What we do/Team/In news

Quantum Computing    Quantum computers are expected to accelerate scientific discovery spanning many different areas such as medicine, AI, material science, and financial predictions. Quantum hardware manipulates with much more complex than binary information that is represented in classical computers. We are interested in quantum algorithms and methods of their hybridization with classical computing systems.

Machine Learning and Data Mining    Many standard machine learning and data mining algorithms are prohibitive for large-scale number of variables. For example, this can happen because of the slow convergence or NP-hardness of underlying optimization problems (such as in support vector machines and cut-based clustering). We are interested in algorithms that cope with such problems.

AI, Literature Based Discovery and Text Mining    Hypothesis generation is becoming a crucial time-saving family of techniques which allow researchers to quickly discover implicit connections between important concepts. We are interested in such techniques and complex text mining problems, in general. Applications include biomedical discovery with scientific texts, healthcare and social media.

Network Science and Graph Algorithms    Computational, modeling, theory and data problems related to complex networks in social/natural/information sciences, and engineering. The analysis often includes frequent pattern discovery, outliers detection, quantitative methods for importance ranking of network elements, time-dependent data analysis, evolution modeling, visualization, and community detection.

Combinatorial Scientific Computing    Discrete optimization problems on large-scale graphs that are used to accelerate the performance of scientific computing algorithms. Examples include (hyper)graph partitioning, reordering, and coloring to improve load-balancing, task mapping, and data locality on HPC.

Multiscale Methods    A broad range of scientific problems involve multiple scales. Traditional monoscale approaches have proven to be inadequate, even with the largest supercomputers, because of the prohibitively large number of variables involved. We develop multiscale approaches in which a hierarchy of coarse scale approximations is used to solve large-scale problems efficiently.

	Group Lead    Dr. Ilya Safro received his Ph.D. from the Weizmann Institute of Science (Israel) under the supervision of Achi Brandt and Dorit Ron. In 2021, he joined the Department of Computer and Information Sciences at the University of Delaware. In 2012-2020, Ilya held assistant and associate professor positions in the School of Computing at Clemson University. He was also a Faculty Scholar of the Clemson University School of Health Research. Before that he was a postdoc and Argonne scholar at the Division of Mathematics and Computer Science at Argonne National Laboratory.
	Anthony Angonne    PhD student in Quantum Science and Engineering. Areas of research: quantum computing, quantum optimization
	Bao Bach    PhD student in Quantum Science and Engineering. Areas of research: quantum computing, quantum machine learning
	Cameron Ibrahim    PhD student in Computer Science. Areas of research: quantum computing, hybrid quantum-classical methods, graph algorithms, optimization
	Jose Luis Falla Leon    PhD student in Physics. Areas of research: quantum computing
	Sristy Sangskriti    PhD student in Computer Science. Areas of research: quantum computing, hybrid quantum-classical methods, graph algorithms, optimization
	Ilya Tyagin    PhD student in Biomedical Data Science and Informatics. Areas of research: machine learning, text mining
	Saeideh Valipour    PhD student in Computer Science. Areas of research: machine learning, text mining

Alumni   Tallayeh Razzaghi, Postdoc (now assistant professor at University of Oklahoma);  Ankit Kulshrestha, Ph.D. (now  research scientist in quantum computing group at Fujitsu Research USA); Xiaoyuan Liu, Ph.D. (now research scientist in quantum computing group at Fujitsu Research USA);  Hayato Ushijima-Mwesigwa, Ph.D. (now research scientist in quantum computing group at Fujitsu Research USA);  Ruslan Shaydulin, Ph.D. (now quantum computing researcher at JPMorgan Chase, former postdoc at Argonne National Laboratory);  Justin Sybrandt, Ph.D. (now at Google Brain);  Ehsan Sadrfaridpour, Ph.D. (now data scientist at Lowe's);  Varsha Chauhan, M.Sc. (now software engineer at Amazon);  Emmanuel John, M.Sc. (now software engineer at Epic Systems);  Krish Matta (now student at CMU);  Zirou Qiu, M.Sc. (now Ph.D. student in University of Virginia);  Mikita Yankouski, B.Sc. (now at Blackboud Systems);  Angelo Carrabba, B.Sc., Honors Thesis (now at Google);  Grace Glenn, B.Sc., Honors Thesis (now at Foursquare); 

Our Research Group in News

• SIAM News: Challenges and Opportunities of Scaling Up Quantum Computation and Circuits
• Quantum Computing and Finance: Researchers explore quantum computing ability to speed solutions for financial sector
• A Quantum Phenomen
• Concentrating on Connections, our work on automated scientific hypothesis generation is featured in UDaily
• Artificial intelligence could aid in fight against COVID-19
• Our quantum computing work in HPC wire
• Combing the best of quantum computing and classical computing
• The best of both worlds: how to solve real problems on modern quantum computers
• Future of Medicine - Man or Machine?
  
  

  Big Data: Future of Medicine - Man or Machine? from Clemson University on Vimeo.

• Summer internship of Ruslan Shaydulin
• Our promo video featured at ACM KDD 2017