جست‌وجوی استاد و پژوهشگر

• Evidence Synthesis
• Systematic Review Methodology
• Health Informatics
• +۳ مورد دیگر

Ian Shemilt is an Associate Professor at the University College London (UCL) , affiliated with the EPPI-Centre within the Institute of Education . As an Associate Director of the EPPI Centre , he contributes to advancing evidence synthesis methods and co-leads key research initiatives. Education : BA (Hons) in Philosophy from the University of East Anglia. Collaborations : Cochrane Collaboration, Campbell Collaboration, GRADE Working Group, and International Collaboration for Automation of Systematic Reviews (ICASR). Research interests include developing and applying automation tools for systematic reviews, transitioning evidence synthesis workflows from document-based to dataset-driven processes, and enabling computable, living evidence syntheses. His work leverages technologies like OpenAlex and large language models to enhance efficiency in health and social care evidence production. The articles highlight his focus on systematic review methodology , automated study identification , and health equity analysis in public health interventions. Projects span living evidence syntheses , machine learning applications , and GRADE framework extensions for policy decision-making. Key contributions include: Developing EPPI-Reviewer software tools for OpenAlex integration. Co-investigating Microsoft Academic Graph for automated study identification. Advancing living systematic reviews through dynamic datasets. Collaborating on ICASR to automate evidence synthesis workflows. His affiliations span multiple institutions, including University of East Anglia (Senior Research Associate since 2005) and University of Cambridge (2011–2015). He has held leadership roles in the Campbell & Cochrane Economics Methods Group and GRADE Working Group .

Nicolas Ballierمشاهده پروفایل

استاد

• Corpus prosody
• Neural machine translation
• Automatic analysis of learner corpora
• +۵ مورد دیگر

Nicolas Ballier is a Professor at Université Paris Cité (formerly Université Paris Diderot), where he teaches and conducts research in linguistics and digital humanities. Previously, he taught at the Université de Rouen and Paris 13. His work focuses on the intersection of linguistics, computational methods, and language learning technologies, with particular expertise in neural machine translation and speech processing. His primary research interests include: Corpus prosody Neural machine translation Automatic analysis of learner corpora Digital humanities Epistemology of linguistics (third revolution of grammatisation) Interpretability of neural machine translation systems Representation of speech in Whisper audio models Dr. Ballier's recent work explores how computers transform linguistic data (the 'third revolution of grammatisation'), with a focus on neural machine translation interpretability and speech analysis using large language models like Whisper. His research bridges theoretical linguistics with practical applications in language learning and translation technologies, particularly focusing on how these technologies can be made transparent and useful for translators and language learners. His 15 most recent publications (2022-2024) demonstrate a strong focus on neural machine translation interpretability, Whisper applications for language assessment, and learner corpus analysis. The publications span multiple prestigious venues including EAMT, ACL, LREC-COLING, and specialized journals in speech technology and computational linguistics, showing consistent productivity and impact in his fields. Dr. Ballier has been PI or team member on numerous European-funded projects including DOKTORAND (2012-2016), KVARK project (2014-2026), PHC Ulysses (2019), multitraiNMT (2021), and LT-LIder project (2024-Nov 2026). He has developed platforms like PAPTAN for neural machine translation experiments and MAKE-NMT VIZ for investigating machine translation interpretability. He has supervised PhD students through collaborative projects and has been involved in research initiatives like DLLA (Deep Learning for Language Assessment) exploring CEFR levels with keylog data, Neuroviz (2021-2022), and SPECTRANS (2020-2022) focusing on specialized neural translation and probing information flow in neural networks.

Dong Zhaoمشاهده پروفایل

دانشیار

• Smart Buildings
• Sustainable Development
• Construction Informatics
• +۵ مورد دیگر

Dong Zhao is an Associate Professor of Construction Management in the School of Planning, Design and Construction at Michigan State University's College of Agriculture and Natural Resources, with a joint appointment in Civil and Environmental Engineering within the College of Engineering. His research pioneers human-building integration for smart and sustainable urban environments through advanced computational approaches. Education: Ph.D. from Virginia Tech Postdoctoral research at Virginia Tech Dr. Zhao's work fundamentally bridges human factors with building systems to optimize energy efficiency, productivity, and occupant health. His expertise spans smart building energy retrofitting, sustainable EV charging infrastructure, virtual design/construction methodologies, and systemic risk mitigation in built environments. This research creates actionable frameworks for transforming cities into responsive, sustainable ecosystems where technology serves human needs. Scientific Awards: NSF CAREER Award ASCE ExCEEd Teaching Award ASCE Best Journal Paper Award Secured major funding from the National Science Foundation, US Department of Transportation, US Economic Development Administration, and Michigan Department of Transportation. Serves as officer for ASCE's Data Sensing and Analysis Committee and reviews for over 50 international journals. As a LEED AP and TEDx speaker, he actively translates research into sustainable building practices and policy initiatives.

• Statistical Analysis and Applications
• Machine Learning
• Data Mining
• +۴ مورد دیگر

Dr. Uğur ERCAN is currently serving as an Associate Professor at Akdeniz University's Department of Informatics. He holds a PhD in Econometrics from Akdeniz University (2016) and a master's degree in Mathematics (2008), with a bachelor's degree in Computer Engineering from Mersin University (2003). PhD in Econometrics (Akdeniz University, 2016) MSc in Mathematics (Akdeniz University, 2008) BSc in Computer Engineering (Mersin University, 2003) His research focuses on statistical analysis, machine learning, data mining, and optimization, with notable applications in agriculture, public health, and consumer behavior. Recent work includes machine learning models for predicting crop quality, alcohol consumption patterns, and energy systems optimization. Scientific output trends show interdisciplinary applications of machine learning across agricultural engineering, health economics, and renewable energy. Publications include predictive models for fruit characteristics, econometric analyses of household expenditures, and optimization algorithms for solar energy systems. He has contributed to 51 publications (Scopus) and maintains active research in quantitative methods, with expertise in neural networks, regression analysis, and ensemble learning techniques. His work aligns with UN Sustainable Development Goals for responsible consumption and production.

• Science and Technology Studies
• Digital Methods
• Generative AI for Social Research
• +۵ مورد دیگر

Anders Kristian Munk is a Professor at DTU Management, Technical University of Denmark, based in the Department of Technology, Management and Economics within the Division for Technology and Business Studies. His research focuses on the critical intersection of technology and society through Science and Technology Studies (STS) and advanced digital methodologies. His primary research domains include: Generative AI integration in social research Digital methods development (notably data sprints) Controversy mapping of sociotechnical issues Computational ethnography and network analysis Critical examination of data imaginaries Urban studies through computational visual methods Recent publications (2023-2025) demonstrate a strategic pivot toward generative AI applications, with significant contributions in developing AI-native research frameworks like synthetic interlocutors and irreductionist mapping. His work uniquely bridges STS theory with practical tool development, exemplified by Gephisto for critical network analysis. Key application areas span public health (e.g., inflammatory bowel disease trend analysis in Africa), cultural industries (film audience analytics), and urban studies (pandemic place attachments). Prof. Munk co-developed the data sprint methodology for collaborative controversy mapping and has contributed to major projects including Climaps and EMAPS. His interdisciplinary approach combines social science rigor with computational innovation through partnerships across computer science, design, and domain-specific fields. Current work emphasizes ethical AI integration and methodological adaptation for complex sociotechnical challenges.

Katrien Verbertمشاهده پروفایل

استاد

• Human-Centered AI
• Explainable AI
• Interactive Recommender Systems
• +۵ مورد دیگر

Katrien Verbert is a Professor at KU Leuven in the Department of Computer Science within the Faculty of Engineering Sciences. She leads the AUGMENT research group, an imec research group focused on Human-Centered AI. Her work spans multiple institutes including DigiSoc – KU Leuven Institute for Digital Society and Leuven.AI - KU Leuven Institute for Artificial Intelligence. She also serves as Program Director of the POC Digital Humanities for the Faculty of Engineering. Her research focuses on creating AI systems that amplify and augment human abilities while preserving human control. This work aims to make AI systems more productive, enjoyable, and fair through visualisation and interaction techniques across various hardware platforms. Verbert investigates how to enable end-users to understand AI model rationale and steer models with input and feedback to increase appropriate trust, acceptance, and accuracy while empowering users as active participants in data-driven solutions. Verbert's research spans applications in learning analytics, precision agriculture, healthcare, media consumption, digital humanities, food & nutrition, fintech, and human resources. Her work emphasizes interactive explanation methods that improve model understanding while enhancing users' ability to steer and refine AI models through domain knowledge. She leads multiple major research projects through 2029 focusing on human-centered explainable AI across diverse application domains. Human-centered Explainable AI Interactive Model Steering Healthcare Applications of AI Educational Technology and Learning Analytics Digital Humanities and AI Collaboration Motivational Design Techniques Verbert has presented her work at prestigious conferences including CHI, Aachen AI Week, and EXPLAIN'AI events. She actively supervises PhD candidates and master's students, with several supervised theses receiving recognition. Her research group continues to advance the field of human-AI interaction through numerous publications and real-world applications.

Murat Elhüseyniمشاهده پروفایل

مدرس

• Engineering Fundamental Field
• Industrial Engineering
• Modeling and Optimization
• +۱ مورد دیگر

Murat Elhüseyni serves as a Lecturer in the Department of Industrial Engineering at Yeditepe University's Faculty of Engineering. His academic career spans multiple institutions including Uskudar University, Bogazici University, and University of Primorska, with expertise in optimization and industrial systems. Education PhD in Industrial Engineering, Bogazici University (2012-2018) Master's in Industrial Engineering, Istanbul Technical University (2010-2012) BSc in Industrial Engineering, Yildiz Technical University (2006-2010) Post-Doctoral Research, Sabanci University (2019-2020) Post-Doctoral Research, University of Primorska (2023-2024) His research centers on Operations Research with emphasis on Modeling and Optimization. Key contributions include vehicle maintenance scheduling under single dead-end track constraints and service level agreements, electricity market modeling to prevent tacit collusion, and biomedical applications using hybrid learning frameworks for gene expression analysis in breast cancer. His methodological approach integrates mathematical modeling, heuristic algorithms, and simulation techniques. Publication trends (2012-2021) reveal consistent focus on industrial optimization problems, particularly vehicle scheduling systems, with expanding applications into energy markets and bioinformatics. His work demonstrates evolution from simulation-based textile workshop scheduling to complex trilevel optimization models for electricity markets. He contributed as a scholarship holder to the TÜBİTAK 1001 project 'Preventing Tacit Collusion in Restructured Electricity Markets' (2020-2021) and serves as a member of INFORMS since 2019.

Sindhu Jagadammaمشاهده پروفایل

دانشیار

• Soil Health
• Carbon Farming
• Sustainable Agriculture
• +۵ مورد دیگر

Sindhu Jagadamma serves as an Associate Professor in the Department of Biosystems Engineering and Soil Science at the University of Tennessee Institute of Agriculture (UTIA), part of the Herbert College of Agriculture. With seven years of service at UT, she has established herself as a leading researcher in soil health and sustainable agricultural practices, particularly focused on addressing the unique challenges of the southeastern United States agricultural landscape. Dr. Jagadamma's educational journey began in Kerala, India, where she earned her BS in Agricultural Sciences and MS in Soil Science from Kerala Agricultural University. After serving four years as an Extension Specialist and Soil Survey Officer for the Kerala state government, she came to the United States as a Ford Foundation International Fellow. At The Ohio State University, she completed both her MS and PhD in Soil Carbon Science under the mentorship of Dr. Rattan Lal, a globally recognized soil scientist and 2020 World Food Prize recipient. She continued her research as a postdoctoral researcher at OSU and Oak Ridge National Laboratory before joining UT as a tenure-track assistant professor in 2016, earning tenure and promotion to Associate Professor in 2022. Her research program centers on developing regionally adaptable approaches to improve soil health in agroecosystems, addressing the limitations of conventional no-tillage practices in the warm, humid southeastern US climate. Dr. Jagadamma investigates innovative strategies including multi-species cover cropping, organic soil amendments, adaptive grazing, and organic farming systems to enhance soil carbon sequestration, nutrient cycling, and overall ecosystem resilience. Her work has demonstrated that building soil health is the most meaningful and cost-effective way to balance agricultural productivity with environmental stewardship in challenging climatic conditions. Analysis of Dr. Jagadamma's recent publications reveals a strong focus on soil carbon dynamics under climate extremes, precision soil health assessment methodologies, and the development of carbon farming practices. Her research spans diverse topics including cover cropping systems, soil health indicators, carbon credit markets, and the impacts of drought and heat on soil processes. A notable trend is her integration of traditional soil science with emerging technologies to address complex agricultural challenges in the context of climate change. AgResearch Mid-Career Faculty Research Excellence Award (2023) University of Tennessee Graduate Student Senate's Graduate Student Mentor of the Year (2023) Gamma Sigma Delta Tennessee Chapter Research Award (2022) BESS Outstanding Research Faculty Award (2018, 2022) Ralph E. Powe Junior Faculty Enhancement Award from Oak Ridge Associated Universities (2017) Dr. Jagadamma has secured over $7 million in research funding, including multiple USDA-NIFA awards as lead principal investigator, an NSF award as co-PI, and USDA-NRCS Partnership for Climate-Smart Commodities awards. Her mentorship philosophy emphasizes cultivating independent research skills through regular individual and group meetings, professional development support, and networking opportunities. To date, she has mentored four PhD and seven MS graduates, with three former mentees now serving as Assistant Professors. Her research group conducts studies on soil processes related to carbon and nutrient cycling, soil health assessment, and developing sustainable management practices specifically adapted to southeastern US agriculture.

Abhilash Chandelمشاهده پروفایل

استادیار

• Precision Agriculture
• Remote Sensing
• Machine Learning in Agriculture
• +۴ مورد دیگر

Abhilash Chandel serves as an Assistant Professor of Precision Agriculture Technologies and Data Management in the Department of Biological Systems Engineering at Virginia Tech, holding a 50% research and 50% extension appointment since December 2021. His work bridges advanced data analytics with practical agricultural solutions, focusing on optimizing crop production through technological innovation. Dr. Chandel's academic foundation includes: Ph.D. in Biological and Agricultural Engineering from Washington State University (2021) M.Tech. in Farm Machinery and Power Engineering from IIT Kharagpur (2015) B.Tech. in Agricultural and Food Engineering from IIT Kharagpur (2015) His research program centers on precision agriculture technologies, specializing in aerial and satellite remote sensing, machine learning applications for crop monitoring, and data-driven water stress detection. Key innovations include AI-enabled mobile tools for real-time field diagnostics, multispectral imagery analysis for disease identification, and evapotranspiration mapping systems for irrigation optimization. His work addresses critical challenges in water management, yield prediction, and sustainable crop production across diverse agricultural systems. Analysis of Dr. Chandel's publication record reveals consistent advancement in UAS-based crop monitoring, with increasing emphasis on AI integration for real-time decision support. His research spans multiple cropping systems including soybeans, peanuts, wheat, and specialty crops, demonstrating particular expertise in thermal-RGB fusion techniques for physiological stress assessment and machine learning frameworks for yield prediction. Recent work shows expanding applications in disease detection (anthracnose, nematodes) and precision nutrient management. Dr. Chandel's contributions have been recognized through multiple awards including: Graduate Student Leadership Award (WSU, 2021) Walter and Vinnie Hinz Outstanding Graduate Student Award (2020) Winner of Microsoft-WSU Digital Ag-Athon (2020) Multiple travel awards and departmental recognitions during graduate studies Through his extension appointment, Dr. Chandel develops practical resources for Virginia farmers including VCE publications on drone-based nematode assessment, peanut maturity mapping, and multispectral disease diagnosis. His work demonstrates strong collaboration with agricultural stakeholders to translate research into field applications, though specific details of graduate student mentoring or major grant funding were not documented in available sources.

Ira Kurtzمشاهده پروفایل

استاد

• Renal Physiology
• Acid-Base Homeostasis
• Molecular Transport Mechanisms
• +۳ مورد دیگر

Dr. Ira Kurtz is a Professor of Medicine at the University of California, Los Angeles (UCLA), and Chief of Nephrology. He is also a Member of the Brain Research Institute. His research focuses on renal physiology, acid-base homeostasis, and molecular transport mechanisms, particularly within the SLC4 transporter family. His work spans structural biology, molecular dynamics, and clinical nephrology. His scientific contributions include structural characterization of SLC4 transporters using cryo-EM and molecular dynamics simulations, revealing key insights into ion translocation pathways and conformational dynamics. He has extensively studied the molecular basis of proximal renal tubular acidosis and electrolyte disorders, with particular emphasis on NBCe1 transporter function and regulation. Dr. Kurtz's publications demonstrate expertise in renal acid-base physiology, membrane transporter characterization, and clinical applications. His work connects fundamental molecular mechanisms with human diseases, including corneal endothelial dystrophies, thrombotic microangiopathy from anti-VEGF treatments, and genetic disorders affecting ion transport. His research methodology integrates computational biology, structural biology, and translational medicine approaches. He has developed novel quantitative models for acid-base analysis and electrolyte disorders, challenging established paradigms like the Stewart approach through critical evaluation of bicarbonate-centered models.

• Econometrics
• Macroeconometrics
• Microeconometrics
• +۷ مورد دیگر

Grigory Gelmutovich Kantorovich is a distinguished Research Professor at the National Research University Higher School of Economics (HSE), where he has been working since 1993. He serves as Head of the Scientific and Educational Laboratory of Macrostructural Modeling of the Russian Economy within the Faculty of Economic Sciences. He holds a tenured professorship since 2018 and has been recognized as a Distinguished Professor by HSE. With 54 years of scientific and teaching experience, Kantorovich has made significant contributions to the field of econometrics and economic modeling. His educational background includes a Candidate of Physical and Mathematical Sciences degree (1974) from Moscow Institute of Physics and Technology, where he specialized in Automatic Regulation and Control Theory. He graduated with honors from the same institution in 1971 with a degree in Flight Dynamics and Motion Control of Aircraft. His extensive professional development includes numerous international programs at Erasmus University Rotterdam, University of Paris 1 - Sorbonne, Harvard University, and the London School of Economics. Kantorovich's research spans multiple areas of econometrics, including macroeconometrics, microeconometrics, financial econometrics, and time series modeling. His work focuses on dynamic modeling of economic development, macrostructural modeling of the economy, and econometric modeling of socio-economic processes. His publications demonstrate a consistent focus on applying rigorous statistical methods to economic problems, with particular attention to risk evaluation in financial markets, structural shifts in time series, and the impact of investments on economic efficiency. Throughout his career, Kantorovich has received numerous prestigious awards including the Medal of the Order "For Merits to the Fatherland" (both I and II degrees), multiple Honorary Certificates from HSE, and recognition as Best Teacher in multiple academic years. He was awarded the "Golden HSE" Award twice for his contributions to teaching and school development. As an academic advisor, Kantorovich has supervised doctoral research and mentored students including Lana Zakharova (on economic crises determinants), Nikolai Shugal (on gross value added modeling), and Elena Nazrullaeva (on capital investments impact). He has also participated in numerous international research projects, including a World Bank project on energy price impacts and grants from the MacArthur Foundation. He leads the Scientific and Educational Laboratory of Macrostructural Modeling of the Russian Economy, where he conducts research on economic modeling and advises on policy-related economic issues. His work bridges theoretical econometrics with practical applications in Russian economic policy and education.

• Biomedical Informatics
• Clinical Decision Support Systems
• Electronic Health Records (EHR) Analysis
• +۳ مورد دیگر

Hyung Paek, MD, MSEE, is a Lecturer in Biostatistics and Clinical Informatician at the Yale School of Public Health. His work bridges Biomedical Informatics with Emergency Medicine , focusing on Clinical Decision Support Systems and Health Equity in electronic health record (EHR) applications. Dr. Paek holds board certifications in Clinical Informatics and Internal Medicine , and his research explores Machine Learning for healthcare workforce predictions, Telehealth Equity for HIV patients, and Opioid Use Disorder interventions in emergency settings. MD, St. Louis University (1996) MSEE, Politechnic University (1986) Board Certified: Clinical Informatics (ABPM, 2017), Internal Medicine (ABIM, 2000) Dr. Paek's recent publications analyze EHR-Driven Predictive Models , Gender Productivity Gaps , and Telehealth Implementation during the pandemic. He frequently collaborates with Yale colleagues including Dr. Edward Melnick , Dr. Gail D'Onofrio , and Dr. James Dziura . His 2023 work in JAMA Network Open highlights emergency department buprenorphine adoption, while 2025 studies examine adaptive decision support systems for addiction treatment.

Massimo Mecellaمشاهده پروفایل

استاد

• Service-Oriented Computing
• Business Process Management
• Process Mining
• +۱۲ مورد دیگر

Massimo Mecella serves as Full Professor at Sapienza University of Rome's Faculty of Information Engineering, Computer Science and Statistics, where he leads the Processes, Services and Software Management research group and participates in the CINI National Cyber Security Lab. His academic foundation stems from a PhD in Computer Engineering earned at Sapienza in 2002. His research expertise spans service-oriented computing, business process management, and cyber-physical systems with emphasis on service composition, process mining, and adaptive distributed architectures. Key technical domains include IoT integration, digital twin development, and security frameworks for complex software ecosystems. Recent publications (2024-2025) demonstrate a pronounced shift toward Large Language Model (LLM) integration in business processes and smart manufacturing. Dominant themes include LLM-driven process modeling, multimodal quality control systems, and digital twin implementations for energy management and production optimization. Methodological innovations focus on retrieval-augmented generation (RAG) for service discovery and fuzzy cognitive maturity assessments. His distinguished recognition includes: ICSOC 2013 Most Influential Paper Award (2003-2012 decade) 2017 Best Paper Award As research group leader, Mecella directs projects in smart manufacturing, process mining, and cyber-physical security, with funding typically sourced from EU initiatives and industry partnerships in aerospace, healthcare, and energy sectors. His team maintains active collaborations with aerospace manufacturers through the MICS SPOKE8 project and develops frameworks like SAMBA for human-in-the-loop manufacturing systems. The Processes, Services and Software Management group operates within Sapienza's engineering faculty infrastructure, utilizing specialized labs for CPS/IoT testing and digital twin simulation, with strong ties to the CINI Cyber Security Lab for security validation.

• Modelling and Requirements Engineering
• Systems Architecture
• Process and Project Management
• +۵ مورد دیگر

Ricardo J. Machado is a Full Professor of Information Systems Engineering at the University of Minho, where he founded this disciplinary area. He currently serves as President of the CCG/ZGDV Institute and has held strategic roles including Vice-Rector of UMinho. He is a member of the Board of GraphicsVision.AI, the leading academic network in Europe in computer graphics and vision. DEng in Electrical and Computer Engineering (U.Porto) MSc and PhD in Computer Science and Engineering Dr. Habil in Information Systems Engineering (UMinho) Certified Software Product Manager (ISPMA) Cybersecurity and Crisis Management programme from Portuguese National Defence Institute Professor Machado's research focuses on modeling and requirements engineering, systems architecture, process and project management, information semantics, ontologies, and cognitive computing. He has developed several methods and tools including the 4SRS method and the shobi-PN meta-model for requirements analysis, software architecture, ontology computation, and project portfolio management. His work bridges theoretical frameworks with practical industrial applications across various sectors. His recent publications demonstrate a strong trend toward digital transformation across multiple domains including smart cities, healthcare systems, industry 4.0, and higher education. The research shows increasing integration of semantic interoperability, logical architecture design, and data management frameworks. His work consistently addresses the challenges of aligning business processes with technical implementations while focusing on practical applications in real-world settings. IEEE MGA Achievement Award APLOG Excellence Award TAA Textbook Award nomination by Springer Professor Machado has supervised nearly one hundred PhD and MSc students, many of whom now hold leadership roles in academia and industry. He has led over 50 R&I projects funded by FCT, ANI, IAPMEI, and the European Union, partnering with institutions including Carnegie Mellon University, MIT, Fraunhofer, and Bosch. His project portfolio spans requirements engineering, systems architecture, and digital transformation initiatives across multiple sectors including healthcare, manufacturing, and smart cities. He founded the SEMAG research group at ALGORITMI and the EPMQ department on Software Engineering and Intelligent Data at CCG/ZGDV. As Director of the ALGORITMI Research Centre, he coordinated UMinho's participation in strategic initiatives such as CEDT, CMU | Portugal, EIT Digital, EUHubs4Data, and Gaia-X Portugal. He also co-founded DTx and ProChild CoLabs, and TICE.pt and CSCP Clusters.

• Agricultural Economics
• Agent-Based Modeling
• Climate Change Adaptation
• +۴ مورد دیگر

Dr. Christian Troost is a Senior Researcher and Lecturer at the Department of Land Use Economics at the University of Hohenheim . His work combines agent-based modeling and bioeconomic simulation to analyze agricultural adaptation to climate variability and resource use decisions across diverse farming systems in Germany, Chile, and Ethiopia . Education : BSc in Geography (Bochum, 2006), MSc in Agricultural Economics (Hohenheim/Wageningen, 2009), PhD in Agricultural Sciences (Hohenheim, 2014) Methodology : Specializes in uncertainty analysis , high-performance computing , and model validation protocols for complex agricultural systems Troost's research advances the MPMAS software package for integrated land system modeling. His 2025-2024 work focuses on pesticide reduction policies , hybrid intelligence systems for biodiversity-productivity tradeoffs, and agroforestry as climate adaptation in Ethiopia. Scientific Recognition : Gerhard-Fürst Prize (2015) Südwestbankpreis (2015) As an educator, he teaches farm system modeling and environmental economics at PhD, MSc, and BSc levels. His collaborative projects span DFG-funded climate change research , BMBF biodiversity initiatives , and German-Ethiopian CLIFOOD graduate programs .