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Federico NutarelliView profile

Assistant Professor

• International Trade
• Health Economics
• Machine Learning
• +3 more

Federico Nutarelli is an Assistant Professor of Economics at the IMT School for Advanced Studies Lucca, Italy. His research focuses on the intersection of machine learning and economic analysis, particularly in international trade, health economics, and industrial organization. He holds a Ph.D. in Economics from IMT Lucca, and previously conducted postdoctoral research at Bocconi University. In 2024, he was a Visiting Scholar at MIT Sloan School of Management. Key research interests include causal machine learning methods to analyze heterogeneous firm responses to economic shocks, pharmaceutical market pricing strategies, and structural demand models. His work bridges methodological rigor with applied relevance, contributing to health economics, trade dynamics, and innovation policy. Recent publications (2020–2025) explore topics such as matrix completion for world trade analysis, machine learning applications in economic complexity, and modeling innovation ecosystems. His work often employs advanced statistical techniques like Shapley values and reinforced Bernoulli processes. No scientific awards were explicitly mentioned in the provided texts. Federico collaborates with institutions like Bocconi University and MIT Sloan, reflecting his interdisciplinary network in economics and data science.

Albert YoonView profile

Professor

• Law and Economics
• Legal Ethics
• Artificial Intelligence in Law
• +4 more

Albert Yoon is a Professor and holds the Michael J. Trebilcock Chair in Law and Economics at the University of Toronto Faculty of Law. He previously served as Associate Dean (Research & Curriculum) from 2018–2020 and has held academic positions at Northwestern University. His research focuses on labor markets in legal professions, legal ethics, and applications of AI to law. He co-founded Blue J, an AI startup aiding tax and legal professionals. Education: BA from Yale, JD and PhD (Political Science) from Stanford. Professional experience includes clerkship at the U.S. Court of Appeals for the Sixth Circuit. Fellowships include the Pierre Elliott Trudeau Fellowship (2022), Princeton University, and Robert Wood Johnson Foundation. Research interests span legal economics, judicial behavior, and technology's impact on law. Notable awards include the Ronald H. Coase Prize and American Law Institute membership. His work bridges empirical legal studies and computational methods, addressing topics like Supreme Court clerkships, AI-driven legal predictions, and tax law analytics. Publications include over 47 scholarly articles in top journals like Chicago Law Review , Stanford Law Review , and Journal of Law & Economics . Recent work examines AI in legal practice, gender disparities in M&A legal teams, and judicial decision-making dynamics.

Manuela TruebanoView profile

Lecturer

• Marine Molecular Biology
• Ecophysiology
• Climate Change Adaptation
• +3 more

Dr Manuela Truebano is a Lecturer in Marine Molecular Biology at the University of Plymouth, affiliated with the School of Biological and Marine Sciences (part of the Faculty of Science and Engineering). She holds a BSc in Marine Biology from the University of Liverpool, an MSc in Shellfish Biology from Bangor University, and a PhD in Molecular Ecophysiology (focusing on thermal stress) from Swansea University and the British Antarctic Survey. Her research group is part of the Ecophysiology and Development Research group within the Marine Biology and Ecology Research Centre. Her teaching portfolio includes module leadership roles in Marine Molecular Biology, Ecophysiology of Marine Animals, Conservation Physiology, and field courses. She has supervised two postgraduate research degrees: Michael Collins (PhD, 2019) and George Mason (ResM, 2021). Dr Truebano’s research focuses on thermal tolerance, hypoxia, and environmental stress responses in marine organisms, particularly invertebrates. She employs molecular and physiological approaches to study climate change impacts, including the development of tools like Dev-ResNet and HeartCV for automated phenotyping. Her work intersects with global sustainability goals, emphasizing organismal resilience to environmental shifts. Her recent publications highlight advancements in understanding transgenerational plasticity, thermal acclimation, and the interplay of multiple stressors. Teaching and learning grants include a 2015 initiative exploring video tutorials for lab skill training. She collaborates extensively in interdisciplinary projects, contributing to both academic and applied marine science.

• Smart Grids
• Renewable Energy Integration
• Optimization of Power Systems
• +4 more

Cuo Zhang is a Lecturer of Power Engineering and ARC DECRA Fellow at the University of Sydney's School of Electrical & Computer Engineering. He holds a B.E. (Hons.) from the University of Sydney (2014) and a Ph.D. in Electrical Engineering from UNSW (2018). His research focuses on smart grids, renewable energy integration, voltage control, and optimization of power systems. Key interests include distributed generation planning, energy storage systems, and demand response mechanisms. Dr. Zhang leads research on enhancing distribution network resilience through advanced control strategies and participates in the Net Zero Institute. He has secured grants such as the 2024 ARC DECRA project on renewables hosting capacity. Supervised students include Yunqing Zhang, working on robust renewables integration. His recent publications emphasize data-driven approaches, decentralized energy trading, and adaptive control for unbalanced networks. He explores machine learning applications in energy management and stochastic optimization under uncertainty. Awards include the University Medal for academic excellence. Awards: ARC DECRA Fellow Grants: 2024 Robust Renewables Hosting Capacity Enhancement Labs/Teams: Member of the Net Zero Institute

Doraiswami RamkrishnaView profile

Professor

• Chemical Engineering
• Biomedical Engineering
• Mathematical Biology
• +5 more

Professor Doraiswami Ramkrishna is the Harry Creighton Peffer Distinguished Professor of Chemical Engineering at Purdue University's Davidson School of Chemical Engineering. His research focuses on applying mathematical methods to chemical and biochemical systems, including population balance modeling, stochastic processes, and cybernetic frameworks for metabolic networks. His work spans crystallization processes, cancer chemotherapy modeling, and personalized medicine. Education: B.S. from the University of Bombay (1960), Ph.D. from the University of Minnesota (1965). He joined Purdue in 1976 after faculty roles at Indian institutions. His awards include membership in the U.S. and Indian National Academies of Engineering, the AIChE Wilhelm and Thomas Baron Awards, and the 2021 William H. Walker Award for Chemical Engineering Literature. Research Interests: Cybernetic modeling of biological systems, population balances in particulate systems, stochastic modeling of rare events, and mathematical approaches to cancer treatment optimization. His group collaborates on projects involving metabolic networks, drug resistance mechanisms, and personalized hydroxyurea therapy for sickle cell disease. Awards: Over 30 honors including the 2021 Walker Award, NAE membership, and Platinum Award from Mumbai University. Advising: Mentored numerous graduate students and research associates, with notable work on lipid metabolism, chemotherapy-induced neuropathy, and crystallization dynamics. Labs/Teams: Leads the Ramkrishna Research Group, collaborating internationally on projects like cancer care engineering and metabolic engineering of bioethanol production.

• Credit Scoring
• Consumer Credit Risk Modelling
• Predictive Analytics
• +3 more

Christophe Mues is a Professor of Data Science and Information Systems at the University of Southampton's School of Management, within the Department of Decision Analytics and Risk. His research focuses on credit scoring, consumer credit risk modeling, and applications of predictive analytics, including machine learning techniques for credit risk assessment. He leads the Information Systems & Business Analytics section and supervises multiple PhD students in Business Studies and Management. His work spans advanced statistical methods for predicting Probability of Default (PD), Loss Given Default (LGD), and loan profitability. He is actively involved in the academic community, serving on the organizing committee for the Credit Scoring and Credit Control conference. His teaching includes topics in information systems and business analytics. Contact: C.Mues@soton.ac.uk. Research Interests: Credit Scoring and Consumer Credit Risk Modelling Predictive Analytics in Finance Machine Learning Applications (Deep Learning, Graph Neural Networks) Non-Traditional Data Integration Credit Model Transparency and Fairness Debt Collection Optimization PhD Supervision: Currently guiding four students in Business Studies & Mngt: Kameswara Rao Korangi, Sarthak Gurnani, Pablo Casas, and Nora Agyei-Ababio. Professional Activities: Leads research groups and contributes to international conferences. His work bridges academic research with practical financial risk solutions, emphasizing ethical AI and regulatory compliance in credit modeling. Biography: Holds a PhD in Applied Economics from KU Leuven (Belgium). Joined the University of Southampton in 2004, advancing from researcher to his current leadership role in Decision Analytics and Risk.

Preeti RaghunathView profile

Lecturer

• Global Media Policy
• Sustainable Technology Governance
• AI and Data Policy
• +5 more

Dr Preeti Raghunath is a Lecturer in Digital Media and Society at the University of Sheffield's School of Sociological Studies, Politics and International Relations. She holds a PhD from the University of Hyderabad and has prior academic roles at Monash University. Her research focuses on global media policy, sustainable technology governance, and AI/ data policies in the Global South. She co-leads the STeMiS research theme and is a Fellow at the Institute for Global Sustainable Development. Her education includes a PhD in Communication (2019) and MPhil in International Relations (2011). Before academia, she worked in journalism, corporate communications, and research. Her work integrates critical policy ethnography and deliberative frameworks, with a focus on South Asia and Southeast Asia. Research highlights include the Deliberative Policy Ecology Approach for media governance, studies on AI policy gender dimensions, and analysis of digital state assemblages. She's contributed to UN-funded projects on AI and women's rights, and her work is part of Columbia University's Global Freedom of Expression curriculum. Teaching includes modules on digital society, research methods, and global data industries. She's secured grants from entities like UN Women, Ford Foundation, and the Open Society Foundations. Awards include the 2018 IAMCR Best Paper Award. Professional roles include Co-Editor of Palgrave's Global Media Policy series and Editorial Board member of Journal of Digital Media & Policy . She collaborates internationally, including partnerships with UNU Macau and India's National Media Policy consultations.

• Nonlinear Dynamics
• Quantum Chaos
• Complex Networks
• +5 more

Ying-Cheng Lai is a Regents' Professor in the School of Electrical, Computer and Energy Engineering at Arizona State University (ASU), where he has been a full-time faculty member since 2005. He holds affiliations with the Center for Biodiversity Outcomes and the Center for Biological Physics. Previously, he served as the Sixth Century Chair in Electrical Engineering at the University of Aberdeen (2009–2017) and returned to ASU as the ISS Endowed Professor (2014–present). His academic journey includes a BS and MS in Optical Engineering from Zhejiang University (1982–1985), followed by MS and PhD in Physics from the University of Maryland, College Park (1989–1992). He completed a postdoctoral fellowship in Biomedical Engineering at Johns Hopkins University School of Medicine (1992–1994). His research focuses on Nonlinear Dynamics and Chaos , Machine Learning applied to complex systems, Relativistic Quantum Chaos , Complex Networks , Mathematical Biology , and Theoretical Ecology . He explores topics such as quantum scars in Dirac materials, synchronization control in networks, and early warning signals for ecological tipping points. His work integrates data analysis techniques with interdisciplinary applications in healthcare, climate science, and cybersecurity. His recent publications highlight advancements in machine learning-driven predictions for critical transitions, quantum transport modeling in graphene, and cybersecurity strategies for power grids. These trends reflect his commitment to bridging theoretical physics with applied engineering solutions. Awards: Regents Professor (ASU's highest faculty honor, 2021) Vannevar Bush Faculty Fellowship (DoD, 2016) Corresponding Fellow of the Royal Society of Edinburgh (2018) Foreign Member of Academia Europaea (2020) Fellow of AAAS (2020) Fellow of the American Physical Society (1999) Ying-Cheng Lai has advised 24 PhD and 20 MS students, supported 15 postdocs, and secured funding from agencies like DOD (AFOSR, ARO, Navy-ONR), NSF, and the National Academies. His grants include projects on quantum billiard systems, sensor applications, and network resilience in multilayer ecological frameworks. He runs a research group focused on advanced topics in electrical engineering and interdisciplinary physics.

Tan Wen ShanView profile

Lecturer

• Stochastic Generation Scheduling
• Power System Flexibility
• Electricity Market Operation
• +8 more

Dr. Tan Wen Shan is a Lecturer in Mechatronics Engineering at Monash University Malaysia, specializing in power systems and renewable energy integration. He holds a PhD in Electrical Engineering from Universiti Teknologi Malaysia (2017), an MEng in Electrical Engineering (2013), and a BEng in Electrical and Electronics Engineering (2011). His research focuses on stochastic generation scheduling, energy storage systems, smart grids, and blockchain applications in energy markets. Education: BEng (Electrical and Electronics Engineering), Universiti Malaysia Sabah, 2011 MEng (Electrical Engineering), Universiti Teknologi Malaysia, 2013 PhD (Electrical Engineering), Universiti Teknologi Malaysia, 2017 Research Interests: Stochastic generation scheduling with renewable integration Power system flexibility and resilience Electricity market operations and blockchain Artificial intelligence in energy systems Publications and Projects: Recent work includes P2P energy trading, transportable energy storage, and resilience-based scheduling under natural disasters. Active projects include sustainable energy bike lanes in Kuala Lumpur and AI-assisted energy storage planning. His articles emphasize renewable energy forecasting, smart grid optimization, and decentralized energy systems. Awards: ITEX 2023 Prize (2023) IBM Call for Code 2022 Green Practice Challenge Accelerator Prize (2022) Teaching and Supervision: Unit Coordinator for TRC3500 (Sensors and Artificial Perceptions) and TRC3600 (Modelling and Control) Supervised multiple final-year projects on energy systems and smart grids (2023–2024).

• Bioinformatics
• Structural Biology
• Computational Biology
• +3 more

Martin Steinegger is a researcher affiliated with Johns Hopkins School of Medicine and previously held roles at institutions such as the Max Planck Institute for Biophysical Chemistry and Technical University of Munich. His research focuses on bioinformatics, protein structure prediction, and computational methods for analyzing large genomic datasets. He has contributed to tools like MMseqs2, ColabFold, and Foldseek, advancing fields like metagenomics and structural biology. His work emphasizes scalable algorithms and open-source software development. Education includes a Master of Computer Science from Ludwig-Maximilians-Universität München (2013-2014) and a Ph.D. from Technical University of Munich (2014-2018). He has also held visiting scholar positions at Seoul National University, Centre for Genomic Regulation, and University of California, San Francisco. Key research interests revolve around protein structure prediction, metagenomic analysis, and developing machine learning frameworks for biological data. His publications highlight innovations in protein language models, structural phylogenetics, and database management systems. Notable contributions include the AlphaFold Protein Structure Database, MMseqs2 sequence search tool, and ColabFold for accessible protein folding predictions. His work bridges computational methods with biological discovery, addressing challenges in structural biology and genomic data interpretation.

• Hepatocellular Carcinoma
• Liver Cancer Stem Cells
• Adiponectin Signaling
• +6 more

Lionel Hebbard is a Professor in the Department of Molecular and Cellular Biology at James Cook University's College of Medicine and Dentistry. His research spans hepatocellular carcinoma mechanisms, metabolic liver disease, and cancer therapeutics with significant contributions to adiponectin biology and sarcopenia assessment in cardiac surgery. James Cook University (Current) Department of Molecular and Cellular Biology College of Medicine and Dentistry Senior Researcher in Liver Cancer Biology His research focuses on hepatocellular carcinoma pathogenesis , particularly adiponectin signaling pathways and liver cancer stem cells. He investigates non-alcoholic fatty liver disease progression to cancer, metabolic drivers of tumorigenesis, and therapeutic targeting using aptamer-based delivery systems. Recent work explores sarcopenia quantification via CT imaging for cardiac surgery risk prediction, demonstrating clinical translation of his molecular findings. His lab employs advanced techniques including CRISPR screening (TARGET-SL platform), in vitro cancer models, and murine tumor systems. Analysis of his 15 most recent publications reveals strong emphasis on translational liver cancer research (60%), cardiac surgery complications (20%), and emerging biotechnologies (20%). Key trends include adiponectin's dual roles in fibrosis and tumorigenesis, sarcopenia as a surgical biomarker, and aptamer-based targeting of cancer stem cells. His work consistently bridges molecular mechanisms with clinical applications, particularly in hepatocellular carcinoma diagnostics and treatment. He mentors multiple doctoral students and early-career researchers including Rhys Gillman and Miriam Wankell. His research is supported by continuous funding from Australian NHMRC and international collaborations with George Jacob (Westmead Institute), Qiao Liang (Bentham Books), and Ranscht Barbara (T-cadherin studies). He leads the Hepatic Cancer Biology laboratory focusing on: Liver cancer stem cell characterization Adiponectin receptor signaling in HCC Metabolic drivers of tumor progression Novel drug delivery systems for liver cancer Translational sarcopenia assessment tools

• Persuasive Communication
• Social Media Influencers
• Digital Advertising
• +4 more

Dr. Sophie C. Boerman is an Associate Professor of Persuasive Communication in the Strategic Communication Group at Wageningen University & Research. She holds a PhD from the University of Amsterdam (2014) and leads research on digital persuasion mechanisms in social media environments. Her work examines how communication from influencers, user interactions, and advertisements shape beliefs, attitudes, and behaviors, with applied focus on sustainable and health decision-making. Research interests center on: Algorithmic persuasion frameworks in digital advertising Privacy calculus and surveillance perceptions in behavioral targeting Effectiveness of sponsorship disclosures across media formats Influencer marketing dynamics and regulatory compliance Behavioral interventions for sustainability and health promotion Her publications demonstrate strong focus on sponsored content disclosure effectiveness, privacy protection behaviors, influencer marketing regulations, and algorithmic persuasion across health/sustainability contexts. Recent works show increased attention to AI ethics, vulnerable populations, and cross-platform disclosure standardization. Honors include: Top 5 finalist for Women in AI Researcher Award 2023 Best Reviewer Award from International Journal of Advertising (2022) Research awards from ICA, EAA, ASCoR, and SWOCC Leadership roles: Board member: European Advertising Academy, SWOCC, GvR, Wageningen Young Academy Chair: Environmental Communication TWG of NeFCA Member: ECoLab, Cosmetic Procedures expert group, Better Personalized Ads initiative Editorial positions include Associate Editor for International Journal of Advertising (current) and former Associate Editor for Journal of Interactive Advertising (2020-2022).

Robert ClarkView profile

Professor

• Industrial Organization
• Banking
• Antitrust
• +5 more

Dr. Robert Clark is a Professor and Stephen J.R. Smith Chair in Economic Policy at Queen's University's Department of Economics (Faculty of Arts and Science). He holds an affiliated professorship at HEC Montréal's Department of Applied Economics and is a Bank of Canada Fellow (2022-2026). His research focuses on antitrust, competition policy, financial markets, and industrial organization. Clark has contributed to landmark studies on cartels, algorithmic pricing, and banking regulation. Education: PhD (2003) and MA (1998) in Economics from the University of Western Ontario, and HBSc (1997) in Quantitative Methods in Economics from the University of Toronto. Research interests include collusion detection, banking fragility, algorithmic competition, and regulatory policy. He has received awards for his work on merger effects and algorithmic pricing, including the 2020 Public Utility Research Prize. Clark advises the John Deutsch Institute and serves as a fellow at CIRANO. He has organized major conferences such as the Montreal Summer IO Conference series (2012–2025) and sessions on Competition Policy at the Canadian Economic Association meetings (2021–2024). His research bridges theoretical models and empirical evidence, addressing real-world economic challenges like cartel behavior and macroprudential policies.

John ClarkView profile

Professor

• Cybersecurity
• Software Engineering
• Artificial Intelligence
• +5 more

John Clark is a Professor of Computer and Information Security at the University of Sheffield since 2017 and Director of the Siemens Digital MINE. Previously, he held roles as Professor of Critical Systems at the University of York (1992–2017) and worked at Logica in security R&D. He studied Mathematics and Applied Statistics at the University of Oxford. His research focuses on cybersecurity, software engineering, and AI applications, particularly in threat modeling, intrusion detection, quantum cryptanalysis, and secure autonomous systems. Clark leads the Security of Advanced Systems research group and has secured grants totaling over £36 million. Notable projects include the EPSRC-funded DAASE (2012–2019) and the Active Building Centre (2018–2022). His work on phishing detection (e.g., analyzing user behavior) and malware analysis has been widely recognized. He has been awarded the Royal Society Wolfson Merit Award (2013), GEECO medals (2005, 2013), and multiple best-paper prizes. Clark’s research spans theoretical and applied domains, including evolutionary computation for cryptanalysis, robotic system security, and smart grid protection. His labs explore areas like digital twin authentication and privacy-aware energy theft detection. He has supervised numerous grants and maintains active collaborations with industry and academia.

Giuseppe ColangeloView profile

Associate Professor

• Innovation Policy
• Intellectual Property Law
• Competition Policy
• +3 more

Giuseppe Colangelo is an Associate Professor of Law and Economics at the University of Basilicata and holds adjunct roles at LUISS Guido Carli (Rome, Italy) and the Transatlantic Technology Law Forum (TTLF). He specializes in innovation policy, competition law, and economic analysis of legal frameworks. His research bridges EU and U.S. antitrust approaches, focusing on digital markets, data governance, and transnational litigation. Education includes a Law degree from LUISS Guido Carli, an LL.M. in Competition Law from Erasmus University Rotterdam, and a Ph.D. in Law and Economics from LUISS. He has been a TTLF Affiliate since 2017. Key research interests include AI-driven collusion, antitrust implications of data accumulation, and interoperability policies. Over 15 publications analyze topics like anti-suit injunctions in SEP litigation, EU-US regulatory divergence in digital platforms, and privacy-antitrust intersections. His work emphasizes transatlantic comparative law and policy recommendations for addressing market power in tech sectors.