Professor Paul D. Sclavounos is a faculty member in the Department of Mechanical Engineering at the Massachusetts Institute of Technology (MIT). He earned his B.Sc. from the National Technical University of Athens in 1977 and Ph.D. from MIT in 1981. Research Interests : Marine hydrodynamics, stochastic control, offshore wind/wave/tidal/solar energy, machine learning applications, magnetohydrodynamic propulsion systems. Notable Contributions : Development of computational tools like SWAN and SML software suites; analysis of nonlinear wave dynamics; integration of AI/ML in marine hydrodynamics. Scientific Recognition : First Prize in National Mathematics Competition (1972), Georg Weinblum Memorial Lecturer (2010-2011), Best Paper Award at OMAE 2019, AEOLOS Scientific Award (2024). Leadership : Director of the Laboratory for Ship and Platform Flows since 1985; advisory roles for US Navy, US Department of Energy, and Det Norske Veritas (DNV). Teaching : Courses in Hydrodynamics (2.016), Advanced Fluid Mechanics (2.25), and Naval Architecture (2.701).
Motahhare Eslami is an Assistant Professor at Carnegie Mellon University’s School of Computer Science, Human-Computer Interaction Institute. Her research bridges human-computer interaction, social computing, and AI ethics. Education : PhD in Computer Science from University of Illinois at Urbana-Champaign, advised by Karrie Karahalios Research Focus : Dr. Eslami investigates algorithmic opacity and user behavior in socio-technical systems, developing frameworks to enhance transparency and stakeholder participation in AI governance. Her work addresses: Algorithmic bias mitigation through participatory audits Ethical implications of generative AI and smart assistants Inclusion of marginalized communities in AI design Transparency mechanisms for opaque algorithms Civic technology and public sector AI Recent Article Trends : Her publications analyze algorithmic harms through lenses of: Medical imaging and data generation Labor market equity and low-wage employment Youth perspectives on AI ethics Content creator experiences with demonetization Explainability in black-box AI systems Scientific Recognition : Best Paper at AAAI HCOMP (2025) Google Academic Research Award (2024) Microsoft AI & Society Fellowship (2024) 100 Brilliant Women in AI Ethics (2023) Teaching Innovation Award at CMU (2023) Advising & Collaborations : Mentors PhD students Shixian Xie, Wesley Deng, Seyun Kim, and former post-doc Jaemarie Solyst. Collaborates with NSF AI Institute for Collaborative Assistance (2022–2027), Amazon, Google, and Microsoft on responsible AI initiatives.
Prof. Dr. Madalina Busuioc is a Full Professor of Public Governance at the Department of Political Science and Public Administration, Vrije Universiteit Amsterdam. She serves as Director of the Graduate School of Social Sciences and co-Director of the R&I Lab on Artificial Intelligence and Digital Governance. Her ERC-funded research explores public accountability in the AI era, with a focus on algorithmic governance and cognitive biases in administrative decision-making. She holds a PhD cum laude from Utrecht University (2010). Her research interests center on public power dynamics, algorithmic governance, and institutional accountability. Notable contributions include work on AI's impact on citizen-state interactions, reputational authority in bureaucracy, and regulatory oversight mechanisms. Her book European Agencies: Law and Practices of Accountability (Oxford UP) and peer-reviewed articles in Public Administration Review , Journal of Public Administration Research and Theory , and Governance highlight her scholarly impact. Teaching contributions include designing the MSc Public Administration: Artificial Intelligence and Governance program, which integrates technical and governance perspectives. Awards include the Haldane Prize (2016) and Fernand Braudel Fellowship (2021). She advises on AI policy through ancillary roles like membership in the Meijers Commission on international law. Her research projects address AI ethics, algorithmic accountability, and regulatory innovation. Recent work explores behavioral dimensions of human-AI collaboration in public services and the societal implications of AI adoption in administrative systems.
Craig Carter is the John G. and Barbara A. Bebbling Professor of Supply Chain Management at Arizona State University’s Department of Supply Chain Management. He holds the Harold E. Fearon Fellow of Purchasing Management title. His research focuses on sustainable supply chain management, ethical buyer-supplier relationships, environmental supply chain practices, and diversity sourcing. He has advised on over 100 Fortune 1000 firms globally and served as an editor for multiple journals, including the Journal of Supply Chain Management and Decision Sciences Journal. Education: Ph.D. in Business from Arizona State University (1996), B.S. in Business from the University of Maryland (1990). His industry experience includes roles at Ryder Systems and the U.S. Department of Transportation. Research emphasizes unintended consequences of sustainability initiatives, behavioral decision-making in supply chains, and supply chain leakage of greenhouse gas emissions. His work bridges theoretical frameworks (e.g., configurational approaches) with practical applications, such as mitigating supply risk and enhancing collaboration. Recent publications explore topics like honesty contagion in negotiations and informal exchanges impacting sourcing collaboration. He has been recognized for editorial contributions and has been actively involved in shaping supply chain management’s academic trajectory through thought leadership. Courses taught include Strategic Procurement, Global Supply Operations, and seminars on supply chain theory. His work often integrates empirical research with real-world case studies, emphasizing actionable insights for practitioners.
Dr. Auzeen Shariati is an Associate Professor and Director of Undergraduate Programs in the Department of Criminology, Law and Society at George Mason University. She holds a Ph.D. in Public Affairs and Criminal Justice from Florida International University (2017), an M.A. in Criminal Law and Criminology from Allameh Tabataba’I University (2010), and a B.A. in Judicial Law from the University of Tehran (2006). Prior to academia, she practiced as a defense attorney at the Iranian Bar Association. Her research focuses on environmental criminology, crime prevention, victimization, policing strategies, and comparative criminal justice systems. Key areas include pandemic impacts on domestic violence, school safety through Crime Prevention Through Environmental Design (CPTED), and policy evaluation in criminal justice. She has published extensively in journals like American Journal of Criminal Justice , Journal of Family Violence , and Security Journal . Recent work examines how the Covid-19 pandemic and social upheavals like the murder of George Floyd influenced domestic violence reporting and victim experiences. Her studies combine quantitative and qualitative methods, emphasizing real-world policy implications. Dr. Shariati teaches courses such as Introduction to Criminology , Law and Justice Around the World , and Evaluation of Crime and Justice Policies . She actively presents at conferences like the American Society of Criminology and has contributed to public discourse on campus safety and criminal justice reform through media engagements.
Dr Sue Caton is a Senior Lecturer in the Department of Social Care and Social Work at Manchester Metropolitan University, within the Faculty of Health and Education. Her research focuses on social and health inequalities affecting people with intellectual disabilities, particularly digital inclusion, mental health medication decision-making, and pandemic impacts. She leads projects such as Digital Lifeline (NIHR-funded), Medications and My Mental Health (NIHR RfPB), and co-leads Our Digital Health (NIHR RfSC), emphasizing co-produced methodologies. Research Projects: Digital Lifeline: Evaluating tablet impact on social connections post-pandemic. Medications and My Mental Health: Empowering informed medication decisions for people with learning disabilities. Our Digital Health: Assessing digital health participation barriers. Key Expertise: Qualitative research, health inequalities, inclusive research design, and participatory methodologies. Her work addresses pandemic-related challenges faced by marginalized groups, including access to healthcare and social support. She has supervised four completed PhD students and currently mentors four PhD candidates exploring topics such as digital inclusion, animal-assisted interventions, and familial perspectives in parenting support. Dr Caton has led evaluations for initiatives like the Shared Lives 16+ project and the Us Too project on domestic abuse, demonstrating expertise in policy-informed research. Her contributions include over 50 peer-reviewed articles, focusing on digital participation, mental health, and pandemic resilience among people with intellectual disabilities. Awards: No specific scientific awards mentioned, though her work is funded by prestigious bodies like NIHR and UKRI. Labs/Teams: Collaborates with interdisciplinary teams including Dudley Voices for Choice, Liverpool John Moores University, and the Universities of Dundee, Warwick, and Birmingham City.
Simon Langlois-Bertrand serves as a Part Time Lecturer in the Department of Political Science at Concordia University, teaching core courses including Introduction to International Relations (POLI205), Sustainability and Governance (POLI208), and Global Energy Politics and Policy (POLI486). His interdisciplinary academic foundation combines engineering and political science, reflected in his educational trajectory: PhD International Affairs, Carleton University M.Sc. Political Science, Université de Montréal M.Ing. Industrial Engineering, École Polytechnique de Montréal B.Ing. Computer Engineering, École Polytechnique de Montréal Langlois-Bertrand's research critically examines energy politics and policy , global environmental governance , and sustainability transitions , with particular emphasis on social-technical dimensions of development and U.S. political dynamics. His work bridges engineering perspectives with political analysis to explore how technological systems interact with institutional frameworks. Analysis of his 15 most recent publications reveals concentrated expertise in North American energy transitions, featuring empirical studies on electricity rate structures, Quebec's carbon policy, and theoretical investigations of uncertainty in energy governance. Key thematic threads include decarbonization pathways, circular economy implementation, and life-cycle policy approaches, predominantly focused on Canadian and Quebec contexts. Scientific awards: No awards documented in source material. Regarding academic mentorship, the provided text contains no information about graduate students supervised or research grants secured. His current research projects indicate ongoing work on the geopolitics of ecological transition and environmental state theory through life-cycle analysis frameworks. No laboratory affiliations or research team memberships are specified in the available documentation.
Eli Ben-Michael is an Assistant Professor jointly appointed in the Heinz College of Information Systems and Public Policy and the Department of Statistics & Data Science at Carnegie Mellon University. He is affiliated with the CMU-NIST AI Measurement Science & Engineering Cooperative Research Center (AIMSEC), contributing to cutting-edge research at the intersection of statistics, policy analysis, and artificial intelligence. His educational background includes a PhD in Statistics from U.C. Berkeley and undergraduate studies at Columbia University where he earned a dual degree in Computer Science and Statistics. Prior to his current position, he completed a postdoctoral fellowship at Harvard University's Institute for Quantitative Social Science and Department of Statistics. Ben-Michael's research focuses on developing innovative statistical and computational methods for causal inference and policy evaluation, with particular emphasis on integrating machine learning techniques to address complex problems in public policy and social science. His work bridges theoretical statistics with practical applications in healthcare, criminal justice, education, and social policy. Current research directions include safe policy learning, sensitivity analysis for clustered data, and methodological innovations for the synthetic control method. His publication record shows a strong trajectory in top-tier journals including Journal of the American Statistical Association, Journal of the Royal Statistical Society, and Proceedings of ICML. Recent work demonstrates increasing focus on policy-relevant applications including abortion legislation impacts, pre-trial risk assessment, and healthcare disparities, while maintaining methodological rigor in causal inference frameworks. Ben-Michael has developed open-source software tools including augsynth and multical R packages, which implement his methodological contributions for synthetic controls and multilevel calibration weighting. These packages have been adopted by researchers in multiple disciplines for causal inference applications.
Dr. Carolyn Conner Seepersad is a Woodruff Professor in the George W. Woodruff School of Mechanical Engineering at Georgia Institute of Technology. She leads the Digital Design and Manufacturing research group and previously founded the Center for Additive Manufacturing and Design Innovation at The University of Texas at Austin. Her research focuses on additive manufacturing, materials design, and process innovation. She holds editorial roles, including Editor-in-Chief of the ASME Journal of Mechanical Design, and has received numerous awards for research and teaching. Education: PhD, Mechanical Engineering, Georgia Tech, 2004 MS, Mechanical Engineering, Georgia Tech, 2001 BA, Philosophy, Politics, and Economics, Oxford University, 1998 BS, Mechanical Engineering, West Virginia University, 1996 Her research interests span design for additive manufacturing, simulation-based materials and structures, and metamaterials. She emphasizes manufacturing-aware design and sustainability. Key contributions include lattice structure optimization, negative stiffness composites, and process-aware manufacturing techniques. Her publications reflect advancements in additive manufacturing processes, materials characterization, and design methodologies. Awards include the ASME Design Automation Award and recognition as a University of Texas System Academy of Distinguished Teachers. Seepersad has advised on grants such as the LEAP-HI GOALI project and contributed to initiatives like the Solid Freeform Fabrication Symposium. Her work bridges academia and industry, emphasizing practical applications and innovation. Labs/Teams: Leads the Digital Design and Manufacturing group at Georgia Tech, previously directed the UT Austin Additive Manufacturing Center.
Padhraic Smyth is a Distinguished Professor and Hasso Plattner Endowed Chair in Artificial Intelligence at the University of California, Irvine (UCI), holding joint appointments in the Department of Computer Science and Department of Statistics. He leads the DataLab research group, focusing on machine learning, AI, and their applications in climate science, healthcare, and education. His research spans probabilistic modeling, deep learning, and human-AI collaboration. Education: PhD in Electrical Engineering from the California Institute of Technology (1988), MSEE (1985), and BEng (1984). Prior to UCI, he worked at NASA's Jet Propulsion Laboratory (1988–1996). Research Interests: Machine learning, AI, pattern recognition, Bayesian methods, climate science applications, algorithmic fairness, and human-AI interaction. He has published over 200 papers and co-authored textbooks like Modeling the Internet and the Web . Awards: ACM Fellow, IEEE Fellow, AAAI Fellow, AAAS Fellow, and ACM SIGKDD Innovation Award recipient. He has held leadership roles in UCI's Center for Machine Learning and Data Science. Key Projects: Human-AI collaboration frameworks, robustness in deep learning, climate modeling using spatio-temporal data, and AI fairness with missing attributes. Collaborates with institutions like NASA and industry partners (e.g., Google, eBay). Labs/Teams: Director of UCI’s Data Science Initiative and HPI Research Center in Machine Learning. Supervises a vibrant PhD program with over 30 alumni in academia and industry.
Ian Grettenberger is an Assistant Professor of Cooperative Extension in the Department of Entomology and Nematology at the University of California, Davis. He leads the Grettenberger Lab, focusing on applied entomology in agricultural ecosystems. His work emphasizes pest management strategies, biological control, and sustainable solutions for invasive species threatening California crops. Education: B.S. in Biology, Western Washington University Ph.D. in Entomology, Pennsylvania State University Research Interests: Grettenberger’s expertise spans pest management of field/vegetable crops, invasive species like the Bagrada bug and western striped cucumber beetle, and resistance management in pests such as the alfalfa weevil. He investigates biological control mechanisms, pesticide efficacy, and economic impacts of pest management policies. Key Contributions: Recent work includes studies on insecticide spray optimization, economic implications of pesticide regulations, and biological control of invasive species. His research bridges laboratory and field settings, addressing challenges in California’s agricultural landscapes. Labs/Teams: The Grettenberger Lab collaborates on applied entomology projects, advancing IPM practices and crop protection strategies.
Ricardo Gutierrez-Osuna is a Professor in the Department of Computer Science and Engineering at Texas A&M University, part of the College of Engineering. He leads the PSI Lab and focuses on machine learning, speech processing, and digital health applications. His research spans topics like wearable sensors, foreign accent conversion, and physiological monitoring. Education: Ph.D. (Computer Engineering, NC State, 1998), M.S. (Computer Engineering, NC State, 1995), B.S. (Electrical Engineering, Universidad Politécnica de Madrid, 1992). Research interests include intelligent sensors, speech processing, machine learning, neuromorphic computation, and mobile robotics. His work bridges computer science and biomedical engineering, with applications in health monitoring and human-computer interaction. Awards: NSF CAREER Award (2002) Ramón y Cajal Award (2005-2010) Texas A&M Barbara and Ralph Cox Fellow (2009) Multiple teaching awards (2009-2010) His lab develops innovative technologies like stress-detecting wearables, biofeedback games, and systems for non-native speech improvement. He collaborates on projects involving voice conversion, glucose prediction algorithms, and multi-modal sensing devices.
Sophie Sanchez is an Associate Professor at Uppsala University's Department of Organismal Biology, specializing in Evolution and Development. Her research focuses on vertebrate evolution, using advanced imaging techniques like synchrotron microtomography to study fossilized anatomy and developmental processes. She has contributed significantly to understanding sensory organ evolution in jawed vertebrates, early tetrapod development, and the structural adaptations of ancient fish. Key work includes studies on placoderms, osteostracans, and the molecular bases of evolutionary innovations. Her findings bridge paleontology with developmental biology, employing cutting-edge microscopy to reveal hidden biological details in fossils. Publications highlight her expertise in fossil musculature, bone histology, and genomic evolution, with high-impact contributions to journals like Nature and Science . Collaborative projects involve global teams analyzing Devonian and Permian vertebrates, emphasizing interdisciplinary approaches to evolutionary questions.
Soheil Salehi is a tenure-track Assistant Professor in the Department of Electrical and Computer Engineering at the University of Arizona, with a joint appointment in Systems and Industrial Engineering. He is the Director of the Privacy-preserving, Intelligent, and Secure Computing (PRISM) Lab, established in August 2022. Prior to this, he was an NSF-Sponsored Computing Innovation Fellow and Postdoctoral Research Fellow at the University of California, Davis. Ph.D., Electrical and Computer Engineering, University of Central Florida, 2020 M.S., Electrical and Computer Engineering, University of Central Florida, 2016 B.S., Isfahan University of Technology, Iran, 2014 Dr. Salehi's research focuses on the intersection of hardware, AI, and security. His work spans hardware and AI-enabled security in IoT , Generative AI for hardware design and security , neuromorphic and biologically-inspired AI hardware , emerging spin-based devices , reconfigurable architectures , low-power VLSI circuits , and digital twins and mixed reality for semiconductor workforce development . He also explores the application of Generative AI in personalized education . His recent publications, spanning 2023–2025, reveal a strong trend toward integrating AI and machine learning into hardware security and design. Key themes include automated secure IC design flows , AI-driven hardware obfuscation , firmware and side-channel attack analysis , security in neuromorphic and spiking neural networks , and educational frameworks using digital twins and generative models . His work appears in top venues like DAC, ICCAD, USENIX Security, IEEE TCAS-I, and ISCAS. Outstanding Reviewer Award, IEEE/ACM Design Automation Conference (DAC), 2023 Best Presentation of the Symposium Award, UC Davis Postdoctoral Research Symposium, 2021 UCF Excellence by a Graduate Teaching Assistant (University-Level), 2016 Nominated for 30-under-30 Award, UCF, 2020 Nominated for Postdoctoral Research Excellence Award, UC Davis, 2022 Dr. Salehi has secured significant research funding as PI and Co-PI, including a $300K NSF SaTC EAGER grant on Generative AI-based Personalized Cybersecurity Tutor, a $174,000 University of Arizona PIF Award, and multiple RII grants totaling over $198K. He has also received industry funding from CHEST. He actively mentors students and leads the PRISM Lab, which focuses on privacy-preserving and intelligent secure computing. His service includes roles as Technical Program Committee (TPC) Member and Session Chair at premier conferences such as DAC, ICCAD, CCS, NDSS, and GLSVLSI. The PRISM Lab, under his direction, conducts cutting-edge research in secure and intelligent hardware systems, with applications in IoT, edge computing, and workforce development. The lab emphasizes interdisciplinary collaboration and innovation in both research and education.
Tiancheng Zhao is a principal researcher at the Binjiang Institute of Zhejiang University and founder of the Om Artificial Intelligence Laboratory (Om AI Lab), dedicated to frontier open multimodal AGI research for building next-generation agents that transform work and life through advanced human-machine interaction. His academic credentials include: Ph.D. in Computer Science from Carnegie Mellon University (2016-2019) under Prof. Maxine Eskenazi, Prof. Louis-Philippe Morency, Prof. William W. Cohen, and Dr. Dilek Hakkani-Tur, with pioneering dissertation “Learning to Converse With Latent Actions” in end-to-end generative conversational models M.S. in Computer Science from Carnegie Mellon University (2014-2016) B.S. in Electrical Engineering from UCLA (2010-2014) with Summa Cum Laude, focusing on speech signal processing under Prof. Abeer Alwan Dr. Zhao’s research centers on multimodal foundation models and agents, tackling three core challenges: Multimodal Models for cross-modal representation learning in high-dimensional data, Learning to Learn for effective skill acquisition from diverse signals (supervised labels, rewards, meta-learning), and AI Agents for open-world understanding and complex decision-making. His work bridges computer vision, natural language processing, and real-world applications including healthcare analytics and remote sensing. Analysis of his 50+ publications reveals accelerating innovation in multimodal large language models (2024-2025), with emphasis on stable vision-language architectures (VLM-R1), agent orchestration frameworks, and domain-specific applications in geospatial analysis and healthcare. Key trends include solving long-tail distribution challenges in satellite imagery, developing human-like zooming capabilities for multimodal LLMs, and creating unified benchmarks for autonomous GUI testing. His scientific recognition includes: National Breakthrough Technology Award by Ministry of Science and Technology (2021) Microsoft Research Best & Brightest PhD (2018) BEST PAPER AWARD at SIGDIAL 2018 Best Paper Nomination at SIGDIAL 2016 Top 1 Outstanding Bachelor of Science Award at UCLA (2014) As Om AI Lab founder, Dr. Zhao leads research teams developing computational building blocks for human-AI collaboration. While specific student mentorship details aren’t public, his extensive publication record with junior co-authors indicates active research supervision. Current projects focus on practical system implementations for real-world multimodal agent deployment across diverse domains.
