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Joren GijsbrechtsView profile

Adjunct Assistant Professor

• Operations Management
• Supply Chain Management
• Machine Learning
• +3 more

Joren Gijsbrechts serves as an Adjunct Assistant Professor in the Department of Operations and Data Science at Católica Lisbon School of Business and Economics, Catholic University of Portugal. His research focuses on data-driven decision making in Operations Management through Machine Learning and Prescriptive Analytics to optimize inventory and transportation systems. Academic background includes: Bachelor in Business Engineering, University of Antwerp, Belgium Master in Business Engineering, University of Antwerp, Belgium PhD, KU Leuven, Belgium His research spans Operations Management, Supply Chain Analytics, and Machine Learning applications for inventory control and transportation logistics. Collaborations include Kellogg School of Management, Olin Business School, Kuehne Logistics University, and Exeter University, with practical implementations for corporate inventory optimization. Recent publications reveal a concentrated trend in deep reinforcement learning for complex inventory challenges (dual sourcing, lost sales, multi-echelon systems) and synchromodal transportation planning. His work bridges theoretical advancements with industrial applications in global supply chain reshoring and real-time logistics decision-making. Gijsbrechts delivers guest lectures and industry workshops on Supply Chain Analytics, translating academic research into business practice. While the provided text contains no details about grants or student advising, his active publication record and corporate engagements demonstrate strong industry-academia integration.

• Health Informatics
• Oncology
• Electronic Health Records
• +5 more

Dr. Ahmad S. Halwani serves as an Associate Professor of Medicine in the Division of Hematology and Hematologic Malignancies within the Department of Internal Medicine at the University of Utah School of Medicine. He concurrently holds positions as a staff physician and health services researcher at the Informatics, Decision Enhancement, & Analytics Sciences (IDEAS) Center of Innovation at the George E Whalen Veterans Administration Medical Center in Salt Lake City. His dual appointments bridge academic medicine and Veterans Affairs healthcare systems, with specialized roles as cancer data subject matter expert and health information technology implementation expert for the VA National Oncology Program. His educational background includes a Medical Degree (M.D.) and Bachelor of Science in Biology with High Distinction from the American University of Beirut. He completed residency training in Internal Medicine followed by fellowships in Medical Oncology and Allergy and Immunology at the University of Iowa Hospitals & Clinics. His board certifications encompass the American Board of Internal Medicine (Medical Oncology sub-specialty) and the Educational Commission for Foreign Medical Graduates. Dr. Halwani's research program centers on leveraging electronic health record (EHR) data to develop clinical decision support tools, analytics platforms, and user interfaces that optimize provider workflows while enhancing care quality, safety, and effectiveness. His work pioneers novel tele-oncology care models and applies advanced computational methods including deep learning and generative large language models to cancer care delivery systems. Key research domains include EHR-based cancer registry development, symptom assessment system implementation, and medication management for oral anticancer therapies. His publication portfolio reveals strong emphasis on Veterans' cancer care with recurring themes in lymphoma management, health disparities analysis, and health information technology implementation. Recent work demonstrates increasing integration of artificial intelligence methodologies within clinical oncology workflows, particularly through EHR data extraction and real-time decision support applications. University of Utah VP Clinical and Translational Scholar (2016) NCI Cancer Clinical Investigator Team Leadership Awardee (2018) National Comprehensive Cancer Network panel member for cutaneous and T-cell lymphoma guidelines As a health services researcher at the VA IDEAS Center, Dr. Halwani leads data analytics initiatives supporting multiple national VA oncology programs including the Breast and Gynecological Cancer System of Excellence, Cancer Care Navigation and Tracking, and Clinical Trial Navigation Center. His work has directly informed congressional reports on Veterans' access to genetic testing and shaped national implementation of symptom assessment systems. He maintains active roles in clinical trial methodology development and serves as subject matter expert for VA oncology informatics infrastructure. His laboratory affiliations include the Division of Epidemiology and VERITAS research group, focusing on translational applications of health informatics in cancer care delivery systems. Current projects involve developing EHR-integrated patient-reported outcome tools and optimizing medication safety systems for oral anticancer therapies within the Veterans Health Administration.

Sharan SrinivasView profile

Associate Professor

• AI-enabled operations engineering
• prescriptive analytics and optimization
• service systems modeling and management
• +1 more

Sharan Srinivas serves as an Associate Professor with a joint appointment in the Department of Marketing and Department of Industrial & Systems Engineering at the University of Missouri-Columbia, while also holding Core Faculty status in the MU Institute for Data Science and Informatics. His academic credentials include: Ph.D in Industrial Engineering and Operations Research from Pennsylvania State University MEng in Industrial Engineering and Operations Research from Pennsylvania State University M.S. in Industrial and Systems Engineering from Binghamton University, State University of New York B.E. in Industrial Engineering from Anna University, India Dr. Srinivas's research integrates advanced analytics across four interconnected domains: AI-enabled operations engineering, prescriptive analytics and optimization, service systems modeling and management, and supply chain logistics. His methodological approach combines machine learning with optimization techniques to address complex operational challenges in healthcare, transportation, and consumer systems, frequently leveraging real-world industry data for validation. Funding for his work comes from major federal sources including the National Science Foundation (NSF), Department of Defense (DoD), and U.S. Economic Development Administration (EDA), alongside state and industry partnerships. He actively mentors PhD, MS, and undergraduate students through research projects and academic advising, incorporating Lean Six Sigma Black Belt methodologies into practical applications. As a Core Faculty member of the MU Institute for Data Science and Informatics, he drives cross-disciplinary collaboration in data-intensive research initiatives while teaching analytics courses across undergraduate, graduate, and MBA programs.

Aasthaa BansalView profile

Professor

• Causal Inference
• Comparative Effectiveness Research
• Health Services Research
• +7 more

Dr. Aasthaa Bansal is a Professor in the Department of Pharmacy at the University of Washington School of Pharmacy . She holds a Ph.D. and M.S. in Biostatistics from UW, and a B.Math in Computer Science (Bioinformatics option) from the University of Waterloo. As a member of the CHOICE Institute and the Center for Pharmacy Practice Transformation , her work bridges biostatistical methodology with healthcare applications. Academic Appointments: Professor, Department of Pharmacy Education: Ph.D./M.S. Biostatistics (UW), B.Math Bioinformatics (Waterloo) Her research focuses on : Sequential Decision Making with Longitudinal Data Predictive Modeling for Clinical Outcomes Value of Information Analysis in Healthcare Comparative Effectiveness Using Real-World Data Dynamic Surveillance Strategies in Oncology Recent publications highlight her expertise in machine learning applications for health economics, cancer treatment optimization , and health disparities analysis , particularly for American Indian/Alaska Native populations. Notable work includes cost-effective personalization frameworks for cancer surveillance and evaluation of digital health interventions. Dr. Bansal leads studies on pharmaceutical outcomes , including: Clinical trial: TrACER Study on CSF Prescribing Colorectal Cancer Chemotherapy Utilization Chronic Myeloid Leukemia Relapse Prediction Healthcare Resource Use in Macular Degeneration She contributes methodological advancements in: Time-Varying Discrimination Metrics Cure Model Bias Evaluation Landmark Analysis Techniques EHR Data Validation

Kate Wolitzky-TaylorView profile

Associate Professor

• Anxiety Disorders
• Cognitive Behavioral Therapy (CBT)
• Comorbid Emotional and Substance Use Disorders
• +3 more

Dr. Kate Wolitzky-Taylor is an Associate Professor and Associate Director at the UCLA Anxiety and Depression Research Center (ADRC) within the Department of Psychiatry & Biobehavioral Sciences at the David Geffen School of Medicine, University of California, Los Angeles. She holds concurrent roles as Director of Clinical Services for the Depression Grand Challenge Innovative Treatment Network and co-PI for the STAND NIMH ALACRITY Center (P50 grant). Dr. Wolitzky-Taylor provides clinical care in the UCLA Faculty Practice Outpatient Clinic and Behavioral Health Services while directing a CBT rotation for psychiatry residents. Education: B.A. summa cum laude in psychology from Emory University; Ph.D. in clinical psychology from the University of Texas at Austin Training: Predoctoral internship at Medical University of South Carolina (T32 grant); Postdoctoral fellowship at UCLA ADRC (2009-2012) Her research focuses on anxiety disorders , comorbid emotional and substance use disorders , CBT mechanisms of change , and community mental health implementation . Recent work examines digital mental health systems , neurobehavioral predictors of treatment response , and integrated interventions for co-occurring conditions. A K23 career development award supported her work on brief CBT programs in SUD clinics. Key article trends highlight her expertise in transdiagnostic approaches, neuroimaging biomarkers, digital health implementation, and integrated behavioral treatments for comorbid conditions across 2012-2025 publications. Predoctoral National Research Service Award (NRSA/NIMH F31) Career Development Award in Patient-Oriented Research (NIDA K23) Dr. Wolitzky-Taylor has extensive experience training clinical psychology doctoral students and psychiatry residents in CBT delivery and research methodology. She supervises ADRC doctoral students and research staff while contributing to NIH-funded studies on emotional disorder treatments.

Shivam GuptaView profile

Associate Professor

• Procurement
• Supplier Management
• Innovation Management
• +1 more

Shivam Gupta is an Associate Professor of Supply Chain Management and Analytics at the University of Nebraska-Lincoln's College of Business. He joined the university in 2018 as an Assistant Professor and was promoted to his current rank in 2024. His office is located in HLH 511K, Lincoln, NE. Education: Ph.D. in Management Science, The University of Texas at Dallas (2011-2016) M.Tech. in Civil Engineering, Indian Institute of Technology Kanpur (2009-2011) B.Tech. in Civil Engineering, Indian Institute of Technology Kanpur (2003-2007) His research expertise spans procurement systems, supplier relationship management, agricultural operations optimization, and incentive mechanism design. He develops analytical models to address challenges in supply chains, focusing on both theoretical frameworks and practical applications in agricultural markets. His work integrates game theory, optimization techniques, and economic analysis to solve complex operational problems. Gupta's publications demonstrate consistent focus on mechanism design and operational challenges, with recent work emphasizing dynamic incentive structures, procurement optimization, and agricultural policy impacts. His articles appear in premier journals including Management Science, Operations Research, and Production and Operations Management. Honors: First Place, INFORMS Public Sector OR Best Paper Competition (2015) Finalist, POMS SCM Student Paper Competition (2016) He teaches undergraduate and graduate courses in business analytics, including SCMA 350: Business Analytics/Information Analysis and SCMA 855: Prescriptive Analytics.

• Industrial Engineering
• Operations Research
• Optimization
• +5 more

Shunichi Ohmori , a Professor at the Faculty of Science and Engineering , Waseda University , holds a Doctorate in Engineering from the same institution. His academic journey includes continuous affiliation with Waseda University since 2011, currently serving as a faculty member and concurrently as Director of Research Institute at the Institute of Global Production & Logistics (2022-2026). Education: Doctorate (2009-2012), Master's (2007-2009), and Bachelor's (2004-2007) from Waseda University His research focuses on industrial engineering and operations research , with specific interests in optimization , facility planning , logistics , and supply chain management . He has developed innovative frameworks for: Robust supply chain design under disruption risks Multi-echelon inventory optimization 3D facility layout planning with pipeline arrangements Collaborative vehicle routing cost allocation Green product portfolio strategies under uncertainty His publication record demonstrates expertise in stochastic optimization (15+ peer-reviewed papers) and heuristic algorithms (14+ conference proceedings). He has received multiple Best Paper Awards (2012-2016) and contributed to curriculum development through courses like Advanced Facilities Planning , Supply Chain Transformation , and Logistics at both undergraduate and graduate levels.

• Digital Transformation
• Manufacturing Industry
• Intelligent Sensing Technology
• +5 more

Sun Yacheng is a Professor at the School of Economics and Management, Tsinghua University, where he actively contributes to research on digital transformation in the manufacturing sector. His expertise bridges economic theory and practical industrial applications. Professor Sun's research focuses on how intelligent sensing technology can drive digital transformation in manufacturing. His work particularly emphasizes the application of IoT, computer vision, and RFID technologies to create closed-loop systems of 'perception-analysis-decision-execution.' He investigates how high-precision 3D spatial reconstruction, end-to-end customer flow tracking, and multi-source data fusion can provide businesses with comprehensive market understanding and process optimization capabilities. His research shows how AI and machine learning can advance analytics from descriptive to predictive and prescriptive models, enhancing business decision-making. As part of Tsinghua School of Economics and Management's 'Two Sessions Through a Financial Perspective' series, Professor Sun provided expert analysis of the Government Work Report's call to accelerate digital transformation in manufacturing. He highlighted the strategic importance of cultivating service providers who understand both industry processes and digital technologies, with particular attention to the challenges facing small and medium enterprises in digital adoption.

Theresa MadreiterView profile

Lecturer

• Knowledge-based maintenance
• Predictive and prescriptive maintenance
• Knowledge discovery from text
• +5 more

Theresa Madreiter is a Lecturer & Doctoral Student at the Institute of Management Sciences within the Faculty of Mechanical Engineering and Industrial Management at Vienna University of Technology (Technische Universität Wien). Her research focuses on Production and Maintenance Management, where she combines engineering expertise with data science approaches to advance industrial maintenance practices. Her educational background includes: Dipl.-Ing. in Industrial Engineering and Mechanical Engineering from the Faculty of Mechanical Engineering and Industrial Management, Vienna University of Technology BSc. in Industrial Engineering and Mechanical Engineering from the Faculty of Mechanical Engineering and Industrial Management, Vienna University of Technology Madreiter's research interests center on knowledge-intensive approaches to industrial maintenance. She explores how knowledge-based maintenance , predictive and prescriptive maintenance systems , and knowledge discovery from text can transform traditional maintenance practices. Her work leverages semantic technology and Natural Language Processing to extract valuable insights from maintenance documentation, and applies predictive data analysis and machine learning techniques to anticipate equipment failures before they occur. This interdisciplinary approach bridges the gap between industrial engineering and data science, positioning her at the forefront of Maintenance 4.0 research. Her publications demonstrate a strong focus on applying text mining and AI techniques to industrial maintenance challenges. The trend in her work shows increasing sophistication in combining multiple data sources (both structured sensor data and unstructured text documentation) to create comprehensive maintenance solutions. Her research spans both theoretical development of algorithms and practical implementation in manufacturing environments, with a particular emphasis on discrete manufacturing systems. Madreiter's scientific achievements have been recognized with several prestigious awards: Schnieder Prize YOUNG MAKER 2021 from acatech Industrial Management - Thesis Award 2020 from Austrian Association for the Promotion of Business Research and Education Best Paper Award for "Combining process monitoring with text mining for anomaly detection in discrete manufacturing" at the Conference on Learning Factories 2022 As a doctoral student and lecturer, Madreiter is actively involved in academic mentoring and education. Her master's thesis on "Design and Development of a Prototype of a Text Understanding Tool for Maintenance 4.0" has served as the foundation for her current doctoral research and multiple research projects including TU-MARS, True_Usage, DigiMain 4.0, and DigiTS-ME. Beyond her formal academic role, she demonstrates significant commitment to social causes through her work with the Computerclubhouse Vienna (CCV), where she leads technology workshops for children from disadvantaged backgrounds. Madreiter is part of research teams working on the intersection of industrial engineering and data science, particularly focused on how AI and text analytics can transform maintenance practices in manufacturing. Her work connects closely with Industry 4.0 initiatives and represents an important bridge between traditional engineering disciplines and emerging data-driven approaches.

• Machine Learning
• Data Analytics
• Social Media Analysis
• +5 more

Dr. Paraskevas Koukaras is an Academic Scholar at the International Hellenic University (IHU) and a Postdoctoral Research Associate at the Information Technologies Institute (ITI) of the Centre for Research and Technology - Hellas (CERTH), affiliated with the School of Science and Technology at IHU. His educational background includes: IT Engineer from the Department of Informatics, Alexander Technological Educational Institute of Thessaloniki (ATEI) PhD in Interdisciplinary data science methods using machine learning for enhanced knowledge acquisition from the School of Science and Technology, International Hellenic University (IHU) Dr. Koukaras' research spans social media analytics, energy systems, and machine learning. His work focuses on energy load forecasting and optimization, data analytics, information modeling, and graph mining in heterogeneous networks. He applies these techniques to address challenges in public health, financial markets, and building energy efficiency through prescriptive analytics. His recent publications (2020-2023) demonstrate a strong focus on applying machine learning to real-world problems, particularly in social media analysis for public health during the COVID-19 pandemic, energy forecasting, and fake news detection. His work consistently employs multi-model approaches leveraging both traditional and deep learning techniques across healthcare, finance, and energy domains. Dr. Koukaras has participated in major European research projects including eDREAM (H2020), DRIMPAC (H2020), PRECEPT, SmartWins, easySRI, and SMACCs, focusing on demand response technologies and energy ecosystems. He contributes to academic training through teaching courses in data science and computing despite no formal advisees being listed. He operates within interdisciplinary research teams at ITI/CERTH and IHU's School of Science and Technology, collaborating on AI applications for energy efficiency, healthcare support, and social media analytics in residential and industrial contexts.

• Quantitative Methods in Business Analytics
• Markov chains and stochastic modeling
• Machine Learning and Prescriptive analytics in Operations
• +4 more

Georgiou Andreas is a Professor in the Department of Business Administration at the University of Macedonia, directing the Master in Business Administration program and serving as academic staff for the Master in Information Systems and Master in Business Analytics and Data Science programs. He earned his B.Sc. in Mathematics (1984) and Doctorate in Operations Research (1989) from Aristotle University of Thessaloniki. His research spans Quantitative Methods in Business Analytics, stochastic modeling, Machine Learning for Operations, Healthcare Simulation, and Multiple Criteria Optimization including AHP and DEA efficiency evaluation. He applies these techniques to solve complex business problems in operations management, marketing, and healthcare decision-making. Professor Georgiou teaches graduate courses including Operations Research, Decision Analysis and Optimization, Simulation Techniques in Business Analytics, and Quantitative Analysis for Business Decisions across multiple programs. With an extensive publication record of 13 books, 36 journal articles, 50 conference papers, and 41 other scholarly works, he actively mentors students in the graduate programs he directs.

• Telecommunications Engineering
• Telematics
• Distributed Systems
• +7 more

María Teresa Ariza Gómez serves as a Full Professor in the Department of Telematics Engineering at the University of Seville, where she has established herself as a leading researcher in telecommunications and educational technology. Her academic career spans over two decades of continuous research contributions and teaching innovation. Professor Ariza Gómez's research interests focus on Telematics Engineering , with specialization in Distributed Systems , CORBA/MMS protocols , and IoT applications . She has pioneered the integration of gamification techniques into engineering education through tools like Kahoot, and developed virtual learning environments that transform programming instruction. Her work bridges theoretical telecommunications concepts with practical industrial applications, particularly in vehicle telematics and service station management systems. Her publication record shows consistent output with recent emphasis on educational technology (2015-2019), including software architecture for health applications and virtual laboratory environments. These works demonstrate her ability to adapt telecommunications expertise to solve problems in diverse domains from healthcare to automotive systems. Professor Ariza Gómez maintains active research through multiple projects including the TIC-154 research group (with continuous funding since 2006) and industry collaborations on Android applications and payment systems for gas stations. Her teaching methodology emphasizes project-based learning , virtual laboratories , and game-based educational resources that enhance student engagement in complex technical subjects. She leads research initiatives that combine telecommunications engineering with practical educational applications, creating innovative teaching tools that have been adopted across engineering disciplines at the University of Seville. Her work on virtualized computing environments has established a flexible, portable model that supports multidisciplinary teaching needs while maintaining technical rigor.

Aurélie LemmensView profile

Professor

• Customer Analytics
• Prescriptive Analytics
• Machine Learning
• +4 more

Prof. Aurélie Lemmens is a Full Professor of Customer Analytics at Rotterdam School of Management (RSM), Erasmus University Rotterdam, where she chairs the Department of Marketing Management. She concurrently serves as Academic Director of the Expert Practice on Customer Analytics at the Erasmus Center for Data Analytics. Previously, she held academic positions at Erasmus School of Economics and Tilburg University, and was a visiting scholar at Harvard Business School. She obtained her PhD from K.U. Leuven (Belgium) following an MSc in Business Engineering from Solvay Business School. Her research develops prescriptive analytics methodologies leveraging consumer data for customer-centric decision-making, organized around three core lifecycle stages: (1) customer acquisition and new product introduction, (2) customer development and engagement, and (3) customer retention. Her work extensively employs machine learning and large-scale computing. Prof. Lemmens' publication portfolio demonstrates consistent focus on quantitative marketing, customer analytics, and methodological innovation, with recent work emphasizing personalization algorithms, donor behavior modeling, and churn optimization techniques. Her research consistently bridges theoretical rigor with practical business applications. Awards and honors include: 2012 IJRM Best Paper Award Marie Curie Grant (European Research Council) Veni and Vidi grants (Dutch Science Foundation) ERC Consolidator Grant Finalist Best Teaching Awards (2017, 2019) for Conjoint Analysis course She teaches Customer Analytics (Business Analytics MSc), Customer Centricity (Marketing Management MSc), and Creating Customer Value (Executive MBA). She leads the Expert Practice on Customer Analytics, coordinating industry-academia collaborations in applied data analytics. Additional information is available via her personal website .

Wolfgang KratschView profile

Research Professor

• Process Mining
• Artificial Intelligence in Business Processes
• Data-Driven Process Management
• +3 more

Prof. Dr. Wolfgang Kratsch serves as Research Professor for Applied AI at Augsburg University of Applied Sciences, Director of the FIM Research Institute for Information Management, and holds a leading position in Fraunhofer FIT's Business Information Systems division. He co-founded and manages the Center for Process Intelligence, driving industry-academia collaboration in digital transformation. His educational background includes B.Sc. and M.Sc. in Business Informatics from the University of Augsburg (2017), followed by a summa cum laude doctorate in data-driven management of process networks from the University of Bayreuth (2020). University of Augsburg: B.Sc./M.Sc. Business Informatics (2017) University of Bayreuth: PhD in Data-Driven Process Network Management (2020) Dr. Kratsch's research centers on data-driven process management , focusing on data extraction, quality assurance, and AI-driven context-sensitive process optimization. His methodology emphasizes design science research yielding prototype implementations for immediate practical use. Key domains include process mining, robotic process automation, and generative AI integration in business workflows, with strong industry applicability. Core Methodology: Design Science Research Technical Focus: Event Log Generation, Object-Centric Process Mining Application Areas: Manufacturing, Healthcare, Transportation His publication trajectory (2021–2025) reveals accelerating integration of generative AI with process mining , particularly in unstructured data extraction (text/video) and automated process improvement. Recent works emphasize practical industry solutions in manufacturing error analysis, airport operations, and medical monitoring, demonstrating consistent collaboration with industrial partners like Munich Airport. No scientific awards were explicitly mentioned in the source material. Dr. Kratsch actively contributes to academia through teaching at Augsburg and Bayreuth Universities, industry project leadership, and startup mentorship. His spin-off credium GmbH (founded 2020) built a 15-person AI/data science team, reflecting his entrepreneurial approach to translating research into market solutions. Current projects prioritize practical AI deployment in serial production and process intelligence systems. Teaching: Lectures/seminars at Augsburg & Bayreuth Universities Startup Experience: credium GmbH (Data Science/AI focus) Industry Projects: Manufacturing optimization, airport operations He leads the FIM Research Institute and Center for Process Intelligence, directing multidisciplinary teams in developing process mining prototypes. His labs focus on bridging academic research with industrial deployment, particularly in video-based process monitoring and generative AI for business process design.