Find Academic Supervisors

• Pediatric Cardiology
• Fetal Cardiology
• Congenital Heart Disease
• +4 more

Frédéric Dallaire is a Professor of Pediatrics at the Université de Sherbrooke and a leading Pediatric and Fetal Cardiologist at the Centre Hospitalier Universitaire de Sherbrooke (CHUS). He directs the Mother-Child Research Axis at CHUS and founded the Canadian Congenital and Pediatric Cardiology Research Network . His research focuses on congenital heart disease risk factors, fetal echocardiography, pediatric cardiovascular reference standards, and clinical knowledge integration. Education : MD (2006) and PhD in Epidemiology (2006) from Université Laval Leadership : Director of CHUS Mother-Child Research Center, founder of Canadian Pediatric Cardiology Network Research Interests span congenital heart disease , fetal cardiology , pediatric echocardiography , and epidemiological studies . He investigates risk factors in repaired tetralogy of Fallot, prenatal detection of heart defects, and vascular parameter normalization. His recent articles highlight machine learning applications in MIS-C/Kawasaki disease prediction epidemiological trends in congenital heart disease standardization of pediatric cardiovascular imaging gender disparities in Kawasaki disease outcomes Scientific Awards include: 2024 RECMUS Award for graduate student supervision 2023 FRQS Clinical Scholar (Senior) 2021 Emerging Academic Leader Award Multiple Dean's Honor List recognitions (2016-2023) His grants secure over $10 million in funding, including major awards from CIHR and Heart & Stroke for congenital heart disease research and rare disease network development.

Erhard RahmView profile

Professor

• Data Integration
• Knowledge Graphs
• Privacy-Preserving Data Analysis
• +4 more

Prof. Dr. Erhard Rahm is a leading computer science academic at the University of Leipzig , affiliated with the Institute of Computer Science under the Faculty of Mathematics and Computer Science. With over 35 years of research contributions, he specializes in scalable data integration, privacy-preserving record linkage (PPRL), and graph analytics for knowledge graphs and digital twins. Key research areas: Data Integration for Knowledge Graphs, Privacy-Preserving Data Analysis, Dynamic Graph Management Recent work focuses on hybrid digital twins, medical image analysis, and secure health data processing 2016-2021: Speaker of GI's Database Systems and Information Systems division Scientific Contributions: His 15 most recent publications (2023-2025) span: Privacy-preserving machine learning (federated learning, PPRL with LLMs) Graph-based data transformations (Temporal Knowledge Graphs, Property Graph Streams) Automated schema matching and entity resolution systems Biomedical data applications (cell confluence assessment, stress detection) Selected Honors: VLDB Ten-Year Best Paper Award (2011) ICDE Influential Paper Award (2013) MSR Outstanding Collaborator Award (2016) Leadership Roles: Currently Vice President of the Gesellschaft für Informatik (since 2022), former DFG reviewer (2016-2024), and active in NFDI infrastructure (2019-2022). He leads research at ScaDS.AI, focusing on big data integration and scalable graph analytics.

• modeling of the educational process
• information technology in education
• databases
• +3 more

Natalia Volodymyrivna Shcherbak serves as a Senior Lecturer in the Department of Computer Systems and Networks at Zaporizhia State Technical University's Faculty of Computer Science and Technology, where she also holds the position of Deputy Dean. With over two decades of academic experience since joining the university in 2001, she has established herself as a dedicated educator and researcher in computer science. Dr. Shcherbak graduated from Zaporizhia State Technical University in 2001 with a specialty in "Computer systems and networks" and a qualification as "systems engineer." Her educational background has provided the foundation for her academic career focused on computer science education and research. Her research interests span several key areas including modeling of the educational process, information technology in education (particularly Learning Management Systems), databases, web services, and Service-Oriented Architecture (SOA). Her scholarly work demonstrates a consistent connection between theoretical computer science concepts and practical educational applications, with particular emphasis on developing methodologies for assessing educational quality and optimizing learning experiences through technology. Analysis of her publication trends from 2014-2024 reveals a strong research trajectory in web services quality assessment, decision-making methods for service selection, and educational process modeling. Her recent work (2022-2024) advances multi-criteria decision-making methodologies for QoS-based web service environments, while her earlier publications (2016-2018) focus on test task quality assessment methods and educational resource development. Quality assessment methodologies for web services Educational process modeling and optimization Test task quality and complexity evaluation Computer science curriculum development Throughout her career, Dr. Shcherbak has taught a diverse range of computer science subjects including Assembly language programming, database organization, algorithms and calculation methods, programming, computer logic, and modern programming methods. Her teaching portfolio demonstrates expertise across both theoretical foundations and practical applications in computer science education.

Wolfgang TressView profile

Professor

• Organic Electronics
• Perovskite Solar Cells
• Device Physics
• +4 more

Prof. Dr. Wolfgang Tress is a faculty member at the ZHAW Zurich University of Applied Sciences , affiliated with the School of Engineering . He leads the Novel Semiconductor Devices Team, focusing on Organic Electronics and Photovoltaics. His research spans perovskite solar cells, device physics, optoelectronic characterization, and mixed ionic-electronic conductors. PhD in Physics from Technical University of Dresden (2012) Master in Electrical Engineering from University of Ulm (2007) His research explores critical aspects of perovskite materials, including device stability, ionic conductivity, and efficiency optimization through advanced modeling and experimental techniques. Key projects involve correlative optoelectronics, interface tailoring, and lead-free perovskite development. Recent publications highlight trends in machine learning applications for perovskite stability analysis, ion migration dynamics, and multifunctional material design. He has authored over 20 peer-reviewed articles in journals like Advanced Energy Materials , Nature Energy , and Science . 2023 : Web of Science Highly Cited Researcher 2019 : ERC Starting Grant 2019 : SPS Award in Applied Physics 2016 : Zeno Karl Schindler Award As project leader, he drives initiatives in perovskite-on-silicon tandem cells, defect engineering, and laboratory digitization. His work combines experimental fabrication with computational modeling to address photovoltaic reliability challenges.

Lydia E. KavrakiView profile

Professor

• Robotics
• Artificial Intelligence
• Computational Biomedicine
• +11 more

Lydia E. Kavraki is the Noah Harding Professor of Computer Science and Professor of Bioengineering, Electrical and Computer Engineering, and Mechanical Engineering at Rice University . As Director of the Ken Kennedy Institute , she leads initiatives in AI, data science, and computing across seven schools and 27 departments. B.A., University of Crete (1989) Ph.D., Stanford University (1994) Her research bridges robotics , AI , and computational biomedicine . In robotics, she pioneered sampling-based motion planning (OMPL library) and recently developed microsecond-level planning frameworks. In biomedicine, her work focuses on peptide-HLA interactions , personalized immunotherapy , and drug metabolite prediction , with tools like APE-Gen and DINC. Recent publication trends highlight her interdisciplinary work: 2022 PNAS Nexus study on HLA charge interactions , 2020 Frontiers in Immunology work on TCR cross-reactivity , and 2017 structural modeling of immune checkpoints. Themes include structure-based drug discovery , AI-driven motion planning , and ethical AI considerations . Scientific honors include: IEEE Frances E. Allen Medal (2023) ACM-AAAI Allen Newell Award (2020) ACM Athena Lecturer (2017) Three National Academy memberships (NAS/NAE/NAM) NSF CAREER, Sloan Fellowship, Whitaker Investigator Kavraki actively mentors students and postdocs, with over 40 alumni in academia and industry. Her Ken Kennedy Institute leadership spans AI in Health Conference organization and industry partnerships .

Maragatha KuchibhatlaView profile

Professor

• Statistical research methodology
• Analysis of repeated measurements
• Latent growth curve models
• +4 more

Maragatha Kuchibhatla is Professor of Biostatistics and Bioinformatics and Professor in Psychiatry and Behavioral Sciences at Duke University School of Medicine. She serves as Senior Fellow in the Center for the Study of Aging and Human Development and leads the Biostatistics, Epidemiology, and Research Design (BERD) Methods Core within the Division of Biostatistics. Her educational background includes a Ph.D. from Texas A&M University (1992). Ph.D., Biostatistics, Texas A&M University, 1992 Kuchibhatla's research specializes in advanced statistical methodologies for complex clinical data, with emphasis on longitudinal modeling (latent growth curve/class models), classification algorithms, and psychiatric clinical trial design. Her work bridges biostatistical innovation with practical applications in trauma recovery, geriatric care, and health disparities research, particularly in comorbid populations. Recent publications (2024-2025) demonstrate strong interdisciplinary focus across trauma surgery, hematology, global health, and emergency medicine. Key trends include development of predictive models for bleeding/thrombosis risks, age-neutral surgical protocols, and implementation of machine learning in resource-limited settings—showcasing her ability to translate statistical theory into life-saving clinical tools. She currently directs 9 major grants totaling over $20 million in active funding: POWER: Predicting Obesity with Enhanced EHR Resources (NICHD, 2022-2027) Machine learning for thyroid cytopathology in LMIC (NCI, 2021-2027) Personalized Targeted Nutrition via StructurEd Delivery (DoD, 2022-2026) Ultrasound-guided nerve block training (SAEM, 2025-2026) Bystander interventions in opioid cardiac arrests (EMF, 2025-2026) Lung transplantation CT-based selection (RSNA, 2025-2026) Trauma nutrition optimization (Fresenius Kabi, 2024-2026) Elderly trauma ventilation care (NIH, 2020-2025) Nasopharyngoscope cancer screening network (NIH, 2019-2025) Her grant portfolio reflects strategic focus on trauma recovery, health equity, and diagnostic innovation. Kuchibhatla collaborates extensively through Duke's BERD Core and the Center for the Study of Aging, contributing biostatistical leadership to multidisciplinary teams addressing critical gaps in trauma care, psychiatric treatment, and global health access.

• Automatic text analysis
• Information search
• IT project management
• +2 more

Natalia Sergeevna Belova is an Associate Professor in the Department of Software Engineering at the Faculty of Computer Science, National Research University Higher School of Economics (HSE University) in Moscow. She has been working at HSE since 2012 with 13 years of scientific and teaching experience. Education: 2010: Candidate of Technical Sciences from Moscow State University of Instrument Engineering and Computer Science, specialty 05.13.11 "Mathematical and software support for computing machines, complexes and computer networks" 2009: Postgraduate study from Moscow State University of Instrument Engineering and Computer Science 2005: Engineer qualification in "Computers, complexes, systems and networks" from Moscow State Academy of Instrument Engineering and Computer Science Research Interests: Dr. Belova's research focuses on automatic text analysis, information search, IT project management, embedded databases, and project-based learning in engineering education. Her work bridges theoretical computer science with practical applications in software engineering education and industry. She has made significant contributions to pattern recognition, face identification systems, and the development of computationally efficient methods for intelligent systems processing multimedia information. Publications Trend: Dr. Belova's research has evolved from foundational work on embedded databases to more recent contributions in computer vision, affect recognition, and AI applications. Her early work (2009-2013) focused on database systems reliability and project-based learning methodologies. Since 2015, her research shifted toward computer vision, pattern recognition, and deep learning applications. Her most recent publications (2022-2025) demonstrate expertise in AI applications across various domains including transportation systems and video analysis. Scientific Achievements: Recipient of the Grant of the President of the Russian Federation for young doctors of science MD-306.2017.9 (2017-2018) Gratitude from the Vice-Rector of HSE University (February 2025) Gratitude from the Faculty of Computer Science, HSE University (July 2023) Multiple publication bonuses for journals in List A and international peer-reviewed publications (2016-2025) Best Teacher award for 2016-2017 Member of the High Professional Potential Group (HSE personnel reserve) Teaching and Mentoring: Dr. Belova has supervised over 25 bachelor's theses on diverse software engineering topics including mobile applications, web platforms, biometric identification systems, and educational software tools. She teaches courses on "Group Dynamics and Communications in Professional Software Engineering Practice" and "Economics of Software Engineering." Her commitment to project-based learning methodology is evident in both her research and teaching practice. Professional Development: Dr. Belova actively participates in advanced training programs, including recent courses on modernization of educational programs in software engineering (2025) and problems of training modern specialists in software engineering (2024).

Mark BarashView profile

Associate Professor

• forensic genetics
• molecular phenotyping
• forensic DNA analysis
• +7 more

Mark Barash is an Associate Professor in the Department of Justice Studies at San Jose State University's College of Social Sciences. His research integrates molecular biology, anthropology, microbiology, human genetics, genomics, bioinformatics, statistics, and machine learning to advance forensic DNA analysis capabilities. University: San Jose State University College: College of Social Sciences Department: Justice Studies Email: mark.barash@sjsu.edu Barash's primary research focuses on developing novel forensic DNA panels using massively parallel sequencing technology and machine learning pipelines to create molecular identikits for criminal investigations. His work addresses critical challenges in DNA recovery from firearms/ammunition and genetic/epigenetic trait prediction for cold cases, missing persons, and counter-terrorism. Key research trends include: Enhancing forensic DNA recovery techniques from challenging surfaces Machine learning applications in DNA phenotyping Development of the "Genetic Witness" tool for appearance prediction Interdisciplinary collaborations with forensic industry partners Validation of probabilistic genotyping systems Microbial profiling for forensic identification His work impacts criminal investigations by addressing declining police clearance rates for homicides (66%) and sexual assaults (32%) in the U.S., aiming to resolve over 250,000 accumulated cold cases. Barash combines his academic work with practical forensic experience, having served over nine years as a forensic DNA officer (Chief Inspector) in the Israel Police, analyzing evidence in approximately 600 criminal cases.

Mizuho IwaiharaView profile

Professor

• Database Systems
• Web Information Systems
• Text Mining
• +3 more

Professor Mizuho Iwaihara is affiliated with Waseda University's Faculty of Science and Engineering and Graduate School of Information, Production, and Systems. Her research focuses on database systems, web information retrieval, text mining, security/privacy, and social media analysis. She has led significant projects on Wikipedia edit history analysis, knowledge graph construction, and privacy-preserving frameworks. Key research areas: Database Query Processing, Web Information Systems, Text Mining, Knowledge Management, Social Media Her recent publications address semantic analysis of collaborative content, topic evolution tracking, and privacy behavior modeling. Over 85 papers with 349 citations reflect her impact in database and social media research. Scientific achievements include: Best Demo Award (2014) for WikiReviz Best Paper Award (2008) at IFIP e-Business Conference EC-Web2006 recognition Grants from Japan Society for the Promotion of Science (JSPS) span multiple projects on knowledge graph development, social content analysis, and privacy-preserving systems. She supervises numerous graduate students and leads the Data Engineering Laboratory at Waseda University.

Rolf de ByView profile

Associate Professor

• Spatial Databases
• GIS Technology
• Spatial Data Infrastructure (SDI)
• +7 more

Rolf de By is an Associate Professor at the GeoInformation Processing (GIP) Department within ITC, University of Twente, with a career spanning over two decades. His work bridges spatial databases, GIS technology, and geoinformatics to enhance system design and implementation for spatial data sharing and analysis. Focus areas include Spatial Data Infrastructure (SDI), biodiversity databases, moving object data, and vague spatial objects. He leads the SDI - Technology theme, collaborating with experts in Web GIS, geoservices, and architecture. His teaching portfolio includes modules on databases, spatial data modeling, and research skills for geoinformatics and land administration programs. Research trends highlight his contributions to geospatial applications in food security, smallholder agriculture, land registration, and ethical AI. He integrates machine learning (e.g., convolutional networks) and IoT technologies into agricultural monitoring systems, emphasizing data-driven solutions for developing economies. Projects span Mongolia and Tanzania, addressing environmental and societal challenges through SDI. As a portfolio manager for the GIP department, de By drives external project acquisition and proposal writing. His academic legacy includes co-authoring foundational textbooks like Principles of Geographic Information Systems , shaping geoinformatics education globally.

Osvaldo ValeriaView profile

Professor

• Forest Management
• Remote Sensing
• Geographic Information Systems
• +11 more

Osvaldo Valeria is a Professor at the University of Quebec in Abitibi-Témiscamingue (UQAT) and co-holder of the UQAT-UQAM Chair in Sustainable Forest Management. He serves as Director of the Masters in Sustainable Management of Forest Ecosystems and Co-Director of the Lac Duparquet Teaching and Research Forest. His work focuses on forestry operations, geomatics and remote sensing within the field of sustainable forest management. Dr. Valeria earned his Ph.D. in Forest Sciences from Laval University in 2004, following an M.Sc. in Forest Sciences from the same institution in 1999. He completed his undergraduate education with a BSc in Forest Engineering from Universidad de Concepcion, Chile in 1991. Professor Valeria specializes in spatial analyses using geographic information systems and remote sensing systems, with additional expertise in database design. His research laboratory conducts studies focused on forest soil mapping, wetland mapping, monitoring forest dynamics following natural disturbances, bryophyte species diversity, and the automatic identification of forest roads and their closure. His work represents the cutting edge of knowledge on geospatial data, specialized software applications, remote sensing data sources, and statistical processing of spatial data in forestry contexts. He actively participates in and leads numerous research projects in sustainable forest management, with particular emphasis on the ecological, economic and social feasibility of sustainable forest management strategies in the boreal forest. An analysis of Professor Valeria's recent publications reveals a strong emphasis on sustainable forest management in the boreal region, with particular attention to carbon dynamics, climate change impacts, and the use of advanced remote sensing technologies like LiDAR. His work bridges ecological, economic, and social considerations in developing sustainable forestry practices, with increasing focus on climate adaptation strategies and carbon sequestration potential in forest ecosystems. Professor Valeria has advised numerous graduate students throughout his career, including over a dozen doctoral candidates and more than twenty master's students. His research is supported by multiple funding agencies including the UQAT-UQAM Chair in Sustainable Forest Management, the FQRNT Forest Fund, Géoïde, the CRSNG, the MRNF, DEC, MDEIE and the RGDF – Center of Excellence. His laboratory represents a significant hub for research and training in forest geomatics and sustainable management practices. Dr. Valeria's laboratory is at the forefront of applying geospatial technologies to forest management challenges. His team works extensively with LiDAR data, satellite imagery, and advanced spatial analysis techniques to address critical questions in sustainable forestry. The lab maintains strong collaborations with other researchers across Canada and internationally, contributing to a robust research environment for graduate students and postdoctoral fellows.

• Machine Learning
• Parallel and Distributed Computing
• Graph Theory
• +6 more

Giuseppe Agapito is an Associate Professor and senior research scientist at Magna Graecia University of Catanzaro, working within the Department of Law, Economics and Sociology. His academic profile spans both teaching responsibilities and active research in computational fields. His research focuses on computational intelligence techniques applied to complex real-world problems, particularly in machine learning, parallel and distributed computing, and graph theory. His work demonstrates strong interdisciplinary applications, especially in biology and omics sciences where he develops methods to leverage the vast amounts of data being produced. Agapito has published over 100 papers in international journals and conference proceedings. His recent publications show a strong focus on graph-based machine learning approaches, sentiment analysis techniques, and bioinformatics applications. His research trajectory indicates a progression from bioinformatics applications toward more general machine learning frameworks with applications across multiple domains. As an active member of the academic community, he serves as a reviewer for scientific journals and participates as chair and program committee member for national and international conferences. His editorial work for PeerJ Computer Science demonstrates his standing in the academic community. His teaching responsibilities include courses on Computer Science and Data Analysis within the Department of Law, Economics and Sociology, suggesting an interdisciplinary approach that applies computational methods to social sciences domains.

Albert J. KooistraView profile

Associate Professor

• Structural Bioinformatics
• Cheminformatics
• Computer-Aided Drug Discovery
• +6 more

Albert J. Kooistra is an Associate Professor in Data Science for Drug Design (DSDD) at the Department of Drug Design and Pharmacology, University of Copenhagen (DK). He heads the Pharmaceutical Data Science team and the Data and Computing Facility, where he develops computational approaches to advance pharmaceutical research and drug discovery. Dr. Kooistra earned his Master's degree in bioinformatics followed by a PhD in computational medicinal chemistry from the Vrije Universiteit Amsterdam (NL), with postdoctoral training in molecular dynamics, structural bioinformatics, and cheminformatics. His research spans structural bioinformatics, cheminformatics, computer-aided drug discovery & design, research tool/database development, and machine learning/AI applications in pharmaceutical sciences. He is the founder of KLIFS (https://klifs.net), a structural kinase database used by over 1,600 researchers monthly, and has made significant contributions to GPCRdb. His work bridges computational science with pharmaceutical applications to accelerate therapeutic development. Dr. Kooistra's recent publications demonstrate his focus on structural databases for drug discovery, particularly in kinase and GPCR research. His work integrates AlphaFold2 predictions, database development, and structural analysis to create valuable resources for the scientific community. MGMS Frank Blaney Award (2021) from Molecular Graphics and Modelling Society for enabling research via scientific databases Educational Innovation Award (2017) from VU University Amsterdam for implementing Virtual Reality in pharmaceutical education Dr. Kooistra has developed innovative educational programs across multiple levels, including AI in Drug Discovery (international PhD course), Hands-on Computational Medicinal Chemistry, and Digital Core Curriculum implementation for undergraduate data science education. His teaching reaches hundreds of students annually, from high school to PhD level. He leads the Pharmaceutical Data Science team and the Data and Computing Facility at the University of Copenhagen, providing essential computational infrastructure and expertise for pharmaceutical research while maintaining active industry collaborations through Synamics Therapeutics (Co-founder/CTO) and Kvantify (Senior Scientific Expert).

• Semantic Data Integration
• Ontology-based Database Design
• Extremely Large Database Design
• +5 more

Ladjel Bellatreche is a Full Professor of Data Engineering at ISAE-ENSMA (National Engineering School for Mechanics and Aerotechnics) in Poitiers, France, where he has served as faculty since September 2010. He leads the Data and Model Engineering Team within the Laboratory of Computer Science and Automatic Control for Systems (LIAS). Prior to his current position, he spent eight years as Assistant and then Associate Professor at Poitiers University. His academic journey includes visiting positions at the University of Québec en Outaouais (Canada), Purdue University (USA), and Hong Kong University of Science and Technology (China). Professor Bellatreche's research focuses on Semantic Data Integration, Ontology-based Database Design, Life Cycle of Extremely Large Database Design, Big Data & Cloud Computing, Green Computing, and Database Deployment. His work bridges theoretical foundations with practical applications in data management systems, particularly addressing challenges in scalability, efficiency, and semantic enrichment of data repositories. His research has evolved from traditional data warehousing to encompass modern big data analytics and energy-efficient database systems. Analysis of his recent publications reveals a strong trend toward addressing the challenges of big data management through innovative approaches in semantic integration, graph-based query optimization, and green computing. His research shows consistent focus on data warehousing evolution, with increasing emphasis on semantic technologies, RDF data processing, and energy efficiency in query processing. The work demonstrates progression from traditional database design to contemporary challenges in data science and advanced analytics. Professor Bellatreche actively contributes to the academic community through editorial roles, including serving as an Editorial Board Member for the International Journal of Reasoning-based Intelligent Systems and Subject Area Editor for the Scalable Computing Journal. He has organized numerous international conferences and workshops including DAWAK, DOLAP, and MEDI, and has served on program committees for over forty international conferences. He is deeply involved in research mentorship and international collaboration, particularly in Africa and Asia, where he co-supervises students and organizes academic events such as ICT-EurAsia and CIIA. His work extends to promoting research capacity building in developing regions through academic partnerships and collaborative projects. As leader of the Data and Model Engineering Team at LIAS laboratory, Professor Bellatreche oversees research initiatives focused on advanced data management systems. His team works on cutting-edge problems in database design, optimization, and integration, with particular expertise in semantic data warehousing, big data analytics, and green computing approaches for database systems.

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +8 more

Dr. Bhushan Bonde serves as a Visiting Professor of Computational Biology at the University of Surrey's Research Directorate, leveraging over 15 years of expertise in artificial intelligence and machine learning for drug discovery and disease treatment development. His work integrates quantum computing applications with deep learning methodologies to advance biomedical solutions. His research spans Systems Biology modeling of pathogens (E.coli, Yeast, M.TB), Genetics-based patient stratification through GWAS/PheWAS, and comprehensive NGS data analysis (DNA-Seq, RNA-Seq, ChIP-Seq). He specializes in omics-scale meta-analysis including causal reasoning and integrative -omics approaches, with extensive Python programming experience for bioinformatics applications. His technical proficiency covers HPC infrastructure management, cloud deployment (AWS/Azure/Google), and container engineering for scientific computing. Dr. Bonde has supervised doctoral research including Maranga Mokaya's Oxford DPhil project on quantum computing for small molecule simulation (2020) and Effrosyni Karakitsou's Surrey MSc thesis on causal reasoning in yeast networks (2015). He has led 10 industrial high-impact projects and maintains active involvement in Agile project management through Azure-DevOps and Git. His infrastructure expertise includes Linux server administration, GPU/CPU computing, and hybrid cloud/on-premise system deployment for bioinformatics applications.