Advisory Board and Editors Data Mining & Machine Learning
Brittany N Lasseigne
Brittany N. Lasseigne, PhD is an Assistant Professor of Cell, Developmental and Integrative Biology at The University of Alabama at Birmingham School of Medicine. She trained in Biotechnology, Science, and Engineering at Mississippi State University (B.S.) and the University of Alabama in Huntsville (Ph.D.) and completed a postdoctoral fellowship in genetics and genomics at the HudsonAlpha Institute for Biotechnology.
Her lab develops and applies genomic- and data-driven strategies (including single-cell and long-read sequencing) to discover biological signatures that might be used to improve patient care and provide insight into the cellular and molecular processes contributing to disease, especially for diseases impacting the brain and/or kidney. Their recent work includes prioritizing drug repurposing candidates for cancers and polycystic kidney disease, evaluating preclinical models and cross-species transcriptomic signatures to improve disease modeling, and applying single-cell and long-read technologies to neurological disease tissues to understand the role that context plays in disease etiology, progression, and treatment.
The Lasseigne Lab is currently focused on integrating genomics data, functional annotations, and patient information with machine learning and regulatory network approaches across diseases that impact the brain or kidney to discover novel mechanisms in disease etiology and progression, identify genome-driven therapeutic targets and opportunities for drug repositioning and repurposing, determine clinically-relevant biomarkers, and understand how cellular context contributes to these diseases. Collectively, these distinct projects all apply genetics and genomics to human diseases and build tools to accelerate future research. Their lab also develops data science software and analytical pipelines that are open-source, well-documented, and hosted by third-party code distributors, critical for facilitating reproducibility and enabling the research community to use the methods they develop.
Yann LeCun
Director of Facebook AI Research (2013-) and Silver Professor at New York University (2003-), affiliated with: Courant Institute, Center for Data Science, Center for Neural Science, and ECE Dept. Founding director of the NYU Center for Data Science (2012-2014); Fellow,NEC Research Institute (2002-2003); Head, Image Processing Research AT&T Labs (1996-2002); Research Scientist Bell Laboratories (1988-1996).
Jens Lehmann
Dr. Jens Lehmann is a researcher at the University of Leipzig. He is co-leading the AKSW („Agile Knowledge Engineering and Semantic Web“) Group and is interested in semantic technologies, machine learning and the data web. He is working on several community research projects, including DL-Learner, DBpedia and LinkedGeoData as well as funded EU projects such as GeoKnow and Big Data Europe. He studied and worked in Leipzig, Oxford, Bristol and Dresden.
Zhiyi Li
Dr. Zhiyi Li received his Ph.D. degree in Electrical Engineering from Illinois Institute of Technology in 2017. He received an M.E. degree in Electrical Engineering from Zhejiang University (Hangzhou, China) in 2014 and a B.E. degree in Electrical Engineering from Xi’an Jiaotong University (Xi’an, China) in 2011. From August 2017 to May 2019, he was a senior research associate at Robert W. Galvin Center for Electricity Innovation at Illinois Institute of Technology. Since June 2019, he has been with the College of Electrical Engineering, Zhejiang University(Hangzhou, China) as a research professor. His research interests lie in the application of state-of-the-art optimization and control techniques in smart grid design, operation and management with a focus on cyber-physical security. He has already authored/co-authored over 60 refereed journal articles in these areas. He is an associate editor of 4 other international journals (IEEE Access, Journal of Modern Power Systems and Clean Energy, Journal of Electrical Engineering and Technology, and IET Journal of Engineering) and a reviewer of over 30 international journals (including IEEE Transactions on Power Systems, IEEE Transactions on Smart Grid, IEEE Transactions on Sustainable Energy, and IEEE Transactions on Power Delivery).
Xing Li
Dr. Xing Li is an Assistant Professor and Associate Consultant in the Division of Biomedical Statistics and Informatics, Department of Health Science Research at Mayo Clinic - voted the best hospital by U.S. News & World Report. Dr. Li completed his PhD in Bioinformatics from The University of Michigan at Ann Arbor, Michigan, USA. Dr. Li also holds a Masters Degree in Biochemistry and Molecular Biology and Bachelors Degree in Microbiology. Dr. Li’s research interests focus on machine learning, bioinformatics, and statistical data mining in large scale data in biomedical research, such as next generation sequencing data (whole genome sequencing, RNA-seq, microarray data), in the file. He has published more than 20 peer-reviewed papers in reputable journals and book chapters in the fields of Bioinformatics and Biostatistics, cancer research, cardiovascular disease, embryonic stem cell (ESC) and induced pluripotent stem cell (iPSC) research, and human genomics, genetics and development, and Microbiology. Dr. Li’s publications have been highlighted as Journal Cover Stories, Journal Featured Articles, Highlights Section Papers, Must Read by Faculty 1000, and ESC & iPSC News, etc. Dr. Li has been developing data analysis tools, such as RCircle and PCA3d, etc. Dr. Li is also a member of American Association for Cancer Research (AACR), International Society for Computational Biology (ISCB), American Statistics Association (ASA) and American Heart Association (AHA).
Jingyi Jessica Li
I am an Assistant Professor in the Department of Statistics and Department of Human Genetics at University of California, Los Angeles. I am also a faculty member in the Interdepartmental Ph.D. Program in Bioinformatics and a member in the Jonsson Comprehensive Cancer Center (JCCC) Gene Regulation Research Program Area. Prior to joining UCLA, I obtained my Ph.D. degree from the Interdepartmental Group in Biostatistics at University of California, Berkeley, where I worked with Profs Peter J. Bickel and Haiyan Huang. I received my B.S. (summa cum laude) from Department of Biological Sciences and Technology at Tsinghua University, China in 2007.
Chun Liang
Professor in Bioinformatics, Biology Department, Miami University, Ohio, USA
Francesca A. Lisi
Assistant Professor in Computer Science at the University of Bari, Italy. Member of the Board of Directors of the Italian Association for Artifiicial Intelligence (AI*IA).
Pengcheng Liu
Pengcheng Liu is a member of IEEE, IEEE Robotics and Automation Society (RAS), IEEE Control Systems Society (CSS) and International Federation of Automatic Control (IFAC). He is also a member of the IEEE Technical Committee on Bio Robotics, Soft Robotics, Robot Learning, and Safety, Security and Rescue Robotics. Dr Liu is an Associate Editor of IEEE Access, PeerJ Computer Science, and he received the Global Peer Review Awards from Web of Science in 2019, and the Outstanding Contribution Awards from Elsevier in 2017. He has published over 70 papers on flagship journals and conferences. He was nominated as a regular Funding/Grants reviewer for EPSRC, NIHR and NSFC and he has been leading and involving in several research projects and grants, including EPSRC, Newton Fund, Innovate UK, Horizon 2020, Erasmus Mundus, FP7-PEOPLE, NSFC, etc. He serves as reviewers for over 30 flagship journals and conferences in robotics, AI and control. His research interests include robotics, machine learning, automatic control and optimization.
Jingyu Liu
Associate Professor at the Mind Research Network; Adjunct Assistant Professor at the Department of Electrical and Computer Engineering, University of New Mexico. Our MRN lab focuses on developing and optimizing methods and software for quantitative analysis of structure and function in medical images with particular focus on the study of psychiatric illness. We work with many types of data, including functional magnetic resonance imaging (fMRI), diffusion tensor imaging (DTI), electroencephalography (EEG), structural imaging and genetic data.
Jose Maria Luna
Dr. Luna is an associate professor at the University of Cordoba, Spain. He received the Ph.D. degree in Computer Science from the University of Granada, Spain. He has published more than 30 papers in top ranked journals, most of them in the pattern mining field. He is author of two books, related to pattern mining, published by Springer: "Pattern Mining with Evolutionary Algorithms" and "Supervised Descriptive Pattern mining”
Gerton Lunter
My group is interested in investigating the processes of evolution and biology using computational methods. We apply machine learning methods (HMMs, Bayesian statistics, particle filters, deep learning) to large data sets to study for example human demographic history or non-coding functional elements in the genome.