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My name is Aamir H. and I have over 16 years of experience in the tech industry. I specialize in the following technologies: Electrical Engineering, Machine Learning, Statistics, Data Science, PCB Design, etc.. I hold a degree in , , Doctor of Philosophy (PhD), Other. Some of the notable projects I’ve worked on include: Variational-Based Nonlinear Bayesian Filtering with Biased Observation, Outlier-Robust Filtering for Nonlinear Systems, Bayesian Heuristics for Robust Spatial Perception, AutoTAP (Smart Sprinkler System), Vending Machine Controller, etc.. I am based in Lahore, Pakistan. I've successfully completed 14 projects while developing at Softaims.
I employ a methodical and structured approach to solution development, prioritizing deep domain understanding before execution. I excel at systems analysis, creating precise technical specifications, and ensuring that the final solution perfectly maps to the complex business logic it is meant to serve.
My tenure at Softaims has reinforced the importance of careful planning and risk mitigation. I am skilled at breaking down massive, ambiguous problems into manageable, iterative development tasks, ensuring consistent progress and predictable delivery schedules.
I strive for clarity and simplicity in both my technical outputs and my communication. I believe that the most powerful solutions are often the simplest ones, and I am committed to finding those elegant answers for our clients.
Main technologies
AI, IoT and Hardware Platforms
16 years
Electrical Engineering
5 Years
Machine Learning
6 Years
Statistics
8 Years
Additional skills
- Electrical Engineering
- Machine Learning
- Statistics
- Data Science
- PCB Design
- Internet of Things
- Deep Learning
- Algorithm Development
- Hardware Prototyping
- Microcontroller Programming
- Embedded System
- Mathematics
- MATLAB
- Digital Signal Processing
Direct hire
Potentially possible
Previous Company
Systems Limited
Experience Highlights
Variational-Based Nonlinear Bayesian Filtering with Biased Observation
State estimation of dynamical systems is crucial for providing new decision-making and system automation information in different applications. However, the assumptions on the standard computational models for sensor measurements can be violated in practice due to different types of data abnormalities such as outliers and biases. In this work, we focus on the occurrence of measurement biases and propose a robust filter for their detection and mitigation during state estimation of nonlinear dynamical systems. We model the presence of bias in each dimension within the generative structure of the state-space models. Subsequently, employing the theory of Variational Bayes and general Gaussian filtering, we devise a recursive filter which we call the Bias Detecting and Mitigating (BDM) filter. As the error detection mechanism is embedded within the filter structure its dependence on any external detector is obviated. Simulations verify the performance gains of the proposed BDM filter compared to similar Kalman filtering-based approaches in terms of robustness to temporary and persistent bias presence.
Outlier-Robust Filtering for Nonlinear Systems
Considering a common case where measurements are obtained from independent sensors, we present a novel outlier-robust filter for nonlinear dynamical systems in this work. The proposed method is devised by modifying the measurement model and subsequently using the theory of Variational Bayes and general Gaussian filtering. We treat the measurement outliers independently for independent observations leading to selective rejection of the corrupted data during inference. By carrying out simulations for variable number of sensors we verify that an implementation of the proposed filter is computationally more efficient as compared to the proposed modifications of similar baseline methods still yielding similar estimation quality. In addition, experimentation results for various real-time indoor localization scenarios using Ultra-wide Band (UWB) sensors demonstrate the practical utility of the proposed method.
Bayesian Heuristics for Robust Spatial Perception
Spatial perception is a key task in several robotics applications. In general, it involves the nonlinear estimation of hidden variables that represent the state of the robot/environment. However, in the presence of outliers the standard nonlinear least squared formulation results in poor estimates. Several methods have been considered in the literature to improve the reliability of the estimation process. Most methods are based on heuristics since guaranteed global robust estimation is not generally practical due to high computational costs. Recently general purpose robust estimation heuristics have been proposed that leverage existing non-minimal solvers available for the outlier-free formulations without the need for an initial guess. In this work, we propose two similar heuristics backed by Bayesian theory. We evaluate these heuristics in practical scenarios to demonstrate their merits in different applications including 3D point cloud registration, mesh registration and pose graph optimization.
AutoTAP (Smart Sprinkler System)
AutoTAP is a smart irrigation system that we developed from end-to-end. Our developed prototype, a smart water management solution, utilizes the concept of internet of things (IoT) infrastructure to optimize the water usage for gardening and small-scale farming. We aimed to increase the functionality of our sensing and controlling nodes by incorporating various sensing, communication, and computation capabilities, with the goal to make it a futuristic product to save water and promote sustainable practices in domestic household water usage. I contributed in the hardware and firmware development of the project and subsequently in the assembly of the product using 3d printing.
Vending Machine Controller
28 Motor Vending Machine Controller. Description: The product developed in this project aims to control the drive of 28 motors of a vending machine. At the heart of the controller lies an ESP32 chip which communicates with the client application over wireless. Since the requirements were to drive 28 motors with limited IOs of ESP32 we have used four I/O expander modules which communicate with the ESP32 chip over an I2C protocol. Tot drive the motors 28 motor drivers were implemented onboard simultaneously. Deliverables: The deliverables of the projects were: An hardware designed comprises of the following components: • ESP32 • IO expanders • Buzzer • An external RTC • Power and motor driving circuitry • PCB design and testing An embedded firmware developed for esp32 with functionalities mentioned in description above.
Education
Bachelor's degree
in Electrical engineering
2007-01-01-2011-01-01
Master's degree
in Electrical engineering
2015-01-01-2018-01-01
Lahore University of Management Sciences
Doctor of Philosophy (PhD) in Electrical engineering
2018-01-01-2023-01-01
Univerisity of Illinois Urbana Champaign (UIUC)
Other in Signal/Control
2024-01-01-2024-01-01
Languages
- English
Github