Advisory Board and Editors Data Mining & Machine Learning
Shalu Jhanwar
Dr. Jhanwar’s research interests lie at the interface of epigenomics, genomics, bioinformatics, and machine learning. She has extensive experience in plant and animal sciences, development biology, and cancer genomics and epigenomics. She has developed machine learning-based tools and bioinformatic analysis pipelines integrating genomic and epigenomic information. In the past, she has identified biomarkers differentiating wild and cultivated varieties of plants using comparative genomic approaches. Upon integrating transcriptomics and chromatin accessibility, presently she is studying the regulatory dynamics underlying structural diversity during organogenesis.
Yaochu Jin
Professor of Computational Intelligence, University of Surrey, UK, Finland Distinguished Professor, Jyvaskyla, Finland, Changjiang Distinguished Professor, Northeastern University, China. Vice President for Technical Activities, IEEE Computational Intelligence Magazine, IEEE Distinguished Lecturer.
Goo Jun
I am currently an assistant professor at the University of Texas Health Science Center at Houston. I work on statistical genetics, computational biology, bioinformatics, and sequence data analysis. With backgrounds in machine learning and data mining, my research is focused on development of computational and statistical methods for analysis of massive data to understand genetics and biology of complex traits. I have been working on the analysis of large-scale next-generation sequencing data, for which I developed statistical models and software pipelines for detecting sample contamination, variant discovery, machine-learning based variant filtering, and genotyping of structural variations. I also work on genetics of diabetes, obesity, and related traits and study of metabolomic and microbiome compositions related to genetics of common and complex traits.
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.
Gregory M Kapfhammer
Focusing on software engineering, software testing, and data science, Gregory M. Kapfhammer is an Associate Professor of Computer Science at Allegheny College.
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.
Jeffrey O Kephart
IBM Research scientist known for seminal work on computer virus epidemiology and immunology, emergent behavior of economies involving software agents, and autonomic (self-managing) computer systems. Author of over 150 refereed papers (h-index > 50) and over 30 issued patents. Led data center energy initiative resulting in multiple commercial offerings from IBM's software, systems and services divisions. Awarded IEEE Fellow for leadership and technical contributions to autonomic computing.
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.