Graduate students in computer science often enter advanced programs with strong technical skills but limited experience in research communication, academic writing, professional development, or navigating ethical issues in academia and industry. To address these gaps, we designed and implemented a project-based, discussion-driven course aimed at first-semester graduate students, many of whom are international. The course integrates scaffolded milestones such as research topic proposals, annotated bibliographies, and peer-reviewed research plans, alongside interactive in-class activities focused on ethics, professional communication, and team collaboration. Over the semester, students worked in teams to explore contemporary computing issues, culminating in a structured research paper and presentation. The curriculum emphasized constructive alignment by mapping each assignment to specific learning objectives, including academic integrity, teamwork, and effective information sourcing. Regular surveys and peer feedback informed iterative course improvements and helped surface student needs and growth areas. This poster presents the course structure, key pedagogical principles, sample student outcomes, and feedback from mid-semester and end-of-semester evaluations. We also reflect on implementation challenges and offer adaptable strategies for instructors looking to build professional development opportunities into graduate-level computer science curricula.

Dr. Hanieh Shabanian is currently a Tenure-Track Assistant Professor in Computer Science at Western New England University. She earned her M.S. and Ph.D. degrees in Computer Engineering from the University of Memphis with a prior undergraduate degree in Software Engineering. She enjoys teaching computer science courses, including data structures and algorithms, image processing, and artificial intelligence. Her research interest and expertise are at the intersection of computer vision, machine learning, and image processing. During her past research in Computational Imaging Research Laboratory and Computational Ocularscience Laboratory, in addition to solving stereo matching problems and 3D reconstruction using recent computer vision and statistical learning techniques including probabilistic graphical models; she has been working on imaging and analysis of ocular structures including cornea and retina of the eye to obtain dense 3D point cloud, facilitating detection of Glaucoma eye disease.