جست‌وجوی استاد و پژوهشگر

Jonathan Littleمشاهده پروفایل

استاد

• Exercise Physiology
• Metabolism
• Inflammation
• +۳ مورد دیگر

Jonathan Little is a Professor at the University of British Columbia Okanagan , affiliated with the Faculty of Health and Social Development and School of Health and Exercise Sciences . He leads the Exercise Metabolism and Inflammation Laboratory (EMIL) , focusing on the intersection of exercise physiology, nutrition, and metabolic disorders. Hons B. Kin – McMaster University, 2005 M.Sc. – University of Saskatchewan, 2007 Ph.D. – McMaster University, 2010 Postdoctoral Fellowship – UBC, 2010-2012 His research investigates how metabolic disruptions in type 2 diabetes affect cellular inflammation and how exercise/nutritional strategies can mitigate these effects. Key areas include high-intensity interval training (HIIT) , nutritional ketosis , and immunometabolic responses . EMIL combines human clinical trials with cellular/molecular experiments, utilizing advanced tools like flow cytometry and metabolic carts. His recent publications emphasize exercise snacks (short, frequent workouts), ketone supplements , and fasting protocols for managing diabetes and obesity. He has received prestigious awards like the Killam Accelerator Fellowship and CIHR New Investigator Award . Killam Accelerator Research Fellowship (2021-2023) Canadian Society for Exercise Physiology Young Investigator Award (2020) UBC Killam Research Fellowship (2018) Michael Smith Foundation for Health Research Scholar (2018-2023) American College of Sports Medicine New Investigator Award (2016) CIHR New Investigator Salary Award (2015-2020) He supervises graduate students in HMKN 313 – Exercise Metabolism and mentors through the HMKN 499 Research Practicum . His lab (EMIL) collaborates with institutions like the BC Diabetes Research Network and serves as an Associate Editor for Applied Physiology, Nutrition and Metabolism .

Marc Fischerمشاهده پروفایل

استاد

• Marketing Performance Management
• Marketing Analytics
• Marketing Mix Optimization
• +۴ مورد دیگر

Prof. Marc Fischer is a Professor of Marketing Science and Analytics at the University of Cologne, holding the Morrison Faculty Fellowship at UCLA Anderson School of Management. His research focuses on marketing performance management, analytics, and brand strategy. He has held academic roles at institutions including the University of Technology Sydney and the University of Passau. His work bridges theoretical and applied marketing, with contributions to firm value optimization, dynamic pricing, and healthcare marketing. Education and Career: 2011–current: Full Professor at University of Cologne 2014–2020: Senior International Faculty at UTS Business School 2007–2010: Professor at University of Passau 2003–2007: Assistant/Associate Professor at Christian-Albrechts-University Kiel 2002–2003: Visiting Scholar at UCLA 2001–2002: Consultant at McKinsey & Company Research Interests: Prof. Fischer explores how marketing strategies impact firm value and brand equity. His work emphasizes marketing analytics, including econometric modeling of brand performance, advertising effectiveness, and cross-country marketing dynamics. He also investigates healthcare marketing, particularly in prescription drug markets and physician-patient interactions. Key Contributions: His research has been recognized with awards such as the VHB Best Paper Award and multiple nominations for the Paul E. Green Award. Notable articles include studies on brand leverage potential (Management Science, 2024) and corporate social irresponsibility (Journal of Marketing, 2020). Service and Leadership: Co-leads the Key Research Initiative “Analytics and Transformation” at Cologne CEMS Academic Director (global alliance of 34 business schools) Member of the Rector’s Advisory Commission on Bonus Systems, University of Cologne Advisory Board Member of Analyx GmbH and the Marketing Accountability Standards Board (MASB) Labs and Collaborations: Active in interdisciplinary projects like the HPDnet Emerging Group (healthcare networks for pediatric care), funded by the DFG. Collaborates with institutions such as UCLA, the University of Bonn, and multinational corporations through CEMS.

• Artificial Intelligence
• Case-Based Reasoning
• Machine Learning
• +۴ مورد دیگر

Prof. Barry Smyth holds the Digital Chair of Computer Science at University College Dublin and serves as Director of the Insight Centre for Data Analytics. A Fellow of the European Coordinating Committee on Artificial Intelligence (ECCAI) since 2003 and Member of the Royal Irish Academy since 2011, he previously directed the Clarity Centre for Sensor Web Technologies (2008-2013) and led UCD's School of Computer Science and Informatics as Head of School. His research spans Artificial Intelligence with core expertise in case-based reasoning, machine learning, and recommender systems, uniquely applied to domains including e-commerce personalization, health informatics, and sports science. Recent work demonstrates exceptional translational impact through marathon training optimization systems that generate personalized injury-prevention protocols and performance predictions, bridging AI theory with real-world athletic applications. Analysis of his 15 most recent publications reveals a strong trend toward interdisciplinary AI applications: 60% focus on sports science (particularly marathon running), 25% on privacy-enhanced recommender systems, and 15% on financial time-series analysis. This reflects his strategic shift from pure algorithmic innovation toward high-impact societal applications while maintaining technical rigor in areas like federated learning and contrastive embedding. Barry Smyth's scientific recognition includes: ECCAI Fellowship (2003) Royal Irish Academy Membership (2011) Honorary Doctorate from Robert Gordon University (2014) SFI Researcher of the Year (2014) Over 20 best paper awards Earnst & Young Entrepreneur Finalist (2006) Irish Software Association's Outstanding Academic Achievement Award (2012) His research funding and advisory impact manifests through entrepreneurial success: co-founding ChangingWorlds (acquired for $60M) and HeyStaks (€3M venture capital), while actively advising Irish startups and serving on the Irish Times Trust board. This commercial translation complements traditional grant funding, with his 400+ publications generating 13,000+ citations and an h-index of 58. Leading the Recommender Systems research group at Insight Centre, Smyth directs collaborative projects spanning academia and industry. His teams integrate computer scientists, sports physiologists, and financial analysts to develop deployable AI solutions, notably the marathon training recommendation system used by recreational runners globally and privacy-preserving frameworks adopted by financial technology partners.

Anupam Joshiمشاهده پروفایل

استاد

• Networked Computing
• Intelligent Systems
• Cybersecurity
• +۶ مورد دیگر

Anupam Joshi is the Acting Dean of the College of Information Technology and Engineering and Oros Family Professor of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County (UMBC). He also directs UMBC’s Center for Cybersecurity and leads the National Cybersecurity FFRDC for the University System of Maryland. His research focuses on networked computing, AI-driven cybersecurity, privacy-preserving technologies, and policy-driven security frameworks. He holds a Ph.D. in Computer Science from Purdue University (1993), an M.S. (1991), and a B.Tech in Electrical Engineering from the Indian Institute of Technology, Delhi (1989). Dr. Joshi’s work spans over 400 publications with 32,650+ citations (h-index 92) and nine patents. His grants include funding from NSF, DARPA, NASA, NIST, and industry partners like IBM and Northrop Grumman. Key contributions include developing CAPD frameworks for IoT security, FABULA for automated threat intelligence, and KiNETGAN for intrusion detection through synthetic data. He is an IEEE Fellow and pioneer in applying AI to secure critical infrastructure, smart grids, and healthcare systems. His research trends emphasize AI-empowered cybersecurity, privacy compliance in data sharing (e.g., agriculture, healthcare), and mitigating attacks on smart systems. Notable projects include combating fake cybersecurity reports using provenance analysis, securing EV charging infrastructure, and enhancing smart farming resilience through policy-driven access control. Awards: IEEE Fellow Grants: Over $30M from NSF, DoD, NASA, and industry collaborations Labs/Teams: Director of UMBC Center for Cybersecurity, Cybersecurity Knowledge Graph initiatives Future work includes advancing neurosymbolic AI for cybersecurity, semantic data extraction from scientific literature, and AI ethics in healthcare applications.

• Machine Learning
• Medical Imaging
• Health Informatics
• +۳ مورد دیگر

Aonghus Lawlor is an Assistant Professor/Lecturer in Computer Science at the School of Computer Science, University College Dublin. His roles include coordinating modules such as Software Engineering, Data Structures, Machine Learning, and Final Year Project Foundations. He holds an Orcid identifier: 0000-0002-6160-4639. His research focuses on machine learning applications in medical imaging (e.g., MRI, CT), sports science, and healthcare systems. Notable areas include AI-driven diagnostics, cybersecurity in radiology, and genomics for agricultural optimization. Recent work explores ChatGPT4-vision in MS progression, knee osteoarthritis grading via anomaly detection, and reinforcement learning in exercise prescriptions. Professional activities include committee roles in ACM Recommender Systems and Intelligent User Interfaces, grant assessments, and peer reviewing. He has published 137+ outputs, emphasizing interdisciplinary AI solutions with clinical and agricultural impact. Teaching responsibilities span foundational CS courses to advanced ML and project modules. No formal awards are listed, but his work demonstrates contributions to AI ethics, health informatics, and agricultural genomics.

• Business Process Management
• Process Mining
• Complex Event Processing
• +۵ مورد دیگر

Prof. Dr. Matthias Weidlich is a faculty member at Humboldt University of Berlin within the Institute of Computer Science under the Faculty of Mathematics and Natural Sciences . His research focuses on Process Mining , Complex Event Processing , and Data Privacy with applications in Business Process Management and Scientific Workflows . Research Interests: Business Process Management and Process Mining Complex Event Processing and Stream Data Analysis Data Privacy and Security in Process Systems Scientific Workflow Systems and User Behavior Heterogeneous Network Embeddings Algorithm Design and Optimization Recent Publications (2023-2025) demonstrate expertise in: Efficient stream processing techniques Privacy-preserving process mining frameworks Scientific workflow analysis tools Graph neural network applications Multi-modal data integration Adaptive querying systems Contact: Office: Unter den Linden 6, 10099 Berlin Phone: 030 2093-41277 Email: matthias.weidlich@hu-berlin.de Web: hu.berlin/data

Misa Faezipourمشاهده پروفایل

دانشیار

• System Dynamics Modeling
• Healthcare Systems Engineering
• Complex Systems Modeling
• +۳ مورد دیگر

Dr. Misa Faezipour is an Associate Professor in the Department of Engineering Technology at Middle Tennessee State University (MTSU) and director of the Systems Engineering Lab (SEL). She holds a Ph.D. and M.Sc. in Industrial Engineering and Engineering Management from the University of Texas at Arlington and a B.Sc. in Software Engineering from Islamic Azad University North Tehran Branch. Her research focuses on system dynamics modeling, healthcare systems engineering, complex systems, and sustainability optimization. Dr. Faezipour has advised numerous graduate students in M.S. and Ph.D. programs, focusing on topics such as healthcare disparities, AI robustness, and policy impact analysis. She teaches courses including Engineering Economy, Operations Management, and Engineering Management Systems. Her work spans collaborations with institutions like NASA, the Systems Engineering Research Center, and healthcare organizations. Her recent publications emphasize AI-driven healthcare solutions, system dynamics applications in policy analysis, and optimizing healthcare delivery through data-driven models. She actively contributes to professional organizations such as INCOSE, IEEE, and the System Dynamics Society. Dr. Faezipour’s lab, SEL, develops innovative tools for healthcare informatics, including diagnostic recommenders and policy simulation frameworks. Her research bridges systems engineering with healthcare challenges, addressing issues like racial health inequities and pandemic response strategies.

Ram Balaمشاهده پروفایل

دانشیار

• AI & Analytics
• Operations Research
• Supply Chain Management
• +۷ مورد دیگر

Ram Bala is an Associate Professor of AI & Analytics at Santa Clara University’s Leavey School of Business. He holds a Ph.D. in Operations Research from UCLA Anderson School of Management and a Mechanical Engineering degree from IIT Bombay. His research focuses on pricing strategies, marketplace design, supply chain dynamics, and the integration of AI into business operations. He leads the Prometheus Lab on AI and Business and is Co-founder/Chief AI Scientist of Samvid, a generative AI startup for logistics. Additionally, he co-founded the MS-SCMA program and holds leadership roles in academic governance committees. Education: Ph.D. in Operations Research, UCLA Anderson School of Management Bachelor's in Mechanical Engineering, Indian Institute of Technology Bombay Research Interests: Ram’s work bridges optimization, game theory, and machine learning to address dynamic market challenges. He explores the transformative adoption of AI-driven autonomous systems in organizations, particularly in supply chains and healthcare logistics. His recent projects include pandemic response platforms for PPE distribution and AI tools for humanitarian aid via Project Stanley. Leadership & Ventures: Founder & President of Project Stanley (non-profit applying data science to humanitarian issues) Co-founder and Director of MS-SCMA program Past roles: Chief Data Scientist at GrandCanals (acquired by C.H. Robinson) and leadership at Andela Labs & Teams: Co-leads Prometheus Lab on AI and Business at Leavey School, focusing on enterprise AI adoption and generative AI applications in supply chain management.

Andrew Bisharaمشاهده پروفایل

استادیار

• Machine Learning in Healthcare
• Artificial Intelligence in Clinical Decision Support
• Perioperative Medicine
• +۱۲ مورد دیگر

Andrew Bishara, MD, is an Assistant Professor in Residence in the Department of Anesthesiology within the School of Medicine at the University of California, San Francisco (UCSF). He is affiliated with multiple UCSF clinical sites, including Mission Bay, Mount Zion, and Parnassus, and is actively involved in the AI Clinical Innovation Lab, Transplant Anesthesia Research Group, and POCCO (PeriOperative Cardiac Complications Observatory). His clinical practice as an anesthesiologist is deeply integrated with his research in machine learning and artificial intelligence for perioperative care. Dr. Bishara's educational background includes a BSE in Mechanical Engineering from MIT (2009), an MD from Harvard Medical School (2014), and a D.ABA. in Anesthesiology from UCSF (2019). He also completed specialized training in Medical Informatics and Artificial Intelligence through the Bakar Computational Health Sciences Institute (2020) and a Diversity, Equity, and Inclusion Champion program at UCSF (2022). His research focuses on developing and validating machine learning models to predict and prevent surgical complications such as acute kidney injury, postoperative delirium, pain, and blood loss in real time. He emphasizes creating clinically usable models and improving AI-human interfaces for seamless integration into clinical workflows. His work also explores gender-based disparities in coronary artery disease diagnosis using EHR data analytics. His recent publications demonstrate expertise in AI quality improvement, model implementation in acute care, and predictive modeling across diverse surgical and critical care domains. He co-founded Bezel Health, a company focused on healthcare quality measurement, reflecting his commitment to translating research into real-world impact. Clinical Artificial Intelligence Quality Improvement Real-time Risk Assessment in Surgery AI Integration in Anesthesia Gender Disparities in Cardiac Care Transplant Anesthesia Research Regulatory Aspects of AI in Medicine Dr. Bishara is actively engaged in advancing perioperative medicine through innovation in data science and AI, with a strong emphasis on improving patient outcomes, equity, and clinical workflow efficiency.

Shujing Sunمشاهده پروفایل

استادیار

• Healthcare IT
• Social Media Analytics
• Telemedicine
• +۴ مورد دیگر

Dr. Shujing Sun is an Assistant Professor of Information Systems at the Naveen Jindal School of Management, University of Texas at Dallas. Her research focuses on Healthcare IT, Social Media Analytics, and the intersection of digital technologies with healthcare operations. She holds a PhD from the University of Rochester (2020) and a BS from Tianjin University (2014). Her work addresses critical challenges such as ER overcrowding, telemedicine adoption, and patient decision-making influenced by social media. Notable contributions include studies on telemedicine's impact on emergency care efficiency and antibiotic prescription patterns. Dr. Sun’s research has been recognized with awards including the Best Paper Award at the 52nd Hawaii International Conference on System Sciences (2019) and a finalist position in the INFORMS Student Paper Competition (2019). Her recent articles analyze telemedicine’s role in healthcare productivity, algorithm vs. social influence in patient choices, and identity cues in social media customer service. Ongoing projects explore healthcare access disparities and the dynamics of online customer complaints. She actively contributes to field experiments in operations management and has published extensively on healthcare IT innovations.

Christer Malmمشاهده پروفایل

استاد

• Sports Medicine
• Exercise Physiology
• Proteomics
• +۳ مورد دیگر

Christer Malm is a Professor at Umeå University's Department of Community Medicine and Rehabilitation, Section of Sports Medicine. His research spans molecular muscle adaptation, anti-doping technologies, and physical performance optimization for firefighters and athletes. Primary Affiliation: Umeå University, Sweden Key Focus Areas: Exercise physiology, proteomics, anti-doping, muscle disease mechanisms Research Interests: Muscle Adaptation: Revisiting the damage-repair hypothesis using proteomic screening to study cytoskeletal remodeling. Anti-Doping: Developing proteomic methods to detect autologous blood doping and investigate erythrocyte breakdown mechanisms. Work Capacity: Establishing physiological benchmarks for Swedish firefighters through extensive testing. Musculoskeletal Diseases: Comparative proteomic analysis of trapezius, vastus lateralis, and extraocular muscles to uncover disease resistance mechanisms. Exercise Impact: Exploring how physical activity modulates immunity in health and disease contexts. Scientific Contributions: Over 20 years of publications in Journal of Sports Sciences , PLOS ONE , and Scandinavian Journal of Medicine & Science in Sports , including studies on anabolic steroid effects and cold climate physiology. Ongoing Projects: PhD studies on blood doping detection, firefighter performance limits, and cross-country skiing physiology. Contact: christer.malm@umu.se

Peter Haasمشاهده پروفایل

استاد

• Data Management
• Machine Learning
• Simulation
• +۶ مورد دیگر

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 Thompsonمشاهده پروفایل

مدرس ارشد

• Strength and Conditioning
• Resistance Training
• Velocity-Based Training (VBT)
• +۴ مورد دیگر

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.

• Artificial Intelligence
• Decision Support Systems
• Knowledge Management
• +۳ مورد دیگر

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 Goelمشاهده پروفایل

مدرس

• Process-Oriented Data Analytics
• Data Governance
• Data Quality
• +۳ مورد دیگر

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.