Mohannad Elhamod

**Why can I help you? **
I had been a software engineer at Microsoft for 6+ years, a research intern at NASA, and just completed my PhD in Computer Science at Virginia Tech. Starting August 2023, I will be a clinical assistant professor at Questrom school of Business at Boston University.
My field of research is applying Machine Learning in different scientific applications, including physics, biology, and business.
My expertise includes, but is not limited to, AI/ML, Computer Vision, Deep Learning, Generative Networks, and interpretability and visualization. I also leverage my past experience being an Azure software engineer, which includes coding, web design, and data structures.

**Why are YOU here? **
In recent years, I have met plenty of people from various backgrounds and disciplines that are overwhelmed by the sudden surge of AI, machine learning, and data science. With a mix of feeling curious and anxious, this new technology is the unknown that makes them wonder whether it is the next revolution or just a hype that will fade in a few years. It is my belief that I can help these people overcome their fears, embrace this new technology without inflating its powers, and master a skill that increases their competitive market value.
Besides ML being my main field of expertise, I am passionate about helping those with the will and desire to hone their software engineering skills, including coding, whether it be as part of their university degree, career, or business.

** What do I offer?**
Working with you, I can offer my academic and industry expertise to help bring the value of software engineering and ML into your career, guide your career growth, advise you on your research, or work with you on your project. Working together, I offer to set you on the path of becoming an outstanding software and ML practitioner.
My philosophy is to work first and foremost build a deep understanding when it comes to the foundations and to introduce advanced topics based on that solid base. Also, I am a believer in learning through practice; this could involve demos, homework, and toy projects.