Advisory Board and Editors Data Mining & Machine Learning
Jayashree Kalpathy-Cramer
Research interests include the use of image processing and machine learning techniques for medical image analysis and retrieval, imaging for radiation therapy, survival analysis for cancer, information retrieval, and statistical modeling.
Lydia E Kavraki
Lydia Kavraki received her B.A. in Computer Science from the University of Crete in Greece and her Ph.D. in Computer Science from Stanford University. Her research contributions are in physical algorithms and their applications in robotics as well as in computational structural biology and biomedciine. Kavraki is the recipient of the ACM Grace Murray Hopper Award; a Fellow of ACM, IEEE, AAAS, AAAI, and AIMBE; and a member of the Institute of Medicine of the National Academies.
Xiangjie Kong
Dr. Xiangjie Kong is currently a Full Professor in the College of Computer Science & Technology, Zhejiang University of Technology (ZJUT), China. Previously, he was an Associate Professor in School of Software, Dalian University of Technology (DUT), China, where he was the Head of the Department of Cyber Engineering. He is the Founding Director of City Science of Social Computing Lab (The CSSC Lab) (http://cssclab.cn/). He is/was on the Editorial Boards of 6 International journals. He has served as the General Co-Chair, Workshop Chair, Publicity Chair or Program Committee Member of over 30 conferences. Dr. Kong has authored/co-authored over 140 scientific papers in international journals and conferences including IEEE TKDE, ACM TKDD, IEEE TNSE, IEEE TII, IEEE TITS, IEEE NETW, IEEE COMMUN MAG, IEEE TVT, IEEE IOJ, IEEE TSMC, IEEE TETC, IEEE TASE, IEEE TCSS, WWWJ, etc.. 5 of his papers is selected as ESI- Hot Paper (Top 1‰), and 16 papers are ESI-Highly Cited Papers (Top 1%). His research has been reported by Nature Index and other medias. He has been invited as Reviewer for numerous prestigious journals including IEEE TKDE, IEEE TMC, IEEE TNNLS, IEEE TNSE, IEEE TII, IEEE IOTJ, IEEE COMMUN MAG, IEEE NETW, IEEE TITS, TCJ, JASIST, etc.. Dr. Kong has authored/co-authored three books (in Chinese). He has contributed to the development of 14 copyrighted software systems and 20 filed patents. He has an h-index of 36 and i10-index of 87, and a total of more than 4200 citations to his work according to Google Scholar. He is named in the2019 and 2020 world’s top 2% of Scientists List published by Stanford University. Dr. Kong received IEEE Vehicular Technology Society 2020 Best Land Transportation Paper Award, and The Natural Science Fund of Zhejiang Province for Distinguished Young Scholars. He has been invited as Keynote Speaker at 2 international conferences, and delivered a number of Invited Talks at international conferences and many universities worldwide. His research interests include big data, network science, and computational social science. He is a Distinguished Member of CCF, a Senior Member of IEEE, a Full Member of Sigma Xi, and a Member of ACM.
Krista H Lagus
Krista H. Lagus is a Finnish professor and researcher specializing in artificial intelligence (AI), natural language processing (NLP), machine learning, and digital social science. She currently serves as a Professor of Digital Social Science at the University of Helsinki, where she integrates AI and digital methods with social sciences to analyze complex social behaviors.
Lagus earned her M.Sc. in Computer Science in 1996 and her Ph.D. in Computer and Information Sciences in 2000 from Helsinki University of Technology (now Aalto University). She has held various research positions, including Academy Research Fellow at the Finnish Academy of Sciences from 2006 to 2012. In 2019, she co-founded the Center for Social Data Science (CSDS) at the University of Helsinki and became its first director.
University of Helsinki
Her notable projects include WEBSOM, a method for visualizing large text collections using self-organizing maps; Citizen Mindscapes, which examines digital communication to gauge public opinion and societal trends; and Morfessor, an algorithm for unsupervised morphological analysis widely used in NLP. Her publications have appeared in leading journals such as IEEE Transactions on Neural Networks, Information Sciences, Neurocomputing, Artificial Intelligence Review, ACM Transactions on Speech and Language Processing, and Cognitive Science.
In addition to her research, Lagus teaches and supervises students in AI and digital social science, contributing to interdisciplinary research and education that leverages AI for societal benefit.
Marco Lapegna
In 1991 Marco Lapegna received his PhD in Applied Mathematics and Computer Science at the University of Naples Federico II (Italy), and since 2001 is a professor of Computer Science at the Department of Mathematics and Applications of the same university.
His main research interests concern methods, algorithms, and software for parallel and distributed computing environments applied to computational mathematics and machine learning, taking into account the influence of the technological evolution on them (cluster computing, multicore computing, grid computing, cloud, and edge computing). He has an active academic life with several institutional coordination duties.
Juan A Lara
Juan A. Lara is Associate Professor and Research Scientist at University of Córdoba, Spain. He is currently member of Department of Computer. He holds a Ph.D. in Computer Science and two Post Graduate Masters in Information Technologies and Emerging Technologies to Develop Complex Software Systems from Technical University of Madrid, Spain. He is author of more than a 40 papers published in international impact journals. His research interests in computer science include data mining, knowledge discovery in databases, data fusion, artificial intelligence and e-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).
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