Shalmoly Mondal, Research is my adventure, and knowledge is my treasure.

Latest Update

• Paper "Situation-based Query Generation for Performance Evaluation of Cloud Managed IoT Applications." accepted at The 24th IEEE International Conference on Mobile Data Management, 23.

• Paper "Situation-Aware IoT Data Generation towards Performance Evaluation of IoT Middleware Platforms." published at the Sensors Journal, 23.

• Selected for the Ewha-Luce International Seminar (ELIS): Expanding Horizons in 2022

• Paper "Modelling IoT Application Requirements for Benchmarking IoT Middleware Platforms" published at The 19th International Conference on Advances in Mobile Computing and Multimedia (MoMM ’21)

My Skills

Every accomplishment starts with the decision to try

Contact Me

Internet of Things

Data Analysis

Python

Technical Writing

About Me

Driven by Curiosity and Tech, Building a Smarter Future (with a dash of Art and Creativity!)

Motivated by a desire to create and solve, I navigate the ever-evolving landscape of technology, leading me to a Bachelor’s and Master’s degree in Computer Science. I recently completed my PhD in the Internet of Things. Eager to explore the potential of technology, I am constantly learning and pushing the boundaries of what’s possible. Currently, I am seeking to apply a unique blend of research, analytical, and technical skills to contribute to data science and drive digital transformation in cutting-edge technologies.

Well, data wasn’t always my thing. Through the exhilarating lens of my IoT-focused PhD, I delved into the power of raw sensor data, revealing patterns and fuelling a drive for impactful problem-solving. I successfully used sensor data to predict traffic congestion, optimized manufacturing, and enhanced smart city efficiency using machine learning. My work includes a system using machine learning (specifically SVM and Random Forest) recognizing worker activities, improving manufacturing processes. I have worked towards addressing night driving hazards, creating deep learning models trained on real-time camera data to identify faded road markings with 72% accuracy. I've also taken a stab at image processing by building a facial recognition system with 93.33% accuracy. Please explore my Projects page for in-depth insights.

Now, I apply my passion for data science in smart buildings, analyzing data from multiple sources to provide air quality recommendations. Transforming raw sensor data into user-friendly insights, I create intuitive monitoring dashboards, aiming to make the environment healthier, greener, and smarter.

Beyond tech, I'm a symphony of hobbies. I am a moderately skilled piano player, an art enthusiast, and an avid reader. When I am not working, I am either doodling, or hopping around Melbourne, exploring the culinary delights, and capturing moments along the way. Additionally, I enjoy doing Zumba to say fit. My motto? There's no such thing as "too many hobbies," just ones waiting to be discovered.

Feel free to reach out to me via email or follow me on my socials. Thank you, and I look forward to connecting with you!

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My devices I can't live without

MacBook Pro 14", M1, 2021

iPad Pro 12.9", M1, 2021

Apps and Tools I use on a regular basis

Coding: VS Code

Notes: One Note, Goodnotes, Notion

Photo Editing: Adobe Lightroom, Snapseed

Design: Sketch

Art: Procreate

Work Experience

IoT Data Analyst

(August 2023 – Current)

BlueIoT, Mulgrave, Melbourne

Duties and Responsibilities:

Designed and developed interactive air quality monitoring dashboards from scratch using React.JS, significantly enhancing user experience. The result is an improved, a more effective and user-friendly approach to summarize air quality information.

Analyzed raw sensor data from multiple sources and performed initial data pre-processing in Excel and conducted basic Exploratory Data Analysis (EDA) using Python libraries such as NumPy and Pandas.

Created interactive visualizations, including charts and graphs, to summarize the key insights from the sensor data.

Set up databases, and executed SQL queries to extract relevant data from Amazon Timestream and Dynamo DB.

Worked closely with the IoT and software development team using agile methodology.

Assisted in migrating existing infrastructure to AWS cloud by assessing the current infrastructure to determine feasibility and requirements for the migration.

Sessional Academic

(March 2020 – Current)

Swinburne University of Technology, Melbourne

Duties and Responsibilities :

Delivering lectures and tutorials for undergraduate and postgraduate courses in big data, IoT, data visualization, and programming (Ruby, JS, and Python).

Developing teaching materials, planning weekly tasks, grading assignments, and monitoring student progress through effective assessment strategies.

Supervising postgraduate projects in IoT, data science, and big data, providing guidance and mentorship to over 150 students throughout the process.

Units:

COS10009: Computer Systems 2021 - 2023

COS30045: Data Visualization 2020 - 2023

COS10004: Introduction to Programming 2021, 2022

COS80023: Big Data 2021, 2022

SWE30011: IoT Programming 2020

System Engineer

(March 2017 – April 2019)

Infosys Limited, Hyderabad, India

Duties and Responsibilities:

Worked on Oracle APPS as an SQL developer specifically focusing on their ERP systems.

Created logical and technical documents, including operational procedures and configuration guides to guide the development team.

Collaborated closely with cross functional teams, including clients to meet project goals, maintain project quality, and timely delivery.

Conducted thorough testing, troubleshoot, and bug fixing.

Received "Rookie of the Year" award - Q2, 2018.

Skills

Languages

Python, Git, ReactJS, R, Latex

Libraries

Data Analysis:

NumPy, Pandas, SciPy

Prophet, Auto ARIMA, Forecast

AI/ML:

scikit-learn, TensorFlow, Keras;

Data Visualization:

matplotlib, seaborn, ggplot2, plotly, d3.js

Frameworks

Flask, Tailwind, Node

Tools

Postman, Git, JIRA, Jupyter Lab, Notion, Logisim, Visual Paradigm, Adobe Lightroom

Cloud Technologies

AWS, Azure, FIWARE, Nectar Research Cloud

Databases

Microsoft SQL, MongoDB, Oracle, DynamoDB

Education

Ph.D. in Internet of Things

Swinburne University of Technology,

June, 2019 - June, 2023

Masters in Computer Science with SPEC. in Information Security

IIT (ISM) Dhanbad,

July, 2014 - May, 2016

Bachelors in Computer Science

Calcutta Institute of Technology,

July, 2010 - June, 2014

Projects

Project 1

Generating data for IoT applications based on situations of applications

This project delves into the fascinating realm of analyzing real-time data, generating realistic simulations, and evaluating their effectiveness in a practical scenario. The primary objective was to generate new data that mimicked the trends and patterns observed in the real-time stream.

React

Python

Node.js

Source Publication

Project 2

Road Marking Detection

Collaborated with Brimbank Council (Victoria) to develop AI models that detect faded bicycle lane road markings at night using real-time data from Nerian cameras. This tackles safety concerns and reduces nighttime accident risks. Our deep learning and computer vision approach achieved 72% accuracy.

Python

Deep Learning

Source

Project 3

High Accuracy Face Recognition System

This project addressed the challenge of face recognition under varying lighting conditions. I implemented a system utilizing eigenfaces and a distance classifier, achieving an initial accuracy of 93.33%. Further, I leveraged advanced image processing and statistical pattern recognition techniques in MATLAB. Notably, the application of Principal Component Analysis (PCA) for feature extraction significantly improved the system's accuracy by 16%. The research findings and contributions to the field of face recognition were presented and published in peer-reviewed journals and international conferences.

MATLAB

Python

Publication

Research And Academic Activities

Research Overview

My research primarily focused on developing methods to simulate synthetic data that mimic real-world scenarios. This involved applying advanced data analysis techniques to gain a deeper understanding of device-generated data. By analyzing real-world device data, these methods provided valuable insights into the performance characteristics of IoT applications.

My work encompassed several significant achievements:

Automated IoT Data Generation Framework: Designed and implemented a comprehensive framework to automate data generation for diverse IoT applications. This empowered researchers and app developers to simulate data for performance testing, significantly reducing manual data creation efforts.

Actionable Insights from Complex Data: Analyzed complex IoT data to extract valuable information, identify trends and patterns, and develop accurate forecasting models. These insights proved instrumental in understanding IoT application behavior and performance, leading to more informed decisions during development and testing phases.

Research Dissemination : Successfully disseminated research findings through publications in peer-reviewed journals and presentations at international conferences.

Supervision: Provided guidance and mentorship to junior researchers and master's students, ensuring the successful execution of research projects.

My research experience encompasses various projects in domains like transportation and manufacturing. These projects involved analyzing large, complex sensor data sets, and I also explored the application of machine learning models for object detection within these domains. Please explore my Projects page for more details.

Publications

Mondal, S.; Jayaraman, P.P.; Haghighi P.D.; Hassani, A.; Georgakopoulos, D. (2024). Introduction to Benchmarking IoT Middleware. Managing Internet of Things Applications across Edge and Cloud Data Centres(pp. 75-96). IET.

DOI

PDF

Mondal, S., Jayaraman, P. P., Delir Haghighi, P., Hassani, A., & Georgakopoulos, D. (2023). Situation-based Query Generation for Performance Evaluation of Cloud Managed IoT Applications. In 2023 24th IEEE International Conference on Mobile Data Management (MDM) (pp. 352-357). IEEE.

DOI

PDF

Mondal, S., Jayaraman, P. P., Delir Haghighi, P., Hassani, A., & Georgakopoulos, D. (2023). Situation-Aware IoT Data Generation towards Performance Evaluation of IoT Middleware Platforms. Sensors, 23(1), 7.

DOI

PDF

Mondal, S.; Hassani, A.; Jayaraman, P.P.; Delir Haghighi, P.; Georgakopoulos, D. (2021). Modelling IoT Application Requirements for Benchmarking IoT Middleware Platforms. In Proceedings of the 23rd International Conference on Information Integration and Web Intelligence (pp. 553-561), Linz, Austria;

Link

PDF

Mondal, S., & Bag, S. (2017). Face Recognition Using PCA and Minimum Distance Classifier. Advances in Intelligent Systems and Computing, 397–405.

Link

PDF

Presentation and Workshops

Presented a paper on query generation for IoT applications at the 24th IEEE International Conference on Mobile Data Management (MDM), Singapore, 2023 Link

Poster presented at the annual Australian Computer Science Week(ACSW), 2022

One of the 21 postgraduate women in STEM selected from 13 countries around the globe to participate in Ewha-Luce International Seminar (ELIS) in 2022.

Presented a paper at the "19th International Conference on Advances in Mobile Computing and Multimedia (MoMM ’21), Linz, Austria, November 29-December 1, 2021" Slides