جست‌وجوی استاد و پژوهشگر

Rajkumar Venkatesanمشاهده پروفایل

استاد

• Marketing Analytics
• Customer Lifetime Value (CLV)
• Mobile Marketing
• +۴ مورد دیگر

Rajkumar Venkatesan is a Professor at the Darden School of Business , University of Virginia, with expertise in marketing analytics and customer lifetime value. He teaches courses such as 'Marketing Technology Products,' 'Marketing Strategy,' and 'Marketing Analytics.' Venkatesan holds a Ph.D. in Marketing from the University of Houston and a B.E. in Computer Science from the University of Madras. Ph.D., Marketing - University of Houston B.E., Computer Science - University of Madras His research focuses on marketing analytics , customer lifetime value , mobile marketing , and the global political economy . He has published extensively in top-tier journals like The Review of Economics and Statistics , Journal of Marketing , Marketing Science , and Harvard Business Review , with notable work on AI-driven marketing strategies and CRM optimization. Venkatesan’s articles highlight trends in predictive modeling , media attribution , and AI applications in marketing. His empirical methods for CLV calculation and studies on multichannel shopping behavior have shaped modern marketing analytics frameworks. Scientific awards include the Don Lehmann Award , ISBM David T. Wilson-Sheth Award , and recognition as a top scholar by the Marketing Science Institute and Poets & Quants . He has advised firms like IBM , Johnson & Johnson , and Flipkart , developing data analytics solutions and executive education programs. Co-author of Cutting Edge Marketing Analytics , Venkatesan also contributes case studies on AI implementation (e.g., Yum! Brands , Flipkart ) and media attribution challenges. His work bridges academic rigor with industry applications, emphasizing marketing ROI and resource allocation .

Aghdas Badieeمشاهده پروفایل

پژوهشگر

• Sustainable Supply Chain Management
• Resilient Cold Chain Management
• Transportation and Logistics
• +۴ مورد دیگر

Dr. Aghdas Badiee serves as a Post-Doctoral Research Associate at Heriot-Watt University's Edinburgh Business School, affiliated with both the Centre for Logistics and Sustainability and the Centre of Sustainable Road Freight. Her academic foundation spans Industrial Engineering with specialized expertise in data-driven decision systems and logistics optimization. Educational Background: B.Sc. in Industrial Engineering - System Planning and Analysis (Grade: 18.07/20), Iran University of Science and Technology M.Sc. in Socio-economic System Engineering - Location-Allocation Optimization (Grade: 19.30/20), Iran University of Science and Technology Ph.D. in Socio-economic System Engineering - Supply Chain Modeling and Sustainable Logistics (Grade: 19.35/20), Iran University of Science and Technology Her research integrates Sustainable Supply Chain Management , Resilient Cold Chain Logistics , and Operations Research methodologies to address complex transportation challenges. Current projects include the Africa Centre of Excellence for Sustainable Cooling and Cold Chain Systems (ACES) and Zero-Emission Cold-Chain initiatives, focusing on food security through sustainable logistics solutions. Her methodological approach combines descriptive, predictive, and prescriptive analytics using simulation, optimization, and data science techniques. Publication trends reveal consistent contributions to high-impact journals like Annals of Operations Research and IEEE Transactions on Fuzzy Systems , with growing emphasis on sustainable cold chain systems (2023-2025). Her work bridges theoretical operations research with practical applications in agri-food distribution, humanitarian logistics, and transportation procurement. Scientific Recognition: Ranked 1st in all academic degrees (B.Sc. 2010, M.Sc. 2012, Ph.D. 2019) Global Talent designation by UKRI (2022) Distinguished PhD Dissertation Award (2019) Reviewer for Annals of Operations Research Journal (2021-present) Member of WORMS (Women in OR/MS) since 2022 Her professional trajectory demonstrates continuous engagement across academia and industry, having served as Lecturer at University of Tehran and Senior Business Analyst at National Iranian Oil Products Distribution Company. Current activities include developing the MILES simulation platform for cold chain optimization and contributing to UN Sustainable Development Goals through sustainable logistics research. She actively participates in professional networks including Production and Operations Management Society while mentoring students in operations research methodologies.

Gerhard Friedrichمشاهده پروفایل

استاد

• Artificial Intelligence
• Knowledge-Based Systems
• Recommender Systems
• +۱۰ مورد دیگر

Gerhard Friedrich is a Full Professor at the University of Klagenfurt, leading the Institute for Artificial Intelligence and Cybersecurity. He previously served as Dean of the Faculty of Technical Sciences (2013–2021). His roles include Coordinator for International Relations of the Faculty of Engineering and Member of the Faculty Conference of Technical Sciences. He holds a PhD in Computer Science from Vienna University of Technology and has extensive industry experience, including heading departments at Siemens Austria and research roles at Siemens Corporate Research and Stanford Research Institute. Research focuses on knowledge-based systems, recommender systems, configuration and planning, and production informatics. His work bridges theoretical AI with practical applications in manufacturing, software development, and business processes. He has authored a book on recommender systems (translated into Japanese and Chinese) and contributed to prestigious journals like Artificial Intelligence and IEEE Transactions . He has organized major conferences such as the German Conference on Artificial Intelligence (2016) and served as editor and program committee member for leading venues. His awards include Fellowships from the European and Asia-Pacific AI Associations (2012, 2023). His advising and grants include leadership in applied AI projects and international collaborations. He directs the Intelligent Systems and Business Informatics research group, emphasizing interdisciplinary innovation.

Giles Warringtonمشاهده پروفایل

استاد

• Chronobiology
• Sports Physiology
• Athletic Performance Optimization
• +۵ مورد دیگر

Giles Warrington is a Professor (Chair) in the Department of Physical Education & Sport Sciences at the University of Limerick, co-director of the Sport and Human Performance Research Centre, and holds affiliations with the Health Research Institute. He specializes in chronobiology, sleep science, environmental physiology, and performance optimization in weight-category sports. Previously, he worked at Dublin City University and the British Olympic Medical Centre. His research focuses on athlete performance, injury prevention, and the physiological impacts of weight regulation in sports like horseracing and combat sports. Warrington holds a BSc (Hons) in Sports Science (1990) and a PhD in Sport and Exercise Physiology (1998). He is a Fellow of the American College of Sports Medicine and a certified Performance Physiologist with Sport Ireland. His 30+ year career includes advising elite athletes and teams at multiple Olympic Games. His research program with the Irish Horseracing Regulatory Board investigates chronic weight regulation in jockeys, while his recent work explores travel impacts on athletes, mental health in sports, and dual-career athlete models. Key contributions include over 90 peer-reviewed articles, injury prevention frameworks, and evidence-based training methodologies. Warrington’s grants and collaborations span international sports organizations and academic institutions. He leads interdisciplinary initiatives addressing athlete health, environmental stressors, and workplace integration for dual-career athletes.

• Database Systems
• Healthcare Information Systems
• Artificial Intelligence
• +۵ مورد دیگر

Carlo Combi is a Full Professor at the Department of Computer Science, University of Verona. He serves as Department Director and holds roles in various academic committees, including the Faculty Board of PhD in Computer Science and the Computer Science Teaching Committee. His research focuses on databases, healthcare information systems, temporal data mining, and business process management. He leads the STARS laboratory and has contributed to projects like PREPARE (prostate cancer evaluation) and EDIPO (neuroimaging genetics). His teaching spans courses on biomedical databases, software engineering, and Python programming. Combi's work integrates ethical considerations into process models and addresses challenges in healthcare workflows, temporal analytics, and AI applications in medicine. Education: Not explicitly listed in provided texts. Research Projects: Includes 30+ projects since 2008, emphasizing healthcare informatics, temporal systems, and data warehousing. Labs/Teams: STARS (Semistructured Temporal clinical Geographical Systems) and collaborations with CBMC (Computational Biomedicine Center). Grants: Multiple national/international grants, including funding for temporal data mining and healthcare process modeling. Awards: None explicitly mentioned, though his leadership roles and extensive publications highlight academic recognition. His research bridges clinical processes, temporal databases, and AI, with applications in acute kidney injury prediction, BPMN extensions for healthcare, and explainable AI in medical systems. Combi actively contributes to open-source solutions for clinical data analysis and integrates ethical frameworks into process management.

Suchi Rajendranمشاهده پروفایل

استادیار

• Prescriptive Analytics
• Predictive Analytics
• Applied Operations Research
• +۱۰ مورد دیگر

Suchi Rajendran is an Assistant Professor with a joint appointment in the Department of Industrial and Systems Engineering and the Department of Marketing at the University of Missouri, Trulaske College of Business. She is also the Director of Undergraduate Studies in ISE and actively leads research in prescriptive analytics, operations research, and data-driven decision-making across healthcare, transportation, and business systems. Education: PhD in Industrial Engineering, Pennsylvania State University Her research focuses on applying advanced analytics to solve complex real-world problems. Key areas include optimizing healthcare delivery systems, supply chain and logistics (notably blood supply chains and port operations), marketing data analytics, and emerging transportation systems such as air taxis and electric vehicle infrastructure. She leverages predictive and prescriptive artificial intelligence to forecast demand and recommend optimal operational strategies. Her recent work, highlighted in university news up to 2025, demonstrates a strong trend toward interdisciplinary, AI-powered solutions in logistics, sustainability, and public service. She frequently collaborates with industry partners like Case New Holland and Schneider Electric, and has secured funding from agencies such as the Alaska Department of Transportation and the National Science Foundation. Scientific Awards and Recognitions: Richard Wallace Faculty Incentive Grant Bob Bloss Faculty Enhancement Grant Winemiller Excellence Award in Data Analytics NSF CHOT Scholar (Penn State) Service Enterprise Engineering Fellow DAAD-WISE Fellowship (Germany) Lean Six Sigma Black Belt Dr. Rajendran is actively involved in mentoring students, having advised honors-winning graduate and undergraduate researchers. She leads an NSF-funded Research Experiences for Undergraduates (REU) program that brings 10 students annually to Mizzou to work on AI-enabled operations engineering. Her grants support hands-on, interdisciplinary research that prepares the next generation of engineers and data scientists. She is affiliated with cutting-edge research initiatives involving simulation modeling, digital communication platforms, and AI integration in industrial systems. Her lab and team focus on developing practical, scalable solutions for logistics, healthcare, and sustainable transportation, often through collaborative, real-world projects with public and private stakeholders.

Jolie L. Changمشاهده پروفایل

استاد

• Sleep Apnea Surgery
• Sialendoscopy
• Minimally Invasive Surgery
• +۵ مورد دیگر

Jolie L. Chang, MD, is a Professor of Otolaryngology, Head and Neck Surgery at the University of California, San Francisco (UCSF), and Chief of the Division of Sleep Surgery and General Otolaryngology. She holds a Medical Degree from UCSF and dual degrees in Electrical Engineering and Computer Science from MIT. Her clinical expertise spans sleep apnea surgery, minimally invasive salivary gland procedures, and ultrasonography for head and neck disorders. Dr. Chang is an Associate Editor of the Laryngoscope journal’s sleep section and Director of Resident Research in the Department of Otolaryngology. Her research focuses on healthcare decision-making, surgical outcomes for sleep apnea therapies, and innovative techniques in sialendoscopy. She co-authored the International Consensus Statement on Obstructive Sleep Apnea and has secured grants from the American Academy of Otolaryngology, Triological Society, and UCSF Academic Senate. Dr. Chang’s awards include the Triological Society Career Development Award, Howard Hughes Fellowship, and inclusion in the Muriel Steele Society for influential women in surgery. Dr. Chang’s academic contributions extend to medical education, mentorship, and leadership training for residents. Her work integrates digital health tools, such as smartphone applications for hearing health promotion and remote diagnostic technologies, to enhance patient care. She actively publishes on topics like opioid prescription trends, patient-reported outcomes, and barriers to sleep surgery access. Education: Bachelor’s and Master’s in Electrical Engineering & Computer Science, MIT Medical Degree, UCSF Residency in Otolaryngology-Head & Neck Surgery, UCSF Awards: Triological Society Career Development Award (2014) Howard Hughes Medical Institute Fellowship (2005) Dean’s Prize for Research (2007) Francis Sooy, MD Award for Clinical Excellence (2024)

• Medical Physics
• Radiation Oncology
• Proton Therapy
• +۳ مورد دیگر

Sara Margareta Cecilia Pilskog serves as an Associate Professor in the Department of Physics and Technology at the University of Bergen, Norway, with dual affiliation at Haukeland University Hospital's Department of Cancer Treatment and Medical Physics. Her research bridges theoretical medical physics and clinical oncology applications, focusing on precision radiotherapy techniques and biological optimization. Her primary research interests center on proton therapy innovation, where she investigates biological optimization strategies using linear energy transfer (LET) and relative biological effectiveness (RBE) modeling. She develops adaptive radiotherapy frameworks to address inter-fractional motion in pelvic cancers, particularly prostate and rectal malignancies. Her work also pioneers neutron-based in-vivo range verification systems and statistical deformation models for dose accumulation. Current projects emphasize reducing treatment margins through anatomical robustness and optimizing biological dose distributions for organ sparing. Analysis of her 15 most recent publications reveals a dominant focus on improving proton therapy precision for pelvic cancers through biological modeling and motion management. Approximately 70% of her work addresses prostate cancer applications, with significant contributions to adaptive strategies for inter-fractional changes and biological optimization techniques. Her research consistently employs Monte Carlo simulations (particularly FLUKA) and clinical data analysis to translate theoretical models into clinically viable solutions. No scientific awards were documented in the provided materials. While specific advising details remain unreported, her collaborative patterns indicate active mentorship within the University of Bergen's medical physics research ecosystem. Her publications consistently involve junior co-authors from clinical physics teams at Haukeland University Hospital, suggesting hands-on supervision of technical staff and research fellows in radiotherapy innovation projects. Grant funding appears primarily channeled through institutional hospital-university partnerships focused on clinical translation of advanced radiotherapy techniques. Dr. Pilskog operates within the University of Bergen's medical physics research cluster that maintains close operational ties to Haukeland University Hospital's radiotherapy department. This integrated academic-clinical environment enables direct implementation of her research on adaptive proton therapy and biological optimization into clinical workflows, with particular emphasis on pelvic cancer treatment protocols. The team utilizes advanced Monte Carlo simulation platforms and clinical treatment planning systems to develop and validate next-generation radiotherapy approaches.

Richard Boyceمشاهده پروفایل

دانشیار

• Clinical Decision Support Systems
• Pharmacovigilance
• Drug Interaction Analysis
• +۳ مورد دیگر

Richard Boyce is an Associate Professor in the Department of Biomedical Informatics at the University of Pittsburgh's School of Computing and Information. His work focuses on clinical decision support systems (CDSS), pharmacovigilance, and distributed knowledge bases. He leads research in drug interaction analysis, particularly involving natural products like cannabis, and develops machine learning tools for healthcare applications. His address is 530 DBMI Building, Pittsburgh, PA 15260, with contact email rdb20@pitt.edu. Boyce’s research integrates clinical and pharmacological data to improve patient safety through advanced analytics. Recent projects include predicting Alzheimer’s disease using EHR data, falls prediction in nursing homes, and optimizing CDSS modules for resource-limited settings. His team also develops open-source knowledge graphs for life sciences, enhancing data interoperability. Education: Details not explicitly listed in the provided text. Labs/Teams: Affiliated with the Department of Biomedical Informatics and School of Computing and Information at Pitt. Publications highlight his contributions to pharmacovigilance signal detection, machine learning in healthcare, and ontology engineering. Notable works include studies on cannabis-drug interactions and CDSS design for clinical practice.

Cynthia M. Boydمشاهده پروفایل

استاد

• Geriatrics
• Comorbidity
• Multimorbidity
• +۵ مورد دیگر

Cynthia M. Boyd is a Professor at Johns Hopkins University School of Medicine , jointly appointed in the Department of Health Policy and Management and Department of Medicine (Division of Geriatric Medicine and Gerontology). She is a core faculty member at the Johns Hopkins Center on Aging and Health and Roger C. Lipitz Center for Integrated Health Care . Education: MD, Duke University School of Medicine (1997) MPH, University of North Carolina at Chapel Hill (1996) BS, Yale University (1992) Research Interests focus on older adults with multiple chronic conditions , including comorbidity , treatment burden , functional recovery , and clinical practice guidelines . Recent projects examine diabetes management in multimorbid elderly , advance care planning , and deprescribing practices . Publications (2025 Highlights) include trials on advance care planning , geriatric pharmacology for dementia patients, and data harmonization in deprescribing research. Her work emphasizes patient-centered care and policy impact for aging populations.

• Pharmacoepidemiology
• Epidemiology
• Observational Research
• +۹ مورد دیگر

D.L. (Daniala) Weir is an Assistant Professor at Utrecht University's Science college, specifically within the Pharmacoepidemiology and Clinical Pharmacology department. Her research bridges clinical pharmacology , epidemiology , and applied data science to enhance medication safety for patients with multimorbidity. Education: B.Sc. in Biological Sciences (University of Alberta, 2012) M.Sc. in Epidemiology (University of Alberta, 2014) Ph.D. in Epidemiology (McGill University, 2019) Postdoctoral Fellowship (University of Toronto, 2021) Her work combines causal inference frameworks with machine learning to address clinical challenges using real-world data from Canada, the UK, and the Netherlands. She collaborates across disciplines with biostatisticians , data scientists , and healthcare professionals , emphasizing patient-centered approaches through engagement with patients and caregivers. Key research areas include: Dynamic treatment regimens Medication-related harm prevention Adverse drug event detection mHealth applications for adherence Her publications demonstrate expertise in applied data science methodologies for time-to-event analysis , predictive modeling , and multimorbidity treatment optimization .

Saravanan Venkatachalamمشاهده پروفایل

دانشیار

• Stochastic Programming
• Large Scale Optimization
• Robust Optimization
• +۶ مورد دیگر

Saravanan Venkatachalam is an Associate Professor in the Department of Industrial and Systems Engineering at Wayne State University. His research focuses on stochastic programming, robust optimization, and discrete event modeling with applications to supply chain management, healthcare, energy systems, and unmanned vehicle operations. Education Ph.D. in Industrial and Systems Engineering, Texas A&M University M.S. in Industrial and Systems Engineering, Texas A&M University B.E. in Production Engineering, PSG College of Technology, India His work addresses decision-making under uncertainty through decomposition algorithms and data-driven approaches. Recent projects include autonomous vehicle path planning with uncertain parameters, community-aware electric vehicle charging networks, and optimization models for radio advertisement placements. Current research trends emphasize stochastic programming for resource allocation in dynamic environments, robust optimization for transportation and energy systems, and multi-vehicle routing under uncertainty. He has supervised multiple graduate students in thesis work related to optimization models and taught core courses such as Operations Research, Deterministic Optimization, and Stochastic Programming at both undergraduate and graduate levels.

• Football
• Tennis
• Nature Sports

Dr. Öğr. Üyesi Aydın Karabulak , currently lecturing at the Department of Physical Education and Sports within Süleyman Demirel University's Faculty of Sports Sciences , combines academic rigor with practical football coaching expertise. Holding a PhD in Physical Education and Sports Teaching from Burdur Mehmet Akif Ersoy University (2022), he has authored research on football-specific training, physical performance metrics, and health-sports intersections. Education: Pre-Degree: Akdeniz University - Machine Drawing and Construction (1993) BSc: Akdeniz University - Physical Education and Sports Teaching (1997) MSc: Süleyman Demirel University - Sports Sciences (2013) PhD: Burdur Mehmet Akif Ersoy University - Physical Education and Sports Teaching (2022) Research Focus: Specializing in football performance analysis, Aydın investigates training methodologies (core stability, functional drills), environmental factors (playing surfaces), and health outcomes (nutrition-exercise interactions, pandemic-related activity patterns). His work bridges theoretical sports science with applied coaching practices. Publication Trends: Recent studies emphasize technology integration in sports, injury prevention, and evidence-based training frameworks. Conference presentations explore player psychology, surface adaptation, and recovery protocols, while co-authored books address contemporary issues in sports pedagogy. Professional Engagement: Head of Department, Süleyman Demirel University (2018-2022) Football Coach, Türkiye Futbol Federasyonu (2011-present) Member, TÜFAD (Türkiye Futbol Antrenörler Derneği) (1995-present)

André Nitzeمشاهده پروفایل

استاد

• Internet of Things
• Digital Business Models
• Software Engineering
• +۹ مورد دیگر

Prof. Dr.-Ing. André Nitze is a faculty member at the Brandenburg University of Technology in the Department of Economics . His research focuses on Internet of Things (IoT) , Digital Business Models , Software Architectures , Cloud Computing , Mobile Computing , and Predictive Analytics . He leads research projects on municipal LoRaWAN infrastructure, rural on-demand transport systems, and user-centered digitalization for sustainable development. Area of Expertise: IoT Applications, Software Engineering, Digital Transformation Current Research: Sustainable Municipal LoRaWAN, Rural Mobility Solutions Projects: risKI - KatKomm (2024-2026): Protocol development for disaster communication InNoWest (2023-2027): Subproject leader for digital sustainability OSLO (2023): Rural on-demand transport software architecture Awards: Best Paper Award at ICDS 2024 for work on LoRaWAN infrastructure Education & Supervision: M.Sc. and B.Sc. in Business Informatics Supervised over 15 theses including topics on GIS analysis, sensor networks, and AI applications

Douglas Allaireمشاهده پروفایل

دانشیار

• Multidisciplinary design optimization
• Bayesian optimization
• Multifidelity methods
• +۴ مورد دیگر

Douglas Allaire is an Associate Professor and Sallie and Don Davis '61 Faculty Fellow in the J. Mike Walker ’66 Department of Mechanical Engineering at Texas A&M University, part of the College of Engineering. He leads the Computational Design Laboratory, focusing on computational methods for complex engineered systems. His research spans multidisciplinary design optimization, Bayesian optimization, machine learning, and materials design. Education: Ph.D., Aerospace Engineering, Massachusetts Institute of Technology (2009) M.S., Aerospace Engineering, Massachusetts Institute of Technology (2006) B.S., Aerospace Engineering, Massachusetts Institute of Technology (2004) Research Interests: Bayesian optimization and uncertainty quantification Materials design using machine learning Autonomous experimentation and data fusion Predictive analytics for engineering systems Recent Trends in Publications: His work emphasizes integrating Bayesian methods with materials discovery, autonomous systems, and high-fidelity modeling. Key themes include optimizing multifidelity systems, inverse microstructure design, and real-time decision frameworks. Awards: ASEM Fellow (2024) AIAA Associate Fellow (2023) ASME Young Engineer Award (2018) Advising & Grants: Advised students like Jaylen James (Ph.D. 2022) and Danial Khatamsaz. Active in securing grants for computational design and materials research. Collaborates with institutions like the American Institute of Aeronautics and Astronautics. Labs & Teams: Directs the Computational Design Laboratory, part of the Engineering Systems Design Group. Engages in interdisciplinary projects with Texas A&M’s Multidisciplinary Engineering program.