Dr. Valentin Soloiu is the Allen E. Paulson Distinguished Chair of Renewable Energy and Professor in the Department of Mechanical Engineering at Georgia Southern University, where he supervises the Renewable Energy, Combustion, Aerospace, Automotive, and Intelligent Vehicles Laboratories . Expert in advanced combustion technologies for biofuels and synthetic aviation fuels Research focus on heat transfer, emissions control, spray dynamics, and intelligent vehicle systems Research Interests span from first/second generation biofuels to smart engine control strategies and engine tribology . His work employs reactivity-controlled compression ignition (RCCI) and partially premixed combustion for NOx and PM reduction . Recent publications analyze synthetic kerosene (S8) , HEFA fuels , and alternative aviation fuels in combustion chambers. His awards include multiple NSF grants , EPA-P3 Prize , and recognition as Distinguished Associate of ASME Engine Division . Editorial roles: 18 journal/conference reviewer positions Leadership: Councilor of Council of Undergraduate Research (CUR) Labs mentor 15+ graduate students and 30+ undergraduates annually in energy research projects that have won 40+ national awards over the past decade.
Dr. Candan Tamerler is a Professor in the Department of Mechanical Engineering at the University of Kansas and Track Director for the Biomaterials & Tissue Engineering graduate program. She also holds the Wesley G. Cramer Professorship and serves as the Associate Vice Chancellor for Research at KU. Current Affiliations Professor, Mechanical Engineering, University of Kansas (since 2013) Track Director, Biomaterials & Tissue Engineering, KU Graduate Program Associate Vice Chancellor for Research, University of Kansas Past Affiliations Research Professor, Materials Science & Engineering, University of Washington Assistant Director, Genetically-Engineered Materials Science & Engineering Center, University of Washington Full Professor, Molecular Biology & Genetics Department, Istanbul Technical University (2002-2010) Visiting Professor, Materials Science & Engineering, University of Washington Dr. Tamerler's research focuses on molecular biomimetics and bio-nanotechnology, developing bio-enabled materials for dental and biomedical applications. Her work bridges molecular biology with materials science to engineer peptide-based interfaces for tissue repair, antimicrobial surfaces, and enzyme immobilization systems. Recent publications highlight her innovations in dental biomaterials, collagen mineralization, and computational design of peptides. Key themes include autonomous strengthening adhesives, bio-nanoreactors, and machine learning approaches to antimicrobial peptide engineering. Scientific Awards Principal Member, Turkish Academy of Sciences Visiting Scientist, University of Westminster Visiting Professor, University of Nagoya Dr. Tamerler has organized international symposia for the American Chemical Society and TMS, and previously founded a multidisciplinary research center at Istanbul Technical University, securing funding for its 40,000 sq. ft. facility.
Giuseppe Iannaccone is a full Professor at the University of Pisa , Department of Information Engineering. His research focuses on quantum electronics , neuromorphic computing , and 2D materials for advanced applications in analog circuits and high-temperature electronics. Lead researcher in QUEPE Quantum Engineering and QUEFORMAL projects Pioneer in neuromorphic chip design using silicon and 2D materials Active in developing high-temperature integrated circuits for industrial applications His recent work explores twisted transition metal dichalcogenides for spintronics, inkjet-printed 2D electronics on paper substrates, and wireless power transfer systems for medical devices. The Google Scholar articles show consistent contributions to analog neuromorphic engines , quantum transport modeling , and steep-slope transistor architectures . Collaborations include Gianluca Fiori and Benjamin Zambrano in neuromorphic hardware development. He actively promotes student well-being through institutional initiatives like the Ufficio Benessere at University of Pisa and has taught RFID and IoT courses for PhD students. Current research integrates MoS2/graphene heterostructures and van der Waals junctions for next-generation electronics.
Joonki Noh serves as Associate Professor in the Department of Banking & Finance at Case Western Reserve University's Weatherhead School of Management, where he joined in 2015 after completing his finance doctorate at Emory University. His academic journey includes dual doctoral training in finance and electrical engineering, reflecting his interdisciplinary expertise. Education PhD in Finance, Emory University (2015) PhD in Electrical Engineering, University of Michigan (2007) MA in Electrical Engineering, University of Michigan (2007) MS in Electrical Engineering, University of Michigan (2005) BS in Electrical Engineering, Seoul National University (2003) Noh's research integrates quantitative finance with computational methods, focusing on empirical asset pricing , market microstructure , and textual analysis enhanced by machine learning techniques. His work examines how linguistic patterns in corporate disclosures affect market reactions, liquidity dynamics in asset pricing, and information diffusion through industry networks. Teaching responsibilities span Investment Management and Financial Modeling in Big Data for both undergraduate and Master of Finance students. His publication record demonstrates evolving expertise from early biomedical engineering research to contemporary finance applications, with recent work leveraging natural language processing on earnings conference calls and liquidity factor modeling. This trajectory highlights his unique ability to transfer methodological rigor across disciplinary boundaries. Academic Honors Shinhan Finance Investment Best Paper Award (2022) Korea America Finance Association Young Scholar Award (2019) Financial News & KAFA Top-Journal Paper Award (2018, 2022) George J. Benston Scholar Award at Emory University (2014) Outstanding Graduate Student Instructor Award (University of Michigan, 2008) Noh actively contributes to academic governance as seminar organizer for the BAFI Department Research Series and committee member for faculty recruitment. His professional service includes editorial roles for the Asia-Pacific Association of Derivatives and conference reviewing for major finance associations. He maintains strong connections with Korean financial institutions through KAFA partnerships while presenting research at premier venues including the American Finance Association and European Finance Association meetings.
Saman Azhari is an Assistant Professor at the Graduate School of Information Production and Systems , Waseda University, Japan. His research focuses on Nanotechnology , Biosensors , and Reservoir Computing for biomedical and robotic applications. Developed self-sterilizing face masks using enzymatic power generation (2025) Engineered smart contact lenses for wireless cholesterol monitoring and ocular diagnostics (2025, 2024) Innovated CNT/PDMS nanocomposites for haptic sensors and in-sensor computing (2024, 2021) Explored 3D nanomaterial reservoir computing with SWNT/POM networks (2023, 2021) Collaborated on microwave-assisted CNT synthesis from waste materials (2018) His recent publications (2025-2023) emphasize flexible biosensors , atomic switch networks , and energy-efficient AI hardware . Key subfields include piezoresistive pressure sensing , synaptic plasticity in nanoparticle systems , and plant-insertable sucrose monitors . He holds patents for tactile sensing devices and mechanical sensors.
Patrizia Scandurra is an Associate Professor at the Department of Management, Information and Production Engineering, University of Bergamo, Italy. Her academic appointment in Computer Science (01/B1) spans from 2022 to 2033. She previously held roles as a researcher at the University of Bergamo (2009-2017) and postdoctoral fellow at the University of Milan (2006-2008). She earned her PhD in Computer Science (2006) and Bachelor's degree (2002) from the University of Catania. Research Focus : Software architectures and formal methods for modeling, validation, and verification of software-intensive systems. Specializations : Runtime analysis of self-adaptive, autonomous, and uncertain systems including IoT-Edge-Cloud applications, embedded systems, and system-on-chip. Collaborations : STMicroelectronics, Atego, Bialetti, and ENEA. Conference Involvement : Program/organizing committees for ICSE, ASE, ISSRE, ICSA, ECSA, SEAMS@ICSE, ABZ, SA-TTA@SAC, FAACS@ECSA. Research Projects : Model-driven development for robotics, adaptive architectures for pervasive systems, big data in smart cities, and digital twins for medical systems. Notable Contributions : Development of the ASMETA formal method community tools and frameworks for rigorous system design. She has published over 100 peer-reviewed works in international journals and conferences.
Professor Ahmed H. Elsheikh is a faculty member at Heriot-Watt University's School of Energy, Geoscience, Infrastructure and Society, within the Institute for GeoEnergy Engineering. He holds a Professor title since 2021, previously serving as Associate (2017-2021) and Assistant Professor (2013-2017). His educational background includes a Ph.D. (2010) and MASc (2002) from McMaster University, and a BASc from Al-Azhar University (1999). Research Interests: Fluid control via AI, predictive machine learning, generative modeling, data assimilation, Bayesian uncertainty quantification, and subsurface engineering. His work addresses challenges in reservoir modeling, CO2 sequestration, seismic inversion, and geophysical data analysis. Publications span 2004-2025 with a focus on machine learning applications in energy systems, stochastic field generation, and subsurface flow modeling. Notable contributions include AI-driven seismic inversion, deep learning for reactive transport, and ensemble-based history matching. Key Achievements: Developed novel methodologies for uncertainty quantification, GAN applications in geological modeling, and real-time reservoir monitoring systems. His work contributes to UN Sustainable Development Goals related to climate action (SDG 13) and affordable clean energy (SDG 7).
Dr. David Linke serves as Department Head of Catalyst Development and Reaction Engineering at the Leibniz Institute for Catalysis (LIKAT) in Rostock, Germany. His research focuses on catalytic processes for chemical transformations, with particular emphasis on alkane dehydrogenation, CO 2 hydrogenation, and olefin metathesis. Dr. Linke's research interests span multiple areas of catalysis and reaction engineering: Development of catalysts for propane and isobutane dehydrogenation CO 2 hydrogenation to valuable hydrocarbons Metathesis reactions for olefin production Advanced characterization of catalytic materials Reaction mechanism elucidation Integration of machine learning with catalytic reaction engineering Analysis of Dr. Linke's recent publications (2021-2025) reveals a strong focus on sustainable chemical processes, particularly those involving CO 2 utilization and alkane dehydrogenation. His work combines experimental catalysis with advanced characterization techniques and increasingly incorporates computational approaches. A significant portion of his research addresses the challenge of developing efficient and stable catalysts for propane dehydrogenation, which is crucial for propylene production. His more recent work also shows growing interest in data science applications for catalysis research, including ontology development and machine learning approaches to kinetic modeling. Dr. Linke has been actively involved in developing research data infrastructure for catalysis through the NFDI4Cat initiative, which aims to establish unified data management practices across the catalysis research community.
Shui Yu is a Professor of the School of Computer Science in the Faculty of Engineering and Information Technology at the University of Technology Sydney (UTS), where he also serves as the Deputy Chair of the UTS Research Committee. His academic career spans over 20 years in Australia and 7 years in China, with additional teaching experience in Hong Kong and Indonesia. He has developed more than 10 units in cybersecurity, computer science, data analytics, and computer games, serving as the Course Director for Computer Science undergraduate programs. Professor Yu's research interests center on cybersecurity, privacy, networking aspects of Big Data, and applied mathematics for computer science. He pioneered the field of 'networking for big data' in 2013 and edited the seminal book 'Networking for Big Data' published in 2015. His work has practical applications in industry, including Amazon Cloud's auto-scale strategy against distributed denial-of-service attacks. Current research focuses include privacy and security concerns associated with big data, security issues in smart grids, anonymous transactions on Blockchain, and anonymous communication for web browsing privacy. Analysis of his recent publications reveals a strong research trajectory spanning cybersecurity, privacy-preserving technologies, networking for big data, and applied mathematics. His work shows increasing focus on quantum-resistant cryptography, federated learning security, and adversarial robustness in AI systems. The interdisciplinary nature of his research bridges theoretical foundations with practical applications in IoT, blockchain, and cloud environments. Fellow of IEEE (2023) Distinguished Lecturer of IEEE Communications Society (2018-2021) Distinguished Visitor of IEEE Computer Society (2022-2024) Professor Yu has secured numerous research grants from the Australian Research Council, including current projects on privacy and fairness in high intelligence models (DP240100955), improved security and privacy for online platforms (LP220200808), and secure blockchain for financial applications (LP220100453). He has served on editorial boards of multiple IEEE journals including IEEE Communications Surveys and Tutorials, IEEE Communications Magazine, and IEEE Internet of Things Journal. His service extends to organizing major conferences such as IEEE Globecom 2015 and IEEE INFOCOM 2016-2017.
Russell T. Johns is a Professor of Petroleum Engineering at Texas A&M University with extensive contributions to enhanced oil recovery methods. Elected to the National Academy of Engineering in 2025, he maintains active leadership roles in the Society of Petroleum Engineers (SPE) including serving as Editor in Chief for all SPE Journals (2018-2020). Educational Background: Ph.D. in Petroleum Engineering, Stanford University (1992) M.S. in Petroleum Engineering, Stanford University (1989) B.S. in Electrical Engineering, Northwestern University (1982) His research program centers on advanced recovery techniques for conventional and unconventional reservoirs, with particular expertise in miscible gas flooding, chemical EOR processes, and thermodynamics of microemulsion systems. Recent work integrates machine learning with traditional reservoir engineering to model complex fluid behaviors under extreme conditions, while maintaining strong experimental validation through laboratory studies of wettability alteration and phase behavior. Publication trends from 2020-2024 reveal consistent focus on computational modeling of enhanced recovery mechanisms, with growing emphasis on AI-driven approaches for relative permeability prediction and precise characterization of microemulsion systems. His work bridges fundamental thermodynamics with practical field applications across diverse recovery methods. Scientific Awards and Honors: Anthony F. Lucas Gold Medal, AIME (2023) IOR Pioneer Award, SPE (2022) International Award in Reservoir Description and Dynamics, SPE (2016) Faculty Pipeline Award, SPE (2013) Cedric K. Ferguson Medal, SPE (1993) Member, National Academy of Engineering (2025) Honorary Member, SPE (2025) Distinguished Member, SPE (2009) Dr. Johns' editorial leadership as SPE Journals Editor in Chief demonstrates his influence in shaping petroleum engineering research discourse. While specific grant details aren't provided, his sustained publication record and SPE recognition indicate substantial ongoing research funding. His mentorship extends through SPE's Faculty Pipeline Award initiatives and editorial guidance for emerging scholars. Current research activities focus on high-pressure/high-temperature microemulsion modeling and low-salinity waterflooding mechanisms, with future directions likely expanding machine learning applications in reservoir simulation and geothermal energy integration.
Iwan Schie serves as Working Group Leader at the Leibniz Institute of Photonic Technology (Leibniz-IPHT) in Jena, Germany, where he leads the Spectroscopy / Imaging Multimodal Instrumentation research group. His work bridges analytical chemistry, biomedical engineering, and clinical applications with a focus on developing Raman spectroscopy-based diagnostic tools. Dr. Schie maintains an active research program with numerous publications in high-impact journals across multiple disciplines. Dr. Schie's research centers on Raman spectroscopy applications in medical diagnostics and environmental monitoring. His work demonstrates particular expertise in developing multimodal imaging systems that combine Raman spectroscopy with complementary techniques like optical coherence tomography and fluorescence imaging. His research spans both fundamental methodological development and clinical translation, with several studies focusing on cancer diagnostics across multiple organ systems including head and neck, bladder, and colon cancers. The environmental applications of his work include microplastic detection and pollen analysis. Analysis of Dr. Schie's publication record reveals a clear trajectory toward clinical implementation of Raman spectroscopy technologies. His recent work increasingly focuses on regulatory-compliant medical device development, with multiple studies conducted in accordance with European Medical Device Regulation standards. The publications demonstrate progression from ex vivo validation studies to in vivo clinical applications, with particular emphasis on workflow integration within surgical settings. His collaborative approach is evident through extensive co-authorship networks spanning physics, engineering, and clinical medicine. Dr. Schie has made significant contributions to advancing Raman spectroscopy methodology, with publications addressing critical challenges in device stability, spectral analysis, and multimodal integration. His work on establishing clinical workflows represents important steps toward routine clinical adoption of these technologies. The practical impact of his research is demonstrated through development of systems like the invaScope Raman endoscopy platform for bladder tumor diagnosis. As Working Group Leader at Leibniz-IPHT, Dr. Schie oversees research activities in spectroscopy and multimodal imaging instrumentation. His team develops advanced optical systems for biomedical applications with particular focus on real-time tissue characterization during surgical procedures. The research environment supports both fundamental methodological development and applied clinical translation, with strong emphasis on regulatory compliance for medical device development.
Jan Rossmeisl is a Professor in the Department of Chemistry at the University of Copenhagen, Denmark, conducting research at the intersection of catalysis, sustainable energy, and electrochemical processes. His work focuses on developing advanced materials for energy conversion applications including fuel cells and carbon dioxide reduction technologies. His research interests span catalysis , green chemistry , and energy conversion , with specific expertise in electrocatalytic reaction mechanisms, high-entropy alloy design, and sustainable pathways for chemical synthesis. Current investigations emphasize computational modeling of surface reactions and experimental validation of novel catalyst systems for renewable energy applications. Analysis of his 2024-2025 publications reveals a dominant focus on electrocatalytic $$\text{CO}_2$$ conversion and oxygen reduction processes. Key trends include the development of high-entropy alloys for catalytic coupling reactions, strain engineering in binary alloys, and non-aqueous electrolyte effects on $$\text{CO}_2$$ reduction. His work bridges theoretical computation with experimental validation to address challenges in sustainable energy conversion and chemical production.
Andrew Terentis is a Professor and the Department Chair of the Department of Chemistry and Biochemistry at Florida Atlantic University. His research integrates Raman spectroscopy, computational methods, and machine learning to study biological systems, with a focus on peptide and nucleic acid structures, cell interactions, and skin cancer diagnosis. Education: Ph.D. from the University of Sydney Research Interests: Dr. Terentis's work centers on the application of Raman spectroscopy for characterizing biomolecular structures and interactions. His group employs computational studies to analyze peptide and oligonucleotide conformations, and has pioneered the use of machine learning with Raman data for skin cancer classification. Additional interests include enzyme kinetics and mechanisms, particularly in methionine metabolism, and the development of pedagogical methods in Physical Chemistry. Publication Trends: Over the past decade, Terentis's research has evolved from fundamental studies of biomolecular structures (e.g., G-quadruplex DNA) to translational applications in oncology. Recent publications highlight a strong emphasis on integrating Raman spectroscopy with deep learning for cancer tissue classification, demonstrating a shift toward interdisciplinary biomedical engineering approaches.
Stephen T. Lam is an Assistant Professor of Chemical Engineering at the University of Massachusetts Lowell, where he also serves as Director of the Nuclear Engineering Program and RHSA Mentor within the Francis College of Engineering. His research focuses on accelerating materials development for nuclear and clean energy applications through computational methods. His educational background includes a Ph.D. in Nuclear Science and Engineering (2020) and an MS in Nuclear Science and Engineering (2017) from the Massachusetts Institute of Technology, and a BS in Chemical Engineering (2013) from the University of British Columbia. Prior to his academic career, Lam worked in the petroleum and chemical processing industries and holds a Professional Engineer license in Canada. Ph.D.: Nuclear Science and Engineering (2020), Massachusetts Institute of Technology MS: Nuclear Science and Engineering (2017), Massachusetts Institute of Technology BS: Chemical Engineering (2013), University of British Columbia Lam's research integrates multi-scale simulation, experimental validation, and data analytics to address materials challenges in nuclear and clean energy systems. His work spans molten salt chemistry for advanced reactors, neural network interatomic potentials, and fusion energy materials. He leads the Lam Research Group, which combines predictive simulation, data analytics, and experiments to accelerate materials development. His recent publications demonstrate expertise in molten salt structure, neural network potentials, and fusion materials, with emphasis on fluoride and chloride salt systems for nuclear applications. The research shows strong interdisciplinary collaboration across computational chemistry, materials science, and nuclear engineering. Among his notable recognitions are the Early Career Research Award (2024) from the U.S. Department of Energy and the Distinguished Faculty Advancement Award (2024) from the U.S. Nuclear Regulatory. He has also received multiple early-career research awards from national laboratories and professional societies. Early Career Research Award (2024), U.S. Department of Energy Distinguished Faculty Advancement Award (2024), U.S. Nuclear Regulatory Best Presentation (2017), Tokyo Institute of Technology Natural Sciences and Engineering Research Council of Canada Postgraduate Scholarship (2017) Lam actively mentors graduate students and postdoctoral researchers, with current advisees working on AI-assisted materials design, molten salt chemistry, and fusion energy applications. He teaches courses in nuclear materials, nuclear science and engineering, transport phenomena, and fundamentals of electricity. The Lam Research Group maintains strong collaborations with national laboratories and industry partners, focusing on developing computational tools to improve understanding of material properties and accelerate materials discovery for next-generation energy technologies.
Calvin Tsay is a Lecturer (Assistant Professor) in the Computational Optimisation Group at Imperial College London, holding the BASF/RAEng Senior Research Fellowship in Scale-Bridging Modelling. Education: PhD Chemical Engineering (UT Austin 2020), BS/BA (Rice University 2015). Research develops optimization methods bridging machine learning and process systems engineering. Specializes in mixed-integer programming for neural networks and Bayesian optimization for energy applications. Awards: President's Medal for Early Career Researcher RAEng Senior Research Fellowship CACE Best Paper (2023) COIN-OR Cup (2022) Leads research on AI-driven chemical process optimization. Supervises PhD students in ML optimization and process control. Collaborates with BASF on industrial applications.
