جست‌وجوی استاد و پژوهشگر

Burkhard Schipperمشاهده پروفایل

استاد

• Game Theory
• Microeconomic Theory
• Experimental Economics
• +۴ مورد دیگر

Burkhard Schipper serves as Professor of Economics at the University of California, Davis, with affiliated status in the Graduate Group of Applied Mathematics. His academic trajectory spans over two decades, establishing him as a leading theorist in strategic decision-making frameworks with applications across economics, finance, and political science. His educational foundation includes: Diplom-Volkswirt, University of Bonn (2000) Dr. rer. pol., Economics, University of Bonn, European Doctoral Program (2003) Schiper's research program centers on game theory, microeconomic theory, and experimental economics, with pioneering contributions to modeling unawareness in strategic interactions. His theoretical work develops formal frameworks for agents operating with incomplete awareness of game structures, while his experimental research investigates biological determinants of economic behavior, particularly steroid hormones' influence on risk attitudes and competitive bidding. This dual approach bridges abstract theory with empirical validation, yielding insights applicable to financial markets, policy design, and organizational behavior. Recent publications (2022-2025) demonstrate consistent focus on unawareness extensions across diverse contexts—from macroeconomic policy to auction design—while maintaining empirical rigor through experimental methods. His work shows increasing integration of biological variables with strategic models, reflecting interdisciplinary innovation in economic theory. His scientific recognition includes: UC Davis Hellman Fellowship (2009-10) Young Economist Award, European Economic Association (2003) Schiper secures major research funding from the Army Research Office and National Science Foundation, supporting his unawareness modeling and experimental programs. As Editor-in-Chief of the B.E. Journal in Theoretical Economics and Associate Editor of Mathematical Social Sciences, he actively shapes scholarly discourse. His teaching encompasses undergraduate and doctoral courses in microeconomics and game theory, transmitting advanced theoretical frameworks to new generations of economists. Though not explicitly detailed in source materials, his research likely operates through UC Davis' experimental economics laboratories with interdisciplinary collaboration across the Graduate Group of Applied Mathematics.

Roberto Tronمشاهده پروفایل

استادیار

• Control Theory
• Robotics
• Computer Vision
• +۴ مورد دیگر

Roberto Tron is an Assistant Professor in the Mechanical Engineering and Systems Engineering departments at the Boston University College of Engineering , with his office located at 110 Cummington Mall. His research integrates control theory, robotics, and computer vision to solve complex multi-agent coordination problems. His primary research interests focus on Riemannian geometry applications , distributed multi-agent systems , and safety-critical control . Key methodologies include Control Barrier Functions (CBFs), Riemannian optimization, and distributed consensus algorithms, with applications spanning autonomous aerial vehicles, robotic manipulation, and multi-robot security systems. Analysis of his recent publications reveals a strong emphasis on safety verification and real-time optimization for autonomous systems. His work consistently bridges theoretical foundations in nonlinear control with practical implementations in robotics, particularly addressing challenges in limited sensor fields of view, distributed task allocation, and noise-robust navigation. The research shows increasing integration of formal methods like Signal Temporal Logic with learning-based approaches. Tron received his Ph.D. from The John Hopkins University and previously conducted post-doctoral research at the GRASP Lab, University of Pennsylvania. His work demonstrates significant contributions to provably safe autonomous systems through frameworks like the Control Barrier Function Toolbox.

• Business Process Management
• Declarative Process Modeling
• Hybrid Process Notations
• +۵ مورد دیگر

Tijs Slaats is an Associate Professor in the Software, Data, People & Society section at the Department of Computer Science, University of Copenhagen. His research focuses on Business Process Management with particular emphasis on declarative and hybrid process notations to provide flexible workflow support for knowledge workers, funded by the Danish Council for Independent Research. His educational background includes: M.Sc. in Information Technology from IT University of Copenhagen Ph.D. in Computer Science from IT University of Copenhagen (supervised by Thomas Hildebrandt) Dr. Slaats specializes in declarative process modeling, particularly Dynamic Condition Response (DCR) graphs which enable flexible workflow systems. His work bridges theoretical foundations with practical applications in cross-organizational settings. Recent research extends into blockchain technologies, smart contracts, and applications in sensitive domains like asylum processing and refugee law. He combines formal methods with empirical evaluation to ensure both correctness and usability of workflow systems. His publication pattern shows increasing application of process mining techniques to blockchain technologies and socially impactful domains, while maintaining core research in Business Process Management. Notable trends include integration of DCR graphs with smart contracts, privacy-preserving techniques for asylum data, and object-centric approaches to process discovery. Research funding includes: Hybrid Business Process Management Technologies project (Danish Council for Independent Research) Technologies for Flexible Cross-organizational Case Management Systems (FLExCMS) industrial Ph.D. project Alongside his academic work, Dr. Slaats maintains strong industry connections through Exformatics A/S where he developed the DCR Graphs workflow solution (www.dcrgraphs.net), and previously worked as a software engineer in the Dutch e-commerce sector. His dual expertise in academia and industry ensures his research addresses real-world workflow challenges while maintaining theoretical rigor.

• Hydrology
• Water Engineering
• Flood Frequency Analysis
• +۲ مورد دیگر

Luigi Cafiero is a PhD candidate in Civil and Environmental Engineering at the Polytechnic University of Turin (Politecnico di Torino), currently in his 38th cycle (2022-2025). Affiliated with the Department of Environmental, Land and Infrastructure Engineering (DIATI) and serving as an external lecturer at the Interuniversity Department of Regional and Urban Studies and Planning (DIST), his doctoral research focuses on non-stationary flood frequency analysis under climate change conditions, specifically developing operational procedures for space-time estimation of flood quantiles. His academic background includes: Bachelor of Science in Mechanical Engineering, Università degli studi di Genova, 2019 Master of Science in Environmental Engineering (Climate Change specialization), 2022, with thesis on Idai cyclone characterization in Beira, Mozambique, developed within the interdisciplinary lab "After the Storm. Designing post-cyclone strategies for the city of Beira, Mozambique" Research interests span Hydrology and Water Engineering , with concentrated expertise in non-stationary flood frequency analysis, climate change impacts on hydrological extremes, and hydrometeorological risk assessment. His work integrates advanced statistical methods with practical flood risk management applications, particularly addressing precipitation extremes in changing climates. Analysis of his 12 publications (2022-2025) reveals a consistent focus on climate-driven flood frequency modeling, featuring quantile-mapping techniques for precipitation-flood relationships, regional flood modeling in the Po River basin, and case studies of extreme events like the Idai cyclone. His research bridges theoretical hydrology with actionable flood risk adaptation strategies. Scientific Awards: None listed in available information. Advising and Grants: As a current PhD candidate, no formal advisees are documented. No grant information is provided in the source materials. Labs and Teams: Previously engaged with the "After the Storm" interdisciplinary lab for Mozambique cyclone recovery strategies. Currently collaborates with Alberto Viglione (PhD supervisor), Miriam Bertola, Paola Mazzoglio, Francesco Laio, and Günter Blöschl on flood frequency research at Politecnico di Torino.

Chunyu Dongمشاهده پروفایل

پژوهشگر

• Ecohydrology
• Snow Hydrology
• Climate Change Impacts
• +۵ مورد دیگر

Chunyu Dong is a Postdoctoral Scholar at the University of California, Los Angeles (UCLA), affiliated with both the Department of Geography and the Institute of the Environment and Sustainability. His research focuses on ecohydrology and snow hydrology, particularly examining climate change impacts on water cycles and terrestrial ecosystems at multiple scales. Dong employs innovative methodologies including remote sensing, hydrological modeling, and time-lapse photography to analyze snow processes and vegetation responses to drought. Ph.D. in Hydrology and Climatology (Heidelberg University, 2016) Developed cloud-free MODIS snow data algorithms Contributed to time-lapse snow monitoring techniques His current work involves interdisciplinary collaboration with Professors Glen MacDonald, Gregory Okin, and Thomas Gillespie on drought impacts in southern California. Projects include analyzing vegetation sensitivity to drought, wildfire-vegetation-climate interactions, and energy infrastructure resilience. Despite lacking formal faculty rank, his research bridges environmental science, climatology, and geographic information systems.

Emily Firstمشاهده پروفایل

استادیار

• Artificial Intelligence
• Theorem Proving
• Machine Learning
• +۳ مورد دیگر

Emily First is an incoming Assistant Professor in Computer Science at Rutgers University New Brunswick starting Fall 2025. She previously served as a postdoctoral researcher at UC San Diego under Sorin Lerner and earned her PhD in Computer Science at UMass Amherst under Yuriy Brun in the Laboratory for Software Engineering Research (LASER). Her research focuses on leveraging AI for theorem proving, working at the intersection of machine learning, software engineering, and programming languages. She specializes in creating tools for automated proof generation in proof assistants like Coq, Isabelle/HOL, and Lean. Her work explores how AI can enhance human reasoning across domains, with applications in software verification and formal logic systems. Recent publications show trends in neuro-symbolic AI, software verification, and LLM integration for formal methods. Her team's research has been recognized with ACM SIGSOFT Distinguished Paper Awards at ICSE, ACL, and ESEC/FSE conferences, along with workshop presentations at AI and Theorem Proving conferences. ACM SIGSOFT Distinguished Paper Award (ICSE 2025) ACM SIGSOFT Distinguished Paper Award (ACL Main 2024) ACM SIGSOFT Distinguished Paper Award (ESEC/FSE 2023) ACM SIGSOFT Distinguished Paper Award (ICSE 2022) Contact: emfirst@ucsd.edu (new email coming soon).

Vicky Dierckxمشاهده پروفایل

استاد مدعو

• Well-being
• Goal setting and motivation
• Cognitive Psychology
• +۴ مورد دیگر

Vicky Dierckx is an Adjunct Professor in the Marketing Department at Virginia Tech’s Pamplin College of Business. She holds a Ph.D. in Psychological Science from Ghent University (Belgium, 2001) and advanced degrees in Artificial Intelligence and Psychology from KULeuven, Belgium. Her academic work focuses on cognitive processes, particularly mental models in reasoning, and has transitioned to well-being education post-2015 U.S. relocation. Education: Ph.D. in Psychological Science (2001), Ghent University MA in Artificial Intelligence (1997), KULeuven MA in Psychology (1996), KULeuven Teaching degree (1996), KULeuven Research Interests: Combines cognitive science expertise with contemporary well-being pedagogy. Specializes in mental model theory’s application to reasoning, goal-setting, and professional success. Active in designing well-being curricula for university and corporate audiences. Teaching & Outreach: Developed well-being programs for Virginia Tech’s Rec Sports and Lifelong Learning Institute, plus workshops for local governments and organizations like VT-ARC. Over 20 years of academic and applied experience in cognitive psychology and wellness education. Grants & Advising: No formal grants or advising roles listed, but extensive collaborative teaching engagements. Publications span cognitive psychology, mental models theory, and temporal reasoning dynamics.

• Information Systems
• Artificial Intelligence
• Data Visualization
• +۴ مورد دیگر

Peter McBrien is an Associate Professor in the Department of Computing at Imperial College London, affiliated with the Faculty of Engineering. He holds dual affiliations with the Distributed Software Engineering group. His research focuses on conceptual modeling, ontology engineering, database systems, and temporal data management. He has been active in advancing techniques for data visualization, semantic web technologies, and relational database integration. Key research areas include: Ontology extraction from relational databases Type inference in transactional systems Schema transformation between heterogeneous models Peer-to-peer data integration protocols Temporal database systems His publication trends emphasize integration of heterogeneous data sources through formal methods, with notable contributions to OWL ontology implementation, hypergraph data models, and benchmarking big data query languages. Recent work focuses on Spark-based semantic reasoning and distributed knowledge exchange systems. Peter McBrien's research has been supported through Imperial College's infrastructure, with ongoing contributions to the AutoMed data integration framework and RoDEx protocols for unreliable networks. His work bridges theoretical foundations of data management with practical implementations in distributed systems.

• Computation Theory and Mathematics
• Computer Software
• Blockchain Technology
• +۳ مورد دیگر

Ben Livshits is a Reader in Computing at Imperial College London's Department of Computing, part of the Faculty of Engineering. His research focuses on computation theory, computer software, and privacy-preserving technologies in blockchain systems. He holds a Ph.D. from Stanford University. Education: Ph.D. in Computer Science, Stanford University, 2007 Research Interests: Livshits specializes in cryptographic protocols for decentralized systems, ZK-Rollups optimization, MEV analysis, and privacy-enhancing technologies. His work bridges theoretical computer science with practical applications in blockchain scalability and security. Recent Research Trends: His 2024-2025 work emphasizes ZK-proof systems (e.g., zk-bench), MEV mitigation strategies, and formal foundations for rollups. He also explores community-driven verification (CrowdProve) and governance mechanisms for DeFi. Affiliations: He is affiliated with the Centre for Cryptocurrency Research and Engineering, contributing to interdisciplinary blockchain research. Grants & Labs: While specific grants are not listed, his work suggests involvement in Imperial's distributed systems and blockchain initiatives.

• Algorithms
• Theoretical Computer Science
• Programming Languages
• +۶ مورد دیگر

Ben Rank is a Researcher at the Max Planck Institute for Software Systems (MPI-SWS), focusing on foundational aspects of computer science with a strong emphasis on theoretical rigor and practical applications. His work bridges multiple domains including algorithms, formal verification, and cyber-physical systems. Research interests include: Algorithmic methods in programming languages and verification Cyber-physical systems design and analysis Security and privacy in distributed systems Social and information systems integration Recent publications explore cutting-edge topics such as adaptive reinforcement learning in shifting environments and ethical considerations in AI systems. No scientific awards or grants are explicitly mentioned in the provided materials.

• Cybersecurity
• IoT Security
• Network Anomaly Detection
• +۵ مورد دیگر

Gabriela F. Ciocarlie is a Researcher at SRI International, focusing on advancing cybersecurity, IoT security, and formal verification techniques. Her work bridges theoretical computer science with practical applications in critical infrastructure protection and manufacturing systems. She has contributed to over 48 publications across conferences like CCS, NDSS, and IEEE venues. Her research interests span adversarial machine learning, secure manufacturing automation, and resilient biomanufacturing systems. Notable projects include developing frameworks for verifying manufacturing design integrity and creating end-to-end security solutions for cyber-physical systems. She has also pioneered work on deployable adversarial attacks against neural networks and automated attack investigation tools like autoMPI. Key collaborations include partnerships with institutions like Columbia University (former affiliation) and industry leaders. Her work often addresses real-world challenges such as pandemic-resilient biomanufacturing and securing critical infrastructure through cyber-physical integration.

Robert Bateyمشاهده پروفایل

استاد

• Biological Chemistry
• Organic Chemistry
• Organoboron Chemistry
• +۷ مورد دیگر

Robert Batey is a Full Professor in the Department of Chemistry at the University of Toronto, serving as Co-Director of the Acceleration Consortium's Medicinal Chemistry Self-Driving Lab (SDL) and former Chair of the Department (2013–2023). His research focuses on organic synthesis, medicinal chemistry, and applications in drug discovery, with a particular emphasis on organoboron chemistry and protease inhibitors. He holds a B.A. from Oxford University and a Ph.D. from Imperial College London. His work bridges synthetic methods, structural analysis, and computational science, including collaborations on machine learning-driven molecule design. Key research areas include: synthetic methods using organoboron compounds, combinatorial chemistry, hetero Diels-Alder reactions, and the development of small molecules targeting ClpP proteases and ubiquitin pathways. His group has synthesized natural products like himeic acid A, kitastatin, and martinelline, as well as novel antibiotics and PROTACs. Recent publications highlight advancements in AI-driven drug design (e.g., GFlowNets for molecular generation), structure-based antibiotics development, and PROTAC complex structure prediction. Awards include the CSC Merck-Frosst Award and multiple teaching/research accolades. Awards: CSC Merck-Frosst Award, Merck Academic Development Award, Bio-Méga/Boehringer Ingelheim Young Investigator Award, and teaching excellence recognition. Advising: Active mentorship of graduate students and postdocs in organic chemistry, with a focus on independent research and problem-solving. Labs: Located in Lash Miller Building (3rd floor), including North Lab (LM 371) and South Lab (LM 370).

Srikanth Rangarajanمشاهده پروفایل

استادیار

• Energy Storage Management
• Thermal Management Systems
• Electronics Packaging
• +۷ مورد دیگر

Srikanth Rangarajan is an Assistant Professor at Binghamton University's School of Systems Science and Industrial Engineering. He holds a PhD and MS from the Indian Institute of Technology Madras (2017) and a BE from Anna University Chennai (2011). His research focuses on energy storage systems, thermal management of electronics, battery optimization, and digital twinning. He previously served as an Associate Research Professor in Mechanical Engineering at Binghamton under Bahgat Sammakia. Rangarajan authored the book Phase Change Material Heat Sinks: A multi-objective Perspective and holds a patent for a rotatable heat sink design. His teaching includes optimization techniques, thermal modeling, and neural networks. Recent work explores virus spread modeling via genetic algorithms, with a preprint under review in Journal of Healthcare Informatics . He has received multiple awards including an Institute Post-Doctoral Fellowship and Research Assistantships from the Indian government. His research bridges thermal engineering with advanced manufacturing and sustainability, addressing challenges in high-power electronics and data center cooling. Education: BE in Mechanical Engineering, Anna University (2011) MS in Thermal Engineering, IIT Madras (2017) PhD in Heat Transfer, IIT Madras (2017) Research Interests: Digital twin systems for battery optimization Thermal energy storage design Advanced electronics packaging Data center cooling innovations Phase change material composites His recent articles highlight cooling solutions for high-density electronics, battery recycling challenges, and predictive models for epidemiological patterns using computational methods. Ongoing work includes embedded cooling technologies for heterogeneous integrated circuits and sustainable thermal management strategies. Awards: Patent: Rotatable Heat Sink (Government of India) Institute Post-Doctoral Fellowship (IIT Madras, 2017) Research Associate, Divecha Centre (IISc, 2017) Half-Time Research Assistantship (MHRD, 2012-2013) Advising & Grants: While no formal advisees are listed, his prior roles indicate involvement in mentorship. His research has been supported by institutional grants including those from the Indian Ministry of Human Resource Development. Labs/Teams: Active in Binghamton's Systems Science and Industrial Engineering lab, collaborating on thermal management and additive manufacturing projects.

Daniel Chaمشاهده پروفایل

استاد

• Environmental Engineering
• Bioremediation
• Waste Management
• +۳ مورد دیگر

Professor Daniel Cha holds a faculty position in the Department of Civil and Environmental Engineering at the University of Delaware. He earned his Ph.D. from the University of California, Berkeley, followed by a Master's from the University of British Columbia and a Bachelor's from McGill University. His academic and industrial career spans over three decades with expertise in environmental biological processes, microbial community dynamics, and sustainable waste management solutions. Dr. Cha has worked as a consulting engineer and held roles at the Sacramento Regional Wastewater Treatment Plant. His research focuses on microbial-driven systems, including energy/fertilizer recovery from food waste, biocomposite material synthesis from wastewater microorganisms, and genomic analysis of mixed culture microbial communities. Key areas also include zerovalent iron-based water treatment technologies, sulfate reduction for acid mine drainage mitigation, and innovative fecal sludge management systems in urban contexts. Notable projects include field demonstrations of breathable membrane toilets in India and pilot-scale bioretention systems integrating biochar. His work bridges environmental chemistry with engineering solutions, addressing contaminants like perchlorate, energetic compounds, and heavy metals in industrial wastewater. Publications span advanced oxidation processes, microbial ecology, and novel membrane technologies. While no formal awards are listed, his extensive patent portfolio includes systems for treating energetic compound wastewaters and biodegradation enhancement methods. His lab collaborates on projects involving both academic and industrial partners, emphasizing scalable environmental engineering solutions.

Kuldeep S. Meelمشاهده پروفایل

دانشیار

• Automated Reasoning
• Formal Methods
• Artificial Intelligence
• +۳ مورد دیگر

Kuldeep S. Meel is the Stephen Fleming Early-Career Associate Professor at Georgia Institute of Technology's School of Computer Science and an Associate Professor at the University of Toronto (currently on leave). His research focuses on the intersection of Formal Methods and Artificial Intelligence, emphasizing scalable automated reasoning techniques. He holds prestigious awards including the 2022 ACP Early Career Researcher Award and the 2019 NRF Fellowship for AI. His work has been recognized with multiple best paper awards at conferences like ICLP, CAV, and IJCAI. Meel's research spans automated reasoning, formal methods, and their applications in AI. He has developed influential tools like ApproxMC and UniGen, advancing model counting and uniform sampling. His academic journey includes roles at NUS and collaborations with institutions globally. Teaching excellence is highlighted by NUS Annual Teaching Awards (2022, 2023). Awards include Distinguished Paper Awards at CAV-23 and CAV-24, and 1st place in Model Counting Competitions. His lab has produced notable advisees securing tenure-track positions worldwide. Current projects explore distribution testing, probabilistic reasoning, and AI verification.