbia, Vancouver, Canada

Collaborated with Clear Seas and marine consultation company JASCO to develop a Decision Support System (DSS) aimed at reducing ship noise signature footprints in the Salish Sea. Designed and trained a GPU-enabled Conditional Convolutional Neural Network (RC-CAN) on UBC ARC Sockeye HPC clusters for far-field acoustic modeling. Developed an automated data pipeline to streamline the simulation process, enabling parallel execution of numerical simulations to enhance computational efficiency and support diverse scenario testing. Integrated the RC-CAN model into the DSS for real-time 3D noise signature mapping and ship route optimization.

Data Science Intern

September 2024 – December 2024

IPEX Technologies Inc. - Mississauga, Canada

Designed and implemented multiple Gaussian Process Regression (GPR) models to predict polymer material properties based on recipe ingredients and processing conditions. Conducted extensive data preprocessing, including cleaning, formatting, and feature engineering, to optimize the representation of key variables. Achieved significant improvement in predictive accuracy of models from naive models (e.g., the R² score for modulus of elasticity prediction improved from 0.63 to 0.82).

Software Engineer

September 2023 – September 2024

UBC Sailbot - Vancouver, Canada

Led the optimization team, formulating the optimization problem, identifying the design variables and constraints, and implementing software for an autonomous sailboat. Developed a decision-making algorithm leveraging sensor data fusion for autonomous navigation of sailbots. Created various test cases, performance benchmarks, and convergence studies to assess different optimization algorithms.

Mitacs Globalink Research Intern

June 2021 – August 2022

Environmental Hydro-Dynamics (EHD) Laboratory - York University, Toronto, Canada

Simulated surge waves in multiphase environments using OpenFOAM, focusing on turbulent structures and vortex dynamics. Developed statistical analysis algorithms for Reynolds stress tensor and conducted comparative simulations. Documented and published findings in journals and conferences.

Research Intern

September 2020 – March 2021

Integrated MechanoBioSystems Lab - National Cheng Kung University, Tainan, Taiwan

Developed a cervical cell segmentation model using Mask R-CNN, achieving high classification accuracy. Utilized deep learning for segmentation of cell regions, achieving over 0.95 accuracy with low variance.