Chen Liu is an Assistant Professor in the Department of Computer Science at City University of Hong Kong and the Principal Investigator (PI) of the Machine Learning and Optimization (MLO) group. His research focuses on building reliable machine learning models, particularly studying robustness and privacy properties of deep neural networks from an optimization perspective. University: City University of Hong Kong Academic Rank: Assistant Professor Students: Supervises multiple PhD, MPhil, and postdoctoral researchers. Education: Holds a Ph.D. (2022) and MSc (2017) in Computer Science from École Polytechnique Fédérale de Lausanne (EPFL), and a BSc (2015) in Computer Science from Tsinghua University. Research Interests: Adversarial robustness, privacy-preserving machine learning, optimization algorithms, dataset distillation, generative models, and theoretical analysis of loss landscapes. His work addresses challenges like catastrophic overfitting, architecture overfitting in distilled data, and stable adversarial training methods. Article Trends: Recent publications explore adversarial robustness under l0/l1 norms, gradient inversion for data reconstruction, evolutionary factor searching in finance, and meta-tuning for out-of-domain few-shot learning. These works emphasize optimization techniques to enhance model reliability and generalization. Scientific Awards: Microsoft Research Ph.D. Scholarship Programme (2017–2019) Advising and Grants: Supervises a diverse team of current and former students, with collaborations across institutions like George Mason University and Zhejiang University. Research supported by academic and industry grants. Labs and Teams: Leads the MLO group, which investigates fundamental ML theory and algorithms to improve system reliability. The group's work spans adversarial training, dataset distillation, and generative model optimization.
Dr. Asieh Hosseini Tabaghdehi is a Senior Lecturer in Strategy & Business Economy at Brunel Business School, Brunel University of London. She serves as Programme Lead for the BSc International Business Programme and Trade2Grow Executive Education Programme. Additionally, she is Impact Lead at the Brunel Centre for AI: Social and Digital Innovation, where she leads the capability area in the Future of Work. Dr. Tabaghdehi is also an economist and social impact advisor for the independent NGO, Social Innovation Movement. Dr. Tabaghdehi earned her PhD in Economics and Finance (2008) and MSc in International Money, Finance, and Investment (2015), both from Brunel University London. She also holds a BA in Theoretical Economics from University of Mazandaran. She completed the Postgraduate Certificate in Academic Practice and is a Fellow of the Higher Education Academy. Dr. Tabaghdehi is internationally recognized for her research on digital transformation, with particular expertise in the ethical integration of artificial intelligence and digital technologies. Her work focuses on how emerging technologies shape industries, labor markets, and society, with emphasis on enhancing SME growth through technological innovation. She explores applications across critical sectors including social care, supply chain management, and environmental sustainability. A central theme in her research is smart data governance, ensuring ethical, transparent, and responsible use of data in decision-making processes. Her research portfolio demonstrates a consistent focus on the intersection of technology, ethics, and business strategy. She has developed frameworks like the Digital Business Auditing Framework, which has been adopted internationally for smart city initiatives. Her work connects academic research with practical policy applications, as evidenced by her presentations as oral and written evidence to the House of Commons Select Committee. Her publications span AI ethics, digital footprint implications, fertility economics, and healthcare cost analysis, showing interdisciplinary breadth while maintaining thematic coherence around digital transformation's societal impact. Scientific Awards and Recognition Semi-finalist: Research Impact Award at Brunel University London, 2023 Staff Award: Exceptional in Collegiality and Supportive to Colleagues at Brunel University London, 2022 Exceptional Performance at Regents University London, 2018-19 Staff Award in Teaching, Learning and Assessment at Regents University London, 2016 Best Lecturer Award at London Brunel International College, 2014 Best Lecturer Award at London Brunel International College, 2013 Dr. Tabaghdehi actively supervises PhD students researching areas including Smart Data Governance, Ethical AI Governance, Digital Innovation Impact, Responsible AI Adoption Strategies, Sustainability, and Future of Labour Market. She has secured research funding from multiple sources including the Economic & Social Research Council (ESRC), Brunel University London, and Brunel Business School. Her current projects include research on AI Adoption and Governance, Youth digital addiction, Algorithm Reliability Framework, and SMEs digital footprints. She has also co-designed the "Digital Adoption" module for the UK Government's Help to Grow Management program, demonstrating the practical application of her research. As a member of multiple professional organizations, Dr. Tabaghdehi serves as an associate practitioner at Social Value International, associate member of the Big Innovation Centre, and member of the All-Party Parliamentary Group on AI. She is also a member of the ESRC Review College, British Academy of Management Review College, and Energy Institute UK, contributing to the broader academic and policy communities through these roles.
Prof. Frieder W. Scheller is affiliated with the Institute of Biochemistry and Biology at the University of Potsdam, Germany. His work centers on advanced biosensing technologies, particularly molecularly imprinted polymers (MIPs), bioelectronics, and biomimetic recognition systems. Research Interests: His primary fields include Bioanalysis, Bioelectronics, Biosensors, Molecularly Imprinted Polymers, Electrochemical Sensing, and Plastibodies. His research bridges chemistry, materials science, and biotechnology to develop synthetic alternatives to biological receptors for medical and environmental applications. The recent publications (2019–2024) highlight a strong trend in designing MIP-based nanofilms for protein and virus recognition, including applications in SARS-CoV-2 detection and enzyme monitoring. These studies focus on improving selectivity, stability, and reliability of electrochemical biosensors using innovative polymer architectures. Scientific Contributions: Developed Strep-tag imprinted polymer platforms for bio(electro)catalysis. Explored ACE2-mimicking MIPs for viral epitope recognition. Investigated challenges in MIP sensor reliability and non-specific binding. Advanced the concept of plastibodies for biomacromolecules, viruses, and cells. Collaborations and Advising: Prof. Scheller has collaborated with over 145 co-authors globally, indicating strong network engagement. While no formal students are listed in the provided text, his collaborative output suggests mentorship and team leadership roles in multidisciplinary research projects involving materials, electrochemistry, and biotechnology. Laboratories and Research Teams: His work is conducted within the Institute of Biochemistry and Biology at the University of Potsdam, likely involving a research group focused on bioanalytical chemistry and sensor development. The frequent co-authorship with researchers like Aysu Yarman and Xiaorong Zhang indicates an active, interdisciplinary team working on next-generation biosensing platforms.
Daria Nemashkalo is a researcher affiliated with the Digital Society Institute and Radio Systems at the University of Twente. Her work focuses on electromagnetic interference (EMI) filter design, time-domain analysis, and multichannel systems, particularly in power electronics and three-phase applications. Key Research Areas: EMI filter performance, mode decomposition, common mode choke saturation, and time-domain measurement techniques. Contributions: Published extensively on EMI mitigation strategies and filter optimization, including work on multichannel testing and real-world implementation challenges. Collaborations: Active in electromagnetic compatibility symposia, notably with peers like Peter Koch and Frank Leferink.
Prof. Dr.-Ing. habil. Gero Mühl is a W2-Professor at the University of Rostock, where he holds the chair for "Architecture of Application Systems" since October 2009. His academic journey includes positions as a Heisenberg Fellow at the Technical University of Berlin (2009), postdoctoral research at TU Berlin (2002-2009), and doctoral studies at TU Darmstadt where he received his Dr.-Ing. degree with distinction in 2002. He completed dual Diplomas in Computer Science (Dipl.-Inform.) and Electrical Engineering (Dipl.-Ing.) from FernUniversität in Hagen in 1998. Prof. Mühl's research focuses on Self-Organizing Distributed Systems , with particular expertise in distributed systems, distributed algorithms, event-based systems, middleware, energy-efficient systems, organic computing, sensor networks, web services, and electronic commerce. His work bridges theoretical foundations with practical implementations in real-world distributed environments. His recent publications show a strong trend toward time-sensitive networking, content-based publish/subscribe systems, and P4 programmable data planes. These works address critical challenges in industrial communication, real-time systems, and network reliability. His research group has made significant contributions to making distributed systems more autonomous, reliable, and efficient. Scientific awards and recognitions include: Nomination for the Berlin Science Award for Young Scientists (2008) Heisenberg Fellowship by the German Research Foundation (DFG) (2008) Best paper award in System Software and Security at SAC 2015 Prof. Mühl has been actively involved in numerous research projects and collaborations, particularly focusing on self-organizing and self-stabilizing systems. His work on the REBECA publish/subscribe middleware represents a significant contribution to autonomous distributed systems. He has supervised numerous students and researchers, contributing to the development of the next generation of computer scientists specializing in distributed systems. His laboratory at the University of Rostock focuses on practical implementations of self-organizing distributed systems, with current projects investigating time-sensitive networking, publish/subscribe systems, and energy-efficient distributed computing. The team combines theoretical analysis with practical system development to address real-world challenges in industrial and commercial applications of distributed systems.
John Driessnack is a nationally recognized expert in Systems and Portfolio/Program/Project Management, currently serving as a Professor at the Naval Postgraduate School, Defense Acquisition University (DAU), and American University (AU). He has also taught at the University of Maryland’s Project Management Center of Excellence. His career spans over a decade of collaboration with federal agencies, focusing on leadership, organizational strategy, and cost analysis in high-reliability endeavors. His research centers on federal government portfolio/program management, optimization of governance structures, and expansion of Integrated Product and Process Development (IPPD) and Integrated Product Team (IPT) frameworks. Notably, he led the strategic review of a major federal health organization in 2019 and co-authored the Guide to Lean Enablers for Managing Engineering Programs (2012). He contributed to the ANSI standard for Earned Value Management and the Section 809 Panel Report Volume III on Portfolio Management. Driessnack holds advanced certifications in defense acquisition (DAWIA Level IIIs) and industry credentials (PMI PMP, PfPM, ICEAA, Scrum CSM). He previously served as a military officer overseeing major defense programs and has since 2004 led senior consulting groups. His patented CREST framework (US Patent Pending 2012/0215574 A1) provides a novel approach to program analysis.
Avinash Kori is a Ph.D. researcher at Imperial College London affiliated with the Safe and Trusted AI Centre for Doctoral Training (CDT). Supervised by Prof. Francesca Toni and Prof. Ben Glocker , his research focuses on Explainable AI (XAI) , causality , and deep learning with applications in medical image analysis and optimization algorithms . His work includes publications on arXiv and conferences like MICCAI , covering topics such as robust segmentation , concept-based explanations , and symbolic reasoning in hyperbolic space . He has also explored stochastic optimization , support vector machines (SVM) , and gradient descent variants , providing theoretical and practical implementations. Recent trends in his publications highlight advancements in robust CNN models , causal logic frameworks , and hyperbolic geometry for hierarchical learning . His research is driven by the need to make AI systems more transparent and reliable for critical domains like healthcare. Scientific Awards: AAAIw Overall Best Paper Award (Feb 2021) for CNN interpretability research. He actively contributes to open-source implementations via platforms like GitHub and shares insights through blogs and paper reviews . His academic journey includes an undergraduate degree in Biomedical Engineering Design with a minor in Machine Learning from Indian Institute of Technology, Madras , followed by research internships at Siemens and Stanford University .
Prof. Dr.-Ing. Jürgen Teich is a full Professor and Chair for Hardware-Software Co-Design at the Department of Computer Science, Friedrich Alexander University Erlangen-Nuremberg (FAU). He serves as Head of Department Computer Science and Vice Dean of the Technical Faculty since August 2024, and has been Speaker of the FAU Research Center Embedded System Initiative (FAU ESI) since 2023. His educational background includes: Diploma degree in Electrical Engineering, University of Kaiserslautern (1989) Dr.-Ing. degree in Electrical Engineering, University of Saarland (1993) Habilitation (PD Dr.-Ing.) entitled "Synthesis and Optimization of Digital Hardware/Software Systems" (1996) Prof. Teich's research focuses on Embedded Systems , Invasive Computing , Hardware-Software Co-Design , and Reconfigurable Computing . His work spans from theoretical foundations to practical implementations, with particular emphasis on resource-constrained systems, many-core architectures, and energy-efficient computing. He has pioneered research in invasive computing paradigms that enable more efficient use of many-core processors by allowing applications to dynamically claim resources. His recent publications reveal a strong trend toward energy-efficient AI deployment on embedded devices , security of embedded systems , and novel memory technologies . There's a clear focus on practical implementations of machine learning on microcontrollers (TinyML), hardware acceleration for data processing, and innovative approaches to power management in self-powered systems. Among his notable scientific awards are: IEEE Fellow (since 2018) Member of Academia Europaea, Section Informatics (since 2011) Member of the National Academy of Science and Engineering (acatech) (since 2018) Member of the German Society of Humboldtians (since 2021) Prof. Teich has been Principal Investigator for numerous DFG-funded projects including SFB/Transregio 89 "Invasive Computing" (2010-2022), SFB 694, and multiple priority programs. He has coordinated large collaborative research efforts across Germany and internationally, with significant funding from DFG and other sources. His research group has produced influential work in embedded systems design and co-design methodologies. He leads the Hardware-Software Co-Design research group at FAU, which focuses on innovative approaches to embedded system design, invasive computing architectures, and efficient implementation of machine learning on resource-constrained devices. The group maintains strong collaborations with industry partners including Intel, Xilinx, and automotive companies.
James Lacefield is a Professor in both the Department of Electrical and Computer Engineering and the Department of Medical Biophysics at Western University. He serves as Director of the School of Biomedical Engineering and maintains his research laboratory in the Amit Chakma Engineering Building. His academic appointments span multiple disciplines, reflecting the interdisciplinary nature of his work in biomedical ultrasound imaging. Dr. Lacefield earned his Ph.D. and B.S.E. in Biomedical Engineering from Duke University. His educational background established the foundation for his current research program that bridges engineering principles with medical applications. His research focuses on the physical acoustics and signal processing aspects of ultrasound imaging, with particular emphasis on quantitative vascular imaging applications. Dr. Lacefield's laboratory develops novel methods for color Doppler, power Doppler, and contrast-enhanced ultrasound imaging, with primary applications in cancer research. Current projects include optimization of high-resolution ultrasound systems for tumor vascular characterization and development of methods to quantify spatial blood flow distribution in tumors. Analysis of his recent publications reveals a strong focus on quantitative ultrasound techniques for cancer applications, with particular attention to tumor perfusion assessment using contrast-enhanced ultrasound. His work demonstrates increasing sophistication in speckle analysis methods to improve the reliability of perfusion measurements in preclinical tumor models. Associate Editor, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control Member, Council of Chairs of Bioengineering and Biomedical Engineering Member, College of Reviewers, Canadian Institutes of Health Research Dr. Lacefield maintains active collaborations with multiple research groups including the Imaging Research Laboratories at Robarts Research Institute. His professional activities include editorial work for major ultrasound journals and participation in national review panels for biomedical research funding.
Gianni Franchi is an assistant professor at ENSTA Paris , affiliated with the Computer Science and Systems Engineering Unit (U2IS) . His work focuses on theoretical deep learning , with a strong emphasis on uncertainty quantification, robustness, and explainability in machine learning models. Current affiliation: ENSTA Paris (U2IS) Academic rank: Assistant Professor Key collaborators: David Filliat, Emanuel Aldea, Andrei Bursuc, Antoine Manzanera His research spans uncertainty quantification , explainable AI , and reliable machine learning . He investigates methods like Bayesian neural networks, ensemble approaches, and deterministic uncertainty models. His work also addresses domain adaptation , self-supervised learning , and autonomous systems , particularly in trajectory forecasting and semantic segmentation for autonomous driving. Recent publications analyze probabilistic modeling for robustness, symmetry-aware Bayesian methods , and multi-modal datasets like InfraParis. He develops frameworks like Torch-Uncertainty and benchmarks such as MUAD for uncertainty types in autonomous driving. Key themes: Uncertainty Quantification Deep Learning Theory Autonomous Systems Explainable AI Dataset Creation Bayesian Methods
Maarten Sap is an Assistant Professor at Carnegie Mellon University's Language Technologies Institute with a courtesy appointment in the Human-Computer Interaction Institute. He also holds a part-time research scientist position at the Allen Institute for AI (AI2) as an AI safety lead. Current affiliations: CMU (2022–present), AI2 (2022–present) Prior: Postdoctoral Researcher at AI2 (2021–2022), Research Intern at AI2 (2018–2019) and Microsoft (2019) His research focuses on enhancing AI systems with social intelligence and addressing social biases in language technology. Key themes include: Ethical AI and Human-Centric Design Narrative Dynamics and Social Context Analysis AI Agents and Social Intelligence Toxic Language Detection and Cultural Bias Mitigation Recent publications examine: AI safety frameworks like HAICOSYSTEM Clinical reasoning alignment (ALFA) Multilingual moderation (PolyGuard) Cultural sensitivity in non-verbal AI (Mind the Gesture) Personality shaping in LLMs (BIG5-CHAT) Scientific Recognition: 2025 Okawa Research Grant Best Paper Runner Up - NAACL 2025 Outstanding Paper - EMNLP 2023 Best Paper - FAccT 2023 Best Paper - WeCNLP 2020 He advises a diverse group of PhD students across CMU and MIT, and has served on multiple program committees including ACL, EMNLP, and FAccT. His work appears in top venues like Nature Machine Intelligence, PNAS, and ACL.
Mohammad Pirani is an Assistant Professor in the Department of Mechanical Engineering at the University of Ottawa, with a joint appointment at the School of Electrical Engineering and Computer Science. Previously, he held postdoctoral and research assistant professor roles at the University of Waterloo, University of Toronto, and KTH Royal Institute of Technology. Education: Ph.D., Mechanical and Mechatronics Engineering, University of Waterloo (2017) MASc., Electrical and Computer Engineering, University of Waterloo (2014) BASc., Mechanical Engineering, Amirkabir University of Technology (2011) His research focuses on resilient and fault-tolerant control in complex systems, including networked control systems and multi-agent systems . He explores intersections with network science , cybersecurity , and machine learning , addressing vulnerabilities in cyber-physical systems like automotive networks. Notable contributions include publications in IEEE Transactions on Control of Network Systems and Automatica , with recent work on network critical slowing down and graph-theoretic resilience strategies. His research trends emphasize reliable learning , security in distributed systems , and data-driven detection of critical transitions . Scientific Awards: Senior Member, IEEE Mohammad Pirani holds a dual appointment at the University of Ottawa and an adjunct professor position at the University of Waterloo. His work bridges mechatronics , robotics and automation , and networked systems , with future directions targeting cybersecurity in cyber-physical systems.
Matthieu Sozeau is a prominent researcher at Inria in the Gallinette team in Nantes, France, and a key contributor and coordinator of the Coq/Rocq proof assistant project. His work bridges theoretical computer science and practical software development, focusing on creating reliable formal verification tools. His research interests span Type Theory, Proof Assistants, Functional Programming, and Unification. He has made significant contributions to the development of Coq (recently renamed to Rocq Prover), particularly through the MetaCoq project which aims to verify Coq's kernel within Coq itself, the Equations plugin for dependent pattern matching, and CertiCoq, a verified compiler from Coq to assembly. His work enables stronger guarantees about formalized mathematics and verified software. Sozeau's publications reveal a consistent focus on foundational aspects of proof assistants. His recent work includes verified type checking ('Coq Coq Correct!'), verified extraction from Coq to OCaml, and sort polymorphism for proof assistants. These contributions advance both theoretical understanding and practical implementation of dependently-typed programming languages. Distinguished paper award for Verified Compilation from Coq to OCaml at PLDI'24 As an academic mentor, Sozeau has supervised PhD students including Théo Winterhalter and Antoine Allioux. He regularly teaches courses on proof assistants, notably at MPRI (Master Parisien de Recherche en Informatique), and actively participates in the academic community through program committees, invited talks, and workshops. His work has significantly influenced both the theoretical foundations and practical applications of interactive theorem proving.
Yuyu Zhou is a Professor in the Department of Geography at The University of Hong Kong. With an extensive publication record of 301 papers and over 18,000 citations, Dr. Zhou is a leading researcher in urban environmental studies, climate change, and sustainability science. Dr. Zhou received their PhD in Environmental Science from the University of Rhode Island (2004-2008) and previously worked as a Research Scientist at Pacific Northwest National Laboratory's Joint Global Change Research Institute (2010-2015). They currently serve as Chief Editor for Earth System Science Data (Copernicus Publications), Associate Editor for Ecological Processes, and Section Editor for All Earth. Dr. Zhou's research focuses on the intersection of urbanization, climate change, and environmental sustainability. Their work spans several key areas including urban heat island effects, vegetation phenology in urban environments, energy modeling, and sustainable urban development. Through innovative remote sensing approaches and spatial analysis, Dr. Zhou investigates how urban environments respond to and influence global environmental change. Analysis of Dr. Zhou's recent publications reveals a strong emphasis on urban environmental challenges, with particular attention to urban heat islands, vegetation dynamics, and climate change impacts in cities. Their work combines remote sensing data with ground observations to develop high-resolution models of urban environmental processes. Recent research has focused on urban greening effects, building energy use under climate change, and environmental justice issues related to urban heat exposure. Dr. Zhou has received significant recognition for their work, as evidenced by the high citation count of their publications. Their research has important implications for urban planning, climate adaptation strategies, and sustainable development policies worldwide. As an educator and mentor, Dr. Zhou advises numerous graduate students and collaborates with researchers globally. Their work with international teams has resulted in significant contributions to understanding urban environmental systems across different geographical contexts.
Jonathan Miles Robker is a Lecturer (Privatdozent) at the University of Münster's Faculty of Protestant Theology. He holds a Habilitation in Old Testament (2018), Doctor of Theology (2011), Master of Theological Studies (2006), and dual Bachelor's degrees in History and Philosophy (2003). Since 2020, he serves as Editor for Biblical Studies and has held research positions at the university since 2013. His research focuses on textual criticism, ancient Near Eastern epigraphy, Deuteronomistic history, and literary analysis of biblical texts. Primary interests include the Book of Kings, history of Israel, and comparative ancient Near Eastern traditions. Robker's publications demonstrate consistent engagement with textual variants across ancient manuscripts, particularly examining the Deuteronomistic History through Septuagint and Masoretic textual traditions. His works frequently analyze political theology, prophetic literature, and the development of biblical canons.
