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Mo AddaView profile

Senior Lecturer

• Network Security
• Distributed Systems
• Wireless Networks
• +5 more

Dr. Mo Adda is a Principal Lecturer at the University of Portsmouth's School of Computing, part of the Faculty of Technology. He holds a PhD in Distributed Systems and Parallel Processing from the University of Surrey. His research focuses on network security, distributed systems, wireless networks, and cybercrime prevention. He leads projects in the Centre for Cybercrime and Economic Crime, exploring fault management in networks, blockchain applications, and IoT forensics. With 16 supervised theses, he advises on topics like energy-efficient cloud systems and machine learning for environmental modeling. His work bridges academia and industry, addressing challenges in software-defined networks, traffic control, and secure data sharing in social networks. Education: PhD in Distributed Systems (University of Surrey) Affiliations: Centre of Excellence in Defence, Risk & Resilience; Portsmouth Centre for Advanced Materials and Manufacturing Research Interests Dr. Adda's research spans: Network security and fault detection mechanisms Blockchain applications in IoT and forensics Energy-efficient cloud data center optimization Machine learning for climate modeling Self-organizing network protocols Grants & Collaborations His projects include collaborations with industry partners on secure data leakage detection in cloud systems and resilient wireless protocols for harsh environments. He has pioneered fault classification systems using clustering algorithms and fuzzy logic. Labs & Teams He contributes to the Centre for Cybercrime and Economic Crime, focusing on digital forensics and network intrusion analysis. His team develops frameworks for privacy management in social networks and proactive routing in software-defined networks.

Adriana SchulzView profile

Assistant Professor

• Computational Design
• Computer-Aided Design (CAD)
• Digital Fabrication
• +4 more

Adriana Schulz is an Assistant Professor in the Department of Computer Science & Engineering at the University of Washington's College of Engineering. She leads a research group focused on computational design, computer-aided design (CAD), and digital fabrication. Her work bridges computer science with practical applications in manufacturing, robotics, and sustainable design. Dr. Schulz received her Ph.D. in Computer Science from MIT in 2018 under the supervision of Professor Wojciech Matusik. Prior to her doctoral studies, she earned a Master's degree in Mathematics from IMPA (Instituto Nacional de Matemática Pura e Aplicada) in Rio de Janeiro, where she worked with Professor Luiz Velho, and a Bachelor's degree in Electronics Engineering from UFRJ (Federal University of Rio de Janeiro). Her research interests center around computational tools that enhance design and manufacturing processes. She develops novel algorithms for CAD systems, computational fabrication techniques, and sustainable design approaches. Her work spans multiple domains including robotics, textiles, electronics, and architecture, with a strong emphasis on creating practical tools that designers and engineers can use in real-world applications. She explores how machine learning, particularly neurosymbolic approaches, can improve design workflows and enable new capabilities in computational design systems. Analysis of her recent publications reveals a strong trend toward more intelligent and user-centered design tools. Her research increasingly integrates machine learning with traditional CAD systems to create more intuitive interfaces, supports sustainable design practices with computational tools, and develops novel fabrication techniques that push the boundaries of what's possible with digital manufacturing. She has made significant contributions to zero-waste fashion design, immersion cooling for high-performance computing, and CAD program understanding through novel representation learning techniques. Innovators Under 35 - MIT Technology Review Bolsa Aluno Nota 10 from FAPERJ Engineer 20000 award Dr. Schulz actively mentors several PhD students and postdoctoral researchers, including Haisen Zhao, Ben Jones, Yuxuan Mei, and others, often in collaboration with colleagues across different departments. Her research has attracted significant media attention, with coverage in major outlets including MIT News, BBC, IEEE Spectrum, Wired, and TechCrunch. Her work on Interactive Robogami was noted as the most read article in the International Journal of Robotics Research in its publication year. She leads a vibrant research group at the University of Washington that focuses on computational design systems, with particular emphasis on creating tools that bridge the gap between digital design and physical fabrication. Her team develops novel algorithms for CAD systems, computational fabrication techniques, and sustainable design approaches that have practical applications across multiple industries.

• Language Learning and Teaching
• Observational Research
• Educational Research
• +7 more

Dr. Nienke Smit is an Assistant Professor in the Department of Education at Utrecht University's Faculty of Social and Behavioural Sciences. Her research focuses on scaffolding processes, adaptive teaching, and classroom observational research from a complex dynamic systems perspective. Specializes in teacher-student interaction Expert in adaptive instruction and observational methods Collaborates with Data Science department Develops tools for classroom observation Research interests include language learning and teaching , judgment and decision-making in education , and professional development of teachers . She leads projects like Eye Get It (joint attention in classrooms) and a replication study on adaptive instruction effectiveness. Her work applies dynamic systems theory to second language development and foreign language pedagogy. Recent publications address translanguaging in multilingual classrooms, replication studies , and process-oriented research . She teaches at Utrecht's Graduate School of Teaching and works on initiatives to reduce teacher shortages through improved training programs.

Michael MoorView profile

Assistant Professor

• Medical AI
• Machine Learning in Healthcare
• Causal Learning
• +3 more

Prof. Michael Moor is a tenure-track Assistant Professor for Medical AI at ETH Zurich's Department of Biosystems Science and Engineering in Basel. Previously, he conducted postdoctoral research at Stanford University under Prof. Jure Leskovec, focusing on medical foundation models. His work spans causal learning, multimodal AI, and sepsis prediction. Moor holds an MD from the University of Basel and a PhD from ETH Zurich's Machine Learning and Computational Biology Lab under Prof. Karsten Borgwardt. Research Interests: Generalist medical AI models Multimodal medical reasoning Zero-shot and few-shot learning Clinical causal inference Retrieval-augmented language models Sepsis prediction systems Key Achievements: Published foundational work in Nature (2023) on generalist medical AI Developed Med-Flamingo multimodal model (2023) Zero-shot causal learning framework accepted to NeurIPS 2023 (Spotlight) International sepsis prediction study with 156k ICU patients (2023) Lab Activities: Leads ETH's Medical Foundation Models group, collaborating with Stanford HAI and NASA Ames. Active in creating medical AI benchmarks like AgentClinic and developing retrieval-augmented systems like Almanac.

Michael BaldeaView profile

Associate Professor

• Process Systems Engineering
• Energy Systems
• Nonlinear Optimization
• +3 more

Michael Baldea is an Associate Professor in the Department of Chemical Engineering at the University of Texas at Austin . He holds a Ph.D. in Chemical Engineering from the University of Minnesota (2006), with prior degrees from 'Babeş-Bolyai' University in Romania (M.Sc. 2001, Diploma 2000). His research group develops theoretical and computational methods for Process and Energy Systems Engineering , focusing on integrated decision-making, performance optimization, and process intensification with industrial validation. Education: Ph.D., Chemical Engineering, University of Minnesota (2006) M.Sc., Interface Process Engineering, 'Babeş-Bolyai' University (2001) Diploma, Chemical Engineering, 'Babeş-Bolyai' University (2000) Research Thrusts: Integrated decision-making in chemical/energy supply chains Process performance monitoring and optimization Process integration and intensification Key applications include grid-responsive chemical plants, intensified distillation/column designs, and renewable energy integration for building systems. Scientific Awards: Frank A. Liddell, Jr. Fellowship NSF CAREER Award (2015-2020) Moncrief Grand Challenges Faculty Award (2014) AIChE Outstanding Young Researcher Award (2017) Implementation : His group has translated research into commercial tools through partnerships with industrial test beds and is working to integrate methods into commercial simulators. They explore predictive approaches for building energy management and strategic capital investment analysis in next-generation energy systems.

• Urban Planning
• Land-Use Regulation
• Urban Development
• +7 more

Raphaël Fischler is a Full Professor and former Dean of the Faculty of Urban Planning and Landscape Architecture at the University of Montreal. He holds dual expertise in urban planning theory, history, and practice, with a focus on North American urban development, land-use regulation, and governance. His academic roles include teaching at McGill University and serving as Dean from 2018–2023, during which he led strategic initiatives like the Quartier de l’Innovation and addressed urban challenges such as housing affordability and metropolitan governance. **Education:** PhD in Urban Planning, UC Berkeley (1993) Master of City Planning, MIT (1987) Master of Science in Architecture, MIT (1987) Bachelor of Engineering, Eindhoven University of Technology (1984) **Research Interests:** Fischler’s work explores urban planning’s historical foundations, particularly zoning’s evolution, and integrates reflective practice into pedagogy. He advises on municipal projects and contributes to policy debates on density, public participation, and sustainable urbanism. His research bridges academia and practice, emphasizing ethical planning and community engagement. **Articles Trends:** His recent publications analyze zoning’s historical role, social justice in urban redevelopment, and governance challenges in Montréal. He critiques systemic issues like housing affordability and advocates for equitable urban policies. **Awards & Recognition:** Fellow of the Canadian Institute of Planners (2020) Émérite Member, Order of Quebec Urban Planners (2014) McGill Principal’s Excellence in Teaching Award (2013) **Grants & Teams:** He collaborates with institutions like the Centre de recherche interdisciplinaire en études montréalaises (CRIEM) and advises public agencies on metropolitan development. His work spans academic leadership, professional practice, and public advocacy, addressing urban challenges through interdisciplinary approaches. **Labs/Teams:** Affiliated with the CRIEM , focusing on Montréal’s urban history and contemporary planning issues. He mentors students in urban policy and design through studio courses and thesis supervision.

• Optimization
• Complex Networks
• Statistical Inference
• +3 more

Stefano NASINI is an Associate Professor at the University of Lille 3, specializing in Quantitative Methods within the Economics and Mathematics Sciences. He holds a HDR (Habilitation à Diriger des Recherches) from the University of Lille 3 (2021), a Ph.D. in Statistics and Operations Research from the Polytechnic University of Catalonia (2015), and a Master in Statistics (2011). His research focuses on optimization, complex networks, statistical inference, and microeconomic applications. He has held academic positions including a post-doctoral role at IESE Business School (2014–2016) and a visiting researcher role at the University of Lisbon (2014). His work spans scheduling optimization, network analysis, financial contagion modeling, and energy system planning. Key contributions include specialized algorithms for large-scale optimization problems and frameworks for decentralized portfolio management. He is a member of the LEM research group and teaches courses in optimization, econometrics, and social network analysis at the Grande École and MSc levels. Publications highlight interdisciplinary applications, including network-based diffusion models, multi-market financial strategies, and dynamic choice analysis. His research bridges theoretical advancements in operations research with practical challenges in economics, energy, and transportation systems. No scientific awards are explicitly listed in the provided materials. His advising roles and grants are not detailed here, but his extensive publication record reflects active collaboration within academic and applied domains.

Anne JENYView profile

Professor

• Accounting
• Auditing
• Financial Reporting
• +5 more

Professor Anne JENY is a Full Professor at IÉSEG School of Management, France, specializing in accounting and auditing. She holds a Ph.D. in Accounting from HEC Paris Business School (2003) and Master's degrees in Audit, Finance, and Economics from the University of Paris Dauphine (1995-1996). Her academic career includes professorships at IÉSEG (2021–present) and ESSEC Business School (2002–2020). Her research focuses on intangible assets valuation , fair value measurement , IFRS adoption dynamics , and transparency in financial reporting . She has published widely on topics such as non-audit services' impact on debt costs, innovation's interplay with accounting systems, and gender disparities in accounting professions. Her work integrates empirical analysis with policy implications, particularly in digital economy contexts and public-private partnerships. A member of the LEM (Laboratory of Economy and Management), she has authored books like Les 12 travaux de l'analyste financier (2021) and contributed to international journals such as Accounting in Europe and European Accounting Review . Her research bridges theoretical frameworks with practical insights into corporate governance and regulatory challenges.

Paul MohaiView profile

Professor

• Environmental Justice
• Public Health Impacts
• Racial and Socioeconomic Disparities
• +3 more

Paul Mohai is a Professor at the University of Michigan’s School of Natural Resources and Environment (SNRE). His work focuses on environmental justice, racial and socioeconomic disparities in pollution exposure, and policy development. He has contributed extensively to understanding historical and contemporary patterns of environmental inequity, particularly regarding hazardous waste siting and school proximity to industrial hazards. Mohai’s research bridges community-based activism, quantitative analysis, and policy advocacy. Education and Affiliations: Affiliated with the Program in the Environment at the University of Michigan. Research Interests: Mohai’s scholarship addresses how marginalized communities bear disproportionate environmental burdens, including air and water pollution, and advocates for equitable policy solutions. He has analyzed longitudinal data to assess demographic changes around hazardous facilities and collaborated with communities to design environmental screening tools. His work emphasizes the intersection of race, class, and environmental health outcomes. Publications: Mohai’s recent work includes studies on cumulative impacts of pollution, Michigan school siting guidelines, and the Flint water crisis. His articles span journals and conference proceedings, highlighting interdisciplinary approaches to environmental justice. Awards & Grants: No specific awards are listed, but his extensive publication record reflects sustained academic contribution. His grants likely support community-engaged research on environmental disparities. Labs/Teams: Collaborations include the Program in the Environment and the Michigan Environmental Justice Coalition.

Danica KragicView profile

Professor

• Robotics
• Computer Vision
• Machine Learning
• +3 more

Danica Kragic is a Professor of Computer Science at the School of Electrical Engineering and Computer Science at the Royal Institute of Technology (KTH) in Stockholm, Sweden. She serves as the Director of the Centre for Autonomous Systems and leads the Robotics, Perception and Learning Lab at KTH. Her research focuses on advancing robotics capabilities through computer vision and machine learning approaches. MSc in Mechanical Engineering from the Technical University of Rijeka, Croatia (1995) PhD in Computer Science from KTH (2001) Professor Kragic's research primarily centers on robotics, computer vision, and machine learning, with particular emphasis on robotic manipulation, grasp planning, and human-robot interaction. Her work bridges theoretical foundations with practical applications, exploring how robots can understand and interact with objects in complex environments. She investigates how visual and tactile sensing can be integrated to improve robotic perception and manipulation capabilities, with applications ranging from industrial automation to assistive robotics. Her recent publications demonstrate a strong focus on advanced grasp planning techniques, tactile sensing for manipulation, and mathematical representations for robotic control. Kragic's research shows increasing integration of machine learning approaches with traditional robotics frameworks, particularly in the areas of grasp synthesis, object recognition, and human-robot collaboration. Her work spans theoretical contributions in mathematical representations of grasps to practical implementations of robotic systems capable of adapting to novel objects and situations. 2007 IEEE Robotics and Automation Society Early Academic Career Award IEEE Fellow ERC Starting Grant (2012) Member of The Royal Swedish Academy of Sciences Member of The Royal Swedish Academy of Engineering Sciences Honorary Doctorate from Lappeenranta University of Technology Professor Kragic's research has been supported by major funding bodies including the EU, Knut and Alice Wallenberg Foundation, Swedish Foundation for Strategic Research, and Swedish Research Council. While specific student names aren't listed in the provided information, her publication record suggests extensive mentorship of PhD students and postdoctoral researchers in robotics and computer vision. Her lab, the Robotics, Perception and Learning Lab, serves as a hub for interdisciplinary research connecting computer science, engineering, and cognitive science perspectives on robotic systems. As Director of the Centre for Autonomous Systems at KTH, Kragic oversees a major research initiative focused on advancing autonomous technologies. Her Robotics, Perception and Learning Lab brings together researchers working on visual perception, machine learning, and robotic manipulation, with particular emphasis on developing systems that can understand and interact with objects in unstructured environments. The lab's work spans theoretical foundations of robotic manipulation to practical implementations of systems capable of learning from experience.

Fabian SuchanekView profile

Professor

• Artificial Intelligence
• Knowledge Bases
• Natural Language Processing
• +3 more

Fabian Suchanek is a full professor at Institut Polytechnique de Paris, specifically affiliated with Télécom Paris. He leads research in the Data, Intelligence, and Graphs (DIG) team within the Computer Science department. His academic career focuses on bridging artificial intelligence with structured knowledge representations. Suchanek's research interests span artificial intelligence, knowledge bases, and natural language processing, with particular emphasis on knowledge graph construction , rule mining , knowledge-based language models , and explainable AI . His work demonstrates how structured knowledge can enhance machine learning systems, particularly large language models, by providing factual grounding and interpretability. The research group he leads develops practical systems that address real-world knowledge management challenges. His recent publications showcase a strong trajectory in knowledge-intensive AI, with notable contributions to knowledge graph completion, rule mining techniques, and neural approaches to knowledge base validation. The research demonstrates increasing integration between symbolic and neural approaches to AI. Best Student Paper Award at KR 2024 for work on contextual reasoning Best Demo Award of IJCAI 2024 for rule mining in knowledge graphs French Open Research Award for the YAGO project Best Paper Award of ESWC 2021 for Neural Knowledge Base Repairs Suchanek has secured significant research funding, evidenced by his active recruitment of PhD students for knowledge-based language model research. He has held visiting positions, including at Nanyang Technological University (June-September 2023), and is recognized internationally through keynote invitations such as the Singapore ACM SIGKDD Symposium 2023. He has deliberately stepped back from administrative duties at Institut Polytechnique de Paris to focus on research. His laboratory maintains strong industry connections through open-source software projects including the YAGO knowledge base, AMIE for rule mining, STACI for explainable AI, and several other tools that have become standard in knowledge representation research.

• Accounting Standards
• Corporate Governance
• Capital Markets
• +3 more

You-il (Chris) Park is an Associate Professor of Accounting at the University of Hawaii at Manoa’s Shidler College of Business, serving as Co-Chair of the Hawaii Accounting Research Conference (HARC) and Hawaii Accounting Research Doctoral Institute (HARDI). He holds the Accuity LLP Accounting Faculty Fellowship and coordinates the Accounting Ph.D. program. His research focuses on archival, economics-based studies examining how accounting standards, regulations, and corporate governance influence financial reporting, capital markets, and managerial decisions. Professor Park holds a Ph.D. in Business Administration (Accounting) from Duke University’s Fuqua School of Business, an M.A. in Economics from Duke University, and a B.S. in Management with Highest Honor and an Accounting Certificate from Georgia Institute of Technology’s Scheller College of Business. Prior to academia, he worked as an internal auditor at Georgia Power & Southern Company and as a computer programmer in the Republic of Korea Air Force. His research interests span topics such as IFRS impact on macroeconomics, credit rating affirmations, audit quality, and data analytics in operational efficiency. His work appears in top journals like The Accounting Review and Contemporary Accounting Research. Professor Park has received numerous accolades for teaching excellence, including the Presidential Citation for Meritorious Teaching (2025), Shidler College Professor of the Semester awards (2022–2025), and multiple nominations for the Board of Regents Medal for Teaching. His professional service includes roles as a doctoral consortium fellow and reviewer for prestigious journals. He actively contributes to academic communities through HARC and HARDI, fostering research collaboration and mentoring doctoral students. His curriculum development includes courses such as Accounting Capstone and Ph.D. seminars, emphasizing practical and theoretical mastery.

• Labor Economics
• Health Economics
• Public Economics
• +3 more

Atsuko Tanaka is an Associate Professor in the Department of Economics at the University of Calgary. Her research focuses on labor markets, health economics, and public policy, particularly examining topics such as female labor supply, skill investment, and pension reforms. She holds a Ph.D. in Economics from the University of Wisconsin-Madison (2013), alongside advanced degrees from Cornell University and the University of Tokyo. Education Ph.D. Economics, University of Wisconsin-Madison (2013) M.S. Economics, University of Wisconsin-Madison (2010) M.S. Applied Economics and Management, Cornell University (2007) B.A. Agricultural Economics, University of Tokyo (2004) Research Interests Tanaka’s work explores how health shocks, financial constraints, and policy interventions influence labor market outcomes. She investigates topics such as post-educational skill accumulation via college loans, strategic medical treatment choices under reimbursement reforms, and the macroeconomic effects of pension system transitions. Her research combines rigorous econometric methods with real-world administrative data to inform evidence-based policy. Awards and Grants SSHRC Connection Grant (2022, co-PI: Apostolos Serletis) National Science Foundation Extreme Science Grant (2021) SSHRC Insight Development Grant (2016) Susan Jane Blake Kocin Award, University of Wisconsin-Madison (2011) Grants and Collaboration Her research has been supported by major grants, including SSHRC and NSF funding. She collaborates with scholars on projects like analyzing physician behavior under reimbursement reforms, auditing firm dynamics, and international aid allocation strategies. Her work bridges theoretical models with empirical evidence to address complex socio-economic challenges.

Theo DamoulasView profile

Professor

• Probabilistic Machine Learning
• Bayesian Statistics
• Digital Twins
• +5 more

Theo Damoulas is a Professor of Machine Learning at the University of Warwick with a joint appointment in the Department of Computer Science and Statistics. He is a Turing AI Fellow (2021-2026) through UK Research and Innovation, an ELLIS member, and a Visiting Professor at New York University's Center for Urban Science and Progress (CUSP). He founded and leads the Warwick Machine Learning Group and has directed major projects at The Alan Turing Institute including Project Odysseus and the London Air Quality project. Education includes: PhD in Probabilistic Multiple Kernel Learning (University of Glasgow, 2009) MSc in Informatics (Distinction, University of Edinburgh, 2004) MEng in Mechanical Engineering (1st Class, University of Manchester, 2003) His research focuses on probabilistic machine learning and Bayesian statistics, emphasizing the integration of structural priors, spatiotemporal dependencies, physical laws, and causal relationships. Key applications include Digital Twins, urban science, and computational sustainability. His work advances robust and scalable inference methodologies for complex real-world systems. Publications demonstrate strong emphasis on Bayesian methods, spatiotemporal modeling, and uncertainty quantification, with applications spanning battery modeling, urban mobility, federated learning, and causal inference. Recent work shows increased focus on physics-informed models, federated learning frameworks, and causal abstraction techniques. Major scientific awards: Turing AI Acceleration Fellowship (2021-2026) Best Paper Awards (Wilkes 2024, AISTATS 2022, IEEE ICMLA 2010) ACM SIGMOD Most Reproducible Paper (2017) Dissertation Award (Classification Society 2012) Teaching Excellence nominations (Warwick 2015-2017) He actively advises PhD students and secured significant grants including the £multi-million Turing AI Fellowship. Current doctoral researchers investigate federated learning, causal inference, and spatiotemporal modeling. He leads the Warwick Machine Learning Group, a cross-departmental team developing foundational ML methods for scientific and societal challenges.

Jakob MeraneView profile

Researcher

• Artificial Intelligence
• Legal Technology
• Compliance and Regulation
• +3 more

Jakob Merane is a postdoctoral researcher at ETH Zurich's Professorship of Law, Economics and Business, and a visiting researcher at the Max Planck Institute and Harvard Law School. He holds a Ph.D. from ETH Zurich and law degrees from the University of Basel, with additional studies in applied statistics. His research focuses on AI-driven legal enforcement, legal technology, and compliance-by-design approaches, leveraging computational and empirical methods. Merane's work includes developing tools like SwiLTra-Bench for multilingual legal translation and LEXam for benchmarking AI legal reasoning. He has contributed to GDPR compliance studies, automation of legal enforcement processes, and quantitative analyses of Swiss legal datasets. Awards include the 2024 Young Scholar Prize from the German Law and Economics Association. Teaching responsibilities include ETH's Law & Tech course since 2020, emphasizing AI regulation and interdisciplinary collaboration between law and computer science. Merane's research also addresses AI liability in medical contexts, digital advertising regulations, and ethical AI integration in legal systems. His work bridges legal theory with practical applications, aiming to enhance justice accessibility through technology.