جست‌وجوی استاد و پژوهشگر

Markus Heinrichمشاهده پروفایل

دانشیار

• Organic Chemistry
• Pharmaceutical Chemistry
• Radical Chemistry
• +۳ مورد دیگر

Prof. Dr. Markus Heinrich is an Associate Professor (W2) of Pharmaceutical Chemistry at the Department of Chemistry and Pharmacy, Friedrich-Alexander-University Erlangen-Nuremberg (FAU). He leads the Heinrich Laboratory, focusing on radical chemistry, drug synthesis, and functionalization methods. His research spans organic chemistry, medicinal chemistry, and materials science, with applications in drug development, nanotechnology, and sustainable processes. Education: Ph.D. in Organic Chemistry (2003), Ludwig-Maximilians-University Munich Habilitation in Organic Chemistry (2009), Technical University of Munich Research Interests: Heinrich's work emphasizes radical-based methodologies for drug synthesis, including arylation reactions, fluorination, and late-stage functionalization of APIs. His lab explores applications in medicinal chemistry (e.g., dopamine receptors, GPCRs), nanomaterials (graphene functionalization), and industrial processes. Notable specialties include Meerwein arylation, titanium-mediated reactions, and photochemical catalysis. Recent Article Trends: Publications highlight advances in radical chemistry (e.g., fluorination under visible light), graphene functionalization, and drug design targeting dopamine receptors and viral signaling pathways (e.g., US28 receptor of human cytomegalovirus). Awards: Liebig Fellowship (Chemical Industry Fund) ADUC Annual Prize for Habilitands (2008) Advising & Grants: Supervised over 40 Ph.D./Master students and apprentices since 2009. Collaborates with institutions like UC Irvine (Overman group) and industry partners (e.g., Sandoz). Active in funding programs (e.g., European Research Council, DFG). Labs & Teams: Heinrich Laboratory at FAU hosts interdisciplinary teams working on radical chemistry, medicinal synthesis, and materials science. Key projects include development of fluorinated drug candidates and graphene-based nanomaterials.

Raquel Campos Valverdeمشاهده پروفایل

پژوهشگر ارشد

• Popular Music
• Ethnomusicology
• Internet Studies
• +۷ مورد دیگر

Dr. Raquel Campos Valverde serves as a Postdoctoral Research Fellow at the School of Media and Communication, University of Leeds, where she investigates digital music cultures through the MUSICSTREAM project. Her work bridges Popular Music studies, Ethnomusicology, and Internet Research with a focus on user experiences in streaming ecosystems and social media environments, particularly within Spanish cultural contexts. Her academic foundation includes: PhD in Arts and Creative Industries from London South Bank University (2019) MMus in Ethnomusicology from SOAS, University of London BHons in Sociology from Universidad de Granada, Spain Campos Valverde's research critically examines how digital infrastructures shape musical experiences, with current emphasis on cultural inequality in streaming taxonomies and algorithmic recommenders. She employs digital ethnography to analyze music circulation across social platforms, exploring moral economies of sharing and imagined listening communities. Her scholarship reveals persistent tensions between platform design and cultural diversity, highlighting how technical architectures reinforce systemic biases in music discovery and consumption. Analysis of her publication trajectory shows evolving focus from social media musicking (2018-2022) toward critical platform studies (2023-2025), with consistent attention to ethical implications of digital music environments. Recent work demonstrates sophisticated interdisciplinary methodology combining interface analysis, ethnographic observation, and critical algorithm studies. Her recognition includes: 2019 Andrew Goodwin Memorial Postgraduate Prize from IASPM UK/Ireland Campos Valverde maintains active engagement with academic communities through editorial contributions to the Open Encyclopedia of Anthropology and IASPM Journal. Her prior teaching role at King's College London demonstrates commitment to knowledge dissemination, while her current research directly addresses urgent questions about equity in digital cultural infrastructure. The MUSICSTREAM project positions her at the forefront of critical platform studies within music scholarship. As core researcher in the MUSICSTREAM initiative, she collaborates with interdisciplinary teams to deconstruct streaming platform architectures, producing actionable insights for more inclusive music technology design through rigorous ethnographic and critical technical approaches.

Riccardo Lazzerettiمشاهده پروفایل

دانشیار

• Privacy-Preserving Technologies
• Secure Multi-Party Computation
• Biometric Security
• +۵ مورد دیگر

Riccardo Lazzeretti serves as Associate Professor at Sapienza University of Rome since September 2022, following progression from Assistant Professor roles (RTD-A/B) at the same institution from 2017-2022. His academic trajectory includes post-doctoral research at the University of Siena's VIPP group, a research grant at the University of Padua, and industry experience at Italian startup Cynny s.p.a. His educational foundation comprises: MSc in Computer Science Engineering, University of Siena (2007) PhD in Information Engineering, University of Siena (2012) Lazzeretti's research centers on privacy-preserving technologies with emphasis on Secure Multi-Party Computation for encrypted signal processing. His work spans biometric security (including multi-biometric authentication), IoT security protocols, blockchain applications in healthcare, and social network misinformation detection. Recent publications demonstrate deep integration of cryptographic techniques with machine learning for resource-constrained devices and critical infrastructure protection. Analysis of his 15 most recent publications reveals dominant trends in IoT/drone security (32% of works), browser/API vulnerabilities (13%), and privacy-preserving architectures (27%). His research consistently bridges theoretical cryptography with practical system implementations, particularly for emerging technologies like NDN and programmable data planes. His scholarly recognition includes: SIGMM Test of Time Paper Honourable Mention (2021) for Multimedia Security contributions IET Biometrics Premium Best Paper Award (2021) Lazzeretti has supervised graduate students at the University of Siena and secured research funding through collaborations with NATO's CMRE, Digital Catapult (UK), and IMT Lucca. Current projects include privacy-preserving consensus algorithms for IoT and blockchain-based health applications. He maintains active partnerships with European research entities including Centre for Maritime Research and Experimentation and Digital Catapult. His laboratory affiliations encompass the VIPP research group at University of Siena and ongoing collaborations with Sapienza's cybersecurity initiatives, focusing on cross-disciplinary teams integrating signal processing, cryptography, and machine learning expertise.

Jolanta Jaśkowskaمشاهده پروفایل

مدرس

• Medicinal chemistry
• Pharmaceutical technology
• Organic synthesis
• +۷ مورد دیگر

Dr hab. n. farm. inż. Jolanta Jaśkowska is a Lecturer at the Faculty of Chemical Engineering and Technology , Tadeusz Kościuszko Cracow University of Technology, working within the Department of Organic Chemistry and Technology . Holding an MSc Eng. in Light Organic Technology (2005) and a PhD in Chemistry (2010) from the same university, she combines extensive academic training with hands-on international experience gained at the Institute of Pharmacology of the Polish Academy of Sciences (2008) and Stanford University (2015). Research Interests Medicinal & Pharmaceutical Chemistry – design and synthesis of bioactive small molecules targeting CNS disorders. Green & Sustainable Chemistry – solvent-free, microwave- and ultrasound-assisted protocols for API synthesis. Serotonergic Ligands – dual 5-HT1A/5-HT7 receptor modulators, trazodone and aripiprazole analogues. Pre-clinical Evaluation – antidepressant, anxiolytic, antipsychotic and anti-amnesic activities in rodent models. Her recent publications (2022-2025) reveal a clear trend toward environmentally benign synthetic methodologies coupled with multimodal CNS ligands . Key themes include 1,3,5-triazine scaffolds as anticancer agents, cholesterol-doxorubicin conjugates for targeted therapy, and advanced arylpiperazine salicylamides as dual receptor ligands. The body of work demonstrates strong synergy between synthetic innovation and pharmacological validation. Scientific Awards & Recognition Special Prize, Polish Chamber of Chemical Industry – 2016 Silver Medal, 10th IWIS Warsaw – 2016 INNOVATORENPREIS Special Award, Nuremberg – 2017 Honorary Mention, Taipei International Invention Show – 2017 Gold Medal, 15th ARCA Zagreb – 2017 Bronze Medal, KIWIE Seoul – 2017 Research Funding & Leadership 2008–2012 – Development of an innovative CNS drug (schizophrenia, depression, anxiety) 2016–2019 – LIDER grant: synthesis and activity of novel CNS receptor ligands 2017–2018 – TRL+ project: new manufacturing route for trazodone Laboratory & Team Her research is conducted in the Organic Chemistry & Technology laboratories (rooms 534 & 538) of the Faculty of Chemical Engineering and Technology, equipped with modern microwave, ultrasound and analytical instrumentation. While no explicit student names are listed, she teaches Organic Chemistry labs, Drug Technology lectures/labs, and seminars on dietary supplements and herbal medicine, indicating active graduate supervision.

Michael B. Jamesمشاهده پروفایل

پژوهشگر

• Programming Languages
• Program Synthesis
• Human-Computer Interactions
• +۲ مورد دیگر

Michael B. James is a researcher at the University of California at San Diego, focusing on Programming Languages , Program Synthesis , and Human-Computer Interactions . He serves on organizing and review committees for conferences like PLDI and SPLASH. Research Interests: Programming Languages, Program Synthesis, Human-Computer Interactions, Functional Programming Conference Roles: PLDI 2023/2024 Volunteers Co-Chair, SPLASH 2023/2021/2020 author, POPL 2020 committee member His recent work explores how programmers interact with code-generating models and leverages type systems for synthesis tasks. Articles span API discovery in Haskell, abstraction refinement, and refinement types for program synthesis. As a GitHub contributor, he maintains the Academic Pages template , demonstrating technical proficiency in web-based academic outreach.

• Condensed Matter Physics
• Quantum Information Science
• Electronic Structure Theory
• +۵ مورد دیگر

Marco Govoni is an Associate Professor at the University of Modena and Reggio Emilia, affiliated with the Department of Physics, Informatics, and Mathematics. His research focuses on quantum defects, electronic structure theory, and many-body perturbation methods (e.g., GW/BSE), with applications to spin defects in semiconductors for quantum technologies, perovskites, and heterogeneous materials. He develops computational tools like the WEST code and explores quantum simulations on high-performance and near-term quantum computers. He teaches courses in General Physics and Theoretical Physics, emphasizing computational methods and material science. Research interests include: condensed matter physics, quantum computing, electronic structure theory, defect engineering, and computational material discovery. His work addresses challenges in simulating large systems, optimizing hybrid functionals, and leveraging GPU acceleration for many-body calculations. Key contributions include studies on self-trapped excitons, spin defects in diamond/SiC, and dielectric-dependent functionals for heterogeneous interfaces. Publications span topics like quantum defect embedding theory, GPU-accelerated Bethe-Salpeter equation solutions, and machine learning for dielectric screening. He collaborates widely, contributing to software interoperability and exascale computing strategies for electronic structure codes.

• Software Engineering
• Cybersecurity
• Internet of Things (IoT)
• +۴ مورد دیگر

Jacques Pasquier is a Professor in the Department of Informatics at the University of Fribourg, Switzerland, and Head of the Software Engineering Group. His academic affiliation falls under the Interfaculty Informatics unit, emphasizing interdisciplinary research and education in computer science. His research and teaching focus on software engineering, cybersecurity, IoT systems, and agile methodologies. Research interests include IoT security, blockchain applications in decentralized networks, and privacy-preserving technologies. He has contributed to frameworks for secure IoT middleware, lightweight communication protocols, and trusted execution environments. His work bridges theoretical advancements with practical implementations, such as the ImputeGAP library for time series imputation and the Universal Explorer for Web of Things interoperability. Publications span topics like secure IoT infrastructure, blockchain-enabled LPWAN systems, and agile development metrics. His articles often address challenges in privacy, scalability, and resilience within distributed systems. While no specific awards are mentioned, his prolific publication record highlights sustained contributions to computer science and engineering. Led the Software Engineering Group, focusing on advancing methodologies for modern software systems. Collaborates on projects involving industry and academic partners to address real-world challenges in IoT and cybersecurity.

Siau-Cheng Khooمشاهده پروفایل

دانشیار

• Program Analysis
• Specification Mining
• Software Verification
• +۴ مورد دیگر

Siau-Cheng Khoo is an Associate Professor at the Department of Computer Science, School of Computing, National University of Singapore (NUS). He serves as Co-Director of the Master of Science (Business Analytics) programme at the NUS Business Analytics Centre. His research focuses on program analysis, specification mining, software verification, and aspect-oriented programming. Education: PhD in Computer Science from Yale University (1992). Research Interests: His work spans scalable code analysis, bug signature mining, neural network verification, and software specification discovery. Key methodologies include static/dynamic program analysis, vector abstraction, and discriminative pattern mining for software dependability. Recent Research Trends: Publications emphasize automated verification techniques (especially for neural networks), scalable refactoring detection, and mining-based bug diagnosis. Earlier work pioneered parameterized partial evaluation and functional aspect-oriented programming. Grants & Supervision: vSCA: Scalable Code Analysis (AcRF Tier 1, 2014–2017, $130,000) Bug Signature Discovery (AcRF Tier 1, 2012–2015) Supervised 7 PhD and 6 MSc graduates; currently advising 1 PhD candidate.

• Software Engineering
• Software Security
• Machine Learning for Code
• +۲ مورد دیگر

Qing Liao is a Researcher at Harbin Institute of Technology, specializing in software engineering with a focus on security, machine learning applications for code, and automation. His work bridges theoretical and practical challenges in modern software systems. Research Interests: Qing's research spans several interconnected areas: Software Security : Vulnerability detection, patch analysis, and configuration security. Machine Learning for Code : Application of ML models (e.g., transformers, graph networks) to code understanding, generation, and API recommendation. Program Analysis : Techniques for static analysis, browser fuzzing, and performance tuning. Automation : Tools for IaC generation, UI-to-code transformation, and configuration optimization. Publication Trends (2022–2026): His recent publications emphasize security (7/11 papers), particularly vulnerability detection using graph learning and static analysis. A secondary focus is ML-driven code automation (4/11 papers), including knowledge distillation, API generation, and UI-to-code systems. Work consistently targets real-world applicability, evidenced by industry-track publications at ASE/ICSE.

• Computer Graphics
• Human-Computer Interaction
• Information Visualization
• +۹ مورد دیگر

Dr. Feng Liu serves as Professor and Chair of the Department of Informatics and Mathematics within Mercer University's College of Professional Advancement, while also coordinating the B.S. in Software Application Development and Human-Computer Interaction program. Her leadership spans curriculum development, department administration, and program coordination across multiple technical disciplines. Her academic credentials include a PhD and MS in Computer Science from Georgia State University (2005, 2000), an MBA in Business Administration from Mercer University (2010), and a BE in Automobile Engineering from Jilin University of Technology (1995). Professional certifications include a UX Certificate from Nielsen Norman Group (2016) and Interaction Design Specialization from UC San Diego, complemented by recent data analysis training through SimpliV. Dr. Liu's research program centers on Human-Computer Interaction with specialized expertise in Computer Graphics, Information Visualization, and immersive technologies including Virtual Reality, Augmented Reality, Web3D, and the Metaverse. Her work uniquely bridges technical innovation with human-centered applications, particularly focusing on user experience design, educational technology, and community impact. She has pioneered two significant educational frameworks: the Community Project Based Learning Model (CPBLM) that integrates real-world service projects into technical coursework, and the Concurrent Learning/Teaching Model (CLTM) enabling seamless online/in-person student collaboration. Recent publications (2021-2024) demonstrate her evolving research trajectory from educational technology toward ethical metaverse development, with particular emphasis on designing child-safe digital environments using AI and psychological principles. Her scholarly output spans ACM conferences, educational technology journals, and interdisciplinary publications addressing both technical innovation and social impact. Dr. Liu's teaching excellence has been formally recognized through Mercer University's 'Excellent in Teaching' award (2021) and three consecutive Teaching Innovation nominations (2017-2019). She maintains active professional engagement as Chairperson of the Web3D User Experience Working Group and through presentations at major conferences including ACM Web3D, ASCUE, and AAC&U. Her departmental leadership has driven the development of Informatics, Computer Graphics, and Data Science programs since 2011, with particular focus on serving non-traditional students through High-Impact Practices. Through her Web3D Consortium leadership and community-focused teaching model, Dr. Liu cultivates research environments where students develop technical skills while addressing real societal challenges. Her current work explores the critical intersection of emerging technologies and human well-being, positioning her at the forefront of responsible innovation in immersive digital spaces.

David Johnsonمشاهده پروفایل

دانشیار

• Information Systems
• Artificial Intelligence
• Data Infrastructures
• +۳ مورد دیگر

David Johnson is an Associate Professor of Information Systems at Uppsala University's Department of Informatics and Media. He also holds positions as a Visiting Fellow at the University of Oxford's Kellogg College and Senior Associate Tutor (Honorary) at Oxford's Department for Continuing Education. His academic background includes a PhD in Computer Science from the University of Reading, UK, and a Postgraduate Certificate in Teaching and Learning in Higher Education from the University of Oxford. Previously, he held research positions at the University of Oxford's Department of Engineering Science, Imperial College London's Data Science Institute, and Oxford's Department of Computer Science. Dr. Johnson's research focuses on data infrastructures supporting data-intensive scientific research and the practices around using data and artificial intelligence. His expertise spans information systems, artificial intelligence, data infrastructures, interoperability, and life sciences research. He has directed significant projects including European-funded research from the EU Sixth Framework Programme to Horizon 2020, with recent work focusing on AI replication practices in life sciences and the adoption of large language models for citizen services. His publication record shows a clear evolution from technical data infrastructure development toward interdisciplinary research addressing ethical considerations, transparency, and trust in AI systems. Recent works increasingly bridge technical aspects with societal implications, policy considerations, and educational frameworks for AI. University of Oxford Teaching Award (Mathematical, Physical and Life Sciences Division), 2016 New & Future Educators Award jointly given by AAAI and ACM, 2022 Dr. Johnson directs the Masters Programme in Information Systems at Uppsala University and serves as course director for several key courses including Data Mining and Applied Machine Learning (Bachelors), Digital Infrastructure (Masters), Artificial Intelligence and Machine Learning (Masters), and Big Data Analytics (Masters). He is section leader for Information Systems at his department and a member of the Uppsala University Computational Social Science Lab. His grant portfolio includes European-funded research projects and a Vinnova-funded project on large language models for citizen services. He maintains active international collaborations and has consulted for the United Nations on big data and data science, demonstrating strong connections between academic research and practical implementation in diverse contexts.

Jimmy Linمشاهده پروفایل

استاد

• Information Retrieval
• Natural Language Processing
• Data Management
• +۲ مورد دیگر

Jimmy Lin is a Professor and David R. Cheriton Chair at the University of Waterloo's David R. Cheriton School of Computer Science. His research focuses on information retrieval, natural language processing, and data management, with applications in large-scale data analysis and AI systems. Previously at the University of Maryland, he earned his PhD from MIT in 2004. PhD in Electrical Engineering and Computer Science, MIT (2004) MS, Mechanical Engineering, Penn State University BSc, Mechanical Engineering, Addis Ababa University His research integrates information retrieval, NLP, and data management to build tools for understanding large datasets. Current interests include neural retrieval models, cross-lingual systems, and efficient search algorithms. Recent publications focus on retrieval-augmented generation (RAG), model efficiency, multilingual NLP, and evaluation methodologies. Trends include hybrid dense-sparse retrieval systems, robustness improvements, and practical toolkits like Anserini and Tevatron. ACM Fellow SIGIR Academy ACL Fellow SAE Ralph R. Teetor Educational Award (2014) NSF CAREER Award (2011) He advises 36+ PhD/Master's students and leads multiple industry collaborations (Primal, RSVP, Twitter). His lab develops open-source tools for retrieval and NLP, focusing on scalable and multilingual applications.

• System and Software Security
• Embedded Systems Security
• Firmware Analysis
• +۴ مورد دیگر

Aravind Machiry is an Assistant Professor at the Elmore Family School of Electrical and Computer Engineering at Purdue University. His research focuses on system and software security, particularly in securing embedded systems, firmware, and leveraging modern tools like static analysis and fuzzing to mitigate vulnerabilities. He leads the Purdue Systems and Software Security Lab (PurS3) , which emphasizes principled yet practical security solutions. Research interests include embedded systems security (e.g., Rust adoption in embedded contexts), vulnerability detection in firmware and network stacks, and securing continuous integration workflows. He has explored topics like language model integration in Android apps, fault injection in API error handling, and mitigating data poisoning in federated learning. His work bridges hardware and software domains, with contributions to secure execution environments (e.g., ARM TrustZone via Tarnhelm) and binary analysis tools like BinTrimmer. While no formal awards are listed, his grant-funded projects (e.g., CAREER award) reflect recognition of his research impact. Advising focuses on graduate students in cybersecurity and embedded systems domains. Key projects include rehosting embedded applications as Linux apps (LEMIX), automated firmware analysis (KARONTE), and feedback-driven binary generation (Cornucopia). His research often addresses scalability challenges in vulnerability detection across large codebases.

• Linked Data
• Statistical Data Integration
• Data Mashups
• +۴ مورد دیگر

Tuan Dat Trinh is a Researcher at Vienna University of Technology's Network Lab, holding a Dr.techn. degree in technical sciences. His work bridges academic research and practical data integration solutions within the university's engineering ecosystem. His primary research focuses on Linked Data processing , statistical data integration , and widget-based mashup platforms . Key contributions include the Linked Widgets framework for open data exploration and StatSpace for statistical data analysis, emphasizing user-friendly interfaces that lower technical barriers for non-experts. His methodology combines semantic web technologies with innovative interaction paradigms like drag-and-block interfaces and autocomplete annotation systems. Analysis of his 15 most recent publications (2014-2017) reveals three dominant research trajectories: (1) environmental data stream integration for real-time monitoring systems, (2) statistical data cube exploration through widget composition, and (3) collaborative mashup approaches for data journalism. His work consistently addresses the tension between data complexity and user accessibility, particularly in open government and public sector contexts. As a core member of the Network Lab, Trinh contributes to developing next-generation data integration infrastructure where semantic technologies meet practical usability requirements. His collaborative projects with researchers like Wetz, Do, and Tjoa demonstrate strong interdisciplinary engagement across computer science and domain-specific applications.

• Data Management
• Database Systems
• Data Discovery
• +۶ مورد دیگر

Raul Castro Fernandez is a prominent researcher in data management and database systems, with a focus on data discovery, integration, and marketplaces. He has collaborated extensively with leading institutions and researchers, contributing to projects like Data Station and Nexus for secure data sharing. His work bridges theoretical innovation with practical implementations in cloud optimization, differential privacy, and LLM-driven data tools. Key Contributions : Data market frameworks, LLM applications in databases, differential privacy platforms Collaborators : Yue Gong, Samuel Madden, Michael Stonebraker, Eugene Wu, Kyle Chard Research Themes Fernandez explores automated metadata management for data catalogs, spatiotemporal data sharing with privacy guarantees, and LLM-based data discovery . His work on stateful stream processing (e.g., SABER system) and cost optimization in cloud analytics shows technical depth. Recent Trends 2023-2025 publications highlight his pivot toward LLM applications in data management, including tabular data representation and hypothesis assessment tools. He also investigates sustainability in HPC through carbon credit systems.