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Constantine E. Kontokosta is Professor of Urban Science and Planning at NYU Marron Institute of Urban Management, Director of Civic Analytics and Urban Intelligence Lab, with cross-appointments at Center for Urban Science and Progress (CUSP) and Department of Civil and Urban Engineering. He serves as affiliated faculty at Wagner School of Public Service and previously held leadership roles including inaugural Deputy Director of CUSP. His educational background includes: PhD, Urban Planning (Minor: Econometrics) from Columbia University MPhil, Urban Planning from Columbia University MS, Urban Planning; Quantitative Analytics from Columbia University MS, Real Estate Finance and Economics from New York University BSE, Systems Engineering - Civil from University of Pennsylvania Kontokosta leverages large-scale data and computational methods to advance urban energy/climate policy, neighborhood dynamics, and bias detection in public decision-making. His research integrates urban planning with data science to develop equitable solutions for sustainable development, with recent projects analyzing COVID-19 disparities through mobility data and creating methods to reduce building emissions. The work emphasizes evidence-based policy, information transparency, and uncovering algorithmic discrimination. His honors include the IBM Faculty Award, UN Data for Climate Action Challenge Award, Goddard Junior Faculty Fellowship, and multiple best paper awards. Key recognitions: 2023 Best Paper Award (ICLR Climate Workshop) 2021 Article of the Year (Journal of American Planning Association) 2017 Microsoft Azure Research Award 2014 IBM Faculty Award 2012 Fellow of Royal Institution of Chartered Surveyors Funded by National Science Foundation, MacArthur Foundation, Sloan Foundation, U.S. Department of Transportation, NYC Mayor’s Office of Sustainability, Lincoln Institute, and HUD, Kontokosta has served on UNEP Sustainable Buildings Council, Royal Institution of Chartered Surveyors Americas Board, and Suffolk County Planning Commission. His entrepreneurial ventures translate research into practical urban solutions. He leads the Urban Intelligence Lab focused on data-driven urban methodologies and Civic Analytics program advancing evidence-based policy through transparent knowledge democratization, with research featured in Nature Communications, PNAS, and major media outlets.

• Urban Science
• Urban Informatics
• Urban Planning
• +6 more

Prof. Dr. Doreen Mollenhauer serves as a Professor at Friedrich Schiller University Jena through a joint appointment with the Helmholtz Institute for Polymers in Energy Applications (HIPOLE), a collaboration between the university and Helmholtz Center Berlin for Materials and Energy (HZB). She is based in the Institute of Technical Chemistry and Environmental Chemistry within the university's research ecosystem. Her research centers on computational simulation of polymers for next-generation energy storage and conversion technologies . This work integrates molecular modeling with materials engineering to develop advanced polymers for batteries, fuel cells, and solar energy systems. The research bridges theoretical chemistry and practical energy applications, addressing critical challenges in sustainable power solutions. Prof. Mollenhauer's working group operates from Jena's new CEEC research complex (Center for Energy and Environmental Chemistry), which features state-of-the-art laboratories for collaborative energy materials research. Her dual affiliation with HIPOLE creates unique opportunities for students to access both academic and large-scale national laboratory resources. Contact: doreen.mollenhauer@uni-jena.de | Phone: +49 3641 9-48986 | Location: Philosophenweg 7a, 07743 Jena

• Polymer Science
• Energy Storage
• Energy Conversion
• +3 more

Christos Makris is an Associate Professor in the Department of Computer Engineering and Informatics at the University of Patras, Greece. His academic career spans over two decades with significant contributions to computer science, particularly in data structures, algorithms, and information systems. He maintains active research collaborations and supervises graduate students in his areas of expertise. Dr. Makris's research spans several key areas in computer science with a strong focus on efficient data organization and processing. His work encompasses Data Structures , Information Retrieval , Data Mining , String Management and Processing Algorithms , Computational Geometry , Internet Technologies , Bioinformatics , and Multimedia Databases . His interdisciplinary approach bridges theoretical computer science with practical applications across various domains including web technologies, bioinformatics, and emergency response systems. Analysis of Dr. Makris's publication record reveals a consistent research trajectory focused on efficient algorithms for information management. His work demonstrates evolution from foundational data structure research in the 1990s to more applied work in web technologies, social media analysis, and machine learning applications in recent years. A notable pattern is his ability to adapt core algorithmic techniques to emerging application domains while maintaining theoretical rigor. Dr. Makris maintains an impressive scholarly record with over 3,000 citations, an h-index of 29, and an i10-index of 71 according to Google Scholar metrics. These indicators reflect the significant impact of his research within the computer science community. As an active faculty member, Dr. Makris maintains regular office hours on Tuesdays from 18:00-20:00 and Thursdays from 12:00-14:00. He is accessible via email at makri@ceid.upatras.gr or makri@upatras.gr for academic inquiries and student supervision.

• Data Structures
• Information Retrieval
• Data Mining
• +5 more

Pierluigi Salvo Rossi is a Professor at the Department of Electronic Systems , Norwegian University of Science and Technology ( NTNU ), with additional roles as Deputy Head of Department (since 2021) and Deputy Manager at the Center for Green Shift in the Built Environment (since 2022). He also serves as a part-time Research Scientist at SINTEF Energy's Gas Technology department. Education: Ph.D. in Computer Engineering, University of Naples “Federico II”, Italy (2005) Dr.Eng. (cum laude) in Telecommunications Engineering, University of Naples “Federico II”, Italy (2002) Research Interests span Wireless Communications , Digital Twins , Machine Learning , and Statistical Signal Processing , focusing on applications like Industrial IoT , Fault Detection , and Energy Systems . His recent Publications highlight trends in Federated Learning , Graph Signal Processing , and Multi-Sensor Anomaly Detection across domains from Natural Gas Pipelines to Subsea Leakages . Scientific Awards include: Exemplary Senior Editor, IEEE Communications Letters (2018) Department Ambassador, NTNU (2016) IEEE Senior Member (since 2011) Professional Roles encompass editorial leadership (e.g., IEEE Sensors Journal) and conference organization (e.g., General Chair for IEEE Sensor Array and Multichannel Signal Processing Workshop, 2022). He leads major funded research projects like PREFERENCE (RCN, 2023-2027) and AUTOSHIP (RCN, 2020-2028).

• Wireless Communications
• Machine Learning
• Digital Twins
• +5 more

Xin Peng is a Professor and Deputy Dean at the School of Computer Science, Fudan University, China. He leads the CodeWisdom research team focusing on intelligent software engineering techniques for development, maintenance, and operation of software systems. His educational background includes a PhD in Computer Science (2001-2006) and Bachelor's degree in Computer Science (1997-2001), both from Fudan University. He progressed through the academic ranks from Assistant Professor (2006-2010) to Associate Professor (2010-2015) and finally to Professor (2015-present). Professor Peng's research interests span Software Analytics, Intelligent Software Development, Microservice systems, and AIOps. His work leverages AI technologies including deep learning and knowledge graphs to develop intelligent software engineering techniques. A significant portion of his recent work focuses on applying Large Language Models to various software engineering tasks, including vulnerability detection, API usage analysis, and test automation. His publication record shows a clear trend toward increasingly sophisticated applications of AI in software engineering, with recent work heavily featuring LLMs for tasks ranging from vulnerability patch porting to resource leak detection. The research spans multiple domains including microservice systems, automotive software, and Web of Things security. Best Paper Award of ICSM 2011 ACM SIGSOFT Distinguished Paper Award of ASE 2018 and 2021 IEEE TCSE Distinguished Paper Award of ICSME 2018, 2019, and 2020 IEEE Transactions on Software Engineering Best Paper award for 2018 Professor Peng serves in numerous leadership roles including Deputy Director of CCF Technical Committee on Software Engineering, Co-Editor-in-Chief of Journal of Software: Evolution and Process, and Associate Editor for ACM Transactions on Software Engineering and Methodology. He has been actively involved in program committees for major software engineering conferences including ICSE, ASE, ESEC/FSE, and ICSME. He leads the CodeWisdom research team at Fudan University, which has developed several benchmark systems including TrainTicket for microservice research. The team's work bridges academic research with industrial applications, particularly in microservice systems analysis and intelligent software development tools.

• Software Analytics
• Intelligent Software Development
• Microservice
• +4 more

Juho Lee is an Associate Professor at the Kim Jaechul Graduate School of AI, Korea Advanced Institute of Science and Technology (KAIST). Previously, he worked as a research scientist at AITRICS and completed his PhD in Computer Science & Engineering at Pohang University of Science and Technology (POSTECH) under Professor Seungjin Choi, followed by postdoctoral research at University of Oxford with Professor François Caron. His research focuses on: Bayesian deep learning Bayesian inference Meta learning Generative models Uncertainty quantification Graph representation learning Professor Lee's work bridges theoretical Bayesian methods with practical deep learning applications. His recent publications demonstrate significant contributions to neural processes, Bayesian optimization, and scalable inference methods. He has developed novel architectures like Set Transformer for permutation-invariant modeling and advanced techniques for uncertainty quantification in deep networks. His research shows a clear trajectory toward making Bayesian principles applicable to large-scale, real-world machine learning problems. Notable contributions include: Set Transformer: A framework for attention-based permutation-invariant neural networks (ICML 2019) Bootstrapping neural processes (NeurIPS 2020) Deep amortized clustering (NeurIPS 2019 workshop) Learning to pool in graph neural networks for extrapolation Professor Lee actively mentors graduate students and has advised numerous PhD candidates who co-author papers with him across NeurIPS, ICML, and ICLR. His research group SIML@KAIST develops scalable and interpretable machine learning methods with strong theoretical foundations.

• Bayesian deep learning
• Bayesian inference
• meta learning
• +5 more

Dr. João Henriques is a Research Fellow of the Royal Academy of Engineering (RAEng) at the Visual Geometry Group (VGG), University of Oxford. His research focuses on advancing computer vision, deep learning, and robotics, particularly in areas like 3D scene understanding, reinforcement learning, and multi-agent systems. He is renowned for developing the KCF and SiameseFC visual trackers, which won the VOT Challenge and are deployed in consumer hardware. His work spans 3D geometry, self-supervised learning, causal inference, and neuro-symbolic systems. Key contributions include methods for egocentric video analysis, unsupervised reconstruction, and robot navigation. He leads the VGG's research on neural feature fields, hierarchical scene understanding, and real-time 3D perception. Recent publications emphasize 3D-aware segmentation, universal place recognition, and neuro-symbolic world modeling for robotics. His research often bridges theoretical guarantees with practical applications, such as medical imaging and autonomous systems. Dr. Henriques collaborates with industry and academia on AI ethics, friendly AI, and interpretable learning. His lab hosts DPhil students advancing creative AI applications, such as generative models for gameplay design and LLM evaluations in real-world editorial workflows.

• Computer Vision
• Deep Learning
• Robotics
• +7 more

Roozbeh Razavi-Far is an Assistant Professor at the Faculty of Computer Science and the Canadian Institute for Cybersecurity at the University of New Brunswick. His research focuses on machine learning, big data analytics, and cybersecurity of cyber-physical systems and IoT devices. He has authored/co-authored over 150 publications and is listed by Stanford as among the top 2% of most cited researchers (2022). His work spans federated learning, transfer learning, quantum machine learning, and dependable AI systems. He serves as an Associate Editor for Neurocomputing, Machine Learning with Applications, and IEEE Transactions on Industrial Cyber-Physical Systems, among others. As an IEEE Senior Member, he chairs IEEE Computational Intelligence and Systems, Man, and Cybernetics Societies. Previously, he directed the Learning System and Cybernetics Group at the University of Windsor (2016–2022). His research interests emphasize security in non-stationary environments, adversarial machine learning defenses, and real-time analytics for smart grids. Awards include NSERC-DG, NSERC-ECR, and USRG grants. He has mentored students who received NSERC Alexander G. Bell, MITACS, and Ontario Graduate Scholarships. His recent publications highlight advancements in privacy-preserving split learning, blockchain-based federated learning security, and graph-based malware detection. He also explores quantum computing applications in AI and cybersecurity frameworks for cyber-physical systems.

• Machine Learning
• Cybersecurity
• Federated Learning
• +5 more

Usman Ali is an Assistant Professor and Adjunct Lecturer at the School of Mechanical and Materials Engineering, University College Dublin. He holds a Ph.D. in 'A data-driven GIS-based approach for multi-scale residential building energy modeling' (2020) from UCD and an M.Sc. in Computer Science from Lahore University of Management Science (2013). His research focuses on machine learning, GIS modeling, urban building energy systems, and energy performance certification. He has contributed to projects like the U.S.-Ireland R&D initiative on building stock classification and energy prediction, and collaborated with the Sustainable Energy Authority of Ireland (SEAI) on energy policy research. His work emphasizes data-driven solutions for energy efficiency, urban sustainability, and policy decision-making. Education: Ph.D., University College Dublin (2020) M.Sc., Lahore University of Management Science (2013) B.Sc., International Islamic University Islamabad (2008) Research emphasizes machine learning applications in energy modeling, GIS integration for urban planning, and energy policy frameworks. His recent work includes synthetic building datasets, occupancy-based energy analysis, and uncertainty quantification in energy systems.

• Machine Learning
• Data Mining
• GIS Modeling
• +4 more

David Saunders is an Associate Professor in the Department of Statistics and Actuarial Science at the University of Waterloo, affiliated with the David R. Cheriton School of Computer Science. His research focuses on quantitative risk management, mathematical finance, and stochastic optimization with applications to credit risk, portfolio optimization, and derivatives pricing. Professor Saunders has held academic positions at the University of Pittsburgh and Cyprus International Institute of Management, and has consulted for institutions like RiskMetrics, TD Bank, and Central Bank of Cyprus. Education: PhD studies at the University of Toronto with industry collaboration at Algorithmics Inc., followed by roles at Cyprus International Institute of Management and University of Cyprus's HERMES Center. His work bridges theoretical stochastic analysis and practical financial risk management challenges. Research interests prominently feature credit risk management, optimal stopping problems, and analytical techniques in finance. Recent work addresses inverse default boundary problems, wrong-way risk in derivatives, and efficient algorithms for portfolio optimization under complex return distributions. Collaborations with financial institutions drive applied research in capital allocation, operational risk modeling, and regulatory compliance. Advising and grants include directing the Professional Science Master's in Mathematical Finance at Pitt, supervising student-industry projects on credit risk and CDO pricing, and securing funding for computational finance initiatives. Active in RiskLab Cyprus and HERMES European Center, his research extends to equity risk management and market risk frameworks.

• Quantitative Risk Management
• Mathematical Finance
• Stochastic Optimization
• +3 more

Nyeema Harris is the Knobloch Family Associate Professor of Wildlife and Land Conservation at the Yale School of the Environment (YSE), part of Yale University. She focuses on wildlife conservation, urban ecology, and the socio-ecological dimensions of human-wildlife interactions. Her work examines how urbanization impacts predator behavior, diets, and biodiversity, while advocating for inclusive sustainability approaches that address equity and justice in environmental scholarship. Education includes a PhD from North Carolina State University, an MS from The University of Montana, and a BS from Virginia Polytechnic Institute and State University. These degrees span wildlife science and conservation biology. Her research interests emphasize urban carnivore ecology, community-based conservation strategies, and systemic racism’s ecological consequences. She integrates spatial modeling with social equity frameworks, as seen in her publications on textured species range maps and critiques of environmental landscapes of fear. Publications highlight trends in urban wildlife adaptation, interdisciplinary methods, and ethical considerations in conservation. Key themes include dietary shifts in predators due to environmental changes, the role of historical data in species forecasting, and mitigating health risks linked to urbanization and gentrification. Dr. Harris actively advises doctoral students and is involved in initiatives like SNAPSHOT USA and Michigan ZoomIN, leveraging citizen science for ecological monitoring. She has not been noted for specific scientific awards in the provided texts. Her office is located in Kroon Hall, Room 223, at 195 Prospect Street, New Haven, CT. She contributes to environmental justice discourse and has been featured in news articles discussing her work on African carnivore range loss, urban rodent control, and systemic racism in urban ecosystems.

• Wildlife Conservation
• Urban Ecology
• Human-Wildlife Interactions
• +3 more

Peng Li is a Professor in the Department of Electrical and Computer Engineering at the University of California, Santa Barbara. His research focuses on integrated circuits, brain-inspired computing, electronic design automation, and hardware machine learning systems. He holds Fellow status in the Institute of Electrical and Electronics Engineers (IEEE). His work emphasizes neuromorphic engineering, spiking neural networks, and the intersection of machine learning with analog circuit design. Education includes a PhD in Electrical and Computer Engineering from Carnegie Mellon University, an MS in Systems Engineering from Xi'an Jiaotang University, and a BS in Information Science and Engineering from the same institution. His research has been recognized with prestigious awards including the ICCAD Ten-Year Retrospective Most Influential Paper Award and multiple Design Automation Conference Best Paper Awards. Key research trends in his articles include advancements in spiking neural networks (SNNs), hardware accelerators for neuromorphic computing, Bayesian optimization for analog circuit design, and robustness in machine learning systems. He explores topics like adversarial robustness, energy-efficient architectures, and data-efficient prediction techniques. His work bridges theoretical machine learning models with practical hardware implementations, particularly in 3D integration and systolic array acceleration. Notable contributions include pioneering hybrid approaches combining formal verification with machine learning for analog circuits (HFMV framework), and innovations in neuromorphic processors such as the 3D Liquid State Machine architecture. His research also addresses challenges in semiconductor manufacturing, including wafer map pattern recognition and failure detection through semi-supervised learning and contrastive methods. Awards highlight his impactful contributions to both design automation and neural computing. His grants and collaborations likely span industry partnerships in semiconductor technology and neuromorphic computing. He leads a lab focused on next-generation hardware-software co-design for intelligent systems, emphasizing energy efficiency and scalability.

• Integrated Circuits and Systems
• Brain-Inspired Computing
• Electronic Design Automation
• +6 more

Vincent Sitzmann is an Assistant Professor at the Massachusetts Institute of Technology (MIT), affiliated with the Computer Science and Artificial Intelligence Laboratory (CSAIL). He leads the Scene Representation Group and is part of the Visual Computing research community at CSAIL. His work focuses on advancing artificial intelligence's ability to perceive and interact with the physical world, particularly through neural fields, 3D scene representations, and robotics. His research bridges computer vision, machine learning, and robotics, aiming to create systems that emulate human perception and decision-making. He holds a dual role in the PI Core/Dual program at MIT and contributes to interdisciplinary efforts in AI & ML, Graphics & Vision, and Robotics. His recent projects include developing generative models for 3D avatars, robust camera pose estimation, and learning-based control for soft robots. He collaborates widely within MIT’s engineering ecosystem and has led initiatives such as the Collaborative Research grant on compositional implicit representations for 3D scene understanding (2022). His lab, the Scene Representation Group, emphasizes scalable 3D reconstruction, material estimation, and embodied AI. Notable technologies include Flowmap for camera calibration and Dittogym for soft robotics control. While no awards are explicitly listed, his work has been featured in top conferences like SIGGRAPH and IEEE Robotics.

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +4 more

Professor Garg Vikas holds the position of Assistant Professor in the Department of Computer Science at the School of Science. His research spans quantum computing, artificial intelligence, and machine learning with applications in computational biology, healthcare, and drug design. He leads the HEALED/Garg project focused on human-steered machine learning for drug discovery and collaborates with institutions like MIT and industry partners. He co-founded YaiYai Oy, providing AI/ML solutions to global sectors. Education: PhD from MIT CSAIL under Tommi Jaakkola, with postdoctoral and industry experience at Amazon, Microsoft, and IBM. His work aligns with UN SDGs, particularly in healthcare and sustainable energy. Research interests include graph neural networks, generative models, and quantum AI. Recent projects involve climate modeling via physics-informed neural ODEs and optimizing quantum circuits using graph autoencoders. Key collaborations include MIT’s MLPDS Consortium and the Finnish Center for Artificial Intelligence. He supervises doctoral researchers like Yogesh Verma and postdocs such as Kogkalidis.

• Quantum Computing
• Artificial Intelligence
• Machine Learning
• +8 more

Dr. Ying He is a Senior Lecturer at the School of Electrical and Data Engineering, University of Technology Sydney (UTS). Her research focuses on wireless communication networks, particularly integrating machine learning with satellite and terrestrial systems. She holds a BEng from Beijing University of Posts and Telecommunications (2009) and a PhD from UTS (2017). Prior to her academic role, she worked on TD-LTE chip design at the Chinese Academy of Sciences. Affiliations : Faculty of Engineering and Information Technology Global Big Data Technologies Centre (GBDTC) Education : BEng in Telecommunications Engineering, Beijing University of Posts and Telecommunications (2009) PhD in Engineering (Telecommunications), UTS (2017) Her research interests include satellite communication (GEO-LEO integration), spectrum sharing, vehicular communication, and applying machine learning to physical layer algorithms. Notable contributions include optimizing beam design in LEO networks and developing secure IoT systems. She supervises PhD/Master’s students and teaches courses like CCNA and capstone projects. Funded projects span satellite networks, IoT security, and supply chain tracking. Recent grants include SmartSat CRC initiatives and collaborations with industry partners like Intel and Ericsson. Her work addresses challenges in 6G, UAV-enabled computing, and resilient quantum algorithms.

• Wireless Communication Systems
• Satellite Communication Networks
• Spectrum Sharing
• +3 more