To address key scientific and societal challenges, the DC-QC program unites the computational and AI efforts across the college and our universities with interdisciplinary, cyber-enabled research.
Computation and data-enabled techniques will be an important factor in solving complex engineering problems in the future.
Future-focused Research and Education in Computational Engineering and Data-driven Methods
The Data-Enabled Computational Engineering and Applied Quantum Computing (DC-QC) initiative at the FAMU-FSU College of Engineering is the hub for cutting-edge research and education in computational engineering and data-driven methods. By uniting the universities’ ongoing AI and computational research efforts, the DC-QC program focuses on developing innovative solutions to critical scientific and societal challenges through interdisciplinary, cyber-enabled approaches.
Vision
DC-QC aims to position the college as a global leader in advancing computational and data-driven methods for practical applications and hardware experiments, integrating emerging technologies into interdisciplinary research and education. Leveraging cutting-edge engineering infrastructure and expertise, we will foster new collaborations, attract top researchers and students and create a dynamic intellectual community that addresses complex engineering challenges on a local, national and global scale.
The DC-QC interdisciplinary graduate program equips students with cutting-edge computational and data-driven skills to solve complex engineering challenges, fostering collaboration and innovation across research fields.
Graduate Initiative
The interdisciplinary graduate program equips students with cutting-edge skills in AI, high-performance computing and quantum technologies to thrive in today's rapidly evolving tech landscape. Offering a multidisciplinary curriculum with courses in modeling, computation, algorithms, quantum computing and more, the program prepares students for careers in national labs, academia and industries leading advanced modeling and simulation. Participants will master high-performance computational engineering, machine learning and quantum computing, while gaining expertise in software tools like MATLAB, Python, TensorFlow and Quirk, bridging physics-based modeling with data science.
Rodney Roberts, Ph.D.
Professor of Electrical & Computer Engineering
Lichun Li, Ph.D.
Assistant Professor of Industrial & Manufacturing Engineering
Core Initiative Faculty
Unnikrishnan Sasidharan Nair, Ph.D.
Assistant Professor of Mechanical Engineering
Christian Hubiki, Ph.D.
Assistant Professor of Mechanical Engineering
Pedro Fernández-Cabán, Ph.D.
Assistant Professor of Civil Engineering
Ebrahim Ahmadisharaf, Ph.D.
Assistant Professor of Civil Engineering
Bayaner Arigong, Ph.D.
Assistant Professor of Electrical & Computer Engineering
Victor DeBrunner, Ph.D.
Professor of Electrical & Computer Engineering
Hui Wang, Ph.D.
Associate Professor of Industrial & Manufacturing Engineering
Yanshuo Sun, Ph.D.
Associate Professor of Industrial & Manufacturing Engineering
Raghav Gnanasambandam, Ph.D.
Assistant Professor of Industrial & Manufacturing Engineering
Associated Researchers
Bryan Quaife, Ph.D.
Associate Professor of Scientific Computing, Florida State University
Arash Fahim, Ph.D.
Associate Professor of Mathematics, Florida State University
Mark Sussman, Ph.D.
Professor of Mathematics, Florida State University
Yanzhu Chen, Ph.D.
Assistant Professor of Physics, Florida State University
Paul van Der Mark, Ph.D.
Director, Research Computing Center, Florida State University
