About Me

🔭 I’m currently working on Speaker Diarization.

🌱 I’m currently learning about MongoDB, Graph VQA, Docker and Kafka.

📫 You can reach me via email at spalkhiw@asu.edu.

📄 Know about my experiences - Resume

âš¡ Fun fact: I hold a Bachelor’s degree in Electronics and Communication

Work Experience

Machine Learning Intern @ Mirwork Inc | (June 2024 - Present)

  • Engineered and optimized data extraction pipelines using YouTube API to process audio, gathering about 70+ hours of content.
  • Designed, implemented, and maintained MongoDB database to efficiently store and manage extensive audio data sets.
  • Utilized YouTube data to fine-tune LLM to improve the accuracy and effectiveness of interview assessment tools.
  • Implemented Speaker Diarization techniques for structuring data, enhancing LLM fine-tuning for interview assessments.

AI Integration Specialist @ Psych for Life | (August 2023 – Present)

  • Achieved 80% reduction in research time by utilizing Prompt Engineering for enhanced efficiency.
  • Leading integration of AI into the writing process for enhanced efficiency, resulting in a streamlined timeframe.
  • Orchestrated the development and implementation of custom prompt engineering solutions, resulting in a 50% reduction in process cycle time and a 30% increase in team productivity.
  • Conducted research, literature reviews, data analysis, and fact-checking for 65+ scientific documents.
  • Spearheaded collaboration efforts between internal teams and Luminosity Labs for development of a scalable platform, streamlining operations and improving cross-departmental communication.
  • Employed caching techniques, resulting in reduction in 70% decrease in rendering delay and improving website performance

Computer Vision Intern @ eInfochips (An ARROW Company) | (Jan 2023 – June 2023)

  • Led a team of 3 to develop a real-time vehicle detection system for visually impaired individuals, achieving 65.4% average precision and 70.7% for car detection using YOLOv5.
  • Achieved an 86.5% reduction in resource usage by filter pruning algorithm while maintaining a mean precision of 42%.
  • Computed CNN models on 16,000 image datasets for vehicle detection, designed to aid visually impaired individuals.
  • Enhanced real-time safety detection in YOLO by implementing Safety Detection Algorithm, achieving a 25 fps detection rate.
  • Applied precision-focused strategies and fine-tuned the model using Indian traffic data to enhance accuracy in detecting vehicles within specific environmental conditions.
  • Collaborated with engineers to streamline training processes, document methodologies, and advance computer vision technology for traffic surveillance and safety applications.

Software Engineering Intern @ Oxvi Respire Solutions | (May 2022 – July 2022)

  • Developed an Android app to plot real-time values from a prototype ventilator with live Data Visualization.
  • Integrated Firebase for real-time communication, enabling data transmission between the device and application.
  • Implemented communication protocols to embedded C components in the prototype for data exchange within a 5ms lag.

Projects

Graph-based QA or Intelligent Traffic Analysis | (July 2022 - Dec 2022)

  • Extracted 70,000+ frames from traffic videos at 10 fps, enhancing usability and improving VQA system accuracy
  • Converted 10,000+ questions into GloVe word embeddings, improving the model’s NLP and question understanding capabilities for Scene Graph Generation

Hate Speech Detection using NLP and AI | (Jan 2024 - Present)

  • Managed a team of 9 in developing and implementing NLP and AI-driven solutions for content filtering.
  • Delegated research by analyzing over 50+ review articles and research papers to enhance content moderation.

Comparative Analysis of ML Models for Student Grade Prediction | (July 2022 - Dec 2022) Publication

  • Analyzed and remodeled multiple classifiers, Random Forest and Decision Tree, by optimizing hyperparameters to improve accuracy by 6% and F1-score by 12% for predicting students’ grades.
  • Demonstrated understanding of Machine Learning fundamentals, including data preparation, model selection, and performance evaluation, with model selection achieving 87.3% accuracy and 0.913 F1-score.
  • Enhanced hyperparameters tuning using GridSearchCV, leading to a significant 41% improvement in F1 score.
  • Conducted comprehensive analysis of student grades to identify correlations between different factors and academic performance, aiming to assist educational institutions in improving student outcomes.

Fault Prediction for Combination Circuits | (June 2022 - Nov 2022)

  • Evaluated and deployed Machine Learning models like Support Vector Classifier, KNN for fault prediction in circuits.
  • Accelerated testing processes and slashed time by 25% spent on fault testing and verification.
  • Accomplished 36% accuracy while predicting faults, which was improved to 54% by Data Engineering.
  • Optimized performance by Feature Engineering, leading to a 40% reduction in computation time.

Movie Booking System | (Sept 2021 - Nov 2021)

  • Collaborated on a C program utilizing Arrays and Queues to simulate theater seating arrangement, allowing users to choose seats and rows and display the total amount due.

Education

  • M.S., Computer Science - Arizona State University (May 2025)
  • B.Tech., Electronics and Communication - Nirma Institute of Technology (May 2023)

Technical Skills

  • Languages : C++, Python, C, Perl, Shell, Kotlin, Linux, Java, Matlab
  • Machine Learning Techniques: Regression, Statistics, Gradient Boosting, Deep Learning, Hyperparameter Tuning, Feature Engineering.
  • Tools & Frameworks : HTML, CSS, YOLO, OpenCV, Flutter, Kafka, Apache, Spark, Docker, SQL
  • Developer Tools : AWS, Git, Anaconda, Spyder, Linux, IntelliJ, VSCode, Android Studio, LaTeX, PowerBI, Tableau, Firebase, Whisper
  • Libraries : NumPy, Pandas, Keras, Scikit-learn, Torch, TensorFlow, PyTorch, Neural Networks, Pyannotate, Seaborn

Publications

Certifications