Hire the Best Digital Signal Processing Specialists

I'm a Telecommunications Engineer and expert in programming (MATLAB, VHDL, Verilog, and Python) and Signal Processing. My skills are included but not limited to: MATLAB Programming, Embedded Design Systems and Embedded Programming (focused on Yocto Linux Customization) FPGA and Digital Design, Python Programming, Signal Processing and Data Analysis, EEG and Bio-Signal Processing, Artificial Intelligence, 5G and Beyond 5G Communication Systems. Compressive Sensing and Sparse Coding, and Reinforcement Learning are also my recent fields of research. I will design your GUI software by PyQt5 and .Net C# tools.

Senior audio software developer, passionate about audio, technology, and music. CEO & Co-Foudner of Edge Audio Labs, a company specialized in audio and music software development. My team and I build audio products end to end, including audio plugins, desktop and mobile apps, DSP, AI for audio, cloud backends, embedded systems, licensing, QA, UI/UX, and launch-ready product support. My expertise includes C++, JUCE, Python, PyTorch, ONNX, FastAPI, AWS, Vercel, VST3, AU, AAX, iLok, Audinate Dante, AoIP, and cross-platform development for macOS, Windows, iOS, Android, and embedded environments. We’ve worked on analog emulation, AI vocal synthesis, voice and singing technology, cloud-based audio systems, MIDI and music analysis tools, AI audio analysis, embedded audio hardware, and distributed real-time audio platforms. I also bring real-world experience in live sound, theatre, and studio work, plus a musician’s perspective as a drummer and guitarist. This helps me understand both the technical and creative side of audio products. Former college professor in the Audio Technology Department at ORT University, with strong experience leading teams and turning complex ideas into polished products.

Worked with major industry leaders and proved as a creative visionary responsible for building powerful, innovative solutions through application of DSP knowledge and Embedded System expertise. • Conceived and productized SigmaStudio for SHARC - graphical programming support for SHARC processors • Architected, executed and productized 14 fixed-point Audio CODECs, 2 Image CODECs. • Designed more than 30 audio and image post processing, control system and communication algorithms. • Developed and supported software algorithm and system components for digital Radio. • Developed, integrated and participated in field trials of 4 major Automotive Amplifiers - Audio subsystem. • Proved and inventor and technologist responsible for managing high-energy development teams. • Awarded 2 US patents and 2 innovations disclosure in wide area of technologies. • Developed 12 Audio/Image CODECS, 10 Audio based systems, several audio post processing algorithms.

I am a Machine Learning Engineer with four years of experience working across deep learning research, large scale AI systems, and production model deployment. Over the years, I have worked extensively in medical imaging, computer vision, NLP, signal processing, and large language models, building systems that range from experimental research pipelines to deployed real world AI applications. My day to day work primarily involves Python, PyTorch, TensorFlow, Keras, HuggingFace Transformers, sentence transformers, scikit learn, OpenCV, Pandas, and NumPy. I enjoy working deeply on both the research and engineering sides of machine learning, especially problems that require understanding model behavior rather than simply applying existing architectures blindly. A large part of my background is research driven. I have authored multiple peer reviewed publications in indexed journals and IEEE conferences, including publications in Neurocomputing, Healthcare Analytics, Engineering Applications of Artificial Intelligence, Telematics and Informatics Reports, and other Elsevier and IEEE venues. My research has focused heavily on explainable AI, healthcare AI, and advanced deep learning systems. Some of my published work includes CARDxnosis, an explainable knowledge driven framework for ECG diagnosis and clinical report generation, an explainable AI system for trustworthy arrhythmia detection, a CNN RNN Attention hybrid architecture for automatic modulation classification, ensemble deep learning approaches for lung cancer detection from CT scans, and SRGAN based white blood cell image generation and classification pipelines. Alongside published work, I am currently involved in research on brain tumor segmentation, ADHD and ASD classification from brain connectome graphs, epileptic seizure prediction from EEG signals, and interpretable tabular learning using graph neural networks combined with Kolmogorov Arnold Networks. Beyond research, I have substantial hands on experience building and deploying production grade AI systems. One of my major recent projects was LaborBERT v4, a domain adaptive transformer fine tuning system processing hundreds of thousands of records through a large scale training pipeline. The project involved multiple experimental setups including contrastive learning, masked language model pretraining, temporal contrastive learning, cross attention based fusion, multi task training, and Matryoshka Representation Learning. I have also built hybrid embeddings plus LLM systems for taxonomy mapping using OpenAI embeddings alongside locally hosted LLaMA and Mistral models through Ollama. In addition, I have worked on deployed clinical AI systems and a portable on device diagnostic AI solution with embedded deep learning models for point of care inference, which gave me valuable experience in optimization, deployment constraints, inference design, and production reliability. My broader project portfolio includes vehicle detection using Mask R CNN, human activity recognition on the Kinetics 700 dataset, facial keypoint detection with MultiRes UNet, semantic segmentation pipeline redesign, Stable Diffusion based image editing workflows, toxic comment classification, RASA based conversational AI systems, and large scale scraping and automation pipelines using Playwright and Selenium. I have also worked with Flask and Django based deployment pipelines and cloud hosted ML systems. From an engineering perspective, I care strongly about clean and maintainable systems. I follow disciplined workflows involving modular code design, Git based version control, reproducible experimentation, structured evaluation, bootstrap validated metrics, and detailed documentation. I am also comfortable preparing scientific reports, research papers, and journal submissions using both LaTeX and Word. What ties all of this together is that I genuinely enjoy solving difficult technical problems, especially the kind that require balancing research depth with practical engineering constraints. I am most motivated by projects where thoughtful experimentation, careful system design, and real world usability matter equally.

I'm an electrical engineer specializing in high-speed analog and digital electronics. • Electronic design and simulation (MATLAB, SPICE, Keysight ADS) • Schematic capture and PCB layout • Test and measurement (DMM, Oscilloscope, Spectrum Analyzer, VNA) • Production documentation (BOMs, Process Flowsheets, Assembly Instructions, Test Plans)

🏆 Certified Data Scientist and ML Engineer with a proven track record in developing robust machine learning, deep learning, computer vision, and audio processing solutions. 🎯 Efficient, Timely, and Results-Driven Services in the Following Fields: ☑ 𝗢𝗯𝗷𝗲𝗰𝘁 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻 • Processing images and videos, including frame extraction. • Creating clean datasets for model training and testing. • Building object detection models from scratch. • Transfer learning and model fine-tuning. • Hyper-parameter optimization. • Evaluating models with real-world data. 𝙏𝙚𝙘𝙝𝙣𝙞𝙘𝙖𝙡 𝙎𝙠𝙞𝙡𝙡𝙨: Pandas, Numpy, Tensorflow, OpenCV, PIL, Scikit-learn ☑ 𝗔𝘂𝗱𝗶𝗼 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 • Processing raw audio files and cleaning noise. • Generating spectrograms, mel-spectrograms, and MFCCs. • Creating and fine-tuning audio classification models. • Utilizing transfer learning for improved performance. 𝙏𝙚𝙘𝙝𝙣𝙞𝙘𝙖𝙡 𝙎𝙠𝙞𝙡𝙡𝙨: Pandas, Numpy, Tensorflow, Librosa, Matplotlib, Scikit-learn ☑ 𝗗𝗮𝘁𝗮 𝗖𝗹𝗲𝗮𝗻𝗶𝗻𝗴 𝗮𝗻𝗱 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 • Balancing datasets and removing irrelevant samples. • Conducting exploratory data analysis. • Extracting business and scientific insights. • Visualizing data with scientific plots and diagrams. 𝙏𝙚𝙘𝙝𝙣𝙞𝙘𝙖𝙡 𝙎𝙠𝙞𝙡𝙡𝙨: Pandas, Numpy, Seaborn, Matplotlib, Scikit-learn ☑ 𝗜𝗺𝗮𝗴𝗲 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 • Expertise in image processing, including medical imaging (e.g., mitosis detection in WSI). • Building and fine-tuning DL models. • Deploying models into applications using transfer learning. 𝙏𝙚𝙘𝙝𝙣𝙞𝙘𝙖𝙡 𝙎𝙠𝙞𝙡𝙡𝙨: Tensorflow, Numpy, Matplotlib, YOLO, OpenCV, Laravel, PHP I am committed to transparent communication and timely delivery of results. I strive to exceed client expectations and ensure satisfaction. Feel free to contact me to discuss potential collaborations. I look forward to working with you! Thank you! 🙏🏼😊