I have a bachelor’s degree in veterinary medicine as well as PhD and master’s degree in animal Behavior and Management. In addition to, two postdoctoral scholar training in the area of Animal Behavior and Welfare at UC Davis. I am currently pursuing board certification from the American College of Animal Welfare (ACAW). I completed my PhD in USDA-ARS Livestock Behavior Research Unit at Purdue University in 2014, where I conducted experimental research to investigate the effect of group housing on the behavior, health, and welfare of dairy and veal calves. My research specialty focus on applied animal behavior, welfare, and stress physiology. My research utilized behavioral and physiological measures to investigate the effects of management practices and housing design, environmental conditions, and nutrition on the health, production, and welfare of animals. To master additional knowledge in epidemiology and biostatistics and advance animal welfare through sound study design and statistical analyses, I completed my second postdoctoral position at the Dairy Epidemiology Laboratory at VMTRC in Tulare, CA.
As a veterinary epidemiologist I specialize in dairy cattle infectious diseases and welfare. I received my veterinary medicine degree from Cairo University (1998), practiced for two years before completing the Food Animal Production Medicine Internship at the Caine Veterinary Teaching Center at the U of Idaho, followed by the Food Animal Reproduction and Herd Health Residency at U of California, Davis. I completed my masters and doctoral degrees at UC Davis in Preventive Veterinary Medicine and Epidemiology, respectively.
Ksenija Baždarić is associate professor at the Department of Basic Sciencies Rijeka University Faculty of health Studies, Croatia. Her academic background lies both in social sciences and biomedicine. She received her master’s degree in psychology (2002) and PhD in social medicine (2012). She teaches medical informatics, statistics and scientific methodology. Her investigation for the PhD thesis ''The Value of Plagiarism Detection Procedure in a Biomedical Journal'' was focused on the detection of similar texts with web-services CrossCheck and eTBLAST in the Croatian Medical Journal (www.cmj.hr) during 2009-2010, and the development of standard operating procedure for detecting and dealing with plagiarism in biomedical journals. She became Research Integrity Editor at the Croatian Medical Journal (http://www.cmj.hr) in 2012 and Chief Editor of European Science Editing (http://www.ease.org.uk/publications/european-science-editing), the offical journal of the European Association of Science Editors (http://www.ease.org.uk/) in 2015.Her current research activities include open science.
Dr. Berghout received her PhD in Biochemistry from McGill University in Montreal, QC where she researched the genetics of complex traits and susceptibility to infectious disease in humans and mouse models. Following that, she spent three years as the Outreach Coordinator for the Mouse Genome Informatics (MGI) database in Bar Harbor, ME. There, she trained researchers in genetics, genomics, data structures and data mining to answer biological questions, and worked closely with other members of the MGI group to develop and optimize the MGI resource. Now her research interests include genetics of all kinds, personalized medicine, big data, and scientific communication. She is currently pursuing projects in precision medicine for analysis of transcriptome data from patients with rare lung diseases (Sarcoidosis, Coccidiomycosis), and integrative network analysis of complex traits including Alzheimer's Disease. She is currently appointed at the University of Arizona's Center for Biomedical Informatics and Biostatistics (CB2) and The Center for Genetics and Genomic Medicine (TCG2M) in Tucson, AZ.
Chris Brown is a clinical trial bio-statistician at the NHMRC Clinical Trails Centre at the University of Sydney. His main area of expertise is in oncology trials but also has experience in cardiology and neonatal research. His main areas of research are in pharmacoepidemiology and statistical methods.
Siqi Bu received the Ph.D. degree from the electric power and energy research cluster, The Queen’s University of Belfast, Belfast, U.K., where he continued his postdoctoral research work before entering industry. Then he was with National Grid UK as an experienced UK National Transmission System Planner and Operator. He is an Associate Professor with The Hong Kong Polytechnic University, Kowloon, Hong Kong, and also a Chartered Engineer with UK Royal Engineering Council, London, U.K.. He is Senior Member of IEEE. His research interests are power system stability analysis and operation control, including wind power generation, PEV, HVDC, FACTS, ESS and VSG.
He is an Associate Editor of IEEE ACCESS and CSEE JOURNAL OF POWER AND ENERGY SYSTEMS, a Guest Editor of IET RENEWABLE POWER GENERATION, ENERGIES and IET GENERATION, TRANSMISSION & DISTRIBUTION, and an Editor of IEEE OPEN ACCESS JOURNAL OF POWER AND ENERGY and PROTECTION AND CONTROL OF MODERN POWER SYSTEMS. He has received various prizes due to excellent performances and outstanding contributions in operational and commissioning projects during the employment with National Grid UK. He is also the recipient of Outstanding Reviewer Awards from IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, IEEE TRANSACTIONS ON POWER SYSTEMS, APPLIED ENERGY, RENEWABLE ENERGY, INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS and JOURNAL OF MODERN POWER SYSTEMS AND CLEAN ENERGY respectively.
Associate Research Professor of Statistics & Biostatistics at Rutgers University, with adjunct appointments in the Deptartment of Genetics and the Center of Alcohol Studies. Particular interest in how statistics is applied, especially in Biology, Medicine, and particularly Human Genetics.
Head of Human and Comparative Genomics Laboratory in the Biodesign Institute at Arizona State University. Affiliated faculty with the Center for Evolution and Medicine, ASU.
My research is at the interface of genetics, statistics, and software development. I am primarily interested in developing statistical models to estimate evolutionary process from large, genomic datasets. Currently most of my research is connected to mutations.
Tianfeng Chai is an Associate Research Scientist at CICS-MD and the Department of Atmospheric & Oceanic Science, University of Maryland, College Park, Maryland, USA. He got his master and bachelor degrees from Tsinghua University in Beijing, majoring in Fluid Mechanics, Engineering Mechanics, and Environmental Engineering. He earned his Ph.D. at the University of Iowa, with his dissertation of "Four-Dimensional Variational Data Assimilation Using Lidar Data" focusing on atmospheric boundary flow. He then worked with Dr. Greg Carmichael to develop chemical transport model adjoints and computational framework for data assimilation applications before moving to working on the NOAA National Air Quality Forecast Capability (NAQFC) project in 2007. He currently works on the inverse modeling problems using HYSPLIT (Hybrid Single Particle Lagrangian Integrated Trajectory Model) to support several projects at NOAA Air Resources Laboratory.
Assistant Professor of Biostatistics, Mayo Clinic. Ph.D., University of Pennsylvania. My research concerns the development and application of powerful and robust statistical methods for high-dimensional "omics" data, arising from modern high-throughput technologies such as microarray and next-generation sequencing. I am particularly interested in methods for microbiome sequencing data. Much of this effort is motivated by ongoing collaborations in projects that study the role of the human microbiome in disease pathogenesis using metagenomic sequencing.
Research interests include statistical genetics, genomics and metagenomics; and high-dimensional statistics.
I am a tenure-track Assistant Professor of Biostatistics and a member in Center for Computational Biology and Bioinformatics at Indiana University School of Medicine. I received a MS in Biostatistics and another MS in Computer Science from the Johns Hopkins University, and PhD in Computer Science and Informatics from Emory University. I also interned in CareerBuilder Data Science and Amazon Machine Learning.
Gary has research interests primarily focussed on statistical (and reporting) aspects in developing and validating multivariable prediction models. He has published over 100 papers on clinical trials, observational studies, systematic reviews, quality of life, propensity scores and prediction models.
Gary is a statistical editor ("hanging committee") for the BMJ.
Gary also led the development of the TRIPOD Statement for reporting clinical prediction models - www.tripod-statement.org.