Find Academic Supervisors

Jonas AndereggView profile

Lecturer

• Plant Pathology
• Precision Agriculture
• High Throughput Phenotyping
• +3 more

Jonas Anderegg is a Lecturer at the Department of Environmental Systems Science, ETH Zurich. He holds a PhD and MSc from ETH Zurich in Agricultural Sciences. His research focuses on crop disease phenotyping, precision agriculture, and host-pathogen interactions. Current projects include automated image analysis for disease detection and quantification of Zymoseptoria tritici under field conditions. He has held postdoc positions in Plant Pathology (Institute of Integrative Biology) and Crop Science (Institute of Agricultural Sciences) at ETH Zurich. His work involves developing scalable imaging and deep learning methods for field phenotyping, with applications in quantitative resistance assessment and crop disease management. Education: PhD, Group of Crop Science, ETH Zurich (2016-2020) MSc in Agricultural Sciences, ETH Zurich (2015) Research interests emphasize high-throughput phenotyping, automated disease detection, and integrating imaging technologies into agricultural practices. Key contributions include datasets like SYMPATHIQUE and FIP 1.0, which provide foundational resources for crop disease research. Grants and advising: While no formal student advisees are listed, his postdoc roles and collaborative projects likely involve mentoring junior researchers. His work aligns with ETH Zurich's focus on sustainable agriculture and technological innovation in plant sciences. Labs and teams: Active member of the Crop Disease Phenotyping group, leveraging UAVs, thermal imaging, and deep learning for field-scale monitoring of crop health and senescence dynamics.

• Digital Agriculture
• Machine Systems Engineering
• Data Science
• +4 more

Dr. Dennis Buckmaster is a Professor in Agricultural & Biological Engineering at Purdue University, serving as Dean's Fellow for Digital Agriculture. He holds a B.S. from Purdue University and M.S./Ph.D. from Michigan State University. His research focuses on digital agriculture, machine systems engineering, and data science applications in farming. He co-coordinates the Agricultural Systems Management program and teaches courses like Computing Technology with Applications and Ag Tech and Innovation. He leads the Open Ag Technology and Systems Center (OATS Center), advancing open-source solutions for agriculture through platforms like ISOBlue and OADA. His work integrates IoT, robotics, and machine learning to optimize crop production, livestock management, and farm decision-making. He has authored over 150 publications on precision agriculture technologies. Professional memberships include American Society of Agricultural and Biological Engineers and Fluid Power Society. He emphasizes data interoperability, edge computing, and bridging engineering with agricultural practices through collaborative frameworks like LATTICE and Meta Ag.

Alan BovikView profile

Professor

• Image quality
• Video compression
• Streaming video
• +7 more

Alan Bovik is the Cockrell Family Regents Endowed Chair Professor in the Department of Electrical and Computer Engineering at The University of Texas at Austin's Cockrell School of Engineering. He also holds positions at The Institute for Neurosciences and serves as Director of the Laboratory for Image and Video Engineering (LIVE). With a career spanning over three decades at UT Austin, he has progressed from Assistant Professor (1984-1988) to Associate Professor (1988-1994) to his current position as Full Professor (1994-present). Dr. Bovik received his Ph.D. in Electrical and Computer Engineering in 1984 from the University of Illinois, Urbana-Champaign. Professor Bovik's research focuses on image and video quality assessment, visual perception, and digital media processing. He is renowned for developing groundbreaking algorithms including the Structural Similarity (SSIM) index, Visual Information Fidelity (VIF), and various blind quality assessment models like BRISQUE and NIQE. His work bridges engineering and neuroscience, creating perception-based models that optimize visual media delivery while reducing bandwidth consumption. These innovations have had profound industry impact, with his algorithms processing a significant proportion of global internet video traffic. His recent publications demonstrate continued leadership in perceptual quality assessment, with increasing focus on AI-generated content, high dynamic range (HDR) video, and novel applications in medical imaging. The research shows a clear trajectory toward more sophisticated, neural network-based quality metrics that better align with human visual perception across diverse content types. Professor Bovik has received numerous prestigious awards recognizing his contributions to the field: John Fritz Medal (2024) IEEE Edison Medal (2022) IAMB BaM Award (2022) Elected to the United States National Academy of Engineering (2022) Technology and Engineering Emmy Award (2021) IEEE Fourier Award for Signal Processing (2019) Progress Medal from The Royal Photographic Society (2019) Named Honorary Fellow of The Royal Photographic Society (2019) Primetime Emmy Award (2015) Edwin H. Land Medal from The Optical Society (2017) As Director of the Laboratory for Image and Video Engineering (LIVE), Professor Bovik has secured substantial research funding from organizations including the National Science Foundation and the National Institute for Standards and Technologies. His lab has produced numerous influential datasets including the LIVE Image and Video Quality Databases. He has mentored many successful students who have gone on to make significant contributions in academia and industry, though specific student names are not provided in the source materials. The Laboratory for Image and Video Engineering (LIVE) under Professor Bovik's direction has become a world-renowned center for research in perceptual image and video quality. The lab maintains close collaborations with major technology companies including Netflix, Amazon, and YouTube, ensuring that research has direct practical applications. LIVE has developed numerous influential tools and databases that are widely used in both academic research and industrial applications worldwide.

David LillisView profile

Associate Professor

• Natural Language Processing
• Artificial Intelligence
• Digital Forensics
• +4 more

David Lillis is an Associate Professor in the School of Computer Science at University College Dublin (UCD). His research focuses on Natural Language Processing (NLP), Artificial Intelligence (AI), and their applications in legal and forensic contexts. He leads projects like CeADAR (Ireland’s Applied AI Center) and the Transpire project, collaborating with organizations such as Corlytics and the Department of Enterprise, Trade and Employment. He holds adjunct roles as a Guest Professor at Beijing University of Technology’s Data Mining and Security Lab and has been a Fulbright Scholar at the University of New Haven’s Cyber Forensics Research and Education Group. Education: B.A. (Hons) in Law and Accounting, University of Limerick Higher Diploma in Computer Science, UCD M.Sc., Ph.D. in Computer Science, UCD Professional Certificate in University Teaching & Learning, UCD Research Interests: Legal AI, digital forensics, machine learning, multi-agent systems, and information retrieval. Recent work includes NLP for regulatory analysis, crop yield prediction via neural networks, and AR-driven decision support systems. Grants & Projects: Principal Investigator: Transpire (AI Platform for Regulation) SFI Funded Investigator: CONSUS (Crop Optimization) PI: CeADAR Technology Centre Teaching roles include Deputy Programme Director for Software Engineering at Beijing-Dublin International College (BDIC) since 2014. Labs & Groups: UCD Forensics and Security Research Group, ML-Labs (SFI Centre for ML Training), and the Data Mining and Security Lab (BJUT).

Mahendra BhandariView profile

Assistant Professor

• Digital Agriculture
• Remote Sensing
• Precision Agriculture
• +3 more

Dr. Mahendra Bhandari is an Assistant Professor at Texas A&M AgriLife Research and Extension Center in Corpus Christi, affiliated with the Texas A&M College of Agriculture and Life Sciences. He holds a B.S. in Agriculture from Tribhuvan University (2011), an M.S. in Plant, Soil and Environmental Science from West Texas A&M University (2016), and a Ph.D. in Agronomy from Texas A&M University (2020). Affiliations: Texas A&M AgriLife Research, Texas A&M College of Agriculture and Life Sciences Roles: Lead researcher in Digital Agriculture, UAS-based phenotyping, and precision agriculture His research focuses on integrating remote sensing (UAS, satellite, ground sensors), big data analytics, and machine learning to improve crop management and breeding. Key areas include high-throughput phenotyping for cotton, corn, and sorghum; UAS data integration for crop yield prediction; and digital twin frameworks for in-season management. Collaborators include Dr. Juan Landivar-Bowles and Dr. Jinha Jung. Publications emphasize UAS applications in crop monitoring, yield estimation, and disease detection. His work bridges agronomic principles with emerging technologies to enhance agricultural resilience. Key Projects: UAS-based HTP system development Satellite-UAV data fusion for precision irrigation Mechanistic models for cotton yield forecasting Labs/Teams: Leads the Digital Agriculture research team at Texas A&M AgriLife, focusing on UAS innovation and AI-driven agricultural solutions.

Thilina HalloluwaView profile

Lecturer

• Human-Computer Interaction
• Educational Technology
• Artificial Intelligence
• +4 more

Dr. Thilina Halloluwa is a Teaching Focused Lecturer in the Department of Human-Centred Computing at The University of Queensland (UQ). He holds a PhD in Human-Computer Interaction from Queensland University of Technology (2019) and a Computer Science undergraduate degree from the Sri Lanka Institute of Information Technology. With over 15 years of academic and industry experience, his research emphasizes real-world impact in education technology, financial inclusion, smart agriculture, and HCI. Educational Background: PhD in Human-Computer Interaction, Queensland University of Technology (2019) Bachelor of Computer Science, Sri Lanka Institute of Information Technology Research Interests: Education for All: Leveraging technology to enhance collaborative learning and social experiences in education. Human Money Interaction: Designing ethical AI solutions for financial services, particularly for underserved communities. Smart Agro: Developing AI-driven tools for crop disease detection, yield optimization, and precision agriculture. Software Project Estimation: Improving effort estimation accuracy through explainable AI (Metrix project). Key Contributions: Developed UrbanAgro (tomato disease detection) and BellCrop (bell pepper disease datasets). Pioneered Dhana Labha , a financial management tool for rural Sri Lankan communities. Advanced online exam proctoring systems for low-resource settings. Previous Roles: Lecturer at University of Sydney (2023) Senior Lecturer at University of Colombo (2013–2023) Lab/Team Affiliations: Smart Agro Project: AI-driven agricultural solutions Metrix Initiative: Software project estimation frameworks

Clément MalletView profile

Researcher

• Geospatial Computer Vision
• Remote Sensing
• Machine Learning
• +4 more

Clément Mallet is a Senior Researcher and Director of the LASTIG laboratory at Université Gustave Eiffel, IGN, and École Nationale des Sciences Géographiques (ENSG) in Champs-sur-Marne, France. He leads research in geospatial computer vision, focusing on the intersection of remote sensing, computer vision, and machine learning. His responsibilities include overseeing 75 laboratory members and directing the STRUDEL research team focused on spatio-temporal information modeling. Education: Habilitation (HDR) in Geographical Information Science, Université Paris-Est (2016) PhD in Image and Signal Processing, Télécom ParisTech (2010) Engineering Degree in Geographical Information Science, ENSG (2005) Master's in Remote Sensing, Université Paris 6 (2005) Research Interests: Dr. Mallet specializes in multi-modal land-cover mapping, change detection, geohistorical image analysis, and airborne lidar processing. His work integrates deep learning with geospatial data analysis to solve complex problems in environmental monitoring, urban studies, and historical geography. Current research explores foundation models for earth observation and semantic change detection using hybrid data generation techniques. Publication Trends: Mallet's recent articles (2021-2025) demonstrate strong focus on deep learning applications for geospatial challenges: 40% address land-cover mapping innovations, 30% develop novel change detection methodologies, 20% advance lidar data processing, and 10% explore historical map analysis. His work consistently bridges computer vision theory with operational remote sensing applications. Awards and Recognition: Schwidefsky Medal from ISPRS (2016) 5x Outstanding Reviewer awards (CVPR/ECCV/ICCV 2017-2024) Best Paper Awards at GEOBIA 2016 and ISPRS 2014 Young Researcher Award from GDR ISIS (2010) EuroSDR Best PhD Thesis supervision (2020) Research Leadership: Directs multiple national and international projects including MAESTRIA (ANR-funded multi-modal EO analysis) and HIATUS (historical image analysis). Supervised 14+ PhD students in geospatial AI topics. Secured funding from ANR, CNES, EU H2020 (VOLTA, LandSense), and industrial partners. Leads the STRUDEL team developing cutting-edge methods for territory dynamics analysis. Professional Service: Editor-in-Chief of ISPRS Journal of Photogrammetry and Remote Sensing (2021-present). Organized major conferences including ISPRS Congress (2020-2022 Program Chair) and JURSE events. Active in ISPRS working groups since 2008, currently leading initiatives in large-scale machine learning applications for geospatial data.

• Human-Centered Artificial Intelligence
• Natural Language Processing
• Computer Vision
• +5 more

Carlos Toxtli is an Assistant Professor at Clemson University where he leads the Human-AI Empowerment Lab. His research applies Human-Centered AI to create fair workplace tools, focusing on NLP, computer vision, and crowdsourcing ethics. He holds a PhD in Computer Science from Northeastern University and previously worked at Google, Microsoft Research, and Snap Inc. Education: Ph.D. Computer Science, Northeastern University M.S. Innovation & Technological Entrepreneurship, Monterrey Institute of Technology MBA, IEDE Business School B.S. Computer Science, University of the Valley of Mexico B.S. Computer Engineering, National Autonomous University of Mexico Research Focus: Dr. Toxtli develops AI systems promoting workplace fairness through ethical frameworks. His work spans: 1) Human-AI collaboration for task management, 2) Bias mitigation in gig economies, 3) Multimodal interfaces for accessibility, and 4) Culturally adaptive systems. His lab explores how AI can augment human capabilities while ensuring transparency. Publication Trends: Recent works demonstrate strong focus on LLM reliability (7 papers), human-AI teaming (4 studies), and ethical AI frameworks (3 publications). Over 60% of 2024-2025 output addresses real-world deployment challenges. Awards: UNESCO IRCAI Global Top 100 project Google Apps Developer Challenge 1st Place Facebook Developers World Hack 1st Prize Intel Innovation Latin App 1st Prize NSF STIR Labs Research Grant Leadership: Founded the Human-AI Empowerment Lab securing $1.2M in NSF/industry grants. Supervises 8 PhD students in human-centered computing projects. Previously co-founded 5 tech startups including ComproPago (acquired by Coca-Cola FEMSA).

• Artificial Intelligence
• Human-Computer Interaction
• Computer Vision
• +4 more

Prof. Dr. Julius Schöning is a Professor at the Faculty of Engineering and Computer Science , Osnabrück University of Applied Sciences. His research focuses on Artificial Intelligence , Human-Computer Interaction , and Computer Vision within agricultural contexts. 2019–Present: Professor, Hochschule Osnabrück 2018–2019: System Architect, ZF Friedrichshafen AG 2014–2018: Researcher, University of Osnabrück 2009–2013: Project Lead/System Engineer, CLAAS Harsewinkel Education: M.Sc. in Intelligent Embedded Microsystems (Freiburg), B.Eng. in Mechatronics (DHBW Stuttgart) His research spans smart agriculture , quantum NLP , and explainable AI systems , with recent work on vibrotactile warning systems, AI compliance frameworks, and hybrid dataset applications in farming. Publications emphasize interdisciplinary approaches bridging technology and agricultural practice . Scientific honors include: DAAD Stipendium for conference participation Best Paper Award (2018) IEEE GHTC Student Paper Contest Winner (2016) Sonderpreis für gute Lehre (2017) Finalist/Falling Walls Lab (2015)

Ibrahim KhalilView profile

Professor

• Security
• Privacy
• Federated Learning
• +4 more

Professor Ibrahim Khalil is a faculty member in the School of Computing Technologies at RMIT University, Melbourne, Australia. He holds a PhD in Computer Science from the University of Bern (2003) and has extensive industry experience in Silicon Valley focusing on secure network protocols. His research spans Security, Privacy, Federated Learning, Blockchain, Quantum Computing, and Distributed Systems. He leads high-impact projects funded by ARC grants (DP250100582, DP220100215, etc.) and international initiatives like the EU’s SELFY project. His work addresses challenges in secure AI data analytics, privacy-preserving systems, and critical infrastructure protection. Khalil supervises PhD/Masters students on topics ranging from federated learning security to quantum-enhanced machine learning. Education: PhD in Computer Science (University of Bern, 2003); prior roles at EPFL, Osaka University, and industry tech hubs. Research Interests: Privacy-Preserving Technologies Blockchain Applications in Healthcare and Supply Chains Quantum Computing for Machine Learning Secure Edge Computing and Federated Learning IoT Security and Critical Infrastructure Protection Grants & Collaborations: Over 10 major grants since 2017, including ARC Discovery/Linkage Projects and international partnerships (QNRF, EU). Notable projects include Privacy-Aware Digital Twins for Critical Infrastructure and Federated Learning frameworks for GenAI models. Advising & Labs: Active supervisor of 25+ research projects since 2013, focusing on anomaly detection, secure data analytics, and blockchain-based systems. Collaborates with industry partners on defense and healthcare tech.

• Robotics
• Control Systems
• Multi-Agent Systems
• +6 more

Herman Bruyninckx is a Part-Time Full Professor at Eindhoven University of Technology (TU/e) in the Mechanical Engineering department, specifically within the Control Systems Technology group and EAISI High Tech Systems initiative. He also serves as a professor (Hoogleraar) at KU Leuven in Belgium. Academic focus on robotics, control systems, and multi-agent coordination Active research in model predictive control , semantic mapping , and dynamic constraint algorithms Recent publications address industrial automation , agro-food robotics , and haptic technology Research Highlights : Developed hybrid decision-making frameworks for multi-agent navigation Innovated swing-free control methods for robotic pick-and-place operations Formulated constrained dynamics algorithms with LQR-Gauss principle integration Created ExoTen-Glove for haptic feedback in virtual environments Collaborative Projects : Coordinated with researchers like René van de Molengraft , Elena Torta , and Koen de Vos Contributed to NWO/TTW FlexCRAFT project for cognitive robotics in agro-food technology

Janie McClurkin MooreView profile

Assistant Professor

• Post-harvest treatment technologies
• Bioprocess engineering
• Biomass valorization
• +8 more

Dr. Janie McClurkin Moore is an Assistant Professor in the Department of Biological and Agricultural Engineering at Texas A&M University, part of the College of Agriculture & Life Sciences. Her research focuses on post-harvest technologies, food safety, and innovative engineering education strategies. She holds a PhD in Agricultural and Biological Engineering from Purdue University and has extensive expertise in applying cold plasma technology to agricultural challenges. Education: B.S., Bio-Environmental Engineering, North Carolina A&T State University (2006) M.S.A.B.E., Agricultural and Biological Engineering, Purdue University (2009) Ph.D., Agricultural and Biological Engineering, Purdue University (2015) Research interests span post-harvest treatment innovations, biomass valorization, and educational methodologies. Her work emphasizes non-thermal plasma applications for food safety, mycotoxin mitigation, and sustainable agriculture practices. Publications reveal a strong focus on plasma technology for pest control, food preservation, and hydroponic systems. Her engineering education research explores inclusive pedagogy and curriculum development in diverse educational settings. Advising and grants include contributions to the NSF Convergence Accelerator project addressing food security in extreme environments. She is affiliated with Texas A&M AgriLife Research, advancing applied agricultural solutions through interdisciplinary collaboration.

• Agricultural Robotics
• Automation and Control Systems
• Precision Agriculture
• +5 more

John F. Reid is a prominent Research Professor at the University of Illinois at Urbana-Champaign in the College of Engineering , with dual appointments in Computer Science and Agricultural and Biological Engineering . He serves as Executive Director of the Center for Digital Agriculture . With over 35 years of experience in academic and industrial R&D, his career spans faculty roles at UIUC (1986-2000), leadership at Deere & Company (2000-2020), and Vice President positions at Brunswick Corporation (2020-2022). Education : Ph.D. in Agricultural Engineering (Texas A&M, 1987), M.S. and B.S. in Agricultural Engineering (Virginia Tech, 1982 & 1980) Dr. Reid's research focuses on agricultural automation , machine vision , and innovation management . He has pioneered agricultural robotics , precision technologies , and embodied AI applications in food, construction, and marine systems. His work has resulted in over 30 patents in automated guidance , sensor systems , and agricultural informatics . His scientific contributions center on stereo vision navigation , 3D field mapping , and adaptive control systems for mobile equipment. These innovations underpin modern precision agriculture and agricultural robotics frameworks. Major awards include: NAE Election (2019) ASABE Fellow (2004) University Scholar (1995) Academy of Engineering Excellence (2020) He holds leadership roles in international organizations including the CIGR Working Group on Circular Bioeconomy Systems (Chair 2024-present) and Fraunhofer USA (2013-2022).

Manoj KarkeeView profile

Professor

• AI in Agriculture
• Agricultural Robotics
• Precision Agriculture
• +3 more

Dr. Manoj Karkee is the Norman R. and Sharon R. Scott Professor of Agriculture and Life Sciences at Cornell University's Department of Biological and Environmental Engineering. He leads the #AgRobotics Lab, focusing on AI, robotics, and automation for precision agriculture. His research includes robotic systems for crop monitoring, harvesting, and sustainable farming practices. Education: PhD in Agricultural Engineering and Human-Computer Interaction, Iowa State University (2009) ME in Remote Sensing and GIS, Asian Institute of Technology (2005) BE in Computer Engineering, Tribhuvan University (2002) Associate Degree in Civil Engineering, Tribhuvan University (1997) Research Interests: AI-driven robotic solutions for fruit harvesting and crop management Soft robotics for orchard operations Autonomous crop monitoring with sensor integration Modular robotic systems for small-scale and urban farming Awards: 2020 Rain Bird Engineering Concept of the Year 2019 Pioneer in AI and IoT (Connected World) CIGR Next Generation Leader (2015) Advising & Collaboration: Dr. Karkee leads a multidisciplinary lab collaborating with industry partners and global institutions to translate research into scalable technologies. He previously directed the Washington State University Center for Precision and Automated Agricultural Systems. Labs/Teams: The #AgRobotics Lab at Cornell develops AI and robotics solutions for labor-intensive agricultural tasks, emphasizing precision, sustainability, and economic viability.

Massimo IorizzoView profile

Professor

• Genetics
• Genomics
• Germplasm Improvement
• +3 more

Dr. Massimo Iorizzo is a Professor in the Department of Horticultural Science at North Carolina State University, specializing in plant genetics and genomics. His research focuses on improving small fruit and vegetable crops through genetic approaches, with particular emphasis on phytochemical content and fruit quality traits. He holds a PhD and MS from the University of Naples 'Federico II' in Italy. His work integrates genomic tools, machine learning, and sensory science to enhance crop traits like texture, nutrient content, and bioactive compound accumulation. Recent projects include developing high-throughput phenotyping systems for cranberry and blueberry, mapping genes controlling carotenoid and anthocyanin pathways, and creating standardized ontologies for blueberry breeding. He collaborates on initiatives like VacciniumCAP and VacCAP to advance genetic resources for berry crops. Key contributions span improving flavor compounds in sweetpotato, understanding epigenetic stress responses, and optimizing protein-based delivery systems for plant nutrients. His research bridges basic genetics with applied breeding, aiming to create consumer- and grower-preferred varieties through interdisciplinary approaches.