Prof. Dr.-Ing. Johannes Henrich Schleifenbaum is a Professor and Chair of Digital Additive Production at RWTH Aachen University, where he leads research in the Profile area Production Engineering (ProdE). His work advances additive manufacturing (AM) through interdisciplinary approaches combining materials science, process engineering, and digital technologies. His research encompasses: Laser powder bed fusion (LPBF) process optimization and defect mitigation Development of novel alloys/composites for AM applications Sustainable manufacturing practices including material recycling Integration of AI/ML for accelerated material and process design Digital tools for automated design and distributed manufacturing Recent publications (2023-2025) demonstrate a strong focus on: Multi-material processing and microstructure control Machine learning-driven alloy development Standardization and scalability of AM processes Advanced simulations for meltpool dynamics and thermal behavior Applications in aerospace, construction, and biochemical engineering He leads the Chair of Digital Additive Production, collaborating with industry partners to translate research into industrial solutions for next-generation manufacturing.
Find Academic Supervisors
Find and connect with academic supervisors, professors, and scholars for your research journey.
Showing 16-30 of 485 supervisors
Profile data may be stale. It is sourced from public faculty pages.
Anja SchmidtView profile
Researcher
Dr. Anja Schmidt is a Postdoctoral Researcher at the Helmholtz Centre for Environmental Research - UFZ , specifically within the Department of Conservation Biology & Social-Ecological Systems. She has been affiliated with UFZ since 2020 and previously worked at the German Centre for Integrative Biodiversity Research (iDiv) and UFZ's Community Ecology Department. Her research focuses on ecosystem processes, particularly decomposition dynamics driven by invertebrates in tropical and agricultural systems, climate change impacts, and biodiversity conservation. PhD in Ecology (UFZ, 2012–2015) Master's in Ecology, Environmental Science, Bioinformatics (University of Giessen, 2008–2011) BSc in Biotechnology, Animal Physiology, Organismic Biology (University of Erlangen/Nuremberg, 2005–2008) Her work explores the interplay between decomposition processes , invertebrate biodiversity , and human-driven environmental changes . She investigates how climate change, land use, and pollution affect soil and aquatic ecosystems, with a focus on multitrophic interactions. Her research integrates experimental platforms like the iDiv Ecotron and UFZ's environmental observatories (TERENO, MOSES) to simulate and monitor real-world conditions. Dr. Schmidt's recent publications highlight her contributions to understanding invertebrate decline and its cascading effects on microbiomes, soil biodiversity assessments, and policy frameworks for conservation. She collaborates extensively with networks such as iDiv, STACCATO, and LEGATO, bridging experimental ecology with societal decision-making through projects like Faktencheck Artenvielfalt . She is associated with experimental infrastructures including: iDiv Ecotron Project (2018–2020) Global Change Experimental Facility (GCEF) TERENO (Terrestrial Environmental Observatories) MOSES (Modular Observation Solutions for Earth Systems) MOBICOS (Mobile Stream Laboratories) River Experiment Leipzig
Moritz PetersenView profile
Associate Professor
Prof. Dr. Moritz Petersen is an Associate Professor of Sustainable Supply Chain Practice and Co-Director of the Center for Sustainable Logistics and Supply Chains (CSLS) at Kühne Logistics University (KLU) in Hamburg, Germany. His academic career began at Hamburg University of Technology, where he completed his doctoral studies in 2017 under Prof. Dr. Wolfgang Kersten, and he has been at KLU since 2016. Research Focus: Decarbonization of logistics, Circular Economy, Blockchain applications in supply chains, and sustainable product development Teaching: Courses on Supply Chain Sustainability, Circular Product Development, and Lean Logistics Operations across KLU’s Bachelor to MBA programs Outreach: Co-hosts the podcast “Das Gleiche in Grün?!”, collaborates on educational children’s books about logistics, and frequently speaks at industry events like the Deutscher Logistik-Kongress Research Trends from his 15 most recent articles center on: Blockchain technology’s role in sustainable logistics Decarbonization strategies across maritime and road freight Circular economy implementation through product design and waste stream analysis Behavioral and organizational barriers to sustainability Public-private partnerships in European logistics Information sharing as a CE enabler Scientific Awards: 2020 Best Paper Award, International Journal of Operations & Production Management (IJOPM) Research Projects include: CREAToR: EU Horizon 2020 project on polymer recycling GATE: German Ministry of Economy-funded GHG emissions data exchange HANSEBLOC: Blockchain applications in logistics ChainLog: Blockchain use cases Development of Logistics Competence Assessment Toolkit
Matthias WeidlichView profile
Professor
Prof. Dr. Matthias Weidlich is a faculty member at Humboldt University of Berlin within the Institute of Computer Science under the Faculty of Mathematics and Natural Sciences . His research focuses on Process Mining , Complex Event Processing , and Data Privacy with applications in Business Process Management and Scientific Workflows . Research Interests: Business Process Management and Process Mining Complex Event Processing and Stream Data Analysis Data Privacy and Security in Process Systems Scientific Workflow Systems and User Behavior Heterogeneous Network Embeddings Algorithm Design and Optimization Recent Publications (2023-2025) demonstrate expertise in: Efficient stream processing techniques Privacy-preserving process mining frameworks Scientific workflow analysis tools Graph neural network applications Multi-modal data integration Adaptive querying systems Contact: Office: Unter den Linden 6, 10099 Berlin Phone: 030 2093-41277 Email: matthias.weidlich@hu-berlin.de Web: hu.berlin/data
Min PengView profile
Professor
Min Peng is a Professor at Wuhan University's School of Computer Science. His research focuses on artificial intelligence, machine learning, natural language processing, and knowledge graphs. He has collaborated extensively with institutions like Hefei University of Technology and the University of Chinese Academy of Sciences. His work bridges theoretical advancements in AI with practical applications in finance, social media analysis, and network optimization. Recent contributions include neural-symbolic reasoning frameworks, contrastive learning for knowledge graphs, and financial benchmarking with large language models. Research interests emphasize scalable machine learning models for complex reasoning tasks, explainable AI, and domain-specific applications in finance and social networks. Over 100 publications span venues like WWW, ACL, and NeurIPS, highlighting interdisciplinary impact. Notable projects include SymAgent (neural-symbolic agent frameworks), PIXIU (financial LLM benchmark), and DTC (commonsense machine comprehension). Key technical trends include integrating large language models with structured data, temporal knowledge graph reasoning, and transfer learning across domains. His work often addresses real-world challenges in data efficiency, interpretability, and cross-domain scalability. Current efforts explore financial LLMs, agent-based reasoning systems, and multimodal applications. While no specific grants or awards are listed in the provided data, his prolific publication record indicates sustained research excellence. Collaboration networks include teams in computer science, electrical engineering, and finance disciplines.
Riccardo TommasiniView profile
Associate Professor
Riccardo Tommasini is an Associate Professor at INSA Lyon , a leading engineering institution in France. He leads the Stream Processing and Knowledge Graphs research within the DB Team at LIRIS laboratory under Professor Angela Bonifati. His academic journey began with a PhD in Computer Science from Politecnico di Milano under Emanuele Della Valle, with a dissertation titled Velocity on the Web to be published as a Springer book. Research Interests : Advancing stream processing for real-time data systems Extending knowledge graphs with dynamic data Designing graph databases for big data applications Creating query languages for heterogeneous data environments Building data engineering pipelines with Apache Airflow Enabling big graph processing in distributed settings Key Contributions : Developed Zodiac framework for Datalog reasoning under rule amendments (ICDE 2025) Co-authored foundational Streaming Linked Data book with Springer (2023) Created RSP4J API for RDF stream processing (ESWC 2021) Designed challenge-based learning curriculum for Data Engineering courses Scientific Recognition : Received ANR JCJC grant for POLYFLOW project (2024) Awarded Best Resource at ESWC 2021 Managed industrial collaborations with Neo4j, InfluxData, and Confluent Advising & Teaching : Supervises Mohamed Ragab (PhD candidate at University of Tartu) Course Leadership : Foundational Data Engineering course at INSA Lyon and University of Tartu Structured around Apache Airflow , Docker, and graph databases
Steve ZdancewicView profile
Professor
Steve Zdancewic is the Schlein Family President's Distinguished Professor and Associate Chair in the Department of Computer and Information Science at the University of Pennsylvania's School of Engineering and Applied Science. He is a leading researcher in programming languages, formal methods, and computer security with over two decades of impactful contributions to the field. His research interests span programming languages, type theory, logic, computer security, quantum programming, and formal verification. Zdancewic has made significant contributions to information-flow security, memory safety, program synthesis, and the verification of low-level systems. His work often bridges theoretical foundations with practical applications, particularly through the development of verified systems using Coq and other proof assistants. Analysis of his recent publications reveals a strong focus on formal verification techniques, particularly using Interaction Trees and the Coq proof assistant. His research trajectory shows consistent evolution from foundational work on information-flow security toward increasingly sophisticated verification of complex systems including LLVM, quantum computing, and distributed systems. His work demonstrates a commitment to building practically useful verification tools while maintaining rigorous theoretical foundations. Distinguished Paper Award for Semantics for Noninterference with Interaction Trees (ECOOP 2023) Schlein Family President's Distinguished Professor (2021) Distinguished Paper Award for Interaction Trees (POPL 2020) Christian R. and Mary F. Lindback Foundation Award for Distinguished Teaching (2018) IEEE MICRO top picks (2013) Alfred P. Sloan Fellow (2009-2010) NSF CAREER award (2004) Zdancewic has advised numerous PhD students who have gone on to successful careers in academia and industry. His research has been supported by significant grants from NSF, including the NSF Expedition on the Science of Deep Specification. He is actively involved in multiple major research projects including Vellvm (verified LLVM), DeepSpec, and quantum programming verification. Zdancewic also co-organizes Penn's PL Club programming languages research group with Benjamin Pierce and Stephanie Weirich.
Harald KrügerView profile
Researcher
Harald Krüger is a Research Scientist at the Max Planck Institute for Solar System Research (Göttingen), specializing in planetary science and space dust analysis. He leads and contributes to multiple space missions, including the DESTINY+ Dust Analyzer (DDA), the Rosetta COSIMA instrument, and the PHILAE Dust Impact Monitor (SESAME-DIM). His research focuses on cometary dynamics, interstellar dust interactions, and solar system formation processes. Krüger holds a PhD from the University of Göttingen and has held research roles at prestigious institutions like the Max Planck Institutes for Nuclear Physics and Astronomy (Heidelberg). He chairs ESA’s working group on comet 67P/Churyumov-Gerasimenko and participates in international astronomical societies. His work integrates advanced instrumentation design with observational data from spacecraft missions, contributing to our understanding of cosmic dust properties and their role in planetary systems. Key projects include analyzing interstellar dust via the DESTINY+ mission and studying Martian moons through the MMX mission. His publications emphasize instrument constraints, cometary dust trail detection, and spacecraft outgassing effects on measurements.
Tasos DagiuklasView profile
Professor
Tasos Dagiuklas is a Professor in the Department of Computer Science and Technology within the School of Engineering and Technology at the University of Bedfordshire. With over 168 publications spanning from 1995 to 2025, he has established himself as a leading researcher in telecommunications and network systems. His extensive publication record demonstrates continuous scholarly contribution across multiple decades in the field. Professor Dagiuklas' research focuses on wireless communications, edge computing, 5G/6G networks, quality of experience (QoE), and federated learning . His work bridges theoretical networking concepts with practical applications, particularly in multimedia delivery and security. He has developed significant expertise in video streaming optimization, network security mechanisms, and resource management in emerging network architectures. His research consistently addresses the evolving challenges of modern communication systems, with recent work increasingly focusing on AI integration in networking. Analysis of his recent publications (2023-2025) reveals a strong trend toward edge computing, federated learning, and security applications in next-generation networks. His work demonstrates a strategic shift from traditional networking concerns to more complex systems involving AI integration, energy efficiency, and heterogeneous environments. The publications show consistent collaboration with researchers across multiple institutions, with particularly strong partnerships with Muddesar Iqbal, Ilias Politis, and Stavros Kotsopoulos. Professor Dagiuklas has made substantial contributions to the academic community through his extensive publication record in high-impact venues including IEEE journals and conferences. His work has evolved from foundational networking research to cutting-edge investigations of AI-enhanced communication systems, reflecting the broader trajectory of the field itself. His research demonstrates both technical depth in specific networking challenges and breadth across multiple application domains.
Tej ChajedView profile
Assistant Professor
- Formal verification of systems software
- Concurrent systems verification
- Crash-safe file systems
- +5 more
Tej Chajed is an Assistant Professor in the Department of Computer Science at the University of Wisconsin-Madison, where he conducts research in formal verification of systems software. His work focuses on building and proving the correctness of critical systems, particularly file systems and concurrent software. Dr. Chajed earned his PhD from MIT in the PDOS group, followed by a one-year postdoc at VMware Research before joining UW-Madison. His academic journey reflects a strong commitment to bridging theoretical formal methods with practical systems implementation. Chajed's research centers on formal verification techniques for systems software, with particular emphasis on concurrent and crash-safe systems . His work aims to eliminate bugs in critical software through mathematical proofs of correctness. Key contributions include DaisyNFS (a verified concurrent file system), the Perennial framework for reasoning about crash safety, and Goose for connecting proofs to Go code. His research spans the intersection of programming languages, operating systems, and formal methods, developing practical tools that bring verification to real-world systems. His recent publications demonstrate a consistent trajectory toward more practical and scalable verification techniques for increasingly complex systems. The research shows progression from foundational verification frameworks to applied work on specific systems like file systems, journaling, and distributed protocols. A notable trend is the focus on making verification more accessible and practical for systems developers, bridging the gap between theoretical formal methods and real-world software engineering. Dr. Chajed serves on numerous program committees including OSDI 2025 PC, PLDI 2024 PC, SySDW 2023 PC, ECOOP 2023 ERC, CPP 2023 PC, POPL 2023 PC, PLDI 2022 PC, POPL 2022 AEC, EuroDW 2021 PC, POPL 2021 AEC, PLDI 2020 AEC, POPL 2020 AEC, and SOSP 2019 AEC, reflecting his standing in the systems and programming languages research community. In teaching, Chajed has developed and instructed courses on systems verification, operating systems, and protocol verification. He previously helped create MIT's 6.826 (Principles of Computer Systems) during his PhD. His passion for technical communication was cultivated during his time as a Communication Fellow in the EECS Communication Lab at MIT, where he continues to offer guidance to students on writing and presentation skills. His research group at UW-Madison focuses on advancing the state of the art in systems verification, with current projects centered around practical verification frameworks for concurrent and crash-safe systems.
Saman AmarasingheView profile
Professor
Professor Saman Amarasinghe is a full Professor in the Department of Electrical Engineering and Computer Science (EECS) at the Massachusetts Institute of Technology (MIT), and Principal Investigator at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL). He leads the Commit compiler research group, which focuses on programming languages and compilers that maximize application performance on modern computing platforms. His work spans multiple academic departments and research centers, with strong affiliations to both MIT's School of Engineering and CSAIL. Professor Amarasinghe's research interests center around high-performance domain-specific languages and compiler technology . His work combines language design with sophisticated compilation techniques to deliver unprecedented performance for targeted application domains. His research spans multiple areas including image processing (Halide), sparse tensor algebra (TACO), graph analytics (GraphIt), stream computations (StreamIt), and bioinformatics (Seq). A significant thread throughout his work is the application of machine learning for compiler optimizations, from Meta optimization in 2003 to the OpenTuner autotuner framework. Analysis of Professor Amarasinghe's recent publications reveals a strong focus on sparse computing , compiler vectorization , and domain-specific language implementation . His work consistently bridges theoretical compiler concepts with practical performance gains across diverse application domains. The progression from earlier work on StreamIt and Halide to more recent projects like GraphIt and TACO shows an evolution toward more specialized, high-performance DSLs targeting specific computational patterns. His 2020-2025 publications particularly emphasize sparse tensor operations, GPU acceleration, and machine learning integration with compiler technology. ACM Fellow (2019) Professor Amarasinghe has made significant contributions to academic entrepreneurship and student development. He founded Determina, Inc. (acquired by VMware) based on security research from his MIT lab and co-founded Lanka Internet Services, Ltd., Sri Lanka's first ISP. As faculty director of MIT Global Startup Labs, his programs across 17 countries have helped create over 20 successful startups. His teaching includes the popular Performance Engineering of Software Systems (6.172) course with Professor Charles Leiserson, as well as innovative project-based courses like the Open Source Software Project Lab and Bring Your Own Software Project Lab. His educational approach emphasizes hands-on experience with compiler and language design concepts. Professor Amarasinghe leads the Commit compiler research group at MIT CSAIL, which has produced numerous influential domain-specific languages and compilers including Halide, TACO, Simit, StreamIt, and GraphIt. The lab maintains strong industry connections through projects like OpenTuner and Determina, and collaborates with researchers worldwide on compiler technology. The group's work spans both theoretical compiler research and practical implementation, with a consistent focus on bridging the performance gap between high-level programming abstractions and hardware capabilities.
Amr RizkView profile
Professor
Professor Amr Rizk is the Director of the Networks and Communication Systems (NCS) Lab at the University of Duisburg-Essen, where he has been serving as Professor since April 2021. Previously, he was Assistant Professor at Ulm University (2019-2021) and completed his habilitation at TU Darmstadt in 2019. His academic journey includes research positions at prestigious institutions including University of Massachusetts Amherst, University of Warwick, and TU Darmstadt where he was an Athene Young Investigator. Professor Rizk's research spans multiple aspects of networking and communication systems with a particular focus on network performance analysis, stochastic modeling, and practical implementations. His work bridges theoretical foundations with real-world applications, especially in content delivery, video streaming, and network protocols. He has made significant contributions to network calculus, quality of experience optimization, and novel approaches to congestion control and caching mechanisms. His publication record demonstrates consistent high-impact contributions across top networking conferences and journals. Recent work shows a growing emphasis on programmable data planes, AI/ML applications in networking, and advanced techniques for network measurement and performance prediction. His research group at Duisburg-Essen maintains strong connections with both academic and industrial partners in the networking ecosystem. Best Paper Award at ACM MMSys Conference (2023) Distinguished TPC Member for IEEE INFOCOM (2020, 2022) Best Paper Award at ACM/USENIX Middleware Conference (2017) Athene Young Investigator Award, TU Darmstadt (2017) Professor Rizk serves as Associate Editor for Elsevier Computer Communications and has extensive experience with research funding bodies as a reviewer. His leadership extends to conference organization, including roles as PC Co-Chair for IEEE MIPR (2023) and Steering Committee member for Workshop on Network Calculus (2022). He maintains active participation in numerous top networking conferences as Technical Program Committee member, reflecting his standing within the international networking research community.
Danh Le PhuocView profile
Researcher
Danh Le Phuoc is a Principal Computer Scientist at Technical University of Berlin, leading research at the PICOM.AI lab where his team develops autonomous information systems for robotics, autonomous vehicles, and IoT systems through pervasive intelligence in complex networks. With over 70 publications and significant academic impact (6811 citations, H-index 31), he has established himself as a notable researcher in semantic technologies. His educational background isn't explicitly detailed in the provided text, but his research expertise spans multiple domains requiring advanced technical knowledge. Le Phuoc's research focuses on bridging theoretical concepts with practical system implementation, particularly in RDF Stream Processing, Semantic Web technologies, and IoT middleware. His work emphasizes building real-world systems that process linked streams and data in real-time, enabling applications in intelligent transportation systems and connected vehicles. His research trajectory shows consistent innovation from foundational work on semantic mashups (2009) through to advanced stream processing frameworks (2017). His publications reveal strong trends toward processing real-time semantic data streams, with increasing focus on scalability, performance optimization, and integration of IoT systems with knowledge graphs. The progression shows movement from basic semantic web pipes to complex, elastic cloud-based stream processing systems. 22 Awards, Honours, Fellowships and Grants 10+ awards for innovative Semantic Web applications Multiple honors for IoT applications As an obsessive builder, Le Phuoc has developed numerous influential systems including The Graph of Things, CQELS (Continuous Query Evaluation over Linked Streams), Semantic Web Pipes, and Linked Sensor/Stream Middleware. His current focus is on ASAP (Autonomous Semantic Stream Processing), a platform for connected vehicles and intelligent transportation systems. His lab appears to maintain active GitHub repositories for several of these systems, suggesting ongoing development and community engagement.
Michael MöhringView profile
Professor
Prof. Dr.-Ing. Michael Möhring is a Professor of Data Science at Reutlingen University's Faculty of Informatics. He serves as Prodekan for the Herman Hollerith Zentrum (HHZ) and leads research in data analytics, Industry 4.0, and process mining. Previously, he held roles as an IT consultant, project manager at Bosch Group/BSH, and academic researcher. Education: Dr.-Ing. (PhD) in Business Informatics M.Sc. in Business Informatics B.Sc. in Business Informatics Research Interests: Focuses on leveraging structured/unstructured data for industrial applications, enterprise architecture management, digital twins integration, and AI-driven decision support. Specializes in bridging technical systems with organizational processes in manufacturing and service industries. Lab Affiliations: AI-Real Lab AIDA Future Mobility Lab Internet of Things Lab Virtual Reality Lab Articles Trends: Recent work emphasizes practical implementations of AI in production failure analysis (language models), energy optimization systems (HollerithEnergyML), and technical debt management in SMEs. Consistently explores data integration challenges across manufacturing, service ecosystems, and digital twin frameworks. Grants & Collaborations: Active in EU-funded projects like 5G-PreCiSe and bwHealthApp. Collaborates with industry partners on digital transformation initiatives through HHZ's applied research programs.
Milos HauskrechtView profile
Professor
Dr. Milos Hauskrecht is a Professor of Computer Science at the University of Pittsburgh's School of Computing and Information. He holds a PhD from MIT (1997) and an M.Sc. from Slovak Technical University (1988). His research focuses on AI, machine learning, and data mining, with applications in medicine and finance. He leads projects in real-time clinical monitoring, anomaly detection, and time-series analysis of EHR data. He has advised numerous PhD and MS students, including notable alumni now at Amazon, DeepMind, and Microsoft. Research interests include reasoning under uncertainty, optimization, and AI-driven medical decision support. Current grants include NIH funding for AI in renal therapy and clinical monitoring. He has published widely in top venues like ICML, NeurIPS, and journals such as Artificial Intelligence in Medicine. His work on conditional outlier detection earned the Homer Warner Award (AMIA 2010). He teaches machine learning and advises on interdisciplinary AI projects.