"I am a computer vision and machine learning specialist with over a decade of experience in both academic and applied research settings. I hold advanced degrees in Computer Science and have worked at institutions such as the National Institute of Standards and Technology (NIST) and Johns Hopkins University, where I contributed to national-level AI research and evaluation efforts. My core areas of expertise include deep learning, computer vision, and transformer-based models, and I have also worked extensively on generative AI, model fine-tuning, and large-scale evaluation of ML systems.

I have taught and mentored students at the undergraduate and graduate levels, both in classroom settings and through one-on-one instruction. As a faculty member at a leading university, I taught multiple AI-related courses, ranging from introduction to artificial intelligence to specialized courses on deep learning and computer vision. More recently, I have been teaching a university-level algorithms course. My students have included college students, graduate researchers, and working professionals seeking to transition into AI roles. I adapt my approach depending on the learner’s background—focusing on conceptual clarity, coding fluency in Python, and practical project-based learning using modern ML frameworks such as PyTorch and TensorFlow.

I genuinely enjoy helping learners demystify complex topics and build real confidence in AI and ML. Whether someone needs help preparing for interviews, tackling assignments, or building a research-grade project, I work to ensure they understand not only how things work, but why they work." less...