جست‌وجوی استاد و پژوهشگر

Roberto Navigliمشاهده پروفایل

استاد

• Natural Language Processing
• Multilingual Natural Language Understanding
• Large Language Models
• +۵ مورد دیگر

Roberto Navigli is a Full Professor in the Department of Computer, Control and Management Engineering at Sapienza University of Rome, where he leads the Sapienza NLP Group. He has held academic positions at Sapienza since 2007, progressing from Researcher to Associate Professor, and now Full Professor. Navigli is internationally recognized for his groundbreaking work in multilingual natural language processing and computational semantics. His research spans multiple areas within Natural Language Processing, with a particular focus on multilinguality , lexical semantics (Word Sense Disambiguation and induction), sentence-level semantics (Semantic Role Labeling and Semantic Parsing), and Large Language Models . As the leader of the Minerva LLM project, he pioneered the development of the first pretrained LLM in Italian. His creation of BabelNet, the largest multilingual encyclopedic computational dictionary covering 271 languages and about 14 million concepts, represents a landmark achievement in computational linguistics. Professor Navigli's recent publications demonstrate a strong focus on advancing multilingual capabilities of language models, addressing challenges in semantic understanding across languages, and developing robust evaluation frameworks. His work on Italian LLM optimization, lexical ambiguity in machine translation, and cross-lingual safety gaps reflects his commitment to building more accurate and reliable language technologies that work effectively across diverse linguistic contexts. AAAI Fellow (2025) EurAI Fellow (2024) ELLIS Fellow (2024) ACL Fellow (2023) Outstanding Paper Awards at ACL 2024, ACL 2023, and NAACL 2021 Best Resource Paper Award at ACL 2022 Prominent Paper Awards from the Artificial Intelligence Journal (2023, 2017) META Prize 2015 for BabelNet Professor Navigli has successfully secured substantial research funding, including two prestigious ERC Grants (MultiJEDI ERC Starting Grant 2010-2016 and MOUSSE ERC Consolidator Grant 2017-2023, totaling around 3 million euros), PI of PRIN 2017 and PRIN 2020 projects (overall 1.4 million euros), and a Google Focused Research Award (around 300k$). He has mentored over 30 PhD students, many of whom have gone on to successful careers in academia and industry. His leadership extends to major international conferences, serving as General Chair of ACL 2025 and Program Co-Chair of ACL-IJCNLP 2021. As the director of the Sapienza NLP Group and co-leader of the Transversal Project on Large Language/Multimodal Models in the FAIR (Future Artificial Intelligence Research) project, Professor Navigli oversees a vibrant research ecosystem focused on advancing the state-of-the-art in natural language understanding across languages. His spinoff company, Babelscape, translates academic research into practical applications for industry.

• Corruption and Governance
• Economic Development
• Political Settlements
• +۵ مورد دیگر

Mushtaq Khan is a Professor in the Department of Economics at SOAS University of London . His research focuses on governance, corruption, industrial policy, and structural transformation in developing countries, with particular emphasis on Bangladesh, Nepal, and Nigeria. Research Interests : Corruption and Anti-Corruption Strategies Political Settlements and Governance Industrial Policy Design Climate Adaptation and Public Health Systems Policy Contributions : His work addresses challenges such as collusive contracting in energy sectors, doctor absenteeism in rural healthcare, and dual-use climate projects as anti-corruption tools. Advising : He has supervised theses on industrial policy in Nigeria, economic transformation in Rwanda, and federalism in Nepal.

Daniel Adelmanمشاهده پروفایل

استاد

• Business Analytics
• Management Analytics
• Operations Research
• +۵ مورد دیگر

Daniel Adelman is the Charles I. Clough, Jr. Professor of Operations Management at the University of Chicago Booth School of Business. He joined the faculty in 1997 after completing his PhD in industrial engineering and operations research at Georgia Tech. Adelman is a leading expert in Business Analytics and Management Analytics, helping companies deploy data and decision analysis to build world-class strategic and tactical management capabilities. Adelman received his PhD in industrial engineering and operations research in 1997, along with a bachelor's degree in industrial engineering and a master's degree in operations research, all from the School of Industrial and Systems Engineering at the Georgia Institute of Technology. Daniel Adelman's research focuses on applying analytical models to solve complex business problems across multiple industries. He has worked with firms from diverse sectors including internet services, chemical distribution, airlines, third party logistics, fiber-optics manufacturing, semiconductor manufacturing, oil, and healthcare. His research integrates real-world data with analytical models to bring structure and discipline to decision and control processes, enabling firms to achieve higher profits with lower risk. Adelman's recent work has concentrated heavily on healthcare analytics, where he leads the Healthcare Analytics Laboratory at Chicago Booth. This lab works with teams of doctoral and MBA students on projects with major healthcare institutions to optimize clinical, operational, and financial outcomes. His research spans foundational operations research including approximate dynamic programming, inventory theory/supply chain management, and revenue management/pricing optimization, as well as examining the linkage between operational performance metrics and financial performance of firms. Adelman's publications show a clear trend toward increasing focus on healthcare applications while maintaining strong theoretical foundations in operations research. His earlier work focused more on general operations management problems like inventory control and supply chain optimization, while his recent publications demonstrate a strategic shift toward healthcare analytics, particularly examining surgical team dynamics, hospital performance metrics, and resource allocation during public health emergencies like the COVID-19 pandemic. George B. Dantzig Prize (1998) for the best dissertation in operations research and management sciences that is innovative and relevant to practice Adelman regularly advises doctoral and MBA students through the Healthcare Analytics Laboratory at Chicago Booth. He has served as Associate Editor for Management Science, currently serves as Associate Editor for Manufacturing and Service Operations Management, and is the Area Editor for Operations and Supply Chain at Operations Research. His industry collaborations include significant projects with Akamai on internet pricing, with GE Global Research Labs on the electricity smart grid, with BP on gasoline supply contract portfolio optimization, and with Symantec on software release planning. Adelman leads the Healthcare Analytics Laboratory at Chicago Booth, which brings together interdisciplinary teams of doctoral and MBA students to work on a portfolio of projects with major healthcare institutions. The lab focuses on optimizing clinical, operational, and financial outcomes through advanced analytics and decision modeling.

Joy Arulrajمشاهده پروفایل

دانشیار

• Data Systems
• Machine Learning
• Database Systems
• +۲ مورد دیگر

Joy Arulraj is an Associate Professor in the School of Computer Science within the College of Computing at Georgia Institute of Technology. His research focuses on data systems, machine learning, and database systems, with a particular emphasis on video analytics and adaptive query processing. He leads the Data Systems and Analytics Group and is developing the EVA AI-Relational Data System. Dr. Arulraj's research interests span data systems, machine learning, database systems, video analytics, and adaptive query processing. His work centers on developing systems that efficiently process complex queries, particularly for video analytics and machine learning workloads. He has made significant contributions to GPU database systems, non-volatile memory database management, and adaptive query processing techniques. His research often bridges the gap between theoretical database principles and practical implementations for modern hardware architectures. His recent publications show a strong trend toward video analytics systems, adaptive query processing for machine learning workloads, and GPU-accelerated database systems. The EVA system represents a major focus of his recent work, providing end-to-end exploratory video analytics capabilities. His research also addresses fundamental database concepts like buffer management, query optimization, and storage management, adapting these principles for modern hardware and application requirements. Dr. Arulraj has advised numerous graduate students including Pramod Chunduri, Gaurav Tarkok Kakkar, Jiashen Cao, and Sayan Sinha. His graduated students have gone on to work at companies like ServiceNow, Meta Research, and the Korean Army. He actively teaches database system courses at Georgia Tech, including Database System Implementation (CS 4420/6422) and Advanced Database System Implementation (CS 4423/6423), where students build database systems from scratch using C++ and the BuzzDB framework. He maintains an active research program with consistent publication output across top database and systems conferences. His work spans from theoretical database principles to practical system implementations, with a recent emphasis on video analytics, machine learning integration with database systems, and leveraging modern hardware like GPUs and non-volatile memory for database applications.

Benjamin Langeمشاهده پروفایل

استاد پژوهشی

• Ethics of AI
• Technology Ethics
• Normative Ethics
• +۳ مورد دیگر

Benjamin Lange is a Research Professor and Junior Research Group Leader in the Ethics of AI and Machine Learning at Ludwig Maximilians University of Munich (LMU) and the Munich Center for Machine Learning (MCML). He is also an Associate Research Fellow at the Oxford Uehiro Institute for Practical Ethics and a member of LMU's Zentrum für Ethik und Philosophie in der Praxis (ZEPP). His research bridges foundational ethical theory with practical challenges in technology, business, and organizational ethics. Lange holds a BSc from the London School of Economics, and a B.Phil and D.Phil from the University of Oxford. He previously held visiting positions at Google’s Responsible Innovation team and the University of Hamburg. His research focuses on ethical frameworks for AI, digital ethics, and corporate responsibility. Key themes include the ethics of human-AI collaboration, moral partiality in relationships, and organizational ethics. He has advised the European Commission on AI governance and developed assurance audit methodologies for algorithmic systems, including work for NYC’s AI Bias Law. His consulting practice focuses on helping organizations build ethical cultures through capabilities like moral imagination and compliance frameworks. Lange’s work has been featured in media such as CBS News, Business Insider, and ZDF heute journal. He is completing a book on the ethics of interpersonal relationships and partiality, aiming to connect philosophical insights with practical applications. His recent publications explore moral imagination in engineering teams, bias mitigation in AI systems, and the ethical implications of digital duplication technologies. He advises Fortune 500 companies, startups, and public institutions across sectors including finance, technology, and education. His approach emphasizes aligning ethical practices with organizational growth and social impact through tailored ethical frameworks and stakeholder engagement strategies.

Myriam Dunn Caveltyمشاهده پروفایل

دانشیار

• Cybersecurity
• Digital Technologies and Political Behavior
• Governance of Digital Technologies
• +۶ مورد دیگر

Myriam Dunn Cavelty serves as Deputy Head of Research and Teaching at the Center for Security Studies (CSS) at ETH Zurich, where she leads significant academic initiatives in cybersecurity and digital governance. She holds formal affiliations as an associate researcher at the Center for Advanced Security Theory at the University of Copenhagen, and has held prestigious visiting positions at Brown University, the University of Darmstadt (as a Mercator Fellow), and the Central European University. Her research expertise centers on how digital technologies influence political behavior and societal values, with particular attention to governance arrangements for digital technologies and their embedding in social, political, and economic processes. Her scholarly contributions span cybersecurity politics, cyber warfare, critical infrastructure protection, and strategic foresight, reflecting an interdisciplinary approach that bridges political science, international relations, and technology studies. Dunn Cavelty's publication record demonstrates remarkable consistency and impact, with works spanning from 2008 to the present. Her research shows an evolution from early analyses of cyber-threat construction to contemporary examinations of AI in security contexts and the political dimensions of cybersecurity governance. Her work consistently addresses the intersection of technological capabilities and political constraints, examining how states and other actors navigate security challenges in the digital domain. Mercator Fellow at the University of Darmstadt Editor-in-Chief of Contemporary Security Policy Advisory work for governments, international institutions, and corporations As an academic leader, she has advised numerous doctoral students and supervised research projects examining the political dimensions of cybersecurity. Her work with the Center for Security Studies involves directing research programs that connect academic inquiry with policy relevance, particularly in the areas of risk analysis and strategic foresight. She has established herself as a leading voice in European cybersecurity scholarship, with particular expertise in the Swiss context and broader European security frameworks.

Toby Jia-Jun Liمشاهده پروفایل

استادیار

• Human-Computer Interaction
• Artificial Intelligence
• Machine Learning
• +۶ مورد دیگر

Toby Jia-Jun Li is an Assistant Professor in the Department of Computer Science and Engineering at the University of Notre Dame, where he leads the SaNDwich Lab. He also serves as the Director of the Human-Centered Responsible AI Lab in the Lucy Family Institute for Data & Society and is a Faculty Fellow at the Institute for Educational Initiatives (IEI). Previously, he was affiliated with Carnegie Mellon University's Human-Computer Interaction Institute (HCII) and GroupLens Research. Dr. Li's research spans the intersection of Human-Computer Interaction (HCI), End-User Software Engineering, Machine Learning (ML), and Natural Language Processing (NLP), with recent work focusing on addressing societal challenges in the future of work through human-AI collaborative approaches. His work has resulted in over 40 publications at premier venues including CHI, UIST, CSCW, ACL, and ICSE, with 8 papers winning Best Paper or Honorable Mention awards. His recent publications demonstrate a strong focus on human-AI collaboration across various domains, including code understanding, privacy, accessibility, and creative tools. The work shows a trajectory toward increasingly sophisticated integration of human-centered design with AI capabilities, particularly using large language models to enhance human productivity and address societal challenges. Google Research Scholar Award recipient Recipient of Yahoo! Fellowship ($100,000/year) Best Paper Award at UIST 2020 Best Paper Honorable Mention Award at CHI 2021 Best Paper Award at CSCW 2024 Best Paper Award at CHI 2025 Dr. Li actively mentors Ph.D. students and has established collaborations with Google, Microsoft Research, IBM Research, Adobe, Verizon, and J.P. Morgan. His research has been supported by NSF, Google Research Scholar Program, AnalytiXIN Initiative, Yahoo! InMind project, and J.P. Morgan. He is currently recruiting Ph.D. students and undergraduate researchers for his SaNDwich Lab, which focuses on developing interactive systems to empower individuals to create, configure, and extend AI-powered computing systems.

Witold J. Heniszمشاهده پروفایل

استاد

• Political Risk Management
• ESG (Environmental, Social, Governance) Impact Analysis
• Stakeholder Engagement
• +۵ مورد دیگر

Witold J. Henisz serves as Vice Dean and Faculty Director of the Impact, Value, and Sustainable Business Initiative at The Wharton School, University of Pennsylvania, holding the Deloitte & Touche Professorship in Management. His research centers on political and social risk identification, ESG impact materiality, and corporate diplomacy frameworks that transform stakeholder relationships into strategic assets. His educational background includes a Ph.D. in Business and Public Policy from UC Berkeley's Haas School and an M.A. in International Relations from Johns Hopkins SAIS. Henisz has served as Departmental Editor at Journal of International Business Studies and Associate Editor at Strategic Management Journal , reflecting his scholarly influence. Research interests converge on geopolitical risk quantification, stakeholder engagement optimization, and sustainable business model innovation. His work demonstrates how political hazards materially impact firm valuation and how corporate diplomacy can mitigate conflict through strategic stakeholder alignment. Recent publications leverage massive media datasets (4+ billion articles) to model populism dynamics, indigenous land conflicts, and ESG-credit risk linkages. Award highlights include the Aspen Institute Ideas Worth Teaching Award (2020), Iron Prof recognition (2019), and Academy of International Business Fellowship. His geostrategy framework Geostrategy by Design (2024) provides executives with actionable methods for navigating geopolitical volatility. Aspen Institute Ideas Worth Teaching Award (2020) Iron Prof recognition (2019) Academy of International Business Fellow (2014-present) Multiple Wharton Excellence in Teaching Awards (2006-2021) Industry Studies Association Best Paper Award (2019) Henisz consults through PRIMA LLC for multinational firms (Rio Tinto, Shell), financial institutions (Eaton Vance, World Bank), and NGOs. His executive education programs train leaders in ESG integration and geopolitical risk management, while his KEROVKA crisis simulation develops real-time strategic response capabilities. Media frequently cites his expertise on ESG controversies, with 50+ major publications featuring his analysis since 2022 including Financial Times , Bloomberg , and Wall Street Journal .

Tiancheng Zhaoمشاهده پروفایل

پژوهشگر

• Multimodal Foundation Models
• AI Agents
• Vision-Language Models
• +۳ مورد دیگر

Tiancheng Zhao is a principal researcher at the Binjiang Institute of Zhejiang University and founder of the Om Artificial Intelligence Laboratory (Om AI Lab), dedicated to frontier open multimodal AGI research for building next-generation agents that transform work and life through advanced human-machine interaction. His academic credentials include: Ph.D. in Computer Science from Carnegie Mellon University (2016-2019) under Prof. Maxine Eskenazi, Prof. Louis-Philippe Morency, Prof. William W. Cohen, and Dr. Dilek Hakkani-Tur, with pioneering dissertation “Learning to Converse With Latent Actions” in end-to-end generative conversational models M.S. in Computer Science from Carnegie Mellon University (2014-2016) B.S. in Electrical Engineering from UCLA (2010-2014) with Summa Cum Laude, focusing on speech signal processing under Prof. Abeer Alwan Dr. Zhao’s research centers on multimodal foundation models and agents, tackling three core challenges: Multimodal Models for cross-modal representation learning in high-dimensional data, Learning to Learn for effective skill acquisition from diverse signals (supervised labels, rewards, meta-learning), and AI Agents for open-world understanding and complex decision-making. His work bridges computer vision, natural language processing, and real-world applications including healthcare analytics and remote sensing. Analysis of his 50+ publications reveals accelerating innovation in multimodal large language models (2024-2025), with emphasis on stable vision-language architectures (VLM-R1), agent orchestration frameworks, and domain-specific applications in geospatial analysis and healthcare. Key trends include solving long-tail distribution challenges in satellite imagery, developing human-like zooming capabilities for multimodal LLMs, and creating unified benchmarks for autonomous GUI testing. His scientific recognition includes: National Breakthrough Technology Award by Ministry of Science and Technology (2021) Microsoft Research Best & Brightest PhD (2018) BEST PAPER AWARD at SIGDIAL 2018 Best Paper Nomination at SIGDIAL 2016 Top 1 Outstanding Bachelor of Science Award at UCLA (2014) As Om AI Lab founder, Dr. Zhao leads research teams developing computational building blocks for human-AI collaboration. While specific student mentorship details aren’t public, his extensive publication record with junior co-authors indicates active research supervision. Current projects focus on practical system implementations for real-world multimodal agent deployment across diverse domains.

Matthew B. Blaschkoمشاهده پروفایل

استاد

• Machine Learning
• Computer Vision
• Medical Image Analysis
• +۵ مورد دیگر

Matthew B. Blaschko is a Professor in the Department of Electrical Engineering at KU Leuven, Belgium. He serves as director of the KU Leuven ELLIS unit and is a fellow in the ELLIS Health program. He is a Core PI in the Flanders AI Research Program, working as a workpackage lead for Decision Support Systems and Medical Imaging. Blaschko is also a member of the KU Leuven Institute for Artificial Intelligence and one of the leaders of the working group on Machine Learning and Data Science. Professor Blaschko received his B.S. from Columbia University, M.S. from the University of Massachusetts Amherst, and Dr. rer. nat. from Technische Universität Berlin (awarded for work at Max Planck Institutes Tübingen). He was a Newton International Fellow at the University of Oxford and received his Habilitation (HDR) from École Normale Supérieure de Cachan. Prior to joining KU Leuven, he was a Permanent Research Scientist in the INRIA Saclay Research Center and a Faculty Member at Ecole Centrale Paris. His research focuses on machine learning techniques applied to visual data, with particular emphasis on calibration in deep learning, medical image analysis, and federated learning. Blaschko's work bridges theoretical foundations with practical applications, as evidenced by technology developed in his research being incorporated into MONA, software for ophthalmic image analysis. His research group has made significant contributions to the fields of model calibration, uncertainty estimation, and medical imaging analysis, with recent publications showing strong trends toward improving reliability of AI systems in medical contexts and advancing theoretical understanding of calibration metrics. Professor Blaschko has been recognized with several awards including the Université Paris-Saclay STIC Doctoral School Best Scientific Contribution Award, Best Paper Award at CVPR 2008, Main Award at DAGM 2008, and Best Student Paper Award at ECCV 2008. Professor Blaschko has supervised numerous PhD and Master's students, with current and former students including Deniz Soysal, Claire Marchal, Dongli Xu, Sebastian Gruber, Jiameng Li, Marco Mezzina, and many others working on diverse topics from Alzheimer's disease analysis to surgical phase recognition. His research has been supported by various funding sources including the Flanders AI Research Program. He has co-organized several influential workshops including the "Another Brick in the AI Wall: Building Practical Solutions from Theoretical Foundations" at CVPR 2025, Commands 4 Autonomous Vehicles workshop at ECCV 2020, and the Learning from Limited Labeled Data workshop series at NIPS 2017 and ICLR 2019. His laboratory focuses on machine learning for medical image analysis, with applications in ophthalmology, neurology, and surgical robotics. The group maintains active collaborations with medical institutions and participates in international challenges such as the KNee OsteoArthritis Prediction (KNOAP2020) challenge.

• Circular Economy
• Sustainability
• Business Models
• +۳ مورد دیگر

Leena Aarikka-Stenroos is a Professor at Tampere University, leading the Research Centre for Managing Circular Economy (ManCE). She holds a Doctor of Science degree in Economics and Business Administration from Turku School of Economics (2011). Her research focuses on circular economy innovation, business models, sustainability transitions, and value creation mechanisms. She has pioneered studies on catalysts for circular economy ecosystems, B2B customer referencing, and the role of digital technologies in enabling circular value chains. Academic Qualification: PhD in Economics and Business Administration (Turku School of Economics, 2011) Roles: Head of Research Centre for Managing Circular Economy, Tampere University Research Interests: Circular Economy, Sustainability, Business Models, Innovation Management, Value Creation Her work contributes to UN Sustainable Development Goals, particularly addressing climate action and responsible consumption. Key contributions include frameworks for circular business model innovation and stakeholder engagement strategies in sustainability transitions. She has received prestigious awards such as the Alex Gofman Award (2016) and multiple best paper recognitions. Her research spans industries like construction, textiles, and healthcare, emphasizing cross-sector collaboration. Recent articles explore emotional dynamics in innovation, ethical value co-creation, and AI-driven value creation in B2B contexts. She actively collaborates internationally, contributing to conferences and editorial boards. Her work bridges theoretical insights with practical business strategies for environmental sustainability.

Jiajun Wuمشاهده پروفایل

استادیار

• Computer Vision
• Machine Learning
• Robotics
• +۵ مورد دیگر

Jiajun Wu is an Assistant Professor of Computer Science and, by courtesy, of Psychology at Stanford University. He holds multiple affiliations including membership in Bio-X, Faculty Affiliate status at the Institute for Human-Centered Artificial Intelligence (HAI), and membership in both the Wu Tsai Human Performance Alliance and Wu Tsai Neurosciences Institute. Dr. Wu earned his Ph.D. and S.M. in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology before joining Stanford. Dr. Wu's research program focuses on creating AI systems that understand and interact with the physical world through the integration of computer vision, machine learning, robotics, and cognitive science. His work emphasizes physics-based modeling combined with deep learning to develop systems capable of perceiving, reasoning about, and predicting physical interactions. Key research areas include 3D scene understanding, neurosymbolic AI approaches, multimodal perception (combining vision, sound, and language), and embodied intelligence for robotics applications. His lab develops novel frameworks that bridge the gap between neural networks and symbolic reasoning to create more interpretable and robust AI systems. Analysis of Dr. Wu's recent publications reveals a strong trajectory toward integrated multimodal understanding for embodied AI. His work increasingly combines vision, sound, and language processing with physical reasoning to create systems that can interact meaningfully with the physical world. There's a clear progression from foundational computer vision research toward practical robotics applications, with significant emphasis on foundation models for robotics, sim2real transfer techniques, and creating comprehensive datasets for embodied AI research. Dr. Wu's exceptional contributions have been recognized with numerous prestigious awards including the NSF CAREER award (2024), Young Investigator Programs from ONR (2024) and AFOSR (2023), the Okawa research grant (2024), and being named to IEEE Intelligent Systems' 'AI's 10 to Watch' (2024). He has received multiple best paper awards at leading conferences including ICRA (2024), SIGGRAPH Asia (2023), and CoRL (2023). Dr. Wu actively mentors a large cohort of students across multiple levels, serving as primary advisor for doctoral candidates, master's students, and numerous independent researchers. His research is supported by substantial funding from major technology companies including Google, Meta, Amazon, Samsung, and J.P. Morgan, as well as government agencies like NSF, ONR, and AFOSR, reflecting the significance and impact of his work in physical AI and multimodal perception systems. Dr. Wu leads a dynamic research group at Stanford that collaborates extensively with the Wu Tsai Neurosciences Institute and Institute for Human-Centered AI. Current projects include developing neurosymbolic models for computer graphics, creating multisensory datasets like OBJECTFOLDER 2.0 for sim2real transfer in robotics, and building foundation models for embodied intelligence that can understand and manipulate objects with human-like physical intuition.

Anil Madhavapeddyمشاهده پروفایل

استاد

• Environmental Computing
• Climate Change Informatics
• Distributed Systems
• +۵ مورد دیگر

Anil Madhavapeddy serves as Professor of Planetary Computing at the University of Cambridge's Department of Computer Science and Technology and directs the Cambridge Centre for Carbon Credits (4C). A Fellow of Pembroke College, he integrates systems research with environmental conservation through the Computer Laboratory's Environment and Energy Group. His career spans industry leadership (NetApp, Citrix, Intel), academic appointments (Cambridge, Imperial, UCLA), and entrepreneurial ventures (XenSource, Unikernel Systems, Docker). Madhavapeddy earned his PhD at Cambridge's Computer Laboratory in 2006. His research bridges computational systems and planetary-scale environmental challenges, with deep expertise in open-source development (OCaml, Xen, Docker, OpenBSD) and technology strategy advising for organizations including Zededa, Tezos Foundation, and Tarides. His work centers on environmental computing and climate informatics, leveraging distributed systems and functional programming to develop sensing infrastructure for conservation. Recent projects focus on carbon credit systems, AI-driven biodiversity monitoring, and sustainable computing architectures that minimize ecological footprints while maximizing analytical capability. Analysis of his 2025 publications reveals a concentrated effort on AI-integrated conservation tools, privacy-preserving carbon accounting, and energy-efficient computing. Key themes include spatial networking for ecological data, LLM-enhanced evidence retrieval in conservation science, and novel metrics for extinction risk assessment—demonstrating computational innovation applied to urgent planetary boundaries. No scientific awards were documented in the source material. Madhavapeddy advises multiple technology firms on strategic development while leading the Cambridge Centre for Carbon Credits, though specific grant funding details remain unreported. He actively contributes to the Environment and Energy Group at Cambridge's Computer Laboratory and directs the interdisciplinary Cambridge Centre for Carbon Credits (4C). His open-source leadership spans critical infrastructure projects including OCaml, Xen, and Docker, fostering collaborative development communities that underpin modern cloud and container technologies.

Elena Simperlمشاهده پروفایل

استاد

• Human-centric AI
• Knowledge Graphs
• Knowledge Engineering
• +۷ مورد دیگر

Elena Simperl is a Professor of Computer Science and Deputy Head of Department for Enterprise and Engagement at King's College London's Department of Informatics. She co-directs the King's Institute for Artificial Intelligence and serves as Director of Research for the Open Data Institute. As a Hans Fischer Senior Fellow at the Technical University of Munich's Institute for Advanced Study, she leads the Trustworthy Knowledge Graphs focus group and contributes to advancing human-centric AI research across European institutions. Professor Simperl obtained her doctoral degree in Computer Science from the Free University of Berlin and her diploma from the Technical University of Munich. Prior to joining King's, she held academic positions in Germany, Austria, and at the University of Southampton, and was a Turing Fellow. Her career trajectory demonstrates consistent leadership in bridging academic research with practical applications in data ecosystems. Her research sits at the critical intersection of AI and social computing, focusing on human-centric approaches to building sociotechnical systems that integrate data, algorithms, and human capabilities. She investigates how to make knowledge engineering more accessible, how to leverage collective intelligence for data quality improvement, and how to design participatory AI systems that address societal challenges like misinformation. Her work spans knowledge graphs, semantic technologies, crowdsourcing, and open data, with particular emphasis on the social dimensions of data-intensive systems and the governance frameworks needed for trustworthy AI deployment. Analysis of her recent publications reveals a strong evolution toward integrating large language models with traditional knowledge engineering practices while maintaining human oversight. There's a clear trajectory from foundational work on knowledge representation toward increasingly applied research addressing real-world challenges in media ecosystems, citizen science, and data governance, with growing attention to policy implications of AI technologies. Fellow of the British Computer Society Fellow of the Royal Society of Arts Hans Fischer Senior Fellow at TUM-IAS (2023) Ranked among top 100 most influential scholars in knowledge engineering of the last decade Included in Women in AI 2000 ranking Professor Simperl has led 14 major European and national research projects totaling millions in funding, including MediaFutures (a Horizon 2020 program tackling online misinformation), QROWD, ODINE, Data Pitch, and ACTION. She currently co-chairs the Croissant working group in ML Commons developing data standards for AI, and serves as president of the Semantic Web Science Association. Her research has directly influenced the development of data ecosystems supporting startups and citizen science initiatives across Europe, demonstrating exceptional ability to translate theoretical advances into practical impact. As Director of Research at the Open Data Institute, she oversees initiatives connecting data entrepreneurs with artists and civic organizations. Her leadership in the MediaFutures project established a data-driven innovation hub that supported 51 startups/SMEs and 43 artists through three open calls, creating a sustainable model for arts-technology collaborations addressing media challenges. Her work with the ODINE project helped create a European ecosystem for data-driven startups, demonstrating her commitment to building practical applications of open data principles.

Mohammad Mohammadi Amiriمشاهده پروفایل

استادیار

• Large Language Models
• Data Valuation
• Federated Learning
• +۵ مورد دیگر

Mohammad Mohammadi Amiri serves as an Assistant Professor in the Department of Computer Science at Rensselaer Polytechnic Institute (RPI), appointed in Fall 2023. His research focuses on advancing artificial intelligence through strategic data utilization, with emphasis on large language models, data valuation, federated learning, and deep learning. Previously, he held postdoctoral appointments at Princeton University and MIT Media Lab, building on his strong educational foundation from Imperial College London, University of Tehran, and Iran University of Science and Technology. Education: Ph.D. in Electrical and Electronic Engineering, Imperial College London (2019) - Best Ph.D. Thesis Award recipient M.Sc. in Electrical and Computer Engineering, University of Tehran (2014) - Ranked 1st among all M.Sc. students B.Sc. in Electrical Engineering, Iran University of Science and Technology (2011) - Ranked 1st among all B.Sc. students Dr. Amiri's research centers on optimizing artificial intelligence systems through innovative data strategies. His work addresses critical challenges in large language models including efficiency, memory usage, alignment, and reasoning capabilities. In data valuation, he develops principled methods to quantify data worth for fair trading platforms. His federated learning research tackles privacy concerns, heterogeneous data distribution, and communication overhead in decentralized environments. The deep learning component explores theoretical foundations to improve model interpretability and robustness. Analysis of his recent publications reveals a strong focus on making AI systems more efficient and accessible, with particular emphasis on large language model optimization, federated learning advancements, and data valuation frameworks. His work bridges theoretical foundations with practical applications in wireless communications and distributed computing environments. Scientific Awards: IEEE Communications Society Young Author Best Paper Award (2022) Best PhD Thesis Award from IEEE Information Theory Chapter of UK and Ireland (2019) Eryl Cadwallader Davies Prize for Outstanding PhD Thesis (2019) EEE Departmental Scholarship at Imperial College London (2015-2019) Ranked 1st among M.Sc. students at University of Tehran (2014) Ranked 1st among B.Sc. students at Iran University of Science and Technology (2011) Dr. Amiri actively mentors graduate students, currently supervising five Ph.D. candidates and one M.Sc. student working on efficient LLM fine-tuning, inference, and storage. His research has attracted significant attention, evidenced by numerous keynote invitations at prestigious institutions including Bell Labs, MIT, King's College London, and various IEEE conferences. He serves on program committees for major conferences including IEEE Globecom and ICC, demonstrating his growing influence in the academic community. His research group operates at the intersection of machine learning and wireless communications, developing innovative solutions for resource-constrained environments while addressing fundamental theoretical challenges in AI systems. Current projects focus on making advanced AI more scalable and accessible through efficiency improvements in model training and inference.