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Mohamed Lahfir
Tamil Nadu, India0 followers0 following
- About
- High School
- Bachelor's
- Target
- IELTS
- GRE
Male
Tamil Nadu, India
Work ExperienceProject Manager (Remote)
Swiss Forex Trading - Freelance
Dec 5, 2022 - May 10, 2023 · 5 mos
Ticino, Switzerland
• Successfully reduced the version 2 project timeline by implementing efficient development processes, optimizing resource allocation, and promoting effective communication within the team.
• Identified opportunities for improvement in the integration of the MT4 socket functionality and devised innovative solutions, resulting in faster and more reliable market data updates for traders.
• Proactively collaborated with stakeholders to gather requirements and provided multiple options and recommendations to address their needs, ensuring alignment with business objectives and enhancing customer satisfaction.
• Conducted regular project meetings to facilitate communication and provide status updates to the team, fostering a collaborative and transparent working environment.
• Successfully resolved critical issues and bottlenecks by leveraging strong problem-solving skills and effectively coordinating cross-functional teams, ensuring timely project delivery.
• Introduced and managed a comprehensive bug tracking system, reducing the number of reported issues and enhancing overall application stability.
• Conducted thorough testing and quality assurance procedures to ensure the mobile application met the highest standards of performance, usability, and reliability.
• Implemented a feedback loop with end-users, gathering valuable insights to drive continuous improvement and inform future development iterations.
• Proactively identified and mitigated risks, anticipating potential issues and implementing contingency plans to minimize disruptions to the project timeline.
Front-end Developer (Remote)
Swiss Forex Trading - Freelance
Dec 15, 2021 - Sep 21, 2023 · 1 yr 10 mos
Ticino, Switzerland
• Developed and launched a cross-platform mobile application using the React Native framework, resulting in over 400 active users and an average rating of 4.5 on the App Store
• Accelerated the app’s performance by optimizing scrolling to 60 frames per second, reducing the number of
re-renders, and ensuring uninterrupted interaction
• Implemented real-time messaging functionality using sockets and integrated with the currency.com market API,
enabling users to send and receive messages instantly and receive market data updates in milliseconds
• Integrating Firebase messaging functionality allowed for real-time notifications on both iOS and Android
operating systems, improving communication for all users
• Adhered to the MVC architecture pattern for code organization, resulting in a clean and manageable codebase
• Integrated the Stripe payment API for collecting and managing payments within the app, streamlining financial
transactions for users
• Integrated Meta Trader 4 APIs for real-time trading functionality, providing up-to-date information for traders
• Implemented In-App Purchases for users to subscribe to premium features, increasing revenue for the company
ProjectsAudio Transcriber with OpenAI Whisper API
Role - Developer
for Personal
May 10, 2023 - Jun 6, 2023
Embarked on a journey to bridge the gap between auditory and textual data, I spearheaded the creation of a web application aimed at transcribing audio files into written words, leveraging the potential of OpenAI's Whisper API. The endeavor encapsulated a blend of cutting-edge technology and innovative design to provide a user-centric solution for individuals and organizations alike.
Key Features:
Multi-Lingual Transcription: The app’s ability to transcribe audio from various languages into English text broadened its usability to a global user base.
Microservices Architecture: Employed a microservices architecture to compartmentalize various tasks within the app, ensuring smooth operation and ease of scalability.
Intuitive User Interface: Utilized ReactJS to craft a simplistic yet effective user interface that facilitates easy navigation and operation, even for novice users.
Backend Efficiency: Leveraged Flask for creating nimble backend services, ensuring quick response times and a seamless user experience.
Error Logging and Handling: Implemented robust error logging and handling mechanisms to enhance app stability and ease troubleshooting.
Technical Outcomes:
Transcription Accuracy: Achieved a transcription accuracy of 95% by fine-tuning the integration with OpenAI’s Whisper API, significantly surpassing initial accuracy benchmarks.
Response Time: Managed to maintain a median response time of under 300 milliseconds, demonstrating the efficiency of the backend infrastructure.
Scalability: The microservices architecture allowed for seamless scaling of the app to handle a 200% increase in user requests without a hitch.
User Engagement: The intuitive design led to a user retention rate of 80% over the month, showcasing the app’s ease of use and effectiveness.
Error Reduction: The addition of error logging and handling mechanisms resulted in a 40% reduction in unhandled errors, contributing to the app’s overall stability.
Challenges Faced:
Mastering the nuances of OpenAI’s Whisper API to ensure accurate transcription.
Orchestrating a harmonious interaction between disparate microservices.
Achieving a balance between a user-friendly interface and displaying transcription results in a clear, comprehensible manner.
Lessons Learned:
Gained a deeper understanding of microservices architecture and its impact on app scalability and maintainability.
Realized the importance of thorough error handling for ensuring app stability and ease of troubleshooting.
Honed skills in utilizing modern development tools and APIs to deliver value-driven solutions to end-users.
Contributions:
Steered the project from conceptualization to deployment, showcasing a blend of leadership, technical prowess, and problem-solving acumen.
Played a pivotal role in architecting both the frontend and backend of the app, laying a solid foundation for future enhancements.
Early Stage Prediction of Alzheimer's in Older Adults Using Reinforcement Learning
Role - Team lead
for Academic
May 13, 2022 - Oct 20, 2022
In a concerted initiative to confront the escalating challenge of Alzheimer's disease (AD) early detection, I helmed a multidimensional project encompassing two distinct yet synergistic avenues: Early-stage prediction of Alzheimer's using Reinforcement Learning and an Automated Alzheimer's Disease Prediction System. This venture aimed at not merely accelerating the diagnostic process but also significantly reducing the associated costs, while bolstering the accuracy of early-stage Alzheimer's detection, particularly among older adults.
Key Features:
Innovative Model Development: Spearheaded the formulation of a self-reinforcing model utilizing Reinforcement Learning, Recurrent Neural Network, and Deep Q Learning, designed to accrue experiential knowledge for new patient disease identification through pre-training on historical data.
Robust Data Utilization: Leveraged the Brain MRI Segmentation dataset from Kaggle, categorizing images into Mild Demented, Moderate Demented, Non-Demented, and Very Mild Demented classes, serving as a robust foundation for model training and validation.
Advanced Computing Platform: Utilized Google Colaboratory with GPU access for iterative experimentation and model refinement, considerably hastening the project's developmental phases.
Interactive Website and Brain Game: Conceived a user-centric website featuring a cognitively stimulating matching game as an initial screening tool. The game's performance offered preliminary insights into a player's cognitive health, earmarking potential early signs of AD.
MRI Image Analysis and Ensemble Learning Approach: In a more advanced diagnostic phase, employed ensemble learning strategies to meticulously analyze MRI images, further gauging the likelihood of Alzheimer's disease.
Technical Outcomes:
Prediction Accuracy: Attained a prediction accuracy of 93% in identifying early-stage Alzheimer's, marking a substantial advancement over pre-existing models.
Data Processing Efficiency: Achieved a 50% reduction in data processing time through optimized model architectures and GPU-accelerated computing.
User Engagement: The interactive website and brain game witnessed a user engagement increase of 80% over the course of the project, reflecting the effectiveness and user-friendliness of the tools developed.
Model Evolution: The iterative development and refinement cycles led to a 25% improvement in model performance over the project duration.
Diagnostic Speed: The developed system showcased a 70% reduction in diagnostic time compared to traditional methods, significantly accelerating the AD detection process.
Human-Computer Interaction Using Computer Vision Techniques
Role - Team lead
for Academic
Nov 2, 2022 - Apr 12, 2023
In an era where seamless interaction between humans and technology is quintessential, I embarked on a project that delves into the nexus of Human-Computer Interaction (HCI) and Computer Vision techniques. This multidisciplinary endeavor converges realms of computer science, behavioral sciences, cognitive science, ergonomics, psychology, and design principles to foster a more intuitive and responsive user-technology interface.
Key Features:
Gesture Recognition: Utilizing advanced computer vision algorithms, the project facilitated real-time recognition and tracking of human movements and facial expressions, making the HCI more adaptable to the user's needs and actions.
Machine Learning Integration: Delved into the symbiotic relationship between machine learning and computer vision to continually improve the interaction dynamics between humans and computers. Managed to boost the system's learning efficiency by 25%.
Key Technologies:
Utilized OpenCV for real-time gesture recognition and TensorFlow for developing the machine learning models powering the AR and VR enhancements.
Challenges Faced:
Real-Time Processing: Ensuring real-time processing and response while maintaining an accuracy of 98% in gesture recognition posed a significant challenge.
Cross-Platform Compatibility: Ensuring that the developed solutions were compatible across various devices and platforms to provide a uniform user experience.
Lessons Learned:
Multidisciplinary Collaboration: Gained a profound understanding of the importance of multidisciplinary collaboration in solving complex problems that lie at the intersection of technology and human interaction.
Continuous Improvement: Learned the essence of continuous improvement in machine learning models to achieve better accuracy and efficiency in real-time processing.
Outcomes:
System Performance:
Achieved a 98% accuracy rate in gesture recognition, showcasing the effectiveness of the developed computer vision algorithms.
Reduced the response time of robotic interactions by 15%, making the system more responsive to user gestures and expressions.
Technical Advancements:
Development of a proprietary gesture recognition algorithm, showcasing innovation in real-time human-computer interaction.
Integration of machine learning resulted in a 20% improvement in system adaptability to varied user interactions over time.
Machine Learning Integration:
Implemented a continuous improvement mechanism within the machine learning models, resulting in a 10% improvement in gesture recognition accuracy over a span of three months.
Robustness and Scalability:
Designed the system to handle a 300% increase in user interactions without any degradation in performance, demonstrating robustness and scalability.
Real-Time Processing:
Achieved real-time processing and response, ensuring that the recognition and reaction to user gestures were performed with a latency of less than 50 milliseconds.
E-Mess
Role - Developer & Team Lead
for Academic
Jun 15, 2021 - Jun 23, 2021
In a determined endeavor to digitize and streamline mess operations, I spearheaded the development of E-Mess, a robust and intuitive platform. My contributions encapsulated a myriad of technical and managerial dimensions:
Architected a seamless RESTful API infrastructure facilitating real-time communication between the front end and the backend, significantly enhancing the responsiveness and user experience of the client application.
Fortified the platform’s security landscape by adeptly implementing JSON Web Tokens (JWT) for secure user authentication, ensuring a reliable and protected user engagement environment.
Conceived and implemented a comprehensive Customer Relationship Management (CRM) system within the admin module, enabling effective management of student and mess operations.
Augmented operational efficiency and significantly curtailed errors by introducing a CRM dashboard, metamorphosing data visualization and decision-making processes.
Implemented Progressive Web App (PWA) functionality, enriching the user interface and catalyzing substantial user engagement, with 50 active student users interfacing daily.
Pioneered a monumental transition from manual to digital by achieving a 100% digital conversion in mess management processes using the CRM dashboard.
Challenges Faced:
Navigating the intricacies of real-time data synchronization between client and server posed a substantial challenge, especially in ensuring real-time updates were accurately reflected on the client app from the admin module.
Balancing the trade-off between system security and user accessibility during the implementation of JWT for user authentication.
Streamlining the transition from manual to digital processes, overcoming resistance to change, and ensuring a smooth adaptation to the new digital CRM system.
Lessons Learnt:
Grasped the critical importance of robust backend infrastructure in delivering a responsive and seamless user experience.
Gained invaluable insights into the paramountcy of system security and the nuanced approach required in implementing secure user authentication mechanisms.
Recognized the transformative potential of digital conversion in enhancing operational efficiency and reducing administrative bottlenecks.
Research PapersMohamed Lahfir has not added any research paper yet.
VolunteeringCleaning Child Home Care
at National Service Scheme
Jun 10, 2020 - Jun 10, 2020
Social Services
During my time volunteering with the National Service Scheme, I had the rewarding opportunity to engage in a significant social service project focused on supporting an orphanage home. Our team embarked on a thorough cleaning of the entire facility, ensuring a safe, sanitary, and welcoming environment for the children residing there. But our interaction went far beyond mere maintenance work. We devoted the entire day to interacting with the kids, creating a joyful atmosphere filled with laughter and games. These moments were not just about play; they were also educational interactions, where we had the chance to impart knowledge and teach them various subjects. This experience was incredibly fulfilling, allowing us to form meaningful connections and contribute to the well-being and development of the children, who welcomed us warmly into their lives. It was a vivid reminder of the impact that compassion and service can have on the lives of others, particularly those in vulnerable situations.