2021 Excellence in Research

2021 Dean's Award for Excellence

See all 2021 Dean's Awards for Excellence.

Joseph L. Gabbard

Associate Professor, Grado Department of Industrial and Systems Engineering

Joseph Gabbard’s current research is focused on the design and evaluation of augmented reality user experiences, which includes exploring new techniques to deliver user interfaces and support user interaction in AR. The work is particularly interested in the nexus between AR interface design and human performance — examining application domains such as emergency response (head-worn AR), civil infrastructure inspection (drone-based AR), and transportation (windshield-based AR). Through his research, he has developed methodologies to quantify effects of AR user interface designs on human performance.
Leading a team of talented principal investigators from Virginia Tech, Texas A&M, and University of Florida through NSF's inaugural Convergence Accelerator Pilot Program, Joseph Gabbard and team were the first one of 43 proposals funded from 504 submissions (8% acceptance rate). They then competed for $5M Phase two funded, where they were one of nine selected to proceed with Phase 2, and were told that their team was tied with one other team for "first place". So in short of 504 original submissions, they were one of the nine teams awarded for Phase 2, or ~1.79%. The work is creating a personalized learning system for Emergency Responders by infusing human augmentation technologies such as augmented reality and exoskeletons.
In working with his Ph.D. students, Joseph Gabbard is committed to inspiring them to find the best within themselves, understanding how to listen and appreciate different perspectives, to value kindness and empathy, assists them with securing jobs in industry and academia, and continues to mentor after they have left Virginia Tech.
Joseph Gabbard also co-chairs the Graduate Admissions and Recruiting Committee, serves on the executive committee for the Center for Human Computer Interaction, and is the liaison for the Virginia Tech College of Engineering his department to the University of Nottingham.

Lingjia Liu

Associate Professor, Bradley Department of Electrical and Computer Engineering

Lingjia Liu’s current research focuses on enabling technologies for 6G cellular networks, studying how domain knowledge of wireless networks can be integrated to customize machine learning tools for the design of these networks. His research has attracted significant attention in both academia and industry. Through the research, he has demonstrated that machine learning can be utilized in wireless systems even under extremely limited training, which has both theoretical significance and practical relevance as training data is extremely precious in wireless systems. From Qualcomm to Samsung, his work has attracted attention and funding from major telecommunication companies.
Lingjia Liu has 200+ publications including 3 book chapters, 80+ journal articles (most of them are IEEE journals such as TWC, TIT, TCOM, etc), 5 editorials, 90+ conference papers (most of them are IEEE flagship conferences such as GLOBECOM, ICC, GlobalSIP, and ISIT), and 20+ U.S. patents. His research received many recognitions in the field including 8 Best Paper Awards (2020 Charles K. Kao Best Paper Award, 2018 IEEE TAOS Best Paper Award, 2018 IEEE TCGCC Best Conference Paper Award, 2016 GLOBECOM Best Paper Award, etc). Besides academic research, Lingjia Liu also has numerous technical contributions to the 4G standards including both 3GPP LTE-Advanced and IEEE 802.16m. He has 20+ granted U.S. patents with 10+ patents listed as essential intellectual property rights (IPRs) in 4G standards. He received the Individual Gold Medal from Samsung and was elected as the 2011 New Faces of Engineering by the National Engineers Week Foundation.
Funding for his research has come from the National Science Foundation (NSF), National Spectrum Consortium (NSC), Air Force Office of Scientific Research (AFOSR), Air Force Research Lab. (AFRL), Defense Advanced Research Projects Agency (DARPA), and industry. His research efforts have been supported in part by over $90 Million in research funding, with Lingjia Liu serving as the principal investigator (PI) on close to $9 Million research grants.
Lindjia Liu is a senior member of IEEE. He was an Editor for the IEEE Trans. Wireless Commun. and IEEE Trans. Commun. He is currently serving as an Associate Editor for IEEE Trans. Neural Netw. & Learning Syst., the EURASIP J. Wireless Commun. and Netw. as well as Wiley's Intl. J. Commun. Systems. He has been serving as the Technical Program Committee Chair of 7 consecutive IEEE GLOBECOM Workshops on Emerging Technologies for 5G.

Chang-Tien Lu

Professor, Department of Computer Science

Chang-Tien leads the Computer Science National Capital Region Program as director, and he is a member and treasurer of the College of Engineering National Capital Region. He is an associate director of the Sanghani Center for Artificial Intelligence and Data Analytics. He has also held numerous positions with ACM: associate editor of the ACM Transactions on Spatial Algorithms and Systems; vice chair of SIGSPATIAL; and GIS General Chair.
Research interests of Chang-Tien Lu include spatial information and urban computing. His research on data management is to fulfill emerging requirements for retrieving, harnessing, analyzing, visualizing, and disseminating massive spatial data. He has made outstanding contributions to spatial information, including spatial outlier detection and transportation data visualization. He has also made seminal contributions to urban computing, including social event forecasting and energy efficiency mining for sustainability. His major contributions include a novel unsupervised approach to detect social events by jointly maximizing local modularity and spatial scan statistics, a generative forecasting model that characterizes the underlying development of events, and a hierarchical Bayesian approach that jointly models the news and social media topics and their interactions.
Through his research, Chang-Tien Lu has developed several novel computational and mathematical models for low sample rate water and energy disaggregation, which can deliver the devices: detailed consumption information to end-users and help them for significant water and energy conservation. He also pioneered a real-time traffic visualization system for evaluating highway traffic flows. The identified traffic patterns are now being used to support intelligent transportation systems, enabling researchers to establish accurate traffic models and allowing travelers to select optimal commuting routes. His collaborative research projects have not only led to the publication of high-quality research papers and the production of innovative systems, but are now being used by professionals in many fields, for example, transportation engineers and watershed managers, to help them make rapid, justifiable and effective decisions in time-sensitive applications.
His recent project, Redistrict, collaborating with Loudoun County Public Schools and published in ACM SIGSPATIAL-2019 (entitled “REGAL: A Regionalization framework for school boundaries”), proposes an automated platform that uses data analytics and machine learning to help public schools - planning divisions better understand school rezoning plans and their potential effect on the community, share their comments and concerns about proposed plans, propose changes to boundaries, and even create their own plans.
Awards that Chang-Tien has been honored with include: Virginia Tech College of Engineering Faculty Fellow; Distinguished Scientist of the Association for Computing Machinery, the Leadership and Service Award from the ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems; and the Outstanding Service Award from the ACM International Conference on Information and Knowledge Management.
Chang-Tien Lu has graduated 12 Ph.D. and 33 MS students, and is currently supervising 10 Ph.D. students of which three are female.

Ranga Pitchumani

George R. Goodson Professor, Department of Mechanical Engineering

Ranga Pitchumani’s current research is focused on energy systems with a specific emphasis on energy conversion and storage, advanced materials for energy applications, energy/water nexus, electric grid integration of renewable energy, and uncertainty quantification and large-scale optimization. Five recent awards are from the U.S. Department of Energy, with him as sole PI or co-PI.
In his field, Ranga Pitchumani has been instrumental in developing innovative technologies, elucidating the underlying fundamentals, developing technoeconomic models, and working with industry and stakeholders for practical applicability.
In working with his Ph.D. students, Ranga Pitchmani has trained them to be creative and critical thinkers and problem solvers, to focus on the fundamental science while not losing sight of the practical applications, honing their oral and written communication skills, and by providing networking opportunities for future success.
Throughout his career, Ranga Pitchumani has received a number of awards: the Hoyt Clarke Hottel Award from the American Solar Energy Society (2017) for leadership in the development of cost-effective solar energy, the Distinguished Alumnus Award from Indian Institute of Technology in Bombay which recognizes alumni who have distinguished themselves in their field of work and done the institute proud, and the Young Investigator Award from the Office of Naval Research. IN addition, he has been named to the Connecticut Academy of Science and Engineering and a fellow of the American Society of Mechanical Engineers.

Nino Ripepi

Associate Professor, Department of Mining and Minerals Engineering

Nino Ripepi is a Project Manager for the Virginia Center for Coal & Energy Research where he has managed research projects in excess of $30 million. Many of these projects have focused on field tests related to mining health and safety, mining risk management, shale gas exploration, carbon sequestration and enhanced coalbed methane and gas recovery including projects funded by the US Department of Energy and the National Institute of Occupational Safety & Health.
Current research led by Nino Ripepi is focused in two different areas: (1) geologic carbon storage and (2) mining health and safety. I have led multiple large US Department of Energy funded projects that are focused on finding suitable locations to safely store anthropogenic carbon dioxide emissions underground for greenhouse gas mitigation reasons. These projects include both geologic characterization efforts to identify promising sites as well as field tests where carbon dioxide has been injected at significant quantities to test the sites ability to act as a storage reservoir. With regards to mining health and safety, my research is focused on ground control in underground mines to ensure the health and safety of mine workers. The research includes visualizing rock structures to identify hazards through Lidar and Photogrammetry, including both hand-held and drone-based options. These results are then utilized in numerical models to help design safe and efficient mine openings with low-risk to injury.
Leading a project that injected over 14,000 tons of carbon dioxide into coalbed methane gas wells in southwest Virginia, Nino Ripepi is testing the ability of coals to safely store carbon dioxide while simultaneously enhancing natural gas production. This test was the second largest test of its kind in the world and could allow for carbon-neutral technologies during the transition from fossil fuels to renewables.
As the principal investigator on a project funded by the US Department of Energy, Nino Ripepi is leading a project that just finished drilling the 2nd deepest well ever drilled in Virginia, greater than 15,000 feet deep, to explore the deep subsurface for oil and gas resources.