Anuj Karpatne, associate professor in the Department of Computer Science in the College of Engineering has won a five-year, $595,738 National Science Foundation Faculty Early Career Development Program CAREER award to explore a unified approach for accelerating scientific discovery using scientific knowledge and data. Karpatne is also a core faculty member at the Sanghani Center for AI and Data Analytics. Read the full story here.
Researchers in three different disciplines at Virginia Tech are partnering in a $15 million grant from the National Science Foundation (NSF) to establish an institute in the new field of “imageomics,” aimed at creating a new frontier of biological information using vast stores of existing image data, such as publicly funded digital collections from national centers, field stations, museums, and individual laboratories.
The goal of the institute is to characterize and discover patterns or biological traits of organisms from images and gain insights into how function follows form in all areas of biology. It will expand public understanding of the rules of life on Earth and how life evolves.
Imageomics is one of five Harnessing the Data Revolution institutes receiving support from the NSF.
Anuj Karpatne, assistant professor in the Department of Computer Science and faculty at the Sanghani Center for Artificial Intelligence and Data Analytics, is serving as one of four co-investigators for the multi-university project led by the Ohio State University. Leanna House, associate professor in the Department of Statistics and faculty at the Sanghani Center, and Josef Uyeda, assistant professor in the Department of Biological Sciences, are designated senior personnel. All three researchers are part of the executive leadership team of the institute and investigators on Virginia Tech’s $1.4 million portion of the grant. Click here to read more about these scientists will apply their expertise to the project.
Having the opportunity to apply state-of-the-art machine learning models to bioinformatics problems as an undergraduate motivated M. Maruf to take a deep dive into machine learning and deep learning as a Ph.D. student in computer science at Virginia Tech which he chose because of its exemplary research and top-notch facilities.
“Dr. Anuj Karpatne’s unique view towards solving real-world problems fascinated me to explore more knowledge-infused machine learning,” Maruf said of his advisor at the Sanghani Center.
Last April, Maruf presented their collaborative paper, “Maximizing Cohesion and Separation in Graph Representation Learning: A Distance-aware Negative Sampling Approach,” at the SIAM International Conference on Data Mining (SDM).
Maruf’s research interests lie in the broad domains of science-guided machine learning and its applications with a focus on integrating domain knowledge into machine learning models to obtain generalized solutions consistent with scientific knowledge.
“In particular, I am developing new algorithms for graph neural networks that allow for better representation with coherent knowledge propagation,” he said.
A black-box neural network model learns solely from training samples and requires a lot of annotated real-world observations to learn the underlying patterns accurately, said Maruf. Moreover, black-box Artificial Neural Networks (ANN) ignore external biological knowledge in the training phase, resulting in inconsistent outputs.
“I am currently addressing these challenges for the fish trait segmentation problem by incorporating biological knowledge into the state-of-the-art ANN model,” he said.
Maruf presented “Biology-Guided Neural Network for Fish Trait Discovery,” at the Society for Integrative and Comparative Biology Virtual Annual Meeting earlier this year.
The Sanghani Center environment, Maruf said, provides its students with multidisciplinary learning and research collaboration opportunites.
His additional work with faculty and other Ph.D. students at 2021 conferences includes
“PID-GAN: A GAN Framework based on a Physics-informed Discriminator for Uncertainty Quantification with Physics”in proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), held in August; and “Learning Compact Representations of Neural Networks using DiscriminAtive Masking (DAM),” which will be included in proceedings of the 35th Conference on Neural Information Processing Systems (NeurIPS) in December.
Maruf received a bachelor’s degree in computer science and engineering from Bangladesh University of Engineering and Technology.
His projected graduation date is Spring 2021 and he plans to pursue an industrial research position.
With restrictions to working in physical office space still in effect, graduate students at the Sanghani Center are working remotely this summer for companies, labs, and programs from coast to coast. Students are not only gaining real-world experience from internships and other opportunities but, in many cases, they are also able to advance their own research interests.
Following is a list of Sanghani Center students and the work they are doing:
Badour AlBahar, a Ph.D. student in electrical and computer engineering, is a computer vision intern at Adobe Vision group in San Jose, California. She is working on human reposing and animation. Her advisor is Jia-Bin Huang.
Sikiru Adewale, a Ph.D. student in computer science, is a software development engineer intern at Amazon Web Service in Seattle, Washington. He is working on data transfer and storage on the AWS snowball device. His advisor is Ismini Lourentzou.
Vasanth Reddy Baddam, a Ph.D. student in computer science, is an research intern at Siemens in Princeton, New Jersey. He is working on contributing to industrial research projects on leveraging machine learning to analyze multi-agent reinforcement learning (MARL) algorithms and implement them. His advisor is Hoda Eldardiry.
Subhodip Biswas, a Ph.D. student in computer science, is working on Bayesian optimization techniques for automated machine learning (AutoML) and robust artificial intelligence systems as part of the Journeyman Fellowship he received from the DEVCOM Army Research Laboratory (ARL) Research Associateship Program (RAP) administered by the Oak Ridge Associated Universities (ORAU). His advisor is Naren Ramakrishnan.
Jie Bu, a Ph.D. student in computer science, is a research intern at Carbon 3D in Redwood City, California. He is working on artificial intelligence-powered computational geometry. His advisor is Anuj Karpatne.
Si Chen, a Ph.D. student in computer engineering, is a research intern at InnoPeak Technology in Seattle, Washington. She is working on research on model privacy protection. Her advisor is Ruoxi Jia.
Kai-Hsiang Cheng, a master’s degree student in computer science, is an intern at GTV Media Group in New York City. He is working on the content management system of the media’s platform. His advisor is Chang-Tien Lu.
Riya Dani, a master’s degree student in computer science, is a software engineer intern at Microsoft. She is working on web application developments under Azure. Her advisor is Ismini Lourentzou.
Debanjan Datta, a Ph.D. student in computer science, is an intern on the Amazon Web Services team at Amazon in Seattle, Washington. He is working on time series characterization and classification. His advisor is Naren Ramakrishnan.
Arka Daw, a Ph.D. student in computer science, is an applied scientist intern at Amazon Web Services Lambda Science Team in Seattle, Washington. He is working on developing an automated causal machine learning framework for setting up experiments and estimating causal effects from observational data. His advisor is Anuj Karpatne.
Yue Feng, a Ph.D. student in electrical and computer engineering, is an intern with the Snap Research Creative Vision Team in Santa Monica, California. She is working on a 3D computer vision project. Her advisor is Jia-Bin Huang.
Chen Gao, a Ph.D. student in electrical and computer engineering, is a research intern at Google in Cambridge, Massachusetts. He is working on creating video panoramas using a cellphone. His advisor is Jia-Bin Huang.
Jianfeng He, a Ph.D. student in computer science, is an intern at Tencent AI Lab in Seattle,Washington. He is working on research about multi-modal dialogue with mentors Linfeng Song and Kun Xu. His advisor is Chang Tien-Lu.
Taoran Ji, a Ph.D. student in computer science, is an intern at Moody’s Analytics in New York City. He is working on analyzing credit and financial data for the global financial markets, which will drive algorithmic improvements in Moody’s Analytics core machine learning and artificial intelligence-driven products. His advisor is Chang-Tien Lu.
Adheesh Juvekar, a Ph.D. student in computer science, is a machine learning and natural language processing intern at Deloitte & Touche LLP. He is working on automatically extracting relevant information from transactional invoices using state of the art deep learning techniques. His advisor is Edward Fox.
M. Maruf, a Ph.D. student in computer science, is a machine learning engineering intern at Qualcomm GNSS/location team in Santa Clara, California. He is applying machine learning techniques to hybrid technology fusion for navigation/positioning in mobile, wearable, automotive, and micro-mobility applications. His advisor is Anuj Karpatne.
Nikhil Muralidhar, a Ph.D. student in computer science, received an Applied Machine Learning Summer Research Fellowship at Los Alamos National Lab in Los Alamos, New Mexico, to work with researchers on physics-informed machine learning for modeling adsorption equilibria in fluid mixtures. His advisor is Naren Ramakrishnan.
Makanjuola Ogunleye, a Ph.D. student in computer science, is an application support engineer intern at Northwestern Mutual in Milwaukee, Wisconsin. His duties include coding, testing, and implementing complex programs from user specifications. He is also performing client data analysis to support engineering technology to improve and facilitate customer success. His advisor is Ismini Lourentzou.
Nishan Pokharel, a master’s degree student in computer science, is a software engineering intern at Capital One in Mclean, Virginia. He is working on network infrastructure automation. His advisor is Chris North
Avi Seth, a master’s degree student in computer science, is serving as a graduate team leader this summer for Virginia Tech’s Data Science for the Public Good program. The group works on projects that address state, federal, and local government challenges around today’s relevant and critical social issues. His advisor is Ismini Lourentzou.
Mia Taylor, a master’s degree student in computer science, is a software development intern at Amazon Web Services in Seattle, Washington. Her team is working with Comprehend AutoML which allows customers to build customized natural language processing models using their own data. Her advisor is Lifu Huang.
Yiran Xu, a Ph.D. student in electrical and computer engineering, is an intern with the Snap Research Creative Vision Team in Santa Monica, California. He is working on 3D human reconstruction and video generation/manipulation. His advisor is Jia-Bin Huang.
Shuaicheng Zhang, a Ph.D. student in computer science, is a natural language processing (NLP) research intern at Deloitte in New York City. He is part of the Audit and Assurance AI innovation team, working on open information extraction on internal control files to help auditors effortlessly process these files. His advisor is Lifu Huang.
Yuliang Zou, a Ph.D. student in electrical and computer engineering, is a research intern at Waymo in Mountainview, California. He is working on the perception problem for self-driving cars. His advisor is Jia-Bin Huang.