Dr. Domenico Striccoli is an Associate Professor in the Department of Electrical and Information Engineering at the Polytechnic University of Bari, Italy. He holds a Dr. Eng. Degree (2000) and Ph.D. in Electronic Engineering (2004) from Politecnico di Bari. His office contact is +39 080 5964225. Research focuses on advanced networking technologies: Internet of Things architectures and security Information Centric Networking paradigms Low Power Wide Area Networks optimization Nanocommunication systems Network performance modeling and evaluation Awards include Best Impact Contribution runner-up at RESTART Workshop (2024) and 2nd Best Paper at ICT-DM Conference (2023). Teaching includes Multimedia Systems and Telecommunication Signal and System for Aerospace courses.
Dr. Vitor Jesus is a Lecturer in Software Engineering and Cybersecurity at Aston University's School of Computer Science and Digital Technologies. His research focuses on privacy-enhancing technologies and cybersecurity frameworks for distributed systems, with particular emphasis on: Consent management architectures and standardization Blockchain-based auditing and data provenance IoT security in smart environments Privacy-preserving data sharing models As co-chair of the COnSeNT workshop series and BSI expert for ISO standards development, he contributes to international privacy frameworks. Funded projects include CyberAlarm (crowd-sourced threat intelligence) and EVBatteries4Planet (supply chain visibility).
Hila Peleg is an Assistant Professor in the Department of Computer Science at the Technion – Israel Institute of Technology, where she co-leads the TecSE lab with Prof. Shachar Itzhaky. Her research lies at the intersection of Programming Languages, Software Engineering, and Human-Computer Interaction, focusing on program synthesis and interactive developer tools. Her research interests center on creating intelligent, theory-driven tools that enhance programmer productivity and correctness. She explores interaction models that integrate formal methods like separation logic into practical synthesis systems, enabling more versatile and reliable code generation. Her work spans both foundational models and real-world applications, from web layout synthesis to computational crafting. The trend in her recent publications shows a strong focus on interactive and practical program synthesis, blending formal verification with user-centered design. She investigates how synthesis can be made more usable through live programming, best-effort results, co-design of tools and languages, and integration with developer workflows. Her work increasingly emphasizes the human aspect of programming tools. Distinguished Paper Award, PLDI 2021 Distinguished Artifact Award, SPLASH 2020 Hila Peleg advises multiple graduate students, including PhD and MSc candidates, and leads the ERC-funded EXPLOSYN project, which supports advanced research in program synthesis. She has taught advanced courses such as User-Centered Programming Tools and seminars in programming languages, and is actively involved in the academic community through conference service and organization. She is a core member of the TecSE lab, which focuses on advancing software engineering through programming language theory and interactive systems. The lab fosters interdisciplinary research at the boundary of formal methods and human-centered tool design.
Samantha Baxter is an Adjunct Associate Professor at the School of Health and Rehabilitation Sciences at MGH Institute of Health Professions, where she teaches in the Master of Science in Genetic Counseling program. She also works as a Senior Clinical Genomics Specialist at the Broad Institute of MIT and Harvard's Center for Mendelian Genomics , focusing on novel gene discovery and variant interpretation. With 12 years of experience in clinical and research laboratories, her expertise spans cardiovascular genetics, post-mortem testing, and IT-facilitated genomic data sharing. BS, Behavioral Neuroscience, Lehigh University MS, Genetic Counseling, Boston University School of Medicine Her research emphasizes genomic sequencing , data infrastructure , and clinical applications through publications on BRCA variant databases, patient matching tools, and hypertrophic cardiomyopathy diagnostics. Key contributions include the development of the matchbox open-source platform for genomic data exchange. Scientific Awards: New Leader Award from the National Society of Genetic Counselors (2012) She actively participates in professional leadership as a chair of multiple NSGC Special Interest Groups and served on the NSGC Board of Directors. Her work bridges clinical genomics and bioinformatics to improve genetic diagnosis and counseling workflows.
Travis Gagie is an Associate Professor in the Faculty of Computer Science at Dalhousie University, where he conducts research on compact data structures with applications in bioinformatics and computational genomics. He is currently teaching CSCI 6905: Compact Data Structures in Computational Genomics and is funded by an NSERC Discovery Grant (RGPIN-07185-2020). His work bridges algorithmic design with real-world challenges in genomic data representation and equitable healthcare. His educational background includes: BSc in Cognitive Science from Queen's University (Canada) MSc in Computer Science from the University of Toronto Dr. rer. nat. in Genome Informatics from Bielefeld University (Germany) Travis Gagie's research focuses on overcoming biases in genomic data analysis, particularly those arising from the use of a single reference genome. He investigates pan-genomic data structures such as variation graphs, founder sequences, and r-index to enable more inclusive and accurate genomic medicine. His work emphasizes scalable indexing methods for diverse populations and rare disease diagnosis, intersecting with ethical considerations in precision medicine. He has collaborated with researchers globally and taught courses in Spain and Chile. The recent articles in his portfolio reflect a strong trend in developing and analyzing data structures for pan-genomic applications. These include variation graphs (vg, minigraph), compact indexes (r-index, MONI/PHONI), and alignment tools (Giraffe, PLAST), all aimed at improving scalability, accuracy, and inclusivity in genomics. His research integrates theoretical computer science with practical bioinformatics challenges, particularly in the context of human and microbial pan-genomes. Although no formal scientific awards are mentioned in the provided texts, his active research program, teaching responsibilities, and grant funding indicate strong academic recognition and productivity. Travis Gagie has previously served as a research assistant at the Italian National Research Council and the University of Eastern Piedmont, completed postdoctoral work at the University of Chile, Aalto University, and the University of Helsinki, and was an associate professor at Diego Portales University. He has also been a visiting researcher at Illumina, the University of A Coruña, and the Czech Technical University. While he is not currently seeking graduate students or interns, he maintains an open-door policy for academic discussion via Webex and email. He emphasizes the importance of ethical considerations in genomics, particularly in relation to Indigenous populations and equitable healthcare access. He is actively involved in academic outreach, recommending seminars such as the CGEM series on equity in genomic healthcare and promoting workshops like Data Structures in Bioinformatics (DSB '21). He supports student learning through video lectures, assignments, and collaborative discussions, often integrating real-world case studies like the Silent Genomes Project to contextualize technical work.
Isabel Cecília Correia da Silva Praça Gomes Pereira is a Coordinator Professor at the School of Engineering of the Polytechnic Institute of Porto (ISEP), where she serves as Director of the Master on Informatics Engineering and Advisor of ISEP Presidency for R&D. She is also a Senior Researcher at GECAD (Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development), a research unit ranked as Excellent by the Portuguese Science & Technology Foundation. Dr. Praça holds a PhD in Electrical and Computer Engineering - Industrial Informatics from the University of Trás-os-Montes e Alto Douro, and completed her post-doctoral studies in Artificial Intelligence - Multi-Agent Systems with support from the Portuguese National Science Foundation (SFRH/BPD/30111/2006). Her academic journey includes progressive appointments from Assistant to Adjunct Professor and finally to Coordinator Professor at ISEP. Her research focuses on Artificial Intelligence applied to cybersecurity and security of AI , with significant contributions to applying AI in various domains including cybersecurity (projects like AIDA, SAFE, VESTA), industry (SeCoIIA, Cyberfactory), and energy systems (SPET, MAS-Society). She leads a research team at GECAD working on these topics and has strong connections with over 100 companies and technology transfer centers across more than 30 countries. Analysis of her recent publications reveals a strong trend toward AI security, with particular emphasis on adversarial attacks against AI systems, secure implementation of AI in critical domains, and privacy-preserving AI techniques. Her work spans both theoretical foundations and practical applications across multiple sectors including energy, healthcare, and network security, demonstrating her ability to bridge academic research with real-world applications. Dr. Praça has been recognized as an expert by several prestigious organizations including the European Union Agency for Cybersecurity (ENISA), where she contributes to working groups on Security of AI and the European Cybersecurity Skills Framework. She also serves as an expert for NATO's Defence Innovation Accelerator for the North Atlantic (DIANA) and represents ISEP in the European Cybersecurity Organization (ECSO). As an educator, she teaches Machine Learning and Multi-agent Systems in the MSc in AI program, and Security of Communications and Infrastructures in the Cybersecurity branch of the MSc in Informatics. She currently supervises 4 PhD students in AI security and privacy and has guided 45 Master's theses, demonstrating her commitment to developing the next generation of researchers and practitioners in her field. Her research is supported by numerous international and national grants, including multiple Horizon Europe and Horizon 2020 projects where she serves as Principal Investigator or Co-PI. She has participated in over 35 R&D projects totaling significant funding, with a strong track record of translating research into practical applications through industry partnerships.
Ştefania-Gabriela Dumbravă is an Associate Professor in Computer Science at the École Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise (ENSIIE), part of Institut Polytechnique de Paris. She leads the ACMES team at Samovar Laboratory (Télécom SudParis) and participates in international working groups including the Property Graph Schema Working Group and European Research Network on Formal Proofs. Education: PhD in Computer Science, Université Paris-Sud (2016) MSc in Computer Science, Jacobs University Bremen (2012) BSc in Mathematics, Jacobs University Bremen (2010) Research Focus: Her work centers on formal methods for designing and verifying graph database algorithms, with emphasis on: certified database engines, property graph schemas, threshold queries, progressive querying techniques, and knowledge graph evolution. She integrates theorem proving (Coq/Isabelle) with practical database applications. Publication Trends: Her recent works demonstrate strong focus on graph database foundations (schemas, query processing) and practical verification techniques. Publications frequently appear in top-tier venues (VLDB, SIGMOD, ICDE) and emphasize both theoretical rigor and real-world applications in areas like bioinformatics, transportation, and networking. Awards & Honors: EASST Best Software Science Paper (ICGT 2025) ICDE/SIGMOD Distinguished Reviewer Awards (2025) SIGMOD Best Paper & Research Highlight (2023) VLDB Best Paper Runner-Up (2022) Students & Grants: Supervises Master's interns on graph database applications. Leads the ANR JCJC VERDI project (2025-2029) on verified distributed graph systems. Actively recruits PhD candidates for this initiative. Labs & Service: ACMES team at Samovar Lab. Serves on editorial boards (TODS, TGDK) and program committees (VLDB, SIGMOD, ICDE). Coordinates VLDB 2026 Demonstrations Track and co-organizes multiple workshops (GRADES-NDA, TGD).
Siqi Wu is an Assistant Professor of Information and Library Science in the Luddy School of Informatics, Computing, and Engineering at Indiana University Bloomington. Previously, they were a postdoc research fellow in the Center for Social Media Responsibility at the University of Michigan School of Information. Dr. Wu is a computational social scientist who collects, models, and analyzes web data at scale, with research focusing on understanding social phenomena through large-scale empirical measurements and designing next-generation sociotechnical systems via data-driven policies and interventions. Dr. Wu's educational background includes: Ph.D. in Computer Science from the Australian National University M.S. in Information Technology from the University of Melbourne B.E. in Electronics Engineering from Tianjin University Dr. Wu's research spans computational social science, large-scale web data analysis, and social media systems. Their work examines YouTube recommendation networks, Twitter data analysis, prevalence estimation techniques, and cross-platform attention dynamics. Recent projects investigate user control over recommendations, harmful content detection, and cross-partisan communication patterns, combining computational methods with social science theories to understand algorithmic influence on online experiences and develop practical interventions. Dr. Wu's publications demonstrate consistent excellence in social media analysis, with particular expertise in measuring attention dynamics across platforms, estimating class prevalence using black box classifiers, and understanding recommendation systems. Their work bridges technical innovation with social science questions, resulting in practical implications for platform design and policy across YouTube, Twitter, and other social media ecosystems. Notable recognitions include: Google PhD Fellowship (2018) ICWSM Best SPC (2022-2024) CSCW 2019 Best Paper Honorable Mention (top 5%) ICWSM 2021 Spotlight Paper selection (top 8) Dr. Wu actively mentors students interested in computational social science, seeking those with strong programming skills, data analysis experience, and passion for understanding social phenomena through computational methods. They serve on program committees for ICWSM, CSCW, and CHI, and their research has practical applications for social media platform design, content moderation policies, and understanding information ecosystems. Dr. Wu has developed several software tools including pyquantifier for prevalence estimation and tools for Twitter and YouTube data collection. Dr. Wu leads research on social media analysis with focus on YouTube and Twitter ecosystems, developing methods to understand recommendation systems, user engagement patterns, and cross-platform dynamics. Their work involves collecting and analyzing large-scale datasets to inform platform design and policy decisions related to algorithmic transparency and user control.
Antonia Saravanou is a Ph.D. graduate from the Department of Informatics and Telecommunications at the National and Kapodistrian University of Athens (NKUA), advised by Prof. D. Gunopulos. She holds an M.Sc. in Advanced Information Systems and a B.Sc. in Computer Science from the same department. Since 2011, she has worked as a Research Scientist and Engineer at NKUA and Athens University of Economics and Business (AUEB), and is affiliated with the Knowledge Discovery in Databases Laboratory (KDDLab) and the Management of Data, Information & Knowledge Group (Madgik). Her research spans Data Mining, Machine Learning, and Anomaly Detection, with a focus on Social Network Analysis, Graph Representations, and Healthcare Applications. She has completed research visits at Stanford University's Geometric Computing Group, Spotify Tech Research, and Bloomberg AI. Education: Ph.D., Informatics and Telecommunications, NKUA M.Sc., Advanced Information Systems, NKUA B.Sc., Computer Science, NKUA Her research explores graph-based methods for event detection in social networks, self-supervised node representation learning, and applications in healthcare and news analysis. Projects include music recommendation systems via graph representations, infant mortality prediction models using birth certificate data, and real-time news monitoring frameworks. She has also worked on knowledge graph applications for news ranking and anomaly detection in sparse time series data. Scientific Awards: Outstanding Reviewer for ICLR 2021 Top Reviewer for NeurIPS 2018 As a teaching assistant, she has supported graduate and undergraduate courses at NKUA, including Mining Big Datasets, Data Mining, and Artificial Intelligence. She actively participates in outreach programs like ACM Student Chapter UoA, Rails Girls Athens, and Django Girls.
Prof. Marian Benner-Wickner is a Professor of Computer Engineering at IU University of Applied Sciences, where he has been a faculty member since 2018. He leads the Industrial Engineering & Management and Industry 4.0 online study programs, focusing on software engineering and gender-neutral e-learning methodologies. His professional background includes roles as an IT specialist trainer at CampusLab GmbH and a software developer at the Fraunhofer Institute for Software and Systems Technology. He holds a PhD in Software Engineering (2016) from the University of Duisburg-Essen, where he managed the “Agenda-Driven Case Management” research cluster. His research interests span software engineering, case management techniques, process flexibility, smart home technologies, and digital youth protection. Notable contributions include work on adaptive case management systems, ontology-based recommendation systems, and e-learning metadata frameworks. Prof. Benner-Wickner actively contributes to organizations like the Wikimedia Foundation, CPS.HUB NRW, and the Society for Computer Science. His publications address topics such as IT integration architectures, automated grading systems, and security frameworks in mobile networks. His recent projects emphasize integrating IT applications, enhancing educational technologies, and optimizing knowledge-intensive business processes through semantic web technologies and process mining. In advising and grants, he oversees company-specific final theses in IT and technology, emphasizing practical industry collaboration. He has developed guidelines for design science research in academic works and advocates for empirical approaches to software process improvement. His leadership in Industry 4.0 programs reflects his commitment to bridging academic research with industrial applications. Leveraging his role as department head, he fosters innovation in curriculum design, particularly in leveraging digital tools for inclusive and adaptive learning environments. His interdisciplinary work intersects computer science, education, and policy, addressing societal challenges like digital youth protection and smart home security.
Zhaohan Xi is an Assistant Professor in the School of Computing at Binghamton University, SUNY, focusing on AI security/privacy and clinical AI in the context of large language models (LLMs). His research spans cybersecurity strategies, healthcare applications, and advanced AI techniques like graph learning and AutoML. He holds a PhD from Pennsylvania State University, with a visiting scholar stint at Stony Brook University's Computer Science Department. Education: PhD: Pennsylvania State University (2020–2024) Visiting Scholar: Stony Brook University (2023–2024) Master’s: Lehigh University (2016–2018) Bachelor’s: Nanjing University of Aeronautics and Astronautics (2012–2016) Research interests include: AI Security/Privacy: Backdoor attacks, adversarial defense, and LLM vulnerabilities Clinical AI: Cardiologist-level diagnostics, drug repurposing, and ECG analysis Cybersecurity: Threat hunting, red/blue teaming, and threat intelligence Graph Learning: Knowledge graphs, GNNs, and decision-making systems Recent publications emphasize LLM robustness (e.g., standardized testing benchmarks), adversarial knowledge extraction (e.g., stealing knowledge graphs via APIs), and cybersecurity applications (e.g., LLMs as threat intelligence tools). His work bridges theoretical AI security with practical healthcare and defense systems. Notable awards include ICLR Notable Reviewer (2025) and Binghamton’s Outstanding Service and Support Award (2025) . He has served as a NSF panelist (AI/cybersecurity) and reviewer for venues like ARR/EMNLP, ACL, and KDD. Internships include roles at Sony AI (2024), Microsoft (2023), and Uber (2022), focusing on AI research and software engineering. No advising details or grants are explicitly mentioned in the provided texts.
Rajesh Dave is a Distinguished Professor in the Department of Chemical & Materials Engineering at the New Jersey Institute of Technology (NJIT). He holds a B.S. from the Indian Institute of Technology Bombay and M.S./Ph.D. degrees from Utah State University, all in Mechanical Engineering. His research focuses on pharmaceutical process engineering, powder technology, and material science, with applications in drug delivery systems and manufacturing optimization. He has pioneered dry coating techniques to enhance powder blend processability and employs machine learning for predictive modeling in pharmaceutical processes. Education: Ph.D., Mechanical Engineering, Utah State University (1983) M.S., Mechanical Engineering, Utah State University (1981) B.S., Mechanical Engineering, Indian Institute of Technology Bombay (1978) Research interests include powder compaction mechanics, surface engineering of pharmaceutical particles, and scalable manufacturing solutions for poorly soluble drugs. His work integrates experimental and computational methods to address challenges in drug formulation, process analytical technology (PAT), and quality-by-design (QbD) frameworks. Recent projects emphasize enhancing drug dissolution rates via nanoparticle engineering and optimizing 3D-printed dosage forms. Scientific Awards: 2022 AIChE PD2M Award for QbD Contributions 2021 Fellow of the National Academy of Inventors (NAI) His research has led to innovations in engineered excipients, continuous manufacturing systems, and predictive models for powder flowability. Collaborative efforts with industry partners focus on translating lab-scale discoveries into industrial applications. He maintains an active laboratory and advises on grants related to pharmaceutical manufacturing excellence.
Dr. Robbie Baldock is a Senior Lecturer and Associate Head (Employability & Placements) at the University of Portsmouth , where he leads the Baldock Laboratory in the School of Medicine, Pharmacy and Biomedical Sciences . His career spans roles at Solent University (Lecturer/Senior Lecturer), University of Gloucestershire, and University of Pittsburgh (postdoctoral research). He holds a PhD from the University of Sussex and is a Senior Fellow of the Higher Education Academy . Current leadership role: Associate Head (Employability & Placements) Academic institutions: University of Sussex (undergraduate), University of Pittsburgh (postdoc) Professional affiliations: Biochemical Society Local Ambassador, Genes Research Area Panel, Early Career Advisory Panel His research interests focus on DNA repair mechanisms in both nuclear and mitochondrial genomes, antibiotic-induced mitochondrial toxicity , and the role of hydroquinine in bacterial resistance . He also explores augmented reality (AR) in bioscience education to enhance student engagement and accessibility. Recent research outputs highlight trends in antibiotic resistance (e.g., efflux pump regulation in Pseudomonas aeruginosa ), mitochondrial DNA protection , and AR applications in education . His 2024 work on machine learning for COVID-19 detection via X-rays demonstrates interdisciplinary innovation. Scientific awards include: Senior Fellow of the Higher Education Academy (AdvanceHE) Dr. Baldock supervises self-funded PhD students and actively participates in degree program validation and external examining (University of Plymouth). His lab at Portsmouth leverages state-of-the-art equipment for fluorescence analysis, flow cytometry, and cell culture studies.
Vladislav Mladenov is a PostDoc at Ruhr University Bochum (Faculty of Computer Science) , affiliated with the Chair for Network and Data Security . He serves as Project Coordinator for the North Rhine-Westphalian Experts on Research in Digitalization (NERD II) and an Associated Principal Investigator at the Cluster of Excellence CASA (Cyber Security in the Age of Large-Scale Adversaries). Academic Background: Dipl.-Ing in IT-Security (Ruhr University Bochum, 2004–2012) Ph.D. in Network and Data Security (2012–2017) Research Interests span Information & Data Security , Authentication & Authorization Protocols , Data Format Security (JSON, XML, PostScript), and Network Protocols Security . His work focuses on vulnerability discovery in enterprise systems, IoT, and web standards. Scientific Contributions include analyses of PDF document security, 3D printing protocols, and Single Sign-On implementations. He has contributed to international standards via ISO/DIN working groups and the PDF Association . Scientific Awards: Excellent Teaching Award (2024) DFG Project Grant (2022) CSAW Best Paper Awards (2019, 2022) Eurobits Excellence Award (2019) CAST IT-Security Award (2017)
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.
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