Maryam Bafandkar, MSc
Tagline:AI Product Manager | Sessional Faculty UofW | Co-Founder Faculty.Bio | Speaker
Canada
Welcome to my Webpage
I'm Maryam Bafandkar, a creative, energetic, tech-savvy entrepreneur and AI expert. I love to connect with people and share our thoughts on business, technology and AI.
Feel free to connect with me and book me for a short chat.
Licenses & Certifications
Product Management Projects
Faculty.Bio - Professional Website Builder for Academics
date: 2023Organization:http://Faculty.Bio/
Description:As a product owner, I was involved with research marketing, working on most sections of the Canvas Business Model, finding user requirements and designing the product strategies, features, and sustainable roadmaps. Developed AI features for SEO purposes.
Clinical user-centric application - ML-based Clinicians Support Decisions Assistant
date: 2020Organization:NRC
Description:As a member of the product and development team, After 6 months of meeting with clinicians and collecting data, we developed a practical application with 96% accuracy which is used in the Montreal Hospital.
Owwwlab - International Web Design Agency
date: 2014Description:As a Co-founder of the company, I was involved in market research, defining the product roadmap, and assigning tasks to the development team. I led the team and managed project finances.
Work Experiences
Product Owner and Co-founder
from: 2024, until: presentOrganization:Faculty.BioLocation:Canada
Description:Impact: Built an online academic platform that’s growing organically with more than 400 hundred users globally.
Achieved: Received 800k funding from Canadian Accelerators, such as NorthForge, and The Forum.- Communicating with academics and industry partners to find out about the pain points, needs and priorities.
- Analyze market trends, competitors, and customer needs to inform strategic decision-making
- Utilize data-driven insights to inform decision-making and strategy development for the founder teams.
- Implement data analysis tools and techniques to extract meaningful business intelligence.
- Facilitate jobs like Sprint Planning and Daily Scrums, and remove roadblocks to complete their tasks efficiently. Track progress, analyze user data, and measure the product’s impact to ensure it delivers value throughout its lifecycle
AI Program Manager
from: 2022, until: 2023Organization:Protein Industries CanadaLocation:Canada
Description:- Designed and Developed an AI Feasibility Assessment Template to measure and score the readiness of AI-based projects from Food and Agricultural companies.
- Lead AI/ML projects from International food and smart-agriculture companies.
- Evaluated AI proposals and projects from mid to large-scale plant-based food and Smart Farm companies.
- Designed AI and Machine Learning Analytics roadmaps for clients based on readiness, aspirations and vision.
** Impact: Managed 5 diverse AI plant-based projects from large and mid-sized companies in Canada and the US.
AI Instructor, Big Data Platform and Machine Learning Course
from: 2021, until: 2024Organization:University of WinnipegLocation:Manitoba, CA
Description:Impact: Got a 92% satisfaction average rate from my students for those 3 years of teaching.
- Developed and delivered comprehensive courses on Machine Learning and Big Data Platforms, with a strong focus on Advanced ML models, like N.N, CNN, Generetic AI Models, LLMs, Google Cloud Platform (GCP), Microsoft Azure Data Analytics Tools, and Amazon Web Services (AWS).
- Designed and implemented practical exercises and labs to reinforce theoretical knowledge.
- Organized data science competitions and hackathons for computer science students.
AI Technology Analyst
from: 2021, until: 2022Organization:Enterprise Machine Intelligence & Learning Initiative(EMILI)Location:Canada
Description:- Impact: Authored 170-page compendium exploring the past decade’s advancements in AI-driven agricultural innovation.
- Provided Innovative Technologies in Precision Agriculture documents by bringing critical research, analysis, and communication with different academia and industrial organizations.
- Communication with partners for potential machine learning deployments in the agriculture sector bringing critical research, analysis, and writing skills.
- Supported the various projects within EMILI with technical advice regarding artificial intelligence and/or machine learning to solve the digital agriculture challenges.
Machine Learning Engineer
from: 2020, until: 2021Organization:National Research Council Canada / Conseil national de recherches Canada(NRC)Location:Montreal, Canada
Description:Impact: Implemented a web-based AI application to assist doctors in making better decisions for patients with COVID-19.
- Designed and developed a web-based clinical machine-learning application using Flask, React.js, HTML, and Python.
- Provided data preparation and data analytics on the Sepsis dataset collected by the clinics in Montreal.
- Implemented machine learning Explainability algorithm using Python, LIME and Dash
- Developed the machine learning explainability models
Deep Learning Researcher
from: 2019, until: 2021Organization:TerraByte research groupLocation:Winnipeg, Manitoba, Canada (full-time)
Description:- Designed and developed deep learning algorithms that worked for digital agriculture purposes.
- Implemented the Machine Learning model based on mathematics and computer science algorithms with a reasonable result on a large dataset of crops and weeds images which are prepared by the Terrabyte team at the University of Winnipeg.
- Prepared an academic paper to publish my experiments on Deep Learning architectures to distinguish between highly similar crops and weeds.
- Impact: Developed ML models to recognize highly similar plant images and published the models and results
Co-founder Owwwlab.com
from: 2016, until: 2020Organization:Envato MarketLocation:Remote US
Description:Impact: Achieved the Elite award on the Envato online market, sold around 7K items worth $700K in 4 years.
- Led all sprint planning, execution monitoring, and delivering the tasks of the projects
- Developed Website templates with desirable UI/UX aligned with market trends.
- Managed 5 different teams and 3 different products with high-efficiency
Recognized design on awwwards.com
Freelance Web Developer
from: 2009, until: 2011Organization:FreelancerLocation:Iran
Description:-
Client Acquisition and Needs Assessment:
- Proactively identified and secured clients by understanding their unique web development needs, conducting thorough consultations, and proposing tailored solutions that align with their business goals.
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Custom Theme Design and Admin Panel Development:
- Designed and developed custom website themes that perfectly align with clients’ branding and requirements. Additionally, created and customized user-friendly admin panels to ensure seamless content management and site maintenance.
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Skills
- Communication and Collaboration
- Product Management
- Leadership
- Agile Methodologies
- Data Analytics and Research
- Scientific Writing and Communication
- User-Centric Approach
- Data Management and Analysis
- GCP and AWS
- Discipline and Consistency
- Deep Learning Frameworks for building and training complex computer vision models.
- Python, Tenserflow, LIME, Flask, Javascript, React, CSS, HTML
Technical AI Projects
Machine Learning Generalist and Specialists CNN Network
date: 2021Organization:Mitacs, Terrabyte, University of Winnipeg
Description:Automatically distinguishing different types of plant images is a challenging problem relevant to both Botany and Computer Science disciplines. Plant identification at the species level is a computer vision task called fine-grained categorization, which focuses on differentiating between hard-to-distinguish object classes. This classification problem is complicated and challenging because of the lack of annotated data, inter-species similarity, the large-scale features in appearance, and a large number of plant species. A plant classification system capable of addressing the complexity of this computer vision problem has important implications for society at large, not only in public computer science education but also in numerous agricultural activities such as the automatic detection of cash crops and non-crop plants (called weeds).
Explaianle AI on X-Ray Chest Images for COVID-19 Patients
date: 2021Organization:NRC
Description:Worked with NRC, JRH hospital and the health community to combat COVID-19 in Canada by using my background knowledge in web designing and developing and utilizing Data science and explainable approaches to chest X-ray images.
A practical COVID-19 prediction and interpretation have been developed to assist the clinician in predicting the severity of the COVID-19 virus for each patient.Data Visualization Project
date: 2020Description:The goal of this project was to visual and crops and weeds images to choose the right images and delete the wrong ones. The impact of project increased the accuracy on the test data by 26%.
After data visualization and clearing process, we split the data into training, validation and test dataset.Exploring Advanced Machine Learning Models for Image Feature Extarction
date: 2020Description:Image feature extraction CNN Architectures on VGG, Resnet, InceptionNet, and XceptionNet. A Gold mine dataset for computer vision is the ImageNet dataset. It consists of about 14 M hand-labelled annotated images which contain over 22,000 day-to-day categories. Every year ImageNet competition is hosted in which the smaller version of this dataset (with 1000 categories) is used with an aim to accurately classify the images. Many winning solutions of the ImageNet Challenge have used state of the art convolutional neural network architectures to beat the best possible accuracy thresholds. In this kernel, I have discussed popular architectures such as VGG16, 19, ResNet, AlexNet etc. In the end, I have explained how to generate image features using pre-trained models and use them in machine learning models.
Data Preprocessing on Big Plant Dataset
date: 2020Organization:Univerrsity of Winnipeg- TerraByte Team
Description:Worked on a million number of labelled plant images. The following steps have been applied:
- Re-labeling images were done to decrease the number of classes, hence, simpler
classification problem; - Filtering plants by age criteria to remove possible noise from the dataset;
- Grouping was done to generate a hierarchical structure of labels.
- Re-labeling images were done to decrease the number of classes, hence, simpler
Tree-CNN Deep Learning Models
date: 2019Organization:Univerrsity of Winnipeg
Description:In a hierarchical CNN classifier, the upper nodes classify the input images into subgroups
[109]. Then, deeper levels classify deeper discrimination, for example, for fruit or human face
classification problem. The upper nodes classify fruit or human face images into subgrouping
like yellow-coloured objects together or human faces together [109]. Then, deeper nodes
classify bigger differences, such as "lemon" v/s "orange" fruits or "old man," v/s "kid boy,"
human faces. Study [109] proves that hierarchical CNN models perform at par or even
better than standard CNNs. In tree-based or hierarchical CNN structures, initial layers of
a CNN from the top of structure learn very general features [87] that have been exploited
for transfer learning [110, 86]. [108] used the hierarchical CNN’s structure to deal with
distinguishing the similar classes. They designed multi-layer hierarchical CNNs where an
abstract higher-level network initially determines which subnetwork a sample should be
directed to. Lower level networks are designed to find discriminating features amongst
similar classes in the subnetwork. Each sub-network is called a class assignment classifier.Digital Agriculture Review
date: 2019Description:To formulate a definition of the concept of digital agriculture, we consider the stages of
agricultural development. Agriculture 1.0 is the first level of agriculture that was based on the
use of manual labour (early 20th century). Agriculture 2.0 is called the "Green Revolution"
(the late 1950s), when fertilizers, pesticides began to be actively used [55]. Fertilizer is a
natural or artificial substance containing the chemical elements that improve the growth and
productiveness of plants [66]. Moreover, a pesticide is any substance used to kill, repel,
or control certain forms of plant or animal life that are considered to be pests. Pesticides include herbicides for destroying weeds and other unwanted vegetation, insecticides for
controlling a wide variety of insects, fungicides used to prevent the growth of moulds and
mildew, disinfectants for preventing the spread of bacteria, and compounds used to control
mice and rats [65].
Moreover, pesticide contamination moves away from the target plants, resulting in
environmental pollution. Such chemical residues impact human health through environmental
and food contamination [104]. It is generally accepted that pesticides play an important role
in agricultural development because they can reduce the losses of farm products and improve
the affordable yield and quality of food [3, 96]. Fertilizers and pesticides are a necessary evil
for industrial agriculture [76]. They have adverse effects on soil, plants, environment, and
human health [11]. Nevertheless, high efficiency in the agricultural sector is not currently
possible without herbicides and pesticides [14]. Also, there is a high demand to produce
more agricultural foods and products to meet the growing population. It is vital to have
precise agriculture with less waste and sustainable outcomes [45].
After agriculture 2.0, using fertilizers and pesticides, came agriculture 3.0 called precision
agriculture (the 1990s and 2000s), and agriculture 4.0 called digital agriculture (early 2010s)
[55]. They utilize digital technologies and technical means in agricultural production making
it possible to bring exact measurements to a new level when information on all agricultural
processes and operations exist in digital form, and, at the same time, the transfer, processing
and analysis of data are automated.
The development of technological advances has been growing year by year [88]. Using
digital agriculture to classify plants is one of the trends [50]. Crop and weeds can largely
vary within the same field. Identifying weeds and removing them from the field contributes
significantly to the final yield. Digital agriculture allows scanning large areas of a plant and
distinguishes between weeds and crops. In recent years, research into digital agriculture
using machine learning methods has become an active area of study. [47] believed weeding
is an effective way to increase crop yields. Their focus is on improving weed and crop
recognition accuracy on weed dataset [59]. They found CNN [33] are favourable for multiclass
crops and weeds recognition with limited labelled data in agricultural recognition tasks.
Read more on my Article on the Link below.Plant Seedling Classification Using Deep Transfer Learning
date: 2020Description:Using a public dataset of 4,234 plant images from the Aarhus University Signal Processing group in collaboration with the University of Southern Denmark, that consists of descriptions under a controlled condition concerning camera radiance and stabilization. The project aims to solve the problem of plant seedling classification problems utilizing the transfer leaning technique by exploring three famous Convolutional Neural Network models, namely AlexNet, VGG16, and Xception CNN architectures to pick the right model and provide solutions to increase its performance and accuracy. Thisproject is compared with the ISI paper worked on the same dataset but with different approaches, published in 2019. The trained model achieved 97% accuracy during testing. The tuned CNN candidate model can significantly deal with overfitting problems to improve the performance of the model.
Teaching History
Big Data Platforms for AI Program at the University of Winnipeg
From: 2021, Until: 2023
Organization:University Of Winnipeg, PACEField:Computer Science
Description:Developed and delivered comprehensive courses on Big Data Platforms, with a strong focus on Google Cloud Platform (GCP) and Amazon Web Services (AWS).
Conducted engaging and interactive classroom sessions, offering in-depth instruction on Big Data Platform principles, GCP, and AWS. Utilized hands-on labs, real-world examples, and case studies to enhance student comprehension.
Designed and implemented practical exercises and labs to reinforce theoretical knowledge. Fostered a hands-on learning environment that encouraged experimentation and problem-solving.
Foundation of Data Science
From: 2021, Until: 2023
Organization:Univerrsity of WinnipegField:Computer science
Description:Lectured the course Foundation of Data Science
Designed course outline and course material (including lectures, labs and activities)
Conducted engaging and interactive classroom sessions, offering in-depth concepts on Statistical Machine Learning models and Python Libraries. Utilized hands-on labs, real-world examples, and case studies to enhance student comprehension.
Business brain? Yep.
I don’t just speak tech, I speak business too!
- I’m enthusiastic about uncovering people’s challenges and crafting solutions that bridge the gap.
- I’m a connector at heart! I love connecting with people, building relationships learning from people and exchanging ideas.
- I love to speak/talk in public, sharing my knowledge and experiences and having people’s feedback
Leadership and Business Projects
Faculty.Bio - Professional Website Builder for Professional Academic People
date: 2023Organization:FacultyBio
Description:- Run Faculty.Bio Business as a Co-Founder and CEO.
- Lead Webdeveloper, Marketing, Sale people
- Manage and organize Faculty.Bio project which is a big online platform.
- Work on collaboration and partnership of project
- Work on business and marketing strategies
AI Program Manager
date: 2022Organization:Protein Industries Canada
Description:- Assessed business needs to align business initiatives with information technology and AI solutions.
- Provide support to the business units and training/documentation on these technology solutions.
- Leading different big-size projects at the same period of time
- Assessed the business plan of projects based on the feasibility, cost and their values for Canadian customers.
Honors & Awards
The Forum E-Series Brusary
date: 2024-04-30Issuer:The Forum
Northforge - Entrepreneurship program
date: 2023-08-01Issuer:NorthForge
Description:I had my initial experience successfully deviating a pitch fro Faculty.Bio, and I’m so honored that I’ve accepted the position of Program Founder at NorthForge. North Forge is a vibrant incubator accelerator and entrepreneur community that empowers innovative science-based, technology-enabled, and advanced manufacturing startups.
Mitacs Accelerate Fellowship
date: 2019-06-01Issuer:Mitacs
Description:I am honored to have been selected as a recipient of the MITACS Accelerate Fellowship, a prestigious recognition that speaks to my commitment to advanced research and collaboration within the academic and industrial realms. The MITACS Accelerate Fellowship is a testament to the merit of the collaborative initiatives undertaken during my academic pursuits.
As a MITACS Accelerate Fellow, I have embarked on a journey of impactful collaboration, engaging in cutting-edge research projects that not only contribute to the academic discourse but also address industry-specific issues. The fellowship has provided me with a unique opportunity to work alongside leading experts in both academic and industrial settings, promoting knowledge exchange, innovation, and the application of research outcomes to solve real-world problems.
Being awarded the MITACS Accelerate Fellowship is a recognition of my dedication to collaborative, applied research that transcends traditional academic boundaries. I am grateful for the support and opportunities provided by MITACS, and I look forward to further contributing to the advancement of knowledge and innovation through this prestigious fellowship.
Top student
date: 2019-02-01Description:I took 2 important courses, named Pattern Recognition and Computer Vision at the University of Winnipeg. I got A+ for both courses. Among 15 students I got selected as a top student for the winter semester 2019.
Toranj - Responsive Creative HTML Template
date: 2008-09-01Description:Toranj is trying to bring the combination of beauty and power to the table and it’s suitable for a wide range of applications. For Creative Portfolio, Photography, Videography, Digital Agency, Interior Design to Personal Blogging and Magazine and even Creative Corporate websites, Toranj has the features alongside a modern design.
Tech Stratup the Week in BC
date: 2024-06-05Issuer:Tech BC
Description:🎉🎉 Today Faculty.Bio has been selected as the hashtag#TechStartup of the Week by BCTech! 🎉🎉
Education
Master's degree
from: 2019, until: 2022Field of study:Applied Computer ScienceSchool:The University of WinnipegLocation:Manitoba, Canada
DescriptionExploring Deep Neural Networks for Plant Image Classification
Bachelor's degree
from: 2004, until: 2010Field of study:Computer Software EngineeringSchool:Islamic Azad University,Science And Research Branch
DescriptionModeling RFID Systems and Security and Privacy Implications
Publicaion
Exploring Deep Neural Networks for Plant Image Classification
ThesisPublisher:University of WinnipegDate:2022Authors:Description:Deep Convolutional Neural Networks (CNN) can be a solution to perform this computer vision task. In this thesis, seven different CNN models are deployed to classify 1 million images - from the TerraByte dataset - of eleven very similar plant species [13]. This robust approach divides the problem into two main steps: the first step, called the generalist, identifies similar plants and separates them into different groups that contain indistinguishable plant species. The second step, called specialist, is used to classify plants within the groups of indistinguishable plants, including five weed and seven crop species, with high accuracy. The generalist-specialist CNN network shows that the hierarchical network outperforms simple CNN models in terms of accuracy and classifying similar plant images. The contributions of this thesis are the exploration of different CNN models and the improved performance of those models by designing and implementing the generalist-specialist CNN models for classifying similar plant images.
Research Interests
- AI in Business Development
- Technical Business Analyst
- AI Product Manager
- Successful Tech Startups
- Technology and AI Management
- Canadian Data Governance
- Economic and Social Impacts of ML on Society
- Cellurar and Regenerative Agriculture