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Peter HaasView profile

Professor

• Data Management
• Machine Learning
• Simulation
• +6 more

Peter Haas is a Professor at the Manning College of Information and Computer Sciences at the University of Massachusetts Amherst, with an adjunct role in Industrial Engineering. Previously, he spent 30 years as a Principal Research Staff Member at IBM Research and held a consulting professorship in Management Science and Engineering at Stanford University. His research focuses on applying probability and statistics to data management, simulation of complex systems, and machine learning scalability. Education : PhD, Operations Research, Stanford University, 1986 MS, Statistics, Stanford University, 1984 MS, Environmental Engineering, Stanford University, 1979 SB, Engineering and Applied Physics, Harvard University, 1978 Research Interests : Haas’s work spans stochastic systems, probabilistic databases (e.g., MCDB and SimSQL), sampling techniques, and simulation optimization. He pioneered methods for managing uncertain data and scalable machine learning, including compressed linear algebra for declarative systems. His recent focus includes in-database decision support and hybrid simulation metamodeling with neural networks. Key Contributions : He developed the Online Aggregation framework (SIGMOD 1997), which earned a Test-of-Time Award in 2007. His work on matrix factorization and distributed stochastic gradient descent (DSGD) revolutionized large-scale machine learning. He also advanced techniques for estimating distinct-values and correlation discovery in databases. Awards : A six-time recipient of IBM’s Pat Goldberg Memorial Award, he is an ACM and INFORMS Fellow. His honors include the VLDB Best Paper Award (2016), EDBT Best Paper (2018), and recognition in Communications of the ACM. Advising & Grants : He advises four current PhD students and has graduated Matteo Brucato. His IBM career included over 30 patents, including foundational work for DB2’s sampling capabilities and IBM Watson analytics. He leads the DREAM Lab, focusing on data systems for exploration and analytics. Labs/Teams : Directs the Data systems Research for Exploration, Analytics, and Modeling (DREAM) Lab, advancing projects like Splash (health system simulation) and SuDocu (document summarization by example).

Stephen ThompsonView profile

Senior Lecturer

• Strength and Conditioning
• Resistance Training
• Velocity-Based Training (VBT)
• +4 more

Stephen Thompson is a Senior Lecturer and Course Leader for the MSc in Strength and Conditioning (S&C) Coaching at Sheffield Hallam University. He holds accreditation as a Strength and Conditioning Coach (ASCC) with the UKSCA and is completing a PhD focusing on Velocity-Based Training (VBT), specifically load-velocity profiling for maximal intensity prediction. His research interests include resistance training, technology in training, and youth development, with publications in journals like Journal of Strength and Conditioning Research and Sports Medicine . He teaches across undergraduate and postgraduate programs in the Department of Sport and Physical Activity, leading courses such as the MSc in Strength and Conditioning Coaching and Applied Sport and Exercise Science. His academic roles include Employability Lead, fostering partnerships with local clubs and organizations for student placements. Thompson’s work spans academia and applied practice, with consultancy roles for elite teams like GB Diving and a Championship football academy. His research emphasizes VBT methodologies, load prescription, and the validation of sports technologies. Key contributions include studies on barbell velocity measurement, 1RM prediction models, and athlete performance profiling. He has secured grants from national governing bodies and collaborated internationally on projects. His expertise bridges theoretical research and practical applications in elite sport conditioning and athlete development.

Michael DanielsView profile

Professor

• Biostatistics
• Bayesian methodology
• Methodology for longitudinal data
• +1 more

Michael Daniels is a Professor and Chair of the Department of Statistics at the University of Florida, holding the Andrew Banks Family Endowed Chair. He previously held faculty positions at the University of Texas at Austin, Iowa State University, and Carnegie Mellon University. He earned his Sc.D. in Biostatistics from Harvard University (1995) and A.B. in Applied Mathematics from Brown University (1991). University: University of Florida College: College of Liberal Arts and Sciences (CLAS) Department: Department of Statistics His research focuses on Biostatistics , Bayesian methodology , and methodologies for longitudinal and causal inference , with applications in cardiovascular health, muscular dystrophy, and healthcare analytics. He has authored influential books like Bayesian Nonparametrics for Causal Inference and Missing Data (2023) and Missing Data in Longitudinal Studies (2008). Key awards include the Lagakos Distinguished Alumni Award (Harvard) and L. Adrienne Cupples Award (Boston University). He has been funded by NIH grants since 2001 and leads collaborative research in chronic disease management, including studies on opioid use in elderly populations and Duchenne muscular dystrophy progression modeling. Grants & Collaborations: NIH-funded studies on muscle degeneration biomarkers Telemedicine and chronic pain management trials Development of clinical trial simulation tools for neuromuscular diseases His work emphasizes Bayesian approaches to handle missing data and causal inference, with applied focus on improving healthcare outcomes through rigorous statistical methods.

• Artificial Intelligence
• Decision Support Systems
• Knowledge Management
• +3 more

Professor Gregoris Mentzas is a faculty member at the National Technical University of Athens, School of Electrical and Computer Engineering, where he directs the Division of Industrial Electric Devices and Decision Systems. His research focuses on AI-enabled decision systems, knowledge management, and semantic technologies applied to digital enterprises and e-government. With over 350 publications, he ranks among the top 2% most cited scientists globally. Research Interests: Artificial intelligence for decision augmentation, big data analytics in personalized health and smart mobility, semantic web technologies, and industrial internet of things. Current projects investigate trustworthy AI frameworks and hybrid intelligence systems for Industry 5.0. Teaching: Leads courses in Digital Enterprise Management, Strategic Information Systems, and Project Management at undergraduate and postgraduate levels, incorporating industry case studies and experiential learning approaches. Awards & Leadership: Top 2% Highly Cited Scientist (PLOS Biology 2021) 5 Best Paper Awards in international conferences Director of Information Management Unit (1997-present) Board Member of Institute of Communication and Computer Systems (2006-2009) Projects & Funding: Secured over €18 million in research grants through 60+ European projects with industry partners including SAP, IBM, and Siemens. Research outcomes led to three technology spin-offs.

Kanika GoelView profile

Lecturer

• Process-Oriented Data Analytics
• Data Governance
• Data Quality
• +3 more

Dr. Kanika Goel is a Lecturer in the School of Information Systems at Queensland University of Technology (QUT), specializing in Business Process Management (BPM), Data Governance, and Process Analytics. She holds a PhD from QUT and has over 9 years of teaching experience, coordinating programs such as BIT Honours (IN10) and Masters of Philosophy (IN80). Her research focuses on process-oriented data analytics, data quality, and process mining, with industry collaborations spanning health, retail, and asset management sectors. She is a Lean Six Sigma Green Belt certified trainer and a Fellow of the Higher Education Academy (FHEA). Dr. Goel has led several industry-funded projects, emphasizing applied research in data governance, process mining, and process improvement. Notably, she received the Vice-Chancellor's Award for Excellence (2019) for innovative BPM integration in research management systems. Her work bridges academic research and real-world applications, contributing to journals like Business and Information Systems Engineering and IEEE Access . She teaches courses on Business Process Technologies, Modern Data Management, and BPM units in QUT's continuing professional education programs. Her articles explore topics like data imperfections in healthcare systems, process standardization strategies, and privacy risks in NoSQL databases. She advocates for digital literacy and has published on initiatives to build tech-savvy communities. Dr. Goel is also involved in supervising research topics such as prescriptive process analytics and process-data governance patterns.

• Addiction Medicine
• Psychiatry
• Neuroscience
• +7 more

Keith Humphreys is a Professor at Stanford University , teaching courses in psychiatry and behavioral sciences. His research focuses on addiction medicine, opioid crisis interventions, and public health policy. He employs interdisciplinary methodologies, including neuroscience and machine learning, to analyze substance use disorders and treatment outcomes. Teaching: PSYC 290 (Teaching in Psychiatry), PSYC 370 (Medical Scholars Research), and others His recent work explores opioid epidemic monitoring via social media, neuromodulation therapies for alcohol use disorder, and policy frameworks for psychedelics and cannabis. Publications span clinical trials, health informatics, and global epidemiology. Key trends in his 15 most recent articles (2023–2025) include: Neuroscience-driven interventions (e.g., theta burst stimulation for addiction) Digital epidemiology (social media analysis for real-time public health insights) Health policy evaluation (opioid regulation, cannabis legalization, overdose mortality) Machine learning applications in predicting treatment outcomes

Moosa TatarView profile

Assistant Professor

• Public Health
• Data Science
• Econometrics
• +5 more

Moosa Tatar is an Assistant Professor at the University of Houston College of Pharmacy , specializing in Pharmaceutical Health Outcomes and Policy . His research combines data science and applied econometrics to analyze public health policies, focusing on drug misuse , metabolic syndrome , liver diseases , and COVID-19 disparities. He holds dual PhDs in Health Services Research (University of Nebraska Medical Center) and Economics (University of Mazandaran), with expertise in machine learning , health equity , and vaccine policy modeling . PhD, Health Services Research, University of Nebraska Medical Center PhD, Economics, University of Mazandaran MA, Economics, Azad University BA, Economics, University of Sistan and Baluchistan His work addresses critical public health challenges through large-scale dataset analysis , including excess mortality during pandemics , vaccine inequality , and economic impacts of health policies . Key themes include social vulnerability , health disparities , and policy evaluation using machine learning and econometric modeling . Tatar's research has informed global and national health interventions related to prescription drug monitoring and hepatitis C screening . He is affiliated with the Society for Medical Decision Making , AcademyHealth , and American Public Health Association , serving as an ad hoc reviewer for journals like JAMA Pediatrics and Value in Health Regional Issues . His contact email is mtatar@Central.UH.EDU .

Georgia BlackView profile

Lecturer

• Sport Science
• Exercise Physiology
• Female Athlete Health
• +3 more

Dr Georgia Black is a Lecturer in Sport and Exercise Science at the School of Human Movement and Nutrition Sciences, University of Queensland. She holds a PhD in Sport Science from Australian Catholic University and a Bachelor of Exercise Science (BExSci) degree. Her research focuses on female athlete performance, including physical qualities optimization, multidisciplinary training approaches, and bone mineral density impacts. University of Queensland Australian Catholic University (PhD, BExSci) Dr Black's research spans diverse sports like Australian Football (AFLW), rugby league, and baseball. Key themes include workload monitoring, match intensity analysis, player rotation strategies, and technological applications in performance tracking. Her recent work emphasizes athlete profiling, test implementation, and injury prevention frameworks across various high-intensity sports, with a consistent focus on female athletes and throwing-dominant disciplines.

• Digital Health
• Human-Computer Interaction (HCI)
• User-Centered Design
• +11 more

Professor Marilyn Lennon is a leading academic in the Department of Computer and Information Sciences at the University of Strathclyde , holding the title of Professor in Digital Health and Care. She founded and directs the Digital Health and Wellness Group (DHaWG) , focusing on multidisciplinary research for designing, evaluating, and implementing digital technologies that enhance individual and population health and wellbeing. First class BSc in Psychology (University of Glasgow, 1998) PhD in Computing Science (University of Glasgow, 2002) Postgraduate diploma in academic practice (Fellow of Higher Education Academy) With over two decades of experience in Human-Computer Interaction (HCI) , usability, and user-centered design, her research spans wearable and mobile technologies for home healthcare, including remote monitoring (predictive falls monitoring), remote diagnosis (colon capsule technology evaluations), and smart home solutions for social care. She specializes in addressing technical and social barriers to real-world health technology implementation, emphasizing robust yet rapid evaluation methods. Recent publications highlight her work in 3D healthcare visualization, telehealth implementation, and medication management systems. Her research combines lab-based usability testing with real-world ('in the wild') evaluations of technologies like smart home systems and telehealth solutions. Key trends include cross-disciplinary collaboration, accessibility for sensory-impaired individuals, and AI-driven healthcare innovations. Scientific Awards MyCity: Glasgow - Gamechanger Award (Gold Medal) 2025 Images of Research 2025 Shortlist Emerald Outstanding Paper Award 2013 Best Paper Award (most replicable scientific paper) 2013 As course director for the Masters in Digital Health Systems , she has supervised 5 PhD students (2 completed) and over 100 honors projects. Current projects include the No Need to Fall initiative funded by the Health Foundation (£400k) and the PREMIO project co-creating clinical trials in advanced breast cancer. She maintains active collaborations with NHS, charities, and international institutions like the University of Waterloo.

• Reproductive medicine
• Menopause
• Premature Ovarian Failure

Clinical Professor Rodney Baber is affiliated with the University of Sydney through its Northern Clinical School in the Department of Obstetrics, Gynaecology and Neonatology . His research focuses on Reproductive medicine , Menopause , and Premature Ovarian Failure , with particular emphasis on hormone therapy applications and postmenopausal health risks. Obstetrics and Gynaecology Menopausal Medicine Reproductive Endocrinology Non-hormonal Therapies Recent publications demonstrate strong engagement with menopausal hormone therapy controversies, cardiovascular risk assessment , non-hormonal treatment alternatives , and postmenopausal complication management . Collaborative work with international experts appears across multiple high-impact journals.

Antonio Dello IaconoView profile

Associate Professor

• Sports Science
• Strength and Conditioning
• Exercise Physiology
• +5 more

Antonio Dello Iacono is an Associate Professor (Reader) in Strength and Conditioning at the University of the West of Scotland's Division of Sport, Exercise, and Health within the School of Health and Life Sciences. With 20 years of combined experience in elite sport practice, teaching, and research, his work focuses on elite performance conditioning, training load monitoring, and technology integration in sports. He leads the Sports Performance Interest Group, coordinating industry-collaborative applied research projects. His research aims to develop personalized exercise prescription models to enhance individualized training outcomes, reduce overtraining risks, and improve health outcomes. Education & Positions Scientific Associate at University of Split (since 2017) Research Focus His primary research goals involve transitioning from rigid exercise protocols to personalized approaches tailored to individual abilities and stressors. Key areas include autoregulation methods in resistance training and interdisciplinary research integration. He contributes to UN Sustainable Development Goals related to health and well-being. Collaborations & Activities Peer reviewer for Frontiers in Physiology (2022) Invited speaker at ECSS conferences (2018–2021) Media engagements include discussions on mental training's physical benefits and collaborative partnerships Lab/Teams Leads the Sports Performance Interest Group, fostering applied research with industry partners.

Erik LinsteadView profile

Associate Professor

• Machine Learning
• GPU Programming
• Autism Spectrum Disorder
• +5 more

Dr. Erik Linstead is an Associate Professor and Senior Associate Dean at Chapman University, affiliated with the Fowler School of Engineering, School of Pharmacy, and George L. Argyros College of Business and Economics. His expertise spans Machine Learning, GPU Programming, Autism Spectrum Disorder, Assistive Technologies, Predictive Analytics, and Virtual Reality. Education: Bachelor of Science, Chapman University Master of Science, Stanford University Ph.D., University of California, Irvine Dr. Linstead's research integrates machine learning with diverse domains, including autism treatment, environmental monitoring, and software engineering. His recent publications focus on coral reef health, land surface temperature trends, and embedded machine learning systems. His scholarly work includes collaborations in remote sensing, medical informatics, and neurodiversity support. Articles highlight his interdisciplinary approach, applying AI to ecological challenges (e.g., Red Sea coral reefs, Nile Basin droughts) and human-centered technologies (e.g., VR therapy for autism, medication adherence analysis).

Rodrigo Rico BiniView profile

Senior Lecturer

• Biomechanics
• Exercise Science
• Sports Science
• +4 more

Rodrigo Rico Bini is a Senior Lecturer at La Trobe University's Rural Health School, where he leads the Discipline of Exercise Science & Physiology and coordinates the Bachelor of Exercise Science program. His research focuses on quantitative analysis of human movement to improve athletic performance and reduce injury risks, specializing in cycling and running biomechanics using motion capture, force measurements, and AI modeling. He holds editorial positions at multiple sports science journals including Journal of Science and Cycling and Sports Biomechanics . His professional affiliations include the Holsworth Biomedical Research Centre and AUT-SPRINZ research institute. Dr. Bini's educational background includes a PhD in Biomechanics from Auckland University of Technology (2009-2011), an MSc from UFRGS Brazil (2006-2008), and a BPhEd from UFRGS (2000-2005). His research demonstrates consistent focus on sports biomechanics applications, with recent work exploring AI-driven motion analysis, equipment ergonomics, and active transportation health impacts. Key thematic trends include technological validation studies (wearables, neural networks), injury prevention strategies, and performance optimization across cycling, running, and occupational activities. Fellow of the International Society of Biomechanics in Sport Mid Career Research Award 2024 - La Trobe Rural Health School Certificate of Excellence in Reviewing: Physical Therapy in Sport AUT Laptop Scholarship First place, II Biofenac Brazilian Award for Exercise Science Dr. Bini supervises graduate students and leads industry collaborations, including an Australia Post-funded project on cycling biomechanics in postal delivery. He directs biomechanics research through the Holsworth Biomedical Research Centre and maintains international partnerships with institutions in New Zealand and Brazil.

Angelos GeorghiouView profile

Assistant Professor

• Game Theory
• Optimal Decision-Making
• Mathematical Modeling
• +7 more

Angelos Georghiou is an Assistant Professor at the University of Cyprus and holds an affiliation with the Desautels Faculty of Management at McGill University . His research bridges game theory, optimization, and risk modeling, with applications in insurance and decision science. Primary Affiliation : University of Cyprus Secondary Affiliation : Desautels Faculty of Management, McGill University Research Focus : Georghiou specializes in derivative-free optimization , entropic risk estimation , and multistage stochastic programming , particularly in mitigating tail risks in insurance and developing robust data-driven prescriptive models. His work emphasizes algorithmic solutions for complex decision-making under uncertainty. Entropic Risk Estimation Regret Minimization Stochastic Optimization Machine Learning Applications Scientific Recognition : 2022 Esdras-Minville Prize (HEC Montréal) for risk-averse regret minimization research

Viktor Sowinski-MydlarzView profile

Lecturer

• Cyber Security
• Data Analytics
• Machine Learning
• +3 more

Dr. Viktor Sowinski-Mydlarz is a Lecturer in Computer Science and Applied Computing at the School of Computing and Digital Media, London Metropolitan University. He also serves as a post-doctoral researcher at the Cyber Security Research Centre. His research focuses on hybrid frameworks combining semantic and machine learning methods for data analysis, with recent projects involving cloud computing, bioinformatics (e.g., 3D modeling of Coronavirus variants), and cyber security threat intelligence. He teaches programming, software engineering, ethical hacking, computer networking, and cloud computing. His academic background includes a PhD in hybrid data processing frameworks. He has contributed to research funded by Lloyds Banking Group and Innovate UK, including Urban Data Space (air quality analysis) and Microsoft Azure Cluster (NHS prescription data statistics with Machine Learning). Dr. Sowinski-Mydlarz's work emphasizes software integration, containerization, and cloud-based data analytics orchestration. His publications span threat intelligence, cyber security policy validation, and AI-driven hybrid platforms.