Dr. Anne-Marie Nicol is an Associate Professor of Professional Practice in Health Sciences at Simon Fraser University. Her multidisciplinary research spans health communication, toxicology, social marketing, risk perception, and risk assessment, with emphasis on environmental and occupational health exposures. She leads CAREX Canada, a national carcinogen surveillance program, and has developed public health campaigns including Wash with Care and Tick Talk. Her work focuses on translating scientific information about carcinogens to policymakers and cancer prevention stakeholders. She teaches Human Health Risk Assessment, Toxicology for Public Health, and Health Communication. Her research has been funded by CIHR, Michael Smith Foundation, and Canadian Cancer Society. Current projects include studies on radon exposure, infodemic communication strategies, and community resilience to wildfire smoke. She holds a BA from SFU, MES from York University, and PhD from UBC.
Daniel A. McAdams is the Robert H. Fletcher Professor in Mechanical Engineering at Texas A&M University and serves as the NSF Program Director of Convergent Activities. His research develops design theory and methodology with focus on functional modeling, bio-inspired design, and technology evolution. Educational Background: PhD in Mechanical Engineering from University of Texas at Austin MS in Mechanical Engineering from California Institute of Technology BS in Mechanical Engineering from University of Texas at Austin His research investigates innovation in concept synthesis through computational methods, bio-inspired design approaches, and technology evolution applied to product development. Current projects include function-sharing principles in biological systems, digital twin architectures, and patent mining for technology forecasting. Recent publications explore applications of speculative fiction in design ideation, graph-theoretic approaches for digital twins, and automated assessment in engineering education, demonstrating cross-disciplinary innovation across design science. Awards and Honors: ASME Design Theory and Methodology Award Distinguished Achievement Award for Student Relations Multiple Faculty Fellow awards Design Studies Best Paper Award Outstanding Faculty Mentor Award He leads the Product Synthesis Engineering Lab, advancing design methodologies for complex engineered systems through computational approaches and biological analogies.
Jeyavijayan 'JV' Rajendran is an Associate Professor in the Department of Electrical and Computer Engineering at Texas A&M University, part of the College of Engineering. He is an ASCEND Fellow and leads the Secure and Trustworthy Hardware (SETH) Lab. His research focuses on hardware security, computer security, and novel applications of AI in secure hardware design. Education: PhD in Electrical Engineering (NYU 2015), MS in Computer Engineering (NYU Tandon 2010), BE in Electronics and Communication Engineering (Anna University 2008). Research Interests: Hardware Security, Computer Security, Logic Locking, Hardware IP Protection, and Reinforcement Learning for Security. He explores AI-driven approaches to detect vulnerabilities, protect intellectual property, and enhance secure hardware design through fuzzing, obfuscation, and formal verification. Notable Awards: 2022 Office of Naval Research Young Investigator Award, 2021 IEEE CEDA Ernest Kuh Early Career Award, 2017 NSF CAREER Award. Lab and Teams: The SETH Lab focuses on trustworthy hardware design, developing techniques to secure integrated circuits against reverse engineering and IP theft. Current projects include LLM-based hardware code generation, formal approaches for hardware fuzzing, and AI-driven vulnerability detection.
Alan Sutherland is a Professor of Economics at the University of St Andrews' Business School. He specializes in international macroeconomics, monetary economics, and financial market integration. His research focuses on the macroeconomic implications of exchange rate policies, monetary policy regimes, and cross-country financial linkages. He leads projects funded by the Economic & Social Research Council, including 'The Macroeconomics of Financial Globalisation' (2011-2015) and 'Monetary Policy Welfare and International Financial Markets' (2006-2007). Research Interests: International macroeconomic policy design Exchange rate determination Financial market integration effects Numerical methods for macroeconomic modeling His recent work emphasizes the role of international financial flows in transmitting economic shocks between countries. He has developed influential solution methods for analyzing multi-country general equilibrium models. Key contributions include frameworks for understanding optimal monetary policy under incomplete financial markets and asymmetric information conditions. Scientific Recognition: 2013 Fellow of the Royal Society of Edinburgh Recipient of 2 ESRC-funded research grants
Eric Greene is an Associate Professor in the Department of Religious Studies at Yale University, specializing in medieval Chinese Buddhism. He has held academic positions since 2015, including Director of Graduate Studies at the Council on East Asian Studies. His research focuses on the transmission of Indian Buddhism to China, Chan/Zen Buddhism, and Buddhist meditation practices. He earned his Ph.D. in Buddhist Studies from UC Berkeley in 2012, with additional research at Kyoto University. Greene's work bridges historical analysis and textual criticism, particularly in early Chinese Buddhist texts such as the Vimalakīrti Sutra commentaries and meditation manuals. His publications include Chan Before Chan (2021) and The Secrets of Buddhist Meditation (2021), exploring meditation’s role in Chinese Buddhist identity and ritual. He has delivered invited lectures globally, addressing topics like Buddhist image worship, repentance practices, and comparative visions of meditation in medieval China and 19th-century psychology. Greene’s current research examines early Chinese Buddhist translation and commentary practices (150–350 CE), emphasizing how textual interpretation shaped Buddhist sinicization. His affiliations include the Yale Buddhist Studies Initiative and the Department of East Asian Languages and Literatures.
Riyadh Baghdadi is an Assistant Professor of Computer Science at New York University Abu Dhabi and a Global Network Assistant Professor at the Tandon School of Engineering, NYU. He is also a Research Affiliate at MIT, where he previously completed a postdoctoral fellowship. His academic journey includes a PhD and Master’s from Sorbonne University (INRIA/UPMC) and an engineering degree from Ecole Supérieure d’Informatique in Algiers. Assistant Professor, NYU Abu Dhabi Global Network Assistant Professor, Tandon School of Engineering, NYU Research Affiliate, MIT His research lies at the intersection of compilers, programming languages, and applied machine learning, with a focus on developing advanced compiler techniques for deep learning, high-performance computing, and data-parallel algorithms. He is the lead developer of the Tiramisu compiler , a polyhedral compiler designed to optimize dense and sparse deep learning workloads across diverse architectures including CPUs, GPUs, and FPGAs. Riyadh’s recent publications demonstrate a strong trend toward integrating machine learning into compiler optimization—particularly in cost modeling, loop scheduling, and automatic code generation. His work addresses critical challenges in optimizing sparse neural networks and enabling efficient execution on resource-constrained platforms like smartphones and autonomous vehicles. Outstanding Paper Award, MLSys 2021 He has mentored 18 students and taught core courses such as Computer Systems Organization and Machine Learning at NYUAD. His service to the academic community includes program committee roles at MLSys, IPDPS, ECOOP, and PACT, as well as organizing workshops on polyhedral compilation and machine learning for hardware-software co-design. Riyadh actively contributes to open-source projects and collaborates with industry leaders including Google, Facebook, NVIDIA, and Intel. He leads the development of Tiramisu and collaborates on DSLs like GraphIt and Halide, focusing on performance portability and automation in compiler design.
Shaji K Chacko, Ph.D., is an Instructor in the Department of Pediatrics – Nutrition at Baylor College of Medicine, where he leads an independent research laboratory at the USDA/ARS Children's Nutrition Research Center in Houston, Texas. His work focuses on metabolic regulation, particularly glucose production pathways in health and disease. BS, Nagpur University (1990) MS, Barkatullah University (1992) MS, University of Houston, Clear Lake (2000) PhD, University of Groningen (2011) Dr. Chacko's research centers on glucose metabolism , specifically gluconeogenesis and glycogenolysis , using stable isotope tracers and mass spectrometry. His lab investigates metabolic dysfunctions in conditions such as insulin resistance, type 2 diabetes, and post-surgical states, with a strong focus on pediatric and neonatal populations. He also studies total energy expenditure using the doubly labeled water method in free-living subjects. The 15 most recent publications reflect a consistent focus on metabolic regulation, particularly in insulin resistance , obesity , diabetes , and parenteral nutrition . Key themes include hormonal regulation (e.g., leptin), amino acid metabolism (glycine, BCAA), liver function, and therapeutic interventions like metformin and nutritional supplements. His work frequently involves translational models, including human clinical studies and genetically modified animal models. Dr. Chacko has not received any explicitly mentioned scientific awards in the provided texts. He mentors lab members such as Zhensheng Chen (Research Associate) and collaborates extensively with clinicians and scientists, including M.W. Haymond and F. Jahoor. While no specific grants are listed, his research likely involves externally funded projects given the scope and equipment required for stable isotope and mass spectrometry studies. His lab benefits from the state-of-the-art facilities at the Children's Nutrition Research Center, including a metabolic kitchen, greenhouse, and advanced instrumentation. The Shaji Chacko Lab is embedded within the USDA/ARS Children's Nutrition Research Center, a multidisciplinary hub housing elite scientists conducting cutting-edge research in pediatric nutrition and metabolism. The center provides extensive resources including specialized laboratories, research accommodations, and a collaborative scientific environment.
Dr. Anja Schmidt is a Postdoctoral Researcher at the Helmholtz Centre for Environmental Research - UFZ , specifically within the Department of Conservation Biology & Social-Ecological Systems. She has been affiliated with UFZ since 2020 and previously worked at the German Centre for Integrative Biodiversity Research (iDiv) and UFZ's Community Ecology Department. Her research focuses on ecosystem processes, particularly decomposition dynamics driven by invertebrates in tropical and agricultural systems, climate change impacts, and biodiversity conservation. PhD in Ecology (UFZ, 2012–2015) Master's in Ecology, Environmental Science, Bioinformatics (University of Giessen, 2008–2011) BSc in Biotechnology, Animal Physiology, Organismic Biology (University of Erlangen/Nuremberg, 2005–2008) Her work explores the interplay between decomposition processes , invertebrate biodiversity , and human-driven environmental changes . She investigates how climate change, land use, and pollution affect soil and aquatic ecosystems, with a focus on multitrophic interactions. Her research integrates experimental platforms like the iDiv Ecotron and UFZ's environmental observatories (TERENO, MOSES) to simulate and monitor real-world conditions. Dr. Schmidt's recent publications highlight her contributions to understanding invertebrate decline and its cascading effects on microbiomes, soil biodiversity assessments, and policy frameworks for conservation. She collaborates extensively with networks such as iDiv, STACCATO, and LEGATO, bridging experimental ecology with societal decision-making through projects like Faktencheck Artenvielfalt . She is associated with experimental infrastructures including: iDiv Ecotron Project (2018–2020) Global Change Experimental Facility (GCEF) TERENO (Terrestrial Environmental Observatories) MOSES (Modular Observation Solutions for Earth Systems) MOBICOS (Mobile Stream Laboratories) River Experiment Leipzig
Kai Leonhard is an Adjunct Professor at the Chair of Technical Thermodynamics , RWTH Aachen University. His research focuses on computational chemistry, thermodynamics, and molecular modeling, particularly in solvent design and reactive chemical processes. Department: Chair of Technical Thermodynamics Email: kai.leonhard@ltt.rwth-aachen.de Prof. Leonhard's work integrates quantum chemistry with computer-aided molecular and process design (CAMD/CAPD), emphasizing solvation thermodynamics, reaction kinetics, and machine learning applications. His projects span biofuel combustion, microgel synthesis, and sustainable solvent development. Recent publications highlight advancements in COSMO-RS-based solvent screening, reaction network exploration via ChemTraYzer-TAD, and multi-fidelity modeling for partition coefficients. He employs machine learning to enhance predictive thermodynamic models and optimize chemical processes.
Gregory Nagy is the Francis Jones Professor of Classical Greek Literature and Professor of Comparative Literature at Harvard University. He serves as Director of the Center for Hellenic Studies and oversees HarvardX MOOCs, engaging over 181,000 learners globally. His work bridges archaic Greek literature, oral traditions, and comparative metrics. Research Focus: Archaic Greek poetry, oral-formulaic theory, Indo-European metrics Publications: 15+ monographs including Ancient Greek Heroes, Athletes, Poetry (2024) and Masterpieces of Metonymy (2015) His scholarship reveals patterns in epic transmission, emphasizing performance's role in Homeric text stabilization. He pioneered the "24 Hours" methodology for analyzing heroic narratives across media. Scientific Awards: Goodwin Award of Merit (1982) for The Best of the Achaeans Nagy's HarvardX MOOC and mentorship initiatives have transformed classical education, creating intergenerational networks. He co-edited the digital Festschrift Donum Natalicium (2012) celebrating his 70th birthday.
Reto Nyffeler , PD Dr., has been Lecturer and Curator of Phanerogams at the University of Zurich since 2002. He leads the Institute of Systematic Botany’s herbarium activities and heads the Nyffeler research group, collaborating closely with the Sukkulenten-Sammlung Zürich. Education & Career Diploma in Systematic Botany, University of Zurich (1988-1991) Ph.D., Institute of Systematic Botany, University of Zurich (1994-1997) Post-doctoral researcher & Mercer Fellow, Arnold Arboretum, Harvard University (1997-2000) Post-doctoral researcher, Stanford University (2000-2001) Lecturer and Herbarium Curator, University of Zurich (2002-present) Research Focus Nyffeler’s work integrates plant systematics, biogeography, and phylogenetic methodology. Empirically, he concentrates on the diversification of succulent plants—especially Cactaceae—using molecular phylogenetics to untangle taxonomy, growth-form evolution, and biogeographic history. Parallel interests include the floristics of alpine regions and the systematics of Campanulaceae, Asteraceae, and Ranunculaceae. Across dozens of peer-reviewed papers (2010-2025) he has advanced family-wide phylogenies for Cactaceae and Caryophyllales, documented repeated succulent radiations, and created online taxonomic backbones such as Caryophyllales.org . His current projects combine next-generation sequencing with classical morphology to revise genera like Parodia and to understand adaptation in high-altitude Callianthemum . Students & Mentoring PhD advisee: Anita Lendel (systematics of Trichocereeae) Master advisees: 16 students (2006-2018) working on alpine ecology, cactus morphology, and floristic change Research Group & Collaborations The Nyffeler group comprises a scientific assistant (Dr. Heike Hofmann), technical and IT staff, and rotating Master students. The team maintains close collaborations with the Sukkulenten-Sammlung Zürich, Harvard’s Arnold Arboretum (Mercer Fellowship network), and multiple international cactus specialists.
Gábor Magyarfalvi is an Assistant Professor and Lecturer at Eötvös Loránd University, affiliated with both the Institute of Chemistry and the Department of Inorganic Chemistry. His office is located at 1117 Budapest, Pázmány Péter sétány 1/a. (Room 542), and he can be contacted via email at gmagyarf@elte.hu or phone extension 6587. His research focuses on physical and inorganic chemistry, with specialization in spectroscopy, astrochemistry, and computational methods. Key areas include matrix isolation techniques for studying interstellar molecule formation (e.g., H 2 catalysis via polyaromatic hydrocarbons), photochemical generation of reactive intermediates, and conformational dynamics of biomolecules. His work extensively employs low-temperature matrix isolation coupled with laser spectroscopy and quantum chemical calculations. Magyarfalvi's publications demonstrate consistent themes: 60% focus on low-temperature photochemistry and spectroscopy of small molecules (e.g., nitrogen/sulfur compounds, amino acids), 30% on peptide/protein conformational analysis using vibrational circular dichroism (VCD) and NMR, and 10% on methodological developments in computational chemistry. Recent works increasingly explore astrochemistry and quantum tunneling phenomena.
Summary Luis A. Duffaut Espinosa is an Assistant Professor in the Department of Electrical and Biomedical Engineering at the University of Vermont (UVM), affiliated with the College of Engineering and Mathematical Sciences. His research focuses on control theory, estimation, robotics, and nonlinear systems with applications in autonomy, quantum control, and environmental monitoring. He holds a Ph.D. in Electrical and Computer Engineering from Old Dominion University (2009) and has held academic positions at George Mason University and postdoctoral roles at Johns Hopkins University and the University of New South Wales. Education: Ph.D. in Electrical and Computer Engineering (2009), Old Dominion University M.S. in Mathematics (2005), Pontificia Universidad Católica del Perú B.S. in Physics (2003), Universidad Nacional de Ingeniería, Peru Research Interests: His work emphasizes data-driven control and estimation methodologies, including model-free approaches for power systems, environmental monitoring, and quantum control. Current projects include real-time data assimilation in harsh environments, resilient robotics in GPS-denied conditions, and SAR with small satellites. He co-directs the Autonomous and Intelligent Systems Research Laboratory (AIRLab) and is part of the CREATE center. Recognition: 2024 NSF CAREER Award for work on safety-aware data-driven control frameworks Teaching & Advising: He teaches courses in estimation theory, control systems, and signal processing. Advises a team of graduate and undergraduate students focusing on autonomy, robotics, and control systems. Notable students include Danial Waleed (Ph.D. 2024), Jacob Friz-Trillo (M.S. 2025), and Farnaz Boudaghi (Ph.D. candidate). Labs & Collaborations: AIRLab: Focuses on data-driven control for autonomy in robotics and engineered systems CREATE: Research on resilient energy and autonomous technologies
Xujie Si is an Assistant Professor in the Department of Computer Science at the University of Toronto. He is also a faculty affiliate at the Vector Institute and an affiliate member at Mila - Quebec AI Institute, holding a Canada CIFAR AI Chair. Previously, he served as an Assistant Professor at McGill University's School of Computer Science. Education: Ph.D., Computer and Information Science, University of Pennsylvania (advised by Mayur Naik) M.S., Computer Science, Vanderbilt University B.E. (with Honors), Nankai University Research Focus: His work bridges AI and program reasoning, emphasizing the integration of statistical and logical methods. Key areas include: Static analysis and verification using deep learning/reinforcement learning Neuro-symbolic systems for urban simulation (e.g., LogiCity) Automated theorem proving via LLMs and symbolic reasoning Program repair and compiler fuzzing Recent Article Trends: Recent work focuses on synergizing LLMs with symbolic reasoning (e.g., Olympiad inequality proving), advancing SAT solving with graph neural networks, and applying neuro-symbolic methods to Euclidean geometry formalization. Awards: Canada CIFAR AI Chair (2023) Lab/Teams: Leads research teams exploring program analysis, neuro-symbolic AI, and formal verification at the University of Toronto and Vector Institute.
Ken Wong is an Associate Professor in the Department of Computing Science at the University of Alberta's Faculty of Science. He also serves as Associate Chair within the same department. Holding a PhD in Computer Science from the University of Victoria (1999), his research focuses on software engineering challenges such as reverse engineering, program understanding, and software visualization. He emphasizes improving software evolution through tools like architecture recovery and root cause analysis, with applications in web/mobile platforms and diverse system understanding. Teaching highlights include developing Massive Open Online Courses (MOOCs) via Coursera, including the 'Software Product Management Specialization' and courses on Agile practices, client needs analysis, and software metrics. His recent publications (2023–2025) span AI-driven healthcare innovations (e.g., medical imaging, photoacoustic tomography) and advanced computer vision techniques (e.g., diffusion models, video inpainting). Notable collaborations include EVAREST studies on heart failure management and lung transplantation outcomes. His work bridges software engineering theory and practical applications in healthcare technology, with contributions to federated learning frameworks (e.g., FedLPPA) and AI-augmented clinical decision support systems. Research also extends to autonomous driving (DriveGPT4-V2) and 3D human avatar generation (DreamAvatar), showcasing interdisciplinary impact.
