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Kok Sheik WongView profile

Professor

• Multimedia Signal Processing
• Cybersecurity
• Data Hiding
• +9 more

Kok Sheik Wong is a Professor and Deputy Head (Research) at the School of Information Technology, Monash University Malaysia. He holds a Doctor of Engineering from Shinshu University, Japan, and Master’s and Bachelor’s degrees in Computer Science and Mathematics from Utah State University, USA. His academic leadership and research excellence are central to his role at Monash. B.S. Computational Mathematics, Utah State University (2002) M.S. Computer Science, Utah State University (2006) M.S. Mathematics, Utah State University (2004) Doctor of Engineering, Shinshu University, Japan (2009) His research focuses on multimedia signal processing and cybersecurity , particularly in data hiding , reversible data hiding , coverless steganography , and multimedia encryption . He is also expanding into digital health , applying AI to mental health in workplace environments. His work aligns with UN SDGs, particularly in health and education. The recent publication trends show a strong emphasis on reversible data hiding , image watermarking , and AI-driven health applications . His interdisciplinary work spans computer science, engineering, and public health, with increasing focus on real-world impact through EU and national grants. He has received several honors, including: Academic of Science Malaysia - Young Scientist Network (2020) Best Paper Award, IWDW 2019 ITEX 2021 Gold Medal for BAITRADAR School of IT Excellence in Research Award (2022) Dr. Wong actively supervises PhD students and leads major research projects, including the EU-funded WAge project. He has served as an associate editor for IEEE Signal Processing Letters and the Journal of Information Security and Applications, and is a member of IEEE IFS and APSIPA technical committees. His grants reflect strong external collaboration and funding in cybersecurity and digital health. He is involved in key research labs and teams through Monash University and international consortia, particularly in the areas of multimedia security and digital health innovation. His leadership in the WAge project connects him with European and Asia-Pacific research networks, enhancing global impact.

Lukas EngelmannView profile

Senior Lecturer

• History of Epidemiology
• History of Science and Medicine
• Digital Health
• +7 more

Lukas Engelmann is a Senior Lecturer at the University of Edinburgh , specifically within the Science, Technology and Innovation Studies department under the School of Social and Political Science . His research focuses on the history and sociology of biomedicine , with particular interest in epidemiological reasoning , visual cultures of disease , digital epidemiology , and decolonial approaches to medical history . The Epidemy Lab , which he founded, explores the historical development of epidemiology and its contemporary influence on data-driven public health and pandemic policy-making . Engelmann's work has been funded by prestigious grants including an ERC Starting Grant (2021-2025) for his research on the history of epidemiological reasoning, and support from the Wellcome Trust for projects examining the social dimensions of digital health . His book 'Mapping AIDS' (2018) established him as a leading scholar in medical visualization , while 'Sulphuric Utopias' (2020) with Christos Lynteris explores the technological history of maritime sanitation and its political implications. Recent publications emphasize the visual and data practices that have shaped epidemiology, including works on epidemic modeling during the COVID-19 pandemic , the history of plague mapping , and the ethical implications of digital phenotyping . He has also contributed to interdisciplinary discussions on syndemics , co-infection epistemology , and the commercialization of bacteriology in the early 20th century. His scientific contributions have earned recognition through editorial roles in journals like Big Data and Society , and collaborative projects such as 'Working with Diagrams' (2022) which investigates the epistemological role of visual tools in medical knowledge production. Scientific Awards and Funding: ERC Starting Grant (2021-2025) Wellcome Trust Institutional Support Fund British Academy/Leverhulme Small Research Grant Chancellor's Fellowship (University of Edinburgh) 'Sulphuric Utopias' listed in The Guardian's 30 Books to Understand the World (2020)

Mengdi WangView profile

Professor

• Reinforcement Learning
• Machine Learning Theory
• Generative AI
• +4 more

Mengdi Wang is a Professor at Princeton University with primary appointments in the Department of Electrical and Computer Engineering and the Center for Statistics and Machine Learning, and courtesy appointments in the Department of Computer Science and Omenn-Darling Bioengineering Institute. She co-directs Princeton AI for Accelerated Invention and is affiliated with the Princeton ML Theory Group and Princeton Language+Intelligence Initiative, with prior visiting roles at DeepMind, IAS, and Simons Institute. Her educational background includes a PhD in Electrical Engineering and Computer Science (with Mathematics minor) from MIT (2013), advised by Dimitri P. Bertsekas at LIDS, and undergraduate studies in Automation at Tsinghua University: PhD: MIT, Electrical Engineering and Computer Science (2013) Bachelor: Tsinghua University, Automation Her research establishes theoretical foundations for machine learning with emphasis on reinforcement learning algorithms, generative AI, and large language models. She investigates data-driven stochastic optimization, statistical limits of reinforcement learning, representation learning, and diffusion models, developing provably robust algorithms for complex systems. Her work bridges theoretical guarantees with real-world applications in healthcare, biotech drug discovery, fintech, and scientific acceleration, focusing on how AI can transform discovery processes across disciplines. Her scientific contributions are recognized by prestigious awards: Young Researcher Prize in Continuous Optimization (Mathematical Optimization Society, 2016) Princeton SEAS Innovation Award (2016) NSF Career Award (2017) Google Faculty Award (2017) MIT Tech Review 35-Under-35 (China region, 2018) WAIC YunFan Award (2022) Donald Eckman Award (American Automatic Control Council, 2024) Professor Wang actively mentors students and recruits undergraduate interns, visitors, and postdocs for her research group. Her work is supported by major grants from NSF, AFOSR, NIH, ONR, Google, Microsoft C3.ai, FinUP, RVAC Medicines, MURI, and GenMab. She serves as Program Chair for ICLR 2023 and Senior Area Chair for NeurIPS, ICML, and COLT, while editing for Harvard Data Science Review and Operations Research. She leads Princeton AI for Accelerated Invention, which develops AI-driven solutions for scientific discovery, collaborating closely with Princeton's ML Theory Group and Language+Intelligence Initiative to advance algorithmic innovation and interdisciplinary applications.

• Machine Learning
• Dynamical Systems
• Algorithmic Information Theory
• +3 more

Boumediene Hamzi is a Senior Scientist at the California Institute of Technology (Caltech) in the Department of Computing and Mathematical Sciences. He is also an Affiliate Fellow of the Data Science Institute at Imperial College London and an External Researcher at the Alan Turing Institute (London, UK). His career includes prestigious roles such as Marie Curie Fellowships and visiting professorships at institutions like Johns Hopkins University. Senior Scientist, Caltech (current) Affiliate Fellow, Data Science Institute, Imperial College London External Researcher, Alan Turing Institute Marie Curie Fellow, Imperial College London (twice) Marie Curie Fellow, Yıldız Technical University and Koç University His research bridges Machine Learning (ML) , Dynamical Systems Theory (DST) , and Algorithmic Information Theory (AIT) , aiming to develop a theoretical framework for ML. Key directions include: ML for Dynamical Systems: Analyzing systems via data in reproducing kernel Hilbert spaces. DST for ML: Using dynamical systems tools to understand ML algorithms. ML for AIT: Approximating Kolmogorov Complexity for compression and prediction. AIT for ML: Explaining ML applicability using AIT tools. He co-leads the Alan Turing Institute's Research Interest Group on "Machine Learning and Dynamical Systems" and co-organizes the One World Seminar Series on the Mathematics of Machine Learning. His work addresses challenges in modeling climate dynamics, biological systems, and complex domains. Scientific Awards Marie Curie Fellow (Imperial College London, Yıldız Technical University, Koç University) Hamzi has organized multiple international symposia and workshops on ML, DST, and AIT intersections, including events at Imperial College London, Fields Institute, and the Alan Turing Institute.

• Nano-Engineering
• Opto-Electronics
• Photonics
• +18 more

Dr. Anna Baldycheva is a Senior Lecturer in Electronic Engineering at the University of Exeter, within the College of Engineering, Mathematics and Physical Sciences. She leads the interdisciplinary STEMM Laboratory, focusing on applied R&D in smart materials, photonics, AI, and IoT. With prior research experience at MIT, Trinity College Dublin, and Tyndall National Institute, she has established herself as an internationally recognized innovator and entrepreneur in emerging technologies. PhD in Electronic and Electrical Engineering, Trinity College Dublin (2008–2012) BSc (Hons) in Physics, St. Petersburg State University (2003–2008) Postgraduate Certificate in Academic Practice, University of Exeter (2016–2017) Postgraduate Certificate in Technology Management, Smurfit Business School (2009–2010) Her research spans Nano-Engineering, Opto-Electronics, Photonics, AI, and IoT , with a strong emphasis on real-world applications. She pioneers work in fluid opto-electronics , graphene nanocoatings , and AI-driven emotion recognition and early cancer detection . Her lab develops smart composite materials for flexible electronics, e-textiles, and structural applications, integrating machine learning into healthcare, education, and communications systems. The recent publications highlight a strong trend toward applied interdisciplinary innovation , combining materials science with AI and photonics for healthcare diagnostics, energy-efficient computing, and educational technology. Her work frequently bridges fundamental physics with commercialization potential, as seen in spin-out technologies like GSurf and the Electronic-Nose for lung cancer detection. Fellow, Royal Microscopical Society (RMS) Fellow, Higher Education Academy (FHEA) Expert, Future and Emerging Technologies, European Commission Featured in Forbes and Forbes Tech Council Editor-in-Chief, InSTEMM Journal Associate Editor, Nature Scientific Reports and Discover Nano Trustee, Royal Microscopical Society Founder, STEMM Global Scientific Society Founder, It’s Her! Women in STEMM Initiative Dr. Baldycheva actively supervises PhD students and has secured industrial collaborations with organizations such as Qinetiq and Lumentum. She leads multiple outreach initiatives, including STEMM Junior for underprivileged children, and serves on the committee for the Jocelyn Bell Brunel PhD Scholarship. She has raised significant research funding through national and international grants, though specific grant names are not listed. She leads the STEMM Laboratory , a multidisciplinary research group with divisions in Smart Composite Materials, Machine Learning & AI, and Opto-Electronics & Photonics. The lab emphasizes industry collaboration and technology transfer, having produced a university spin-out (GSurf) and multiple media-highlighted innovations.

• Sound and Music Computing
• Human-Computer Interaction
• Human-Centered Computing
• +6 more

Dr. Sam Ferguson is a Senior Lecturer at the School of Computer Science, University of Technology Sydney (UTS), with a multidisciplinary background in music performance, cognitive science, and psycho-acoustics. His research explores the intersection of sound, music, and human experience through creative coding, machine learning, and interactive systems. Key Research Areas: Sound and Music Computing, Human-Computer Interaction, Creative Coding, Cognitive Science, Installation Art, and Acoustics. Current Projects: ARC Linkage project on creative coding and multiplicitous media; industry collaborations on IoT-based audiovisual systems. Recent Publications: Focus on spatial audio complexity, gestural interaction with networked sound, music emotion recognition frameworks, and robotic performance through genre-based cultural platforms. Leadership Roles: Director of Teaching & Learning Engagement; former Deputy Head of School (Teaching and Learning); active in ACM Creativity and Cognition Steering Committee. Teaching: Courses like Digital Media Studio , Prototyping Physical Interaction , and Data Processing using R within UTS's interdisciplinary Software Development Studio.

Guido PerboliView profile

Professor

• Operations Research
• Logistics
• Last-mile Delivery
• +9 more

Guido Perboli is a Full Professor in the Department of Management and Production Engineering (DIGEP) at the Polytechnic University of Turin, where he also serves as Logistics Coordinator and Project Coordinator for activities supporting relationships with government bodies. He is a member of the Interdepartmental Center CARS@PoliTO (Center for Automotive Research and Sustainable Mobility) and serves as Director of the ICT for City Logistics and Enterprises (ICElab@Polito) research center, which he founded in 2016. His research interests span a broad range of topics including Operations Research, Logistics, Last-mile Delivery, Sustainable Logistics, Combinatorial Optimization, Stochastic Programming, Business Development, and Lean Business methodologies. His work particularly focuses on City Logistics, Green Logistics, and the application of emerging technologies like Blockchain and AI in supply chain management. He has developed GUEST, a Lean Business methodology for innovation processes from early idea definition to implementation. Professor Perboli's recent publications demonstrate a strong focus on urban logistics, last-mile delivery optimization, blockchain applications in supply chains, and the integration of AI techniques in transportation systems. His work shows an increasing trend toward interdisciplinary research that combines optimization methods with emerging technologies to address sustainable urban mobility challenges. Professional Recognition: CASE Best Paper award from IEEE Conference on Automation Science and Engineering (2011) Effective member of INFORMS (2019-present) Effective member of EURO (1995-present) Effective member of AIRO (1995-present) Associate Editor for Journal of Applied Research and Technology (2020-present) Associate Editor for Sustainability (2018-present) Professor Perboli actively advises PhD students and has supervised numerous research projects, including EU-funded initiatives like SINFONICA, HESTER, and 5G-LOGINNOV. He serves as Scientific Director for multiple commercial research projects focused on blockchain, IoT, and AI applications in logistics. Beyond academia, he is Chief Scientific Officer of Arisk S.p.A., a fintech company specializing in business crisis prediction using AI and machine learning. His research group, ICElab@Polito, focuses on two main areas supporting urban growth: logistics and enterprises. The center collaborates with numerous companies including Amazon, DHL, and FCA, addressing real-world challenges in urban logistics and supply chain management through innovative research approaches.

Neil F. LoboView profile

Research Professor

• Medical Entomology
• Vector-Borne Diseases
• Malaria Transmission Dynamics
• +13 more

Neil F. Lobo is a Research Professor in the Department of Biological Sciences at the University of Notre Dame, where he has held progressive academic positions since 2004. His work lies at the intersection of medical entomology, infectious disease epidemiology, and public health policy, with a focus on mosquito-borne diseases such as malaria and dengue. He conducts field and laboratory research across multiple continents, collaborating with global health programs to inform vector control strategies. Ph.D. in Molecular Biology, University of Notre Dame (1995–2000) B.Sc., St. Joseph’s Arts and Science College, Bangalore, India (1990–1994) Dr. Lobo's research centers on understanding the biological and behavioral traits of Anopheles mosquitoes that influence malaria transmission. His lab investigates vector bionomics, species composition, insecticide resistance, and the impact of human behavior on disease spread. A major focus is residual transmission—malaria cases that occur despite existing interventions like bed nets and indoor spraying. His work emphasizes evidence-based decision-making for national malaria control programs. His recent publications span topics including spatial repellents, human exposure patterns, and malaria eradication strategies. Collectively, these studies reflect a strong commitment to operational research that bridges scientific discovery with real-world implementation. His work is published in high-impact journals such as The Lancet , Malaria Journal , and American Journal of Tropical Medicine and Hygiene , and often involves multi-institutional, international collaborations. Scientific awards and honors: No specific awards mentioned in the provided text. Dr. Lobo has advised numerous research projects and contributed to major global health initiatives, including collaborations with ministries of health in Africa and Southeast Asia. His lab supports operational research to evaluate intervention efficacy, vector surveillance, and transmission drivers. He has secured funding for long-term field studies in countries such as Indonesia, Kenya, Tanzania, Zambia, and Ethiopia, enabling data-driven policy recommendations for vector control. The Lobo Lab operates with a strong commitment to equity, inclusion, and anti-racism in science. It fosters an open, intersectional environment that values diverse perspectives to enhance scientific rigor and global applicability. The lab collaborates with academic institutions, NGOs, and public health agencies worldwide to advance health standards, particularly for populations disproportionately affected by preventable diseases.

Teemu MalmiView profile

Professor

• Management Control
• Performance Measurement
• Management Accounting
• +7 more

Teemu Malmi is a Professor in the Department of Accounting at the School of Business, Aalto University, Finland. He has been an influential figure in management accounting research, particularly in management control systems and performance measurement. His academic qualifications include a Doctoral degree (1997), Licentiate degree (1994), and Master's degree (1990), all in Business and Economics from the Helsinki School of Economics. His research interests span management control, performance measurement, digitalization in finance, public sector accounting, and organizational behavior. His work often integrates empirical analysis with case studies, including a notable investigation into Nokia’s management control challenges. He has published extensively in top-tier journals and contributed to major handbooks in accounting and information systems. The recent trend in his publications (2020–2025) reflects a growing emphasis on digital transformation, blockchain, data analytics, and the evolving role of finance functions. His research increasingly bridges traditional accounting with technology and public policy, especially in healthcare financing and sustainability. Scientific Awards: “Thirst for knowledge” (“Tiedon Jano”) award by JOKO Executive Education Oy (2001) Teemu Malmi has supervised at least five theses and led externally funded research projects, including the SOTE/Kaks project (2015–2016) on social and healthcare services. He has been actively involved in academic service, such as serving on editorial boards, hosting international scholars, presenting keynote lectures, and participating in funding organization committees. His media appearances demonstrate his engagement in public discourse on welfare policy and regional financing in Finland. There is no indication of part-time status, retirement, or former staff designation; he remains an active academic.

• Road Safety
• Connected and Automated Vehicles
• Driver Behaviour
• +5 more

Evita Papazikou serves as a Lecturer in Transport Engineering at the School of Engineering, University of the West of England (UWE Bristol), where she contributes to the Centre for Transport and Society and collaborates with the Bristol Robotics Laboratory's Connected & Autonomous Vehicles Centre. Her academic qualifications include: Civil Engineering (BEng and MEng) from Aristotle University of Thessaloniki MSc in Planning, Organisation, and Management of Transport Systems, Aristotle University of Thessaloniki PhD in Automated Systems and Driver Behaviour (Road Safety) from Loughborough University, sponsored by the Insurance Institute for Highway Safety with access to SHRP2 NDS data Dr. Papazikou's research focuses on road safety, connected and automated vehicles, driver behaviour analysis, and smart infrastructure. She investigates accident causation through statistical modeling, develops driver monitoring systems, and explores human factors in transportation. Her work integrates traffic simulation with mobility data fusion from vehicles, sensors, and infrastructure to enhance safety in future mobility systems, particularly in cooperative, connected, and automated environments. Her recent publications (2023-2025) reveal a concentrated research trajectory examining safety impacts of dedicated lanes for autonomous vehicles, parking policy implications in automated eras, and driver fatigue management. She consistently employs naturalistic driving data and traffic microsimulation to analyze driver-vehicle-environment interactions, with increasing emphasis on real-world intervention effectiveness and environmental sustainability in mobility systems. Scientific Awards: No specific awards were mentioned in the provided information. Dr. Papazikou has secured significant research funding through competitive programs including Horizon 2020, Innovate UK, and the Department for Transport. Her project portfolio demonstrates substantial industry collaboration, particularly with Ford, and includes: LEVITATE: Assessing societal impacts of Connected and Automated Vehicles SafetyCube: Developing an innovative road safety decision support tool i-DREAMS: Creating a smart driver and road environment assessment system DDRST: Building a data-driven road safety tool for hotspot identification TRIP: Developing a driver culpability assignment tool for road injury prevention She actively contributes to interdisciplinary research through her affiliations with the Centre for Transport and Society and the Bristol Robotics Laboratory's Connected & Autonomous Vehicles Centre, where she bridges engineering, human factors, and policy development for next-generation transportation systems.

• Bioinformatics
• Systems Biology
• Disease Module Identification
• +6 more

Zelmina Lubovac is a Senior Lecturer in BioInformatics at the School of Bioscience, University of Skövde. She serves as both a Course Coordinator for multiple undergraduate and graduate courses in bioinformatics and a Programme Coordinator for Master's level programs. Her academic work focuses on the intersection of computational methods and biological applications, particularly in disease analysis and biomarker discovery. Dr. Lubovac's research spans several key areas in bioinformatics and systems biology: Disease module identification in complex biological networks Multi-omics integration (genomics, proteomics, metabolomics) for biomarker discovery Machine learning applications in RNA-seq and other high-throughput biological data Development of bioinformatics software tools for network analysis miRNA analysis in cancer and neurological disorders Her recent publications (2022-2024) demonstrate a strong focus on applying computational approaches to understand disease mechanisms, particularly in pancreatic cancer and multiple sclerosis. She has developed several widely-used bioinformatics tools including MODalyseR, MODifieR, and TFTenricher that facilitate disease module analysis and gene network interpretation. Her work often involves collaborative research with clinical teams to translate computational findings into potential diagnostic applications. Dr. Lubovac has been involved in significant research projects including: BIO-AID (Biomedical AI-driven data analytics): Oct 2020 - Sep 2024 Systems Biology DMDPipe: Mar 2018 - Feb 2021 She actively contributes to both undergraduate and graduate education at the University of Skövde, coordinating multiple courses and programs in bioinformatics and bioscience, with a clear emphasis on preparing students for careers at the intersection of biology and computational science.

Stefano MerlerView profile

Research Fellow

• Epidemiology
• Infectious Diseases
• Mathematical Modelling
• +4 more

Stefano Merler is a Research Fellow at the Fondazione Bruno Kessler in Trento, Italy. He leads the Dynamical Systems research unit, focusing on the mathematical modelling of infectious diseases transmission, particularly analyzing the effects of population heterogeneity on disease spread and control.

Renate EganView profile

Professor

• Techno-economic analysis
• Energy Data Analysis
• Machine Learning in Energy Systems
• +3 more

Renate Egan is a Professor and Deputy Head of School (Engagement) at the School of Photovoltaics and Renewable Energy Engineering , University of New South Wales (UNSW). She leads UNSW's activities in the Australian Centre for Advanced Photovoltaics , a national research consortium involving multiple Australian institutions. Her research focuses on: Techno-economic analysis of photovoltaic technologies Energy data analytics for decentralized systems Electricity market restructuring Technology transfer and commercialization Recent work examines machine learning applications in energy demand forecasting, thermal storage optimization, and bushfire resilience. She collaborates extensively across academia, industry, and government sectors. Key affiliations include: Co-Founder of Solar Analytics (Australia's largest independent energy monitoring provider) Executive Committee member of the IEA PV Power Systems program

Michael Horn-von HoegenView profile

Professor

• Surface Physics
• Ultrafast Dynamics
• Electron Diffraction
• +4 more

Prof. Dr. Michael Horn-von Hoegen is a full professor in the Faculty of Physics at the University of Duisburg-Essen , Germany. His research focuses on ultrafast structural dynamics , surface physics , and 2D materials , particularly using electron diffraction and plasmonic imaging techniques. He leads the Horn-von Hoegen Group , which plays a central role in the Collaborative Research Center CRC 1242 Non-Equilibrium Dynamics of Condensed Matter in the Time Domain , where his team investigates driven phase transitions and phonon systems with sub-femtosecond temporal resolution. Location: Office Window MF260, Faculty of Physics, Lotharstr. 1-21, 47057 Duisburg Contact: Tel. +49 (203) 379 1439 | Fax +49 (203) 379 1555 His research spans ultrafast electron diffraction of photo-induced phase transitions in atomic wires and topological materials , with recent breakthroughs on Kibble-Zurek dynamics in the Si(001) surface and chiral plasmon polaritons . The group’s 15 most recent publications (2025-2022) address phenomena such as negative thermal expansion in 2D materials , electron-phonon coupling in Pb/Si heterostructures , and quantum pathway analysis in Bismuth films . These works are categorized under disciplines like Condensed Matter Physics , Nanooptics , and Ultrafast Dynamics , with subfields including Ising Model Transitions , Plasmon Focusing , and Time-Resolved Diffraction . Prof. Horn-von Hoegen serves as DFG Liaison Officer for the University of Duisburg-Essen, providing guidance on Deutsche Forschungsgemeinschaft (DFG) proposals . His group has mentored notable researchers including Dr. Simon Sindermann (postdoc at IBM), Dr. Anja Hanisch-Blicharski (Leopoldina Fellow), Dr. Hichem Hattab (Leopoldina Fellowship), and Dr. Marin Petrovic (Humboldt Fellow). The group’s laboratory facilities include advanced ultrafast electron diffraction and photoemission microscopy systems, enabling studies of atomic-scale processes such as molecular dynamics simulations of laser-excited surfaces and domain wall motion in Si(553)-Au systems .

Stephan A. SieberView profile

Professor

• Bioorganic Chemistry
• Antibiotic Development
• Chemical Proteomics
• +5 more

Professor Stephan A. Sieber is a leading researcher in bioorganic chemistry at the Technical University of Munich (TUM), where he holds the Chair of Organic Chemistry II within the TUM School of Natural Sciences. His research program focuses on developing new drugs against multidrug-resistant bacteria through a multi-disciplinary approach that integrates synthetic chemistry, functional proteomics, microbiology, and protein biochemistry. His laboratory has made significant contributions to identifying unprecedented antibacterial targets beyond the scope of current antibiotics and exploiting these for chemical manipulation. Recent work has increasingly incorporated machine learning approaches to accelerate antibiotic discovery, with notable publications on AI-guided pipelines, drug-target interaction prediction, and high-throughput screening optimization. Sieber's research has resulted in the discovery of new active substances, some of which are currently being optimized for medical applications. His group's publications reveal a strong focus on chemical proteome mining, natural product mode of action studies, and novel antibacterial target identification. The lab has published extensively in top journals including Nature Chemistry, Nature Communications, and ACS Central Science. Inhoffen Medal (2024) Max Bergmann Medal (2023) ERC Advanced Grant (2023) Merck Future Insight Prize (2020) Klaus Grohe Prize (2020) ERC Consolidator Grant (2016) Professor Sieber leads an active research group that maintains a strong presence in the scientific community through regular publications, conference presentations, and collaborations. His laboratory website and BlueSky presence (@sieberlab.bsky.social) demonstrate ongoing research activities and engagement with the broader scientific community. He has successfully secured significant research funding including multiple ERC grants that have supported his innovative work in antibiotic discovery.