جست‌وجوی استاد و پژوهشگر

Bernhard J. Bergerمشاهده پروفایل

مدرس

• Software Security
• Static Code Analysis
• Machine Learning
• +۴ مورد دیگر

Bernhard J. Berger is a Lecturer in the Department of Computer Engineering at the Institute of Embedded Systems, Hamburg University of Technology (TUHH). His research focuses on software security, static code analysis, machine learning, optimization, and research data management. He has held significant roles such as Program Committee member for ICPC 2025 and MSR 2025, and has received awards including the Best Reviewer Award (ICPC 2023) and Best Engineering Paper Award (SCAM 2019). His work spans interdisciplinary applications including maritime systems security, GPU-accelerated AI, and evolutionary algorithms. Recent studies emphasize AI-driven security tools (e.g., ML-SAST) and domain-specific language approaches to optimization (EvoAl). He has contributed to over 30 peer-reviewed publications, with notable work in IEEE Transactions on Software Engineering and Science of Computer Programming. Berger collaborates closely with industry through DAAD review committees and serves on artifact evaluation boards for ISSTA and ARES conferences. Education: Doctoral Thesis (2022), Diploma in Computer Science (2007) Key Projects: ArchSec tool suite, Threat Modeling Frameworks, Bauhaus static analysis methodology Lab Affiliation: Embedded Systems Design Group His advisory roles include Deputy of TUHH's Election Verification Committee and Session Chair at IEEE Congress on Evolutionary Computation 2023. Current research trends integrate machine learning with static analysis for automated vulnerability detection, while also exploring explainable AI techniques for neural network optimization.

John Clarkمشاهده پروفایل

استاد

• Cybersecurity
• Software Engineering
• Artificial Intelligence
• +۵ مورد دیگر

John Clark is a Professor of Computer and Information Security at the University of Sheffield since 2017 and Director of the Siemens Digital MINE. Previously, he held roles as Professor of Critical Systems at the University of York (1992–2017) and worked at Logica in security R&D. He studied Mathematics and Applied Statistics at the University of Oxford. His research focuses on cybersecurity, software engineering, and AI applications, particularly in threat modeling, intrusion detection, quantum cryptanalysis, and secure autonomous systems. Clark leads the Security of Advanced Systems research group and has secured grants totaling over £36 million. Notable projects include the EPSRC-funded DAASE (2012–2019) and the Active Building Centre (2018–2022). His work on phishing detection (e.g., analyzing user behavior) and malware analysis has been widely recognized. He has been awarded the Royal Society Wolfson Merit Award (2013), GEECO medals (2005, 2013), and multiple best-paper prizes. Clark’s research spans theoretical and applied domains, including evolutionary computation for cryptanalysis, robotic system security, and smart grid protection. His labs explore areas like digital twin authentication and privacy-aware energy theft detection. He has supervised numerous grants and maintains active collaborations with industry and academia.

• Computational Biology
• Bioinformatics
• RNA Bioinformatics
• +۴ مورد دیگر

Hosna Jabbari serves as Associate Professor in Biomedical Engineering and cross-appointed in Electrical and Computer Engineering at the University of Alberta's Faculty of Engineering, directing the Computational Biology Research and Analytics Laboratory (COBRA Lab) focused on RNA-centric diagnostics and therapeutics development. Education: BSc in Computer Science, University of Victoria MSc in Computer Science - Bioinformatics, University of British Columbia PhD in Computer Science - Bioinformatics, University of British Columbia Research Focus: Dr. Jabbari pioneers RNA structure-function characterization through computational biology to decode disease mechanisms. Her work integrates transcriptomics , RNA-RNA/protein interaction analysis , and aging research with advanced machine learning and quantum computing approaches, emphasizing explainable AI for medical applications in RNA therapy development. Publication Trends: Recent work (2018-2024) demonstrates sustained innovation in RNA pseudoknot prediction algorithms applied to viral pathogenesis (notably SARS-CoV-2) and therapeutic design, with increasing integration of quantum computing and AI methodologies reflecting her interdisciplinary trajectory in computational genomics. Advising & Grants: Actively recruiting undergraduate researchers for funded projects including non-DNA life research, AI-driven vaccine development (comparative analysis and self-amplifying RNA platforms), and aging studies. She instructs BME 415/615 (Bioinformatics Algorithms) and MED 621 (Grant Writing), providing hands-on research training and grant preparation mentorship. Laboratory: As COBRA Lab Director, she leads a globally connected research network advancing RNA bioinformatics through algorithm development, fostering collaborations across virology, aging research, and therapeutic design domains.

• Modelling & Simulation
• Health Policy and Planning
• Data Science
• +۳ مورد دیگر

Nikolas Popper is a Senior Scientist and Research Fellow at the Technische Universität Wien (TU Wien), affiliated with the Department of Data Science (E194-04). He leads the DEXHELPP initiative (Decision Support for Health Policy and Planning) as its director and chairs the COMET Initiative. His primary research focuses on modeling and simulation of dynamic systems, particularly in healthcare, logistics, and biomedical engineering. Key areas include hybrid simulation, comparative modeling, and the integration of data-driven methods into policy decision-making. Popper's academic roles include teaching courses on modeling and simulation, health technology assessment, and advanced simulation techniques. He has pioneered the use of agent-based models to address public health challenges, notably during the COVID-19 pandemic, where his work supported Austrian policy decisions. His interdisciplinary projects span railway optimization, vaccine strategy modeling, and patient-centric health technologies. He holds leadership positions in multiple academic societies, including ISPOR (Austrian Chapter President), EUROSIM, and ASIM. Notable awards include the 'Österreicher des Jahres' (Research Category, 2021) and the Gerhart-Bruckmann-Preis (2021). His research has been funded by grants from the Austrian government, FFG, and FWF, addressing topics like vaccination prioritization and pandemic response systems. Popper’s contributions extend to founding initiatives like the 'drahtwarenhandlung' science production studio and the dwh GmbH spin-off from TU Wien. He actively engages in public discourse on data-driven decision-making and pandemic modeling through media and conferences.

Davide Mottinمشاهده پروفایل

دانشیار

• Graph Theory
• Machine Learning
• Data Mining
• +۴ مورد دیگر

Davide Mottin is an Associate Professor at the Department of Computer Science, Aarhus University. His primary research focuses on graph theory, machine learning, and data mining, with significant contributions to knowledge graphs, algorithm design, and interdisciplinary applications in drug discovery and material science. He holds a leadership role in large international conferences such as CIKM 2024 as a Program Chair. His research explores scalable graph algorithms (e.g., subgraph matching, alignment), robust knowledge graph cleaning, and leveraging large language models for scientific tasks. Mottin has pioneered work on spectral methods for graph analysis (e.g., NetLSD, VERSE embeddings) and developed frameworks for interactive data exploration (e.g., X2Q, MetaExp systems). Key contributions include FUGAL for graph alignment and Ucode for community detection Active in reproducibility efforts, as seen in retraction notices and algorithmic redesigns Focus on practical applications in drug discovery via evolution-based models (EvolMPNN) He has authored over 60 peer-reviewed publications and holds grants supporting interdisciplinary research at the intersection of computer science and life sciences. Mottin is affiliated with the university's AI and data science initiatives, contributing to both theoretical advancements and real-world system implementations.

Hasindu Malshan Gamaarachchiمشاهده پروفایل

مدرس

• Bioinformatics
• Genomics Data Processing
• Embedded Systems
• +۳ مورد دیگر

Hasindu Malshan Gamaarachchi is a Lecturer in the School of Computer Science and Engineering at UNSW Sydney since 2023, and a visiting scientist at the Garvan Institute of Medical Research's Genomic Technologies Group. He completed his PhD in Computer Science and Engineering at UNSW Sydney (2020) and holds a BSc (Hons.) in Computer Engineering from the University of Peradeniya (2015). His research focuses on bioinformatics software and hardware systems for real-time nanopore sequencing analysis, embedded computing, and GPGPU/FPGA acceleration. Education: PhD in Computer Science and Engineering, UNSW Sydney (2020) BSc Engineering (Hons.) in Computer Engineering, University of Peradeniya (2015) Research Interests: Design and optimization of domain-specific systems for genomics, including nanopore sequencing analysis, algorithmic efficiency, and hardware-software co-design. Key applications include real-time data processing and embedded systems for bioinformatics. Grants & Awards: ARC Discovery Early Career Researcher Award (DECRA) 2023 (Sole CI) ARC Discovery Projects 2023 (CI with Sri Parameswaran) 2021: Outstanding PhD Thesis Award (ABACBS), Dean's Award (UNSW) 2020: Third place at ACM SRC Awards span academic excellence (e.g., IET-YPS 2016) to bioinformatics innovation. Collaborates with industry and institutions like Garvan for genomic technologies.

• Human-Computer Interaction
• Artificial Intelligence
• Data Visualization
• +۴ مورد دیگر

Tracy Yuexi Chen is an Assistant Professor in the Department of Computer Science at the University of Maryland, College Park. Previously worked at Adobe Research as a research scientist and conducted postdoctoral work at Stanford University. Holds a PhD in Human-Centered Computing from Georgia Tech. Leads the Human-Data Interaction Research Group focused on AI-driven systems for data visualization and communication. Current research: Human-centered AI, example-driven visualization generation, data analysis planning Key applications: Medical imaging, collaborative writing, responsive document design Research combines empirical studies, theoretical frameworks, and interactive systems. Recent work spans visual analytics of event sequences, SVG chart analysis, and AI-assisted tutorial creation. Has received significant recognition including an NSF CAREER Award and DARPA STTR contracts. 2025 IEEE VIS paper on VisAnatomy 2024 CHI papers on collaborative writing and what-if analysis 2023 EuroVis papers on visualization evaluation Scientific awards include: NSF CAREER Award (2023) Honorable Mention at IEEE VIS 2024 Active in academic service as Associate Editor for IEEE Transactions on Visualization and Computer Graphics. Sponsors research through DARPA contracts and maintains international collaborations across Austria, Germany, and FL.

• Genomics
• Parasitic Diseases
• RNA Interference
• +۱۱ مورد دیگر

Christian Tschudi is the John Rodman Paul Professor of Epidemiology at the Yale School of Public Health, within the Department of Epidemiology of Microbial Diseases. He is also affiliated with the Yale Institute for Global Health, the Center for RNA Science and Medicine, and the Yale Combined Program in the Biological and Biomedical Sciences (BBS). His research is centered on the biology of trypanosomes, the causative agents of African sleeping sickness and nagana, with a focus on understanding the molecular basis of pathogen infectivity. Dr. Tschudi’s research interests span genomics, parasitic diseases, RNA interference, tropical medicine, and trypanosomiasis . His laboratory employs advanced bioinformatics and genetic techniques to dissect parasite-specific functions, particularly in RNA metabolism and gene regulation. A major focus is on the transcriptome of Trypanosoma brucei , where his team has developed high-throughput RNA-Seq protocols and generated comprehensive transcript maps at single-nucleotide resolution. This work has uncovered novel genes, regulatory RNAs, and mechanisms involved in developmental transitions and host adaptation. The recent articles highlight a strong trend in RNA biology, gene regulation, and developmental mechanisms in trypanosomes. His team investigates long non-coding RNAs, RNA modifications, RNA-binding proteins, and nuclear organization in the context of infectivity. These studies are crucial for identifying potential drug targets and understanding the basic biology of these neglected pathogens. Elected member, Connecticut Academy of Science and Engineering (2020) John Rodman Paul Professorship, Yale University (2016) Fellow, American Academy of Microbiology (2014) Honorary Master of Arts Degree, Yale University (2013) Burroughs Wellcome Fund New Investigator Award in Molecular Parasitology (2000) Research Project Award in Parasitology and Tropical Medicine, MacArthur Foundation (1984) James Hudson Brown-Alexander B. Coxe Fellowship, Yale School of Medicine (1982) Pre-doctoral Fellowship, European Molecular Biology Laboratory (1981) Pre-doctoral Fellowship, Swiss National Science Foundation (1978) Dr. Tschudi has been actively involved in mentoring and scientific service. He co-directs an NIH-sponsored Global Infectious Disease Training Program (D43) in translational research on leishmaniasis and emerging infectious diseases. He has served on numerous NIH study sections, including as a member of the NIAID Board of Scientific Counselors (2011–2017), and as an ad-hoc member in 2019 and 2020. He also serves on editorial boards of journals such as mBio , PLoS Neglected Tropical Diseases , and Molecular and Biochemical Parasitology . His laboratory is part of the Center for RNA Science and Medicine at Yale, reflecting his deep engagement with RNA-centric research. He collaborates extensively with experts in the field, including Dr. Serap Aksoy (Yale) on tsetse fly vector biology and Dr. Shulamit Michaeli (Bar Ilan University) on RNA modification. His work is supported by NIH grants and aims to identify candidate molecules for chemotherapy against trypanosomatid diseases.

• Computational Biology
• Genomics
• Protein Interaction Networks
• +۳ مورد دیگر

Dr. Maricel Gabriela Kann is an Associate Professor in the Department of Biological Sciences at the University of Maryland, Baltimore County (UMBC). She holds a BS and MS in Chemistry from the Universidad de la República (Uruguay), a PhD in Chemistry from the University of Michigan, and completed postdoctoral training at the NIH's National Center for Biotechnology Information. Her research focuses on computational approaches to annotate the human genome, elucidate disease mechanisms through protein interactions, and predict the role of genetic mutations in diseases. She teaches courses like BINF 313 (Intro to Bioinformatics) and BINF 495 (Bioinformatics Seminar), emphasizing both foundational and advanced bioinformatics principles. Dr. Kann has secured grants such as the 2024 ANID-funded project on drug development targeting cancer mutations and a 2025 UMB ICTR grant for AI-driven multi-omics lung cancer therapy. Her work spans translational bioinformatics, domain-centric analysis of cancer mutations, and integrative approaches linking genotype to phenotype. She has contributed to over 50 peer-reviewed publications, including seminal works on protein domain landscapes, regulatory variant prediction, and hybrid curation methods. Her research integrates computational methods with experimental biology, addressing challenges in precision medicine and disease gene discovery. Collaborations involve developing tools for variant prioritization, biomarker identification, and multi-omics data integration. Dr. Kann’s contributions have been recognized through invited lectures at institutions like the University of Andres Bello and keynotes at Indo-US conferences on big data in disease biology.

Maricel Kannمشاهده پروفایل

دانشیار

• Computational Biology
• Bioinformatics
• Systems Biology
• +۱۲ مورد دیگر

Maricel Kann is an Associate Professor in the Department of Biological Sciences at the University of Maryland, Baltimore County (UMBC), with an affiliate appointment in the Computational Sciences and Engineering Department. Her research integrates computational biology, bioinformatics, and systems biology to understand protein networks, domain-level functional impacts of genetic variants, and molecular mechanisms in diseases such as cancer. Ph.D., University of Michigan, Ann Arbor, 2001 Postdoctorate, National Center for Biotechnology Information, NIH, 2007 Her research focuses on developing computational methodologies to analyze protein domain interactions, prioritize disease-associated variants, and derive molecular signatures of cancers like prostate and breast cancer. She leads projects such as DMDM (Domain Mapping of Disease Mutations) and EMU (Extractor of Mutations), which enable domain-level mutation analysis and text-mining of disease-related variants. Her work emphasizes interdisciplinary collaboration with experimentalists and the integration of genomic, functional, and evolutionary data. Her recent publications reflect a strong trend in domain-centric cancer genomics, statistical modeling of mutation hotspots, and computational frameworks for interpreting non-coding variants and personal genomes. She has contributed significantly to benchmarking in computational biology and the development of tools for translational bioinformatics. NCI Transition Career Development Award (K22) 2009-2012 NIH Intramural Research Training Award 2002-2007 NIH Fellows Award for Research Excellence 2003 Susan Lipschutz Award for Women Graduate Students 1999 Sloan Foundation Summer Graduate Fellowship 1998 Graduate Fellowship of the Organization of the American States 1996-1998 Dr. Kann mentors numerous graduate and undergraduate students through projects like EMU and PINTT (Protein INteraction Text-mining Tool), fostering interdisciplinary training. She has served on editorial boards (e.g., Journal of Biomedical Informatics), advisory committees (PubMedCentral, UniProt), and as organizer for major conferences in bioinformatics. She currently teaches BIOL 495: Seminar in Bioinformatics and is actively recruiting Ph.D. students in computational biology and bioinformatics. Her lab collaborates with researchers across institutions and is part of several interdisciplinary programs including the Chemistry and Biology Interface Program at UMBC and the University of Maryland Greenebaum Cancer Center. She is also involved with the University of Maryland School of Medicine’s Program in Biochemistry and Molecular Biology.

• Data Analytics
• Artificial Intelligence
• Machine Learning
• +۵ مورد دیگر

Prof. Youakim Badr is a Professor of Data Analytics in the Engineering Division at Temple University's Great Valley campus. His research focuses on advancing interdisciplinary fields such as artificial intelligence, blockchain technology, cybersecurity, and Internet of Things (IoT) systems. He has contributed to over 134 publications, emphasizing trust frameworks in decentralized systems, privacy-preserving machine learning, and smart service ecosystems. Research Interests: Prof. Badr’s work spans AI-driven systems, blockchain applications for identity management, secure IoT architectures, and innovative frameworks for data analytics. He actively explores how AI can enhance service ecosystems and address societal challenges aligned with UN Sustainable Development Goals (SDGs), particularly in healthcare and smart infrastructure. Collaborations: His recent research involves international collaborations in blockchain-based trust systems, cybersecurity for AI, and IoT-enabled medical devices. These efforts have led to impactful solutions like the TrustLend platform for decentralized lending and frameworks for resilient dynamic data-driven applications. Advising & Grants: While specific student names are not listed, his research has been supported by grants focusing on AI ethics, secure smart cities, and crisis management systems. He has co-authored seminal papers on federated learning, adversarial testing for AI, and artifact-centric process modeling. Labs/Teams: His contributions include foundational work in the TrustLend platform and collaborations on resilient DDDAS (Dynamic Data-Driven Application Systems) for crisis environments. He also leads initiatives integrating business artifacts with modern IT infrastructure.

Min Hao Kuoمشاهده پروفایل

استاد

• Alzheimer's Disease
• Tau Protein
• Neurodegeneration
• +۷ مورد دیگر

Min Hao Kuo serves as Professor in the Department of Biochemistry & Molecular Biology at Michigan State University, with concurrent faculty appointments in the Genetics & Genome Sciences Program, Neuroscience Program, and Cell & Molecular Biology Program. His research program bridges molecular neuroscience and biochemistry, focusing on pathological mechanisms in neurodegenerative disorders. His academic credentials include: Ph.D. in Biochemistry from the University of Rochester (1995) B.S. in Medical Technology from National Taiwan University (1986) Dr. Kuo's research centers on Alzheimer's disease pathogenesis, specifically investigating hyperphosphorylated tau protein dynamics, aggregation mechanisms, and neurotoxicity. Current work explores tau oligomerization, liquid-liquid phase separation (coacervation), and cross-talk with inflammatory mediators like galectin-3. His secondary research trajectory examines fundamental cell biology processes in yeast models, including quiescence regulation and lipid metabolism in aging. Methodologically, he employs biochemical assays, mass spectrometry for proteoform analysis, and innovative in vivo imaging techniques using single-domain antibodies. Analysis of his 15 most recent publications (2022-2025) reveals a dominant focus on tau-centric Alzheimer's research, comprising 13 of 15 articles. Key thematic clusters include: (1) tau aggregation inhibitors and drug repurposing (e.g., apomorphine, sulfonamides), (2) advanced imaging of tau pathology, and (3) molecular interactions between tau and co-factors (heparan sulfate, RNA, heme oxygenase-1). Two articles maintain his historical yeast research focus, examining cell division in quiescence. His scholarly recognition includes: MSU College of Natural Science Dissertation Completion Fellowship (2010) Sigma Xi Graduate Student Award (2009) Departmental Outstanding Graduate Student Teaching Award (2009) Future Academic Scholars in Teaching Fellowship (2006-2007) Mentorship activities are evidenced through graduate student awards and dissertation fellowships, though specific advisee names aren't documented. His teaching portfolio includes BMB 829: Special Problems in Macromolecular Analysis & Synthesis. Research funding appears institutionally supported through MSU fellowships, with likely external grants given the publication volume and technical scope. His laboratory maintains active research in tau biochemistry and neurodegeneration, utilizing capillary electrophoresis-mass spectrometry, cell-based aggregation assays, and transgenic models. Collaborative networks are evident through multi-institutional publications focusing on tau imaging and therapeutic development.

• Conceptual Modelling
• Enterprise Modeling
• Requirements Engineering
• +۳ مورد دیگر

Monique Snoeck is a full professor and coordinator of the Information Systems Engineering Research Group (LIRIS) at KU Leuven's Faculty of Economics and Business (FEB). She serves on the FEB Council and Campus Council while contributing to DigiSoc and Leuven.AI institutes as a member focused on digital society and AI initiatives. Her research pioneers conceptual modelling, enterprise modeling, requirements engineering, and model-driven engineering, notably through the MERODE methodology and MERLIN prototyping tool. Current work integrates AI with learning analytics for education and develops automatic UI generation from conceptual models, alongside capacity-building projects for Cuba's software industry using model-driven approaches. Recent publications reveal converging trends in LLM-enhanced conceptual modeling education, XAI applications for credit risk analysis, and digital process twins for business scenario simulation. Key themes include model validation techniques, IoT application patterning, and multi-model integration for domain-process alignment. She supervises PhD candidates Tiukhova, E. (credit risk/XAI) and Verbruggen, C. (multi-modelling), leading grants including 'Explanations in AI for Learning Analytics' (2025-2029), 'Personalized Feedback Generation' (2024-2028), and 'Cuba Software Industry Capacity Building' (2024-2029) as promotor. As LIRIS leader, she drives model-driven engineering research while collaborating with Leuven.AI on AI integration and DigiSoc on digital public service co-creation initiatives.

Thomas Baarمشاهده پروفایل

استاد

• Cyber-Physical Systems
• Formal Verification
• Model-Driven Engineering
• +۳ مورد دیگر

Prof. Dr. Thomas Baar is a Professor at HTW Berlin, specializing in cyber-physical systems, formal verification, and model-driven engineering. His research focuses on hybrid systems, automated driving platforms like CeCar, and improving usability of verification tools such as KeYmaera. He contributes to software engineering education and legacy system modernization through domain-specific languages (DSLs) and graphical editors. Baar's work spans academia and industry, with collaborations in aerospace safety, multiagent systems, and cybersecurity for control systems. His research interests include formal methods for hybrid systems, software reliability, and educational platforms for autonomous systems. He has published extensively on topics like verification frameworks, syntax improvements for formal tools, and safety analysis methodologies. His interdisciplinary approach bridges theoretical computer science with practical applications in aerospace, automotive, and industrial automation. Key contributions include the CeCar platform for autonomous driving research, modularization enhancements for KeYmaera, and DSL-based solutions for legacy software maintenance. Baar collaborates with institutions like the IFAF Berlin and participates in EU-funded projects focusing on smart cities and sustainable technologies. His publications reflect a strong emphasis on verification techniques, with over 15 peer-reviewed articles since 2015. He is affiliated with the HTW Berlin's Wilhelminenhof Campus and actively engages in knowledge transfer initiatives to bridge research and industry needs.

Rolf Drechslerمشاهده پروفایل

استاد

• Formal Verification
• Quantum Computing
• RISC-V Architecture
• +۴ مورد دیگر

Rolf Drechsler is a Full Professor and Head of the Group of Computer Architecture at the University of Bremen's Institute of Computer Science since 2001, and Director of the Cyber-Physical Systems Group at DFKI Bremen since 2011. He holds an adjunct professorship at the Indian Statistical Institute and has been affiliated with Duke University. Education: Diploma (1992) and Dr. phil. nat. (1995) in Computer Science from Goethe University Frankfurt Academic Leadership: Dean of Mathematics and Computer Science Faculty (2018-2025), Vice Rector for Research (2008-2013) His research focuses on formal verification , RISC-V architectures , and quantum/in-memory computing . Recent work explores LLM integration in hardware testing and polynomial-based verification techniques. Publications from 2024-2025 span IEEE Transactions , DATE , and DAC , emphasizing automated verification , quantum circuit mapping , and LLM-driven testbench generation . Scientific Awards IEEE/ACM Best Paper Awards (2013, 2018) Berninghausen-Preis for Innovative Teaching (2018) IEEE Fellow (2015) Founder Award for Solvertec (2013) He has served on program committees for DAC, ICCAD, DATE, and founded graduate schools in Embedded Systems and System Design under Germany's Excellence Initiative.