Robert MacCurdy is an Assistant Professor at the Department of Mechanical Engineering, University of Colorado Boulder . He leads the Matter Assembly Computation Lab (MACLab) focused on automating robot design and fabrication. His research bridges computational design and advanced manufacturing to create "robots that walk out of the printer." The lab develops tools like OpenVCAD , an open-source volumetric multi-material geometry compiler.
Professor Nagi Gebraeel serves as the Georgia Power Early Career Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Institute of Technology, where his research integrates predictive analytics, machine learning, and optimization for industrial IoT applications. His work focuses on real-time equipment diagnostics, prognostics, and operational decision-making in critical infrastructure systems. Education: Ph.D. in Industrial Engineering (2003), Purdue University M.S. in Industrial Engineering (1998), Purdue University Research Focus: Dr. Gebraeel develops statistical learning algorithms for IoT-enabled maintenance, repair, and operations (MRO), with emphasis on federated learning frameworks for distributed fault diagnosis and cybersecurity protection against Industrial Control System (ICS) attacks. His research spans manufacturing, power generation, and deep space habitats through NASA's HOME Space Technology Research Institute, where he pioneers self-aware habitat systems. Recent work addresses data heterogeneity in high-consequence industrial environments using causal-informed analytics. Publication Trends: His 2024-2025 publications demonstrate a strong trajectory toward distributionally robust optimization for maintenance logistics, federated learning architectures for distributed fault diagnosis, and prognostics for complex systems like offshore wind farms and industrial robots. Key themes include handling imbalanced data in fault diagnosis, state-space representations for interdependent systems, and cybersecurity integration in manufacturing networks. Awards and Recognition: NSF CAREER Award (2007) SAE Aircraft Electrical Power System Recognition Award (2008) SAE Materials Modeling and Testing Recognition Award (2006) IEEE-AUTOTESTCON Certificate (2006) Fellow of the Institute of Industrial and Systems Engineers Advising and Funding: Dr. Gebraeel mentors doctoral students including Michael Ibrahim (2025 IISE Best Student Paper winner), Heraldo Rozas (now Assistant Professor at University of Chile), Ayush Mohanty, and Nazal Mohamed. He secured a $500,000 NSF grant in August 2025 for AI-driven cybersecurity in distributed manufacturing networks and leads NASA-funded research on deep space habitat systems. His work bridges academic research with industry applications through Georgia Tech's Strategic Energy Institute collaborations. Research Infrastructure: He directs the Analytics and Prognostics Systems laboratory at Georgia Tech's Manufacturing Institute and leads the Predictive Analytics and Intelligent Systems (PAIS) research group. Previously, he served as associate director of Georgia Tech's Strategic Energy Institute (2014-2019), fostering data science applications in energy systems.
Burak Ozdoganlar is a Ver Planck Endowed Chair Professor of Mechanical Engineering at Carnegie Mellon University (CMU) and Associate Director of the Engineering Research Accelerator. He holds courtesy faculty positions in Biomedical Engineering and Materials Science and Engineering. Ozdoganlar earned his Ph.D. in Mechanical Engineering from the University of Michigan (1999), M.S. degrees from Ohio State University (1993, 1995), and a B.S. in Aeronautical Engineering from Istanbul Technical University (1991). Ph.D., Mechanical Engineering, University of Michigan (1999) MS, Mechanical Engineering, Ohio State University (1995) MS, Aeronautical and Astronautical Engineering, Ohio State University (1993) BS, Aeronautical Engineering, Istanbul Technical University (1991) Ozdoganlar’s research focuses on multi-scale manufacturing processes (macro/micro/nano), precision engineering , structural dynamics , and modal testing , with applications in biomedical device fabrication , microneedle arrays , soft electronics , and 3D ice printing for vascular networks. His work bridges computational modeling with experimental validation. Recent scientific awards include the 2023 AIMBE College of Fellows induction, ASME Fellow (2019), and NSF CAREER Award (2006). He served as interim CTO of the Advanced Robotics for Manufacturing (ARM) Institute and chaired the ASME-MED Manufacturing Equipment Technical Committee. Ozdoganlar leads projects in scalable manufacturing for implantable medical devices , bioelectric medicine , and wearable robotics . His lab develops 3D ice-printed vascular templates for tissue engineering and liquid metal circuits for soft electronics, funded by institutions like the Manufacturing Futures Institute and ARPA-H.
Dr. V.M. (Bala) Balasubramaniam is a Professor in the Department of Food Science and Technology at The Ohio State University. He holds editorial roles in food engineering journals and is a Fellow of IFT and IUFoST. His research focuses on clean food manufacturing technologies, particularly high-pressure and nonthermal methods, emphasizing microbial inactivation and nutrient preservation. He teaches unit operations in food engineering and contributes to industry via short courses and pilot plant demonstrations. Education: B.S. (Tamil Nadu Agricultural University), M.S. (Asian Institute of Technology), Ph.D. (Ohio State University). Research Interests: Thermal/nonthermal processing, food safety/quality modeling, and innovative applications of high-pressure technologies. Recent work includes ultra-shear technology development and superheated steam sanitation. Over 100 scientific papers, 20 book chapters, and co-edited books on high-pressure processing. Awards include the 2021 IFT Research & Development Award and 2017 Calvert L. Willey Award. Advising: Supervises graduate students like Liz Astorga Oquendo and Shruthy Seshadrinathan. Industrial outreach includes a USDA consortium for ultra-shear commercialization. Labs/teams focus on pilot-scale equipment testing and microbial efficacy studies.
Prof. Raul Fangueiro is a Full Professor and Vice-Dean at the School of Engineering, University of Minho, Portugal. As a Senior Researcher, he leads FIBRENAMICS – Institute of Innovation on Fiber-Based Materials and Composites. His work spans advanced materials (nano, smart, composites) and structures (3D, auxetic, multiscale) with applications in defense, healthcare, construction, and automotive sectors. Supervised over 20 PhD and Post-Doc researchers Scientific coordinator of 20+ national/international research projects Author of 200+ journal papers (H-index: 47), 500+ conference publications, 36 books, 40 patents Research focuses on nanotechnology , electrospinning , and sustainable material systems : Auxetic composites for personal protection Biodegradable nanofibers for medical use Smart textiles for biological/chemical resistance Recycled mineral/wood-based composites Graphene-reinforced green materials Multiscale fiber architectures Scientific recognition includes: Top 2% most influential scientist (Elsevier/Stanford 2020) Founder of AUXDEFENSE and ICNF conferences Editorial board member of leading composite journals Advisor to European Defense Agency/NATO working groups Active in industry-academia partnerships through spin-offs (Sciencentris, B4Logic, Beyond Composites, Givaware, Pixartidea) and collaborative projects with institutions like Instituto Superior Técnico, University of Aveiro, and international universities.
Gian Antonio Susto is an Associate Professor at the Department of Information Engineering , University of Padova . With a Ph.D. in Information Technology and post-doctoral experience at National University of Ireland, Maynooth, he leads research in Machine Learning , Semiconductor Manufacturing , and Industrial IoT . His work bridges Anomaly Detection , Continual Learning , and Algorithmic Fairness with applications in Hydroelectric Power Plants , Particle Accelerators , and Smart Mobility . B.Sc. and M.Sc. in Controls Engineering, University of Padova (cum laude) Ph.D. in Information Technology, University of Padova (2013) Post-Doc at National University of Ireland, Maynooth (2012-2013) Assistant Professor at University of Padova (2013-2021) His research focuses on Explainable AI , Virtual Metrology , and Deep Learning for manufacturing and infrastructure monitoring. Recent projects include the AIMS5.0 (AI for Manufacturing Sustainability) and MICS (Circular Economy in Italy) initiatives. His publications span Engineering Applications of Artificial Intelligence , IEEE Transactions , and Information Processing & Management , with 15+ recent papers on topics like Fault Diagnosis , Continual Learning , and Fair Ranking . Key scientific awards include: IEEE CCTA Best Student Paper Award (2021) IP&M 2020 Ph.D Paper Award Best Industry Paper Award, European Workshop on Advanced Control and Diagnosis (ACD 2019) He has supervised Ph.D. students on projects involving Particle Accelerators , Plant Behavior Modeling , and Explainable AI , with alumni now at institutions like Max Planck Institute , IBM , and Scripps Research . Current teaching includes Reinforcement Learning and Explainable Machine Learning at graduate and Ph.D. levels.
Nedim Pervan is a Full Professor at the Faculty of Mechanical Engineering, University of Sarajevo, Bosnia and Herzegovina. His academic position focuses on mechanical engineering with emphasis on product design, structural analysis, and biomechanical applications. He maintains an active research profile with numerous publications and collaborations across various engineering disciplines, with office hours every workday from 09:00 to 10:00 in room 314. Professor Pervan's research interests span multiple domains of mechanical engineering. He has made significant contributions to additive manufacturing, particularly in polymer gear production and analysis. His work explores mechanical properties, failure mechanisms, and service life of polymer gears manufactured through additive processes. Additionally, he has conducted extensive research on external fixation devices used in orthopedic treatments, analyzing their biomechanical characteristics and structural stability under various loading conditions. His expertise extends to finite element analysis, structural optimization, and the application of 3D scanning technologies within Industry 4.0 contexts. His research demonstrates a strong connection between theoretical engineering principles and practical applications across automotive, medical devices, and manufacturing industries. His recent publication record reveals a strong trend toward interdisciplinary research bridging mechanical engineering with biomedical applications and advanced manufacturing technologies. A significant portion of his work focuses on polymer gears and additive manufacturing, examining material properties and performance characteristics. Another substantial research stream involves biomechanical engineering, particularly the analysis of external fixation devices. His publications demonstrate a methodological approach combining experimental testing with finite element analysis. More recently, his research has expanded into 3D scanning applications in manufacturing and the electrification of transportation systems in Bosnia and Herzegovina. Professor Pervan has been involved in numerous research projects that have advanced the capabilities of the Faculty of Mechanical Engineering. These include the "Integrated Intelligent CAD System for Interactive Design, Analysis and Prototyping of Compression and Torsion Springs" (2022), "Opremanje Laboratorije za razvoj i dizajn proizvoda" (2020), and "Modernizacija Laboratorije za ispitivanje mašinskih konstrukcija" (2019-2020). These projects have focused on developing advanced laboratory facilities, intelligent CAD systems, and equipment for mechanical design and analysis, with several specifically targeting 3D scanning technology implementation. His collaborative work extends across multiple research teams within the Department of Mechanical Constructions at the University of Sarajevo. He frequently collaborates with researchers including Adis Muminović, Elmedin Mešić, and Muamer Delić on projects related to additive manufacturing, biomechanical engineering, and structural analysis. His research group appears actively involved in both theoretical and applied engineering research with practical industrial and medical applications, contributing significantly to Bosnia and Herzegovina's engineering research landscape.
Douglas A. Loy is a full Professor at the University of Arizona with joint appointments in the Department of Materials Science and Engineering and the Department of Chemistry and Biochemistry, and additional affiliations with the BIO5 Institute and the School of Mining and Mineral Resources. A fifth-generation Arizonan, he earned his BS in Chemistry from the University of Arizona (1983), MS in Chemistry from Northern Arizona University (1986), and PhD in Organic Chemistry from the University of California, Irvine (1991). Before returning to academia he spent 14 years at Sandia National Laboratories and then led the Polymer and Nanomaterials Synthesis Team at Los Alamos National Laboratory. Research Interests Sol-gel & polysilsesquioxane chemistry: fundamental studies and unconventional routes to hybrid organic-inorganic materials. Tetrazine polymer chemistry: synthesis, click modification, and application in antioxidant foams and UV-stable sunscreens. 3-D printing of glasses & ceramics: additive manufacturing of micro-optics, multi-refractive-index glass objects, and transparent devices using silica and silsesquioxane resins. Energy & biomaterials: new materials for energy storage, polymer-ceramic bone scaffolds, and smart packaging films. Across more than 70 recent publications (2012-2025), the dominant themes are advanced additive manufacturing of specialty glasses and ceramics, design of photochemically stable sunscreen systems, and development of multifunctional polymer-ceramic composites for biomedical and energy applications. The work integrates molecular-level organic synthesis with macro-scale materials processing, enabling applications ranging from holographic micro-optics to lunar in-situ resource utilization. Scientific Awards & Recognition While specific honors are not listed in the provided text, Loy is described as a “distinguished member of technical staff” at Sandia National Laboratories, indicating prior recognition for his research achievements. Funding & Collaborative Teams At the University of Arizona his group pursues federally and industrially funded projects spanning NSF, DOE, and NASA programs, particularly in advanced manufacturing and energy materials. He collaborates closely with the BIO5 Institute for biomedical applications and with the School of Mining and Mineral Resources for resource-based materials research. No explicit student lists are included in the text. Laboratory & Facilities Loy’s laboratories are located in Mines and Metallurgy 338B at the University of Arizona, equipped for sol-gel synthesis, polymer processing, and state-of-the-art 3-D printing instrumentation including multi-photon lithography systems for micro-optics fabrication.
Dr. Wade Smith is a Senior Lecturer within the School of Mechanical and Manufacturing Engineering at the University of New South Wales. He is an active member of the WAVES research group (Wear, Aeroacoustics and Vibration in Engineering Systems) and conducts his research in the Tribology and Machine Condition Monitoring laboratory. His primary research interests include vibration-based diagnostics of rotating machinery, prognostics of rotating machinery, gear wear monitoring and prediction, simulation and modeling of rotating machines for diagnostic applications, and signal processing of machine vibration signatures using cyclostationarity. His work has significant applications in industrial machinery health monitoring and predictive maintenance systems. Dr. Smith's recent publications demonstrate a consistent focus on advanced diagnostic techniques for rotating machinery, with particular emphasis on gear systems and bearings. His research integrates traditional mechanical engineering principles with modern signal processing and machine learning approaches to develop more effective condition monitoring solutions. He actively supervises PhD and Masters students on projects related to gear diagnostics, wear monitoring, and vibration analysis. His current research projects include gear diagnostics in planetary gearboxes using internal sensors, gear wear monitoring and prediction, sliding contact-induced vibration studies, and transmission-error-based gear diagnostics. Dr. Smith's laboratory is equipped with specialized facilities including gearbox test rigs (both planetary and parallel configurations), a rolling element bearing test rig, an engine test rig, friction rig, tribometer, high-quality microscope, and extensive instrumentation for vibration analysis. His research has attracted collaborations with institutions including Queensland University of Technology, SpectraQuest (USA), Weir Minerals, University of Technology Sydney, RWTH Aachen University (Germany), and Safran.
Samuel Jean Bassetto is an Associate Professor in the Department of Mathematics and Industrial Engineering at Polytechnique Montréal. He serves as Director of the Continuous Improvement Laboratory (LABAC) and holds membership in multiple prestigious research groups including the Research Group on Globalisation and Management of Technology (GMT), Poly-Industries 4.0 Laboratory, Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT), and Institute for Data Valorization (IVADO). Dr. Bassetto's research spans multiple disciplines, focusing on continuous improvement through the integration of engineering, artificial intelligence, cognitive science, psychology, and design. His primary sphere of excellence is in New Frontiers in Information and Communication Technologies, with secondary expertise in Modeling and Artificial Intelligence and Human Health. He develops tools that place humans at the center of technology to enhance organizational performance while respecting human rhythms and cognitive limitations. His recent publication portfolio reveals a strong interdisciplinary approach, with research bridging industrial engineering, cognitive neuroscience, and AI ethics. His work addresses practical challenges in lean manufacturing assessment, racial bias in medical AI systems, cognitive data collection in natural environments, and condition monitoring for industrial machinery. The research consistently demonstrates a commitment to developing practical solutions that integrate human factors with technological innovation. NSERC Synergy Prize for Innovation recipient Principal investigator on multiple research grants from NSERC, FRQ, and MITACS Collaborations with over a dozen institutions across multiple countries Supervision of over 150 highly qualified personnel throughout his career Dr. Bassetto teaches specialized courses including CAP7011 (Creativity in Research), IND8444 (Continuous Improvement), IND8203 (Industrial Launch), and previously taught IND8178 (Production). His teaching philosophy emphasizes practical application, with courses featuring hands-on exercises, real-world scenarios, and gamification techniques to enhance learning. His supervision portfolio includes numerous Ph.D. and Master's students working on topics ranging from human-technology collaboration to reinforcement learning for production management. Through LABAC, Dr. Bassetto leads research initiatives focused on developing human-centered tools for continuous improvement in organizational settings. The laboratory conducts projects related to industrial IoT applications, cognitive aspects of process improvement, and the development of practical frameworks for organizations to enhance performance while maintaining respect for human rhythms and cognitive capabilities.
Matteo Brunelli is Associate Professor of “Mathematical Methods of Economics and Actuarial and Financial Sciences” at the University of Trento , Department of Industrial Engineering, and Adjunct Professor (docent) at Lappeenranta University of Technology , Finland. He is nationally habilitated as Full Professor in Italy and has held long-term visiting positions at Berkeley, Turku, Auckland, JAIST and Binghamton. Education: Ph.D. (Doctor of Science) in Information Technologies, Åbo Akademi University, Finland, 2011 – graded Eximia cum laude approbatur M.Sc. in Economics, University of Trento, 2007 – grade 110/110 cum laude B.Sc. in Economics, University of Trento, 2005 Research focus: Brunelli’s work sits at the intersection of multi-criteria decision analysis , operations research and computational optimisation . He develops axiomatic foundations and algorithms for pairwise comparison matrices , consistency indices , the best-worst method and fuzzy preference relations , and applies them to energy planning, sustainable inventory, maintenance scheduling, 3-D printer selection, and blockchain governance. His 2023-2025 articles reveal intensified interest in uncertainty modelling (Dempster-Shafer theory), bi-objective optimisation of inventory and maintenance, and group decision protocols that integrate probabilistic or active-learning components, demonstrating both methodological depth and practical relevance. Scientific awards & grants: Academy of Finland Postdoctoral Researcher grant (€254 670, 2014-2017) Claudio Dematté Research Grant (€19 000, 2008) Teacher of the Year Award, Aalto University (2013 – both Spring & Autumn semesters) Bernard Roy Award 2021 for outstanding contribution to Multiple Criteria Decision Aiding (under-40 category) Supervision & funding: While specific doctoral students are not listed, Brunelli currently supervises graduate theses at Trento and has continuously held competitive national grants. His Academy of Finland project “Consistency of valued preference relations for decision analytics methods” financed three years of full-time research and international collaboration. Editorial & community roles: He serves on the editorial boards of International Journal of General Systems and Mathematical and Computational Applications , and acts as area editor for Journal of Multi-Criteria Decision Analysis , positioning him among the key gatekeepers of the MCDA community.
Boxin Zhao is a Professor and University of Waterloo Endowed Chair in Nanotechnology in the Department of Chemical Engineering at the University of Waterloo. His research spans surface science, bionanomaterials, and biomimetic adhesion, with a focus on advanced polymers, hydrogels, and smart materials for biomedical and industrial applications. He leads the Surface Science and Bio-nanomaterials Laboratory Group, which develops innovative materials such as self-cleaning surfaces and antimicrobial coatings. Education: Doctorate in Chemical Engineering, McMaster University (2004); Master's from Chinese Academy of Sciences (1999); Bachelor's in Mineral Engineering from Central South University (1996). He has held prestigious fellowships, including the NSERC Postdoctoral Fellowship and IMMS Research Fellow at UC Santa Barbara and Los Alamos Lab. Research Interests: Biomimetic adhesion, soft robotics, polymer composites, and advanced manufacturing. Notable contributions include gecko-inspired adhesives, self-healing hydrogels, and conductive polymers for electronics. Recent work focuses on 3D-printed medical hydrogels, soft robotic devices, and microplastic degradation. His lab collaborates with industries like Proctor & Gamble and 3M, addressing real-world challenges in materials science and biomedical engineering. Awards: 1996 IET Scholarship, 2005 NSERC Postdoctoral Fellowship, 2007 IMMS Research Fellow. Teaching: CHE 313 (Heat/Mass Transfer), CHE 612 (Interfacial Phenomena). Lab Facilities: Advanced equipment for nanomaterial synthesis, surface characterization, and soft robotics prototyping.
Ronald Hedden is a Professor of Practice in the Department of Chemical and Biological Engineering at Rensselaer Polytechnic Institute (RPI), where he focuses on innovations in undergraduate education and polymer science. Previously, he served as an Associate Professor at Texas Tech University (2009–2017). His current research emphasizes Virtual Reality (VR) integration into chemical engineering education, including the development of a Virtual Chemical Plant (VCP) simulation to provide safe, cost-effective access to process equipment. His research interests span chemical engineering, polymer science, soft materials, and nanomaterials. Notable projects include applying VR for teaching process safety and dynamics, as well as exploring nanocomposite materials and membrane technologies. He also investigates polymer rheology and structure-property relationships using advanced characterization techniques like NMR and SANS. Hedden teaches both core chemical engineering courses and interdisciplinary engineering subjects. His work bridges academic research and practical applications, with contributions to biofuel refining, asphalt modification, and nanoparticle incorporation in polymers. While no specific awards are listed, his extensive publication record highlights impactful contributions to materials science and educational technology. His advisory work involves student projects on VR simulations and materials engineering. He collaborates on initiatives like the VCP platform, aimed at advancing safety training and process control education. Hedden’s career reflects a commitment to both cutting-edge research and transformative pedagogy in engineering education.
Michael Organ is a Full Professor at the University of Ottawa's Department of Chemistry and Biomolecular Sciences, affiliated with the Faculty of Science. He also serves as Director of the Centre for Research and Innovation in Catalysis. His research focuses on catalysis, flow chemistry, and medicinal chemistry, emphasizing sustainable and efficient synthesis methods. Organ has held adjunct roles at the University of Toronto and has extensive industry collaborations, including with GlaxoSmithKline and Abbvie. Education: PhD (University of Guelph, 1992), MSc (University of Guelph, 1988), Hons. BSc (University of Guelph, 1986). Research Interests: Catalysis, microwave-assisted continuous synthesis, reactive intermediates in flow systems, and drug discovery methodologies. His work bridges organic chemistry with engineering, developing scalable and green processes. Publications & Impact: Over 200 publications, including seminal works in Journal of the American Chemical Society and Chemistry – A European Journal . Key contributions include the Pd-PEPPSI-IPent catalyst and the MACOS flow chemistry platform. Awards: NSERC John C. Polanyi Award (2018), Encyclopedia of Reagents Best Reagent Award (2017), Raymond Lemieux Award (2016). Recognized internationally for catalytic innovations. Grants & Funding: Over $45M in research funding, including NSERC Discovery Grants and industry partnerships. Notable projects include CFI JELF grants for sustainable manufacturing and pandemic-related flow chemistry for SARS-CoV-2 diagnostics. Labs & Teams: Leads the Organ Group, collaborating with chemical engineers and industry partners. Specializes in reactor design, catalyst development, and continuous processing systems.
Dr. Sheng Yang is an Assistant Professor in the School of Engineering at the University of Guelph. He leads the Design Innovation and Intelligent Manufacturing (DIIM) lab, focusing on advancing additive manufacturing, generative design, and smart manufacturing technologies. His research integrates IoT, big data analytics, and bio-inspired design to address challenges in aerospace, green energy, and healthcare. Key areas include computational design for additive manufacturing, data-driven mass customization, and digital twin-based optimization. Education: Ph.D. in Mechanical Engineering from McGill University (2019), followed by a Postdoctoral Fellowship at McGill (2019–2020). Joined University of Guelph in 2020. Research interests span energy efficiency, complex system optimization, and personalized healthcare products. Recent work emphasizes digital twin synchronization in robotics, machine learning for quality prediction, and sustainable additive manufacturing processes. Notable awards include the 2019 Association of Commonwealth Universities Blue Charter Fellowship and 2018 ASME Best Paper Award. His lab actively seeks partnerships in personalized healthcare, product design, and smart manufacturing. Grants and collaborations focus on advancing manufacturing technologies and sustainability. No formal advisees listed, but active in graduate training through lab projects. The DIIM lab explores cutting-edge solutions for industrial and societal challenges through interdisciplinary approaches.
