Doç. Dr. Muhammed Aras is an Associate Professor in the Department of Mechanical Engineering at Baskent University (Başkent Üniversitesi), with a research focus on sustainable machining processes, tool wear monitoring, and surface roughness optimization. His work spans advanced manufacturing technologies, energy storage systems, and biomedical device design. PhD in Manufacturing Engineering (2018), Gazi Üniversitesi MSc in Mechanical Education (2013), Gazi Üniversitesi BSc in Mechanical Engineering (2010), Tabriz Islamic Azad University Research interests include sustainable machining (dry/hard turning, cooling-lubrication strategies), tool wear analysis (CBN, ceramic and coated inserts), and surface integrity optimization using AI-based methods (firefly algorithm, TOPSIS, Grey Relational Analysis). His 15 most recent articles (2017-2024) examine topics like: Surface roughness prediction in dry hard turning Energy storage technology viability assessments Cutting parameter optimization for various steels Acoustic/vibration monitoring in machining He has received scientific awards including the Teşvik Ödülü (Encouragement Award, 2015). His patent on an automatic orthognathic surgery articulator and book chapters on tool monitoring systems demonstrate his multidisciplinary impact.
Dr. Jay Pujara is a Research Associate Professor of Computer Science at the University of Southern California (USC) and Director of the Center on Knowledge Graphs. He is also a Principal Scientist at the Information Sciences Institute (ISI) and leads research teams in data science and AI. Ph.D., University of Maryland, College Park (2016) M.S. and B.S. in Computer Science, Carnegie Mellon University Research Interests include artificial intelligence, probabilistic models, knowledge graph construction, statistical relational learning, NLP, and streaming inference. His work focuses on scalable algorithms for big data and uncertainty modeling in dynamic environments. Recent Publications highlight advancements in knowledge graphs, LLM reasoning, and table understanding. Notable topics include non-verbal abstract reasoning , faithful conversational datasets , and KGQA re-ranking . Scientific Awards : SWSA Ten-Year Award (2023), Outstanding Paper (IUI 2019), Top Reviewer (NeurIPS 2018), Best Paper (SRL Workshop 2016) Advising & Grants : Mentored 12+ graduate students, including Ph.D. advisees on topics like causal modeling and neuro-symbolic tasks. Secured NSF funding for table understanding in paleoclimate studies.
Sara Hägg is a Senior Lecturer at the Karolinska Institutet , affiliated with the Department of Medical Epidemiology and Biostatistics . She is also a Docent in molecular epidemiology. PhD in Computational Biology (Linköping University, 2009) MSc in Molecular Biology (Stockholm University, 2003) BSc in Computer Science (Stockholm University, 2003) Her research focuses on human biological aging , including measurement of aging markers (telomere length, epigenetic clocks, frailty index), causal pathway analysis, and identification of geroprotectors for age-related diseases. She utilizes longitudinal twin studies (SATSA, GENDER, HARMONY), UK Biobank, and Swedish cohorts with methods like Mendelian randomization and genome-wide analyses . Recent articles demonstrate trends in epidemiological aging research , with emphasis on cardiovascular aging , neurological disease interactions , metabolic profiling , and epigenetic clocks . Her work often involves multivariable modeling and cross-cohort validation . Leadership roles include Director of LifeGene Core Facility (2024-) and Founding Board Member of the Nordic Aging Society (2023-). She serves on expert groups for the Swedish Twin Registry and Strategic Research Area in Epidemiology and Biostatistics .
Christopher Ferrie is an Associate Professor at the University of Technology Sydney (UTS), where he is affiliated with the Faculty of Engineering and Information Technology and the Centre for Quantum Software and Information (QSI). His academic career spans quantum information science, machine learning, and scientific education, with a strong emphasis on both theoretical research and public engagement through science communication. Full-time faculty member at UTS Active researcher in quantum information science Director of the Centre for Quantum Software and Information Author of numerous scientific publications and popular science books Dr. Ferrie earned his PhD in Applied Mathematics from the Institute for Quantum Computing and University of Waterloo in Canada in 2012. His doctoral work focused on quantum information and laid the foundation for his subsequent research career in quantum computing and related fields. Dr. Ferrie's research interests span several interconnected domains within quantum information science. His primary focus is on quantum estimation and control, with particular emphasis on applying machine learning techniques to solve statistical problems in quantum information science. He investigates how quantum systems can be characterized, controlled, and optimized for practical applications. His work bridges theoretical quantum physics with practical implementations, exploring how quantum phenomena can be harnessed for computational advantage. Recent research directions include quantum machine learning, quantum neural networks, and quantum optimization algorithms, with applications ranging from quantum state tomography to solving combinatorial optimization problems. Analysis of Dr. Ferrie's recent publications reveals a strong focus on practical quantum computing challenges. His work consistently addresses the intersection of quantum information theory and machine learning, with particular emphasis on making quantum algorithms more efficient, interpretable, and robust against noise. A significant portion of his recent research explores variational quantum algorithms and their optimization, reflecting the current priorities in near-term quantum computing. His publications also demonstrate growing interest in quantum machine learning applications and the development of techniques for quantum error mitigation and characterization. Dr. Ferrie has secured multiple research grants supporting his work in quantum computing and related fields. His funded projects span quantum control, quantum probability, quantum machine learning, and statistical decision theory, reflecting the breadth of his research program. While specific major awards aren't detailed in the available information, his sustained funding and publication record indicate significant recognition within the quantum information science community. Dr. Ferrie is actively involved in research supervision and teaching, with current funding supporting multiple PhD students and postdoctoral researchers. His teaching responsibilities include courses on quantum computing, where he introduces students to the fundamentals of quantum information processing. His research group at the Centre for Quantum Software and Information focuses on developing novel quantum algorithms and exploring the practical implementation challenges of quantum computing. The Centre for Quantum Software and Information at UTS serves as the primary research environment for Dr. Ferrie's work. This center brings together researchers working on various aspects of quantum computing, from hardware development to algorithm design and applications. Dr. Ferrie's team within the center focuses specifically on quantum software development, quantum algorithm design, and the application of machine learning techniques to quantum information problems. The collaborative environment enables interdisciplinary research that bridges theoretical quantum physics with practical computing applications.
Dr. Mattia Andreoletti is a Lecturer at the Department of Health Sciences and Technology at ETH Zurich, working within the Professorship for Bioethics. His research spans philosophy of medicine and bioethics, with a focus on the ethical dimensions of AI in healthcare, dementia policy, drug regulation, and clinical reasoning. PhD from the European School of Molecular Medicine (SEMM), affiliated with the European Institute of Oncology (IEO, Milan) ORCID: 0000-0003-1880-0770 His work intersects with clinical ethics, particularly examining replicability in scientific research, evidential pluralism in drug regulation, and the ethical landscape of digital biomarkers. He contributes to courses such as Ethics in Drug Development and Ethics Workshop: The Impact of Digital Life on Society . The 15 most recent articles (2025-2023) highlight his engagement with AI ethics, dementia prevention, regulatory science, and clinical reasoning. Topics include ethical frameworks for digital biomarkers, evidential pluralism in drug approval, and philosophical foundations of rehabilitation sciences.
Professor Anna Marie Munster is a distinguished academic and artist at UNSW Art and Design (College of Fine Arts), where she has held a full-time tenured position since 2001. Her work bridges theoretical scholarship with creative practice, establishing her as a leading figure in digital media art and critical theory. University: University of New South Wales School: College of Fine Arts (UNSW Art and Design) Position: Professor Qualifications: PhD from UNSW (2002), BA (Class 1 Hons with University Medal) from University of Sydney (1984) Professor Munster's research explores the intersections of art, technology, and philosophy, with particular focus on statistical visuality, radical empiricism, the politics and aesthetics of machine learning, and more-than-human perception. Her work integrates process philosophy with contemporary media practices, examining how time, movement, and sonicity shape our experience of digital environments. She approaches these topics through both theoretical scholarship and creative practice, often collaborating with artist Michele Barker on multi-channel audiovisual installations. Her publications reveal a consistent trajectory exploring the relationship between embodiment and digital media, with recent work focusing on AI and machine learning in cultural contexts. Munster's creative output demonstrates sophisticated integration of technical innovation with philosophical inquiry, particularly in how her installations challenge conventional perceptions of time and space. ARC, UNSW Student Council Award for Excellence in Postgraduate Supervision (2017) Dean's Award for Excellence in Postgraduate Supervision (2015) Highly commended for Materializing New Media at Prix Ars Electronica (2008) Winner of National Digital Art Awards 'The Harries' (2006) University Press of New England Publishing Award (2005) Professor Munster has completed over 20 PhD and Masters supervisions at UNSW and been involved in another 10 secondary supervisions. She currently supervises five PhD candidates working on diverse topics including coding materialities, critical bio-textiles, schizophrenia and XR technologies, sound and resonance, and affective drawing. Her ARC-funded research projects demonstrate sustained commitment to interdisciplinary work, particularly at the intersection of art, science, and technology. Her creative practice with Michele Barker has resulted in numerous commissioned installations exploring perception, embodiment, and the relationship between humans and technology.
Samuel Jean Bassetto is an Associate Professor in the Department of Mathematics and Industrial Engineering at Polytechnique Montréal. He serves as Director of the Continuous Improvement Laboratory (LABAC) and holds membership in multiple prestigious research groups including the Research Group on Globalisation and Management of Technology (GMT), Poly-Industries 4.0 Laboratory, Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT), and Institute for Data Valorization (IVADO). Dr. Bassetto's research spans multiple disciplines, focusing on continuous improvement through the integration of engineering, artificial intelligence, cognitive science, psychology, and design. His primary sphere of excellence is in New Frontiers in Information and Communication Technologies, with secondary expertise in Modeling and Artificial Intelligence and Human Health. He develops tools that place humans at the center of technology to enhance organizational performance while respecting human rhythms and cognitive limitations. His recent publication portfolio reveals a strong interdisciplinary approach, with research bridging industrial engineering, cognitive neuroscience, and AI ethics. His work addresses practical challenges in lean manufacturing assessment, racial bias in medical AI systems, cognitive data collection in natural environments, and condition monitoring for industrial machinery. The research consistently demonstrates a commitment to developing practical solutions that integrate human factors with technological innovation. NSERC Synergy Prize for Innovation recipient Principal investigator on multiple research grants from NSERC, FRQ, and MITACS Collaborations with over a dozen institutions across multiple countries Supervision of over 150 highly qualified personnel throughout his career Dr. Bassetto teaches specialized courses including CAP7011 (Creativity in Research), IND8444 (Continuous Improvement), IND8203 (Industrial Launch), and previously taught IND8178 (Production). His teaching philosophy emphasizes practical application, with courses featuring hands-on exercises, real-world scenarios, and gamification techniques to enhance learning. His supervision portfolio includes numerous Ph.D. and Master's students working on topics ranging from human-technology collaboration to reinforcement learning for production management. Through LABAC, Dr. Bassetto leads research initiatives focused on developing human-centered tools for continuous improvement in organizational settings. The laboratory conducts projects related to industrial IoT applications, cognitive aspects of process improvement, and the development of practical frameworks for organizations to enhance performance while maintaining respect for human rhythms and cognitive capabilities.
Christian List is Professor of Philosophy and Decision Theory at Ludwig Maximilian University of Munich, where he serves as Co-Director of the Munich Center for Mathematical Philosophy (MCMP). Previously, he was Professor of Philosophy and Political Science at the London School of Economics until 2020. His work bridges philosophy, economics, and political science with a particular focus on individual and collective decision-making and the nature of intentional agency. Professor List's research spans multiple interconnected domains: theories of individual and collective choice (particularly social choice theory and judgment aggregation), free will and consciousness, the philosophy of mind and action, and the foundations of the social sciences. His work on group agency, developed in his influential book Group Agency with Philip Pettit, has reshaped debates about corporate entities and collective intentionality. His more recent work on free will, culminating in his book Why Free Will is Real , presents a scientifically grounded defense of free will against reductionist skepticism. His recent publications reveal a sophisticated integration of formal methods with deep philosophical questions, particularly regarding consciousness, probability aggregation, and the relationship between different levels of explanation. List's work consistently demonstrates how mathematical precision can illuminate fundamental philosophical problems while maintaining relevance to broader social and scientific contexts. Scientific Awards and Recognition: Elected Fellow of the British Academy (2014) Member of Academia Europaea (2023) Member of the Bavarian Academy of Sciences and Humanities (2022) Joseph B. Gittler Award from the American Philosophical Association (2020) Philip Leverhulme Prize in Philosophy (2007) 5th Social Choice and Welfare Prize (2010) List has supervised numerous PhD students and early-career researchers, many of whom have gone on to prominent positions in philosophy and related fields. His collaborative work with Franz Dietrich on judgment aggregation has been particularly influential. As Co-Director of the Munich Center for Mathematical Philosophy, he has secured substantial research funding and established MCMP as a leading international hub for formal and mathematical approaches to philosophical problems. Through the Munich Center for Mathematical Philosophy, List leads a vibrant research community that brings together philosophers, economists, political scientists, and mathematicians to tackle foundational questions using rigorous formal methods. The center hosts regular workshops, seminars, and visiting scholars, creating a dynamic intellectual environment that bridges disciplinary boundaries.
Ali Ghodsi is a Professor at the University of Waterloo and Director of the Data Science Lab, with affiliations at the Vector Institute. His research spans machine learning, deep learning, and artificial intelligence, with applications in natural language processing, bioinformatics, and computer vision. His group develops theoretical frameworks and algorithms for analyzing large-scale datasets, focusing on neural network architectures, knowledge distillation, and model efficiency. Current projects include deep learning for identity control, computational antibody design, and generative AI/large language models. Ghodsi has authored influential tutorials on diffusion models, graph neural networks, and large language models. Notable research contributions include computational methods for de novo peptide sequencing from mass spectrometry data, green simulation-assisted reinforcement learning, and efficient natural language processing models. His lab maintains collaborations with industry partners including Google, Amazon, and Roche.
Jesper Rindom Jensen is an Associate Professor in the Department of Electronic Systems at Aalborg University, Denmark, under the Technical Faculty of IT and Design. He is the Head of the Audio Analysis Lab, a leading research group in audio signal processing, since 2023. His work bridges theoretical signal processing and practical applications in artificial intelligence and audio systems. Full Name: Jesper Rindom Jensen Institution: Aalborg University School: The Technical Faculty of IT and Design Department: Department of Electronic Systems Research Lab: Audio Analysis Lab Email: jrj@es.aau.dk Office: Fredrik Bajers Vej 7B, B5-206, 9220 Aalborg Øst, Denmark Education: M.Sc. in Electronic Systems, Aalborg University (cum laude, 2009) Ph.D. in Signal Processing, Aalborg University (2012) Research Interests: Jesper Rindom Jensen's research centers on audio signal processing, with a strong emphasis on artificial intelligence, speech enhancement, noise reduction, beamforming, and multichannel systems. His work applies to diverse domains including robot and drone audition, spatial audio, and active noise control. He develops novel filtering techniques, including variable span linear filters and harmonic beamformers, to improve speech quality and intelligibility in noisy and reverberant environments. Publication Trends: His recent publications (2023–2025) show a strong trend toward integrating deep learning with classical signal processing, particularly in direction-of-arrival estimation, underwater acoustics, and robust multichannel systems. There is a clear focus on real-world applications, including sound zone control, active noise control, and limited-data scenarios using knowledge distillation. His work consistently emphasizes robustness, efficiency, and practical deployment. Scientific Awards and Recognition: AAU Talent for emerging research leaders Recipient of a competitive postdoc grant from the Danish Independent Research Council Advising and Grants: Jesper has supervised multiple PhD and master’s students, including Nørholm, Karimian-Azari, Zhang, and Wang. He has led significant research projects such as 'Sound Processing for Robots and Drones' (2018–2020) and participated in others related to joint audio-visual tracking and speech enhancement. His research has been supported by national funding bodies, reflecting its innovation and impact. Labs and Teams: He is a founding and core member of the Audio Analysis Lab at Aalborg University, which focuses on cutting-edge audio signal processing and AI-driven solutions. The lab fosters interdisciplinary collaboration and has produced numerous publications, datasets, and real-world applications. Jensen’s leadership since 2023 underscores his pivotal role in shaping the lab’s research direction.
Mark S. Handcock is a Distinguished Professor in the Department of Statistics and Data Science at the University of California, Los Angeles (UCLA), where he leads research at the intersection of statistical methodology and applied problems in social sciences, epidemiology, and environmental science. His work bridges theoretical statistics with real-world challenges through innovative methodological development. His primary research interests encompass statistical models for social networks, network inference, methodology for hard-to-reach population surveys, spatial processes, demography, and environmetrics. Handcock has pioneered advances in exponential-family random graph models (ERGMs) and developed foundational R packages like ergm and tergm within the statnet suite, enabling sophisticated network analysis across disciplines. Analysis of his recent publications (2023-2025) reveals three dominant research thrusts: (1) Antarctic sea ice modeling using Bayesian reconstruction and temporal variability analysis, (2) epidemiological modeling of infectious disease transmission dynamics (particularly COVID-19), and (3) methodological innovations in network inference and causal analysis over stochastic networks. His work consistently integrates advanced computational statistics with domain-specific applications in climate science, public health, and social systems.
Slava Jankin is a Professor of Data Science and Government at the University of Birmingham’s School of Government, where he also serves as Deputy Director of the Institute for Data and AI and Founding Director of the Centre for Artificial Intelligence in Government. He is concurrently a Fellow and Founding Director of the Data Science Lab at the Hertie School in Berlin. Previously, he held a Professorship at the University of Essex and has worked at University College London (UCL) and the London School of Economics (LSE). His research bridges computational methods, governance, and climate policy, with a focus on AI applications in public institutions, climate-health surveillance, and misinformation resilience. Jankin earned a PhD in Political Science from Trinity College Dublin (2009), a Postgraduate Diploma in Statistics (2006), and a BSc from Belarus State Economic University (2002). **Education**: • PhD in Political Science, Trinity College Dublin (2009) • Postgraduate Diploma in Statistics, Trinity College Dublin (2006) • BSc Econ with Distinction, Belarus State Economic University (2002) **Research Interests**: Jankin’s work integrates AI and computational methods with governance challenges, including climate policy, health surveillance, and institutional effectiveness. He leads initiatives like the Lancet Countdown’s climate-health monitoring and the CATALYSE project on climate impacts. His research also explores digital twins for governance systems and the role of cultural diversity in societal resilience against misinformation. **Grants & Collaborations**: He advises the UN and EU on AI and data science, co-leads the Lancet Countdown, and collaborates with institutions like the Alan Turing Institute. His applied work includes developing AI tools for public service optimization and policy simulations. **Labs & Teams**: Directs the Centre for AI in Government (University of Birmingham) and the Hertie School’s Data Science Lab, fostering interdisciplinary teams to advance computational methods in public policy.
Julian Berger is a postdoctoral researcher at the Max Planck Institute for Human Development in the Center for Adaptive Rationality , where he explores how to enhance decision-making through hybrid human-AI systems. He is also a fellow of the Joachim Herz Foundation and has received funding from the Foundation of German Business and the Danish Data Science Academy. Education: M.A. Psychology in Business and Economics, Universidade Catolica Portuguesa (2021) B.A. Politics, Administration and International Relations, Zeppelin Universität (2018) His research spans human-AI collaboration , collective intelligence , and interpretable machine learning . A recurring theme in his work is developing methods to combine human expertise with AI capabilities for accuracy in domains like medical diagnostics , credit scoring , and football analytics . He has authored publications in high-impact venues such as PNAS , Nature Human Behavior , and Science and Medicine in Football . Scientific awards and funding include: Fellowship for interdisciplinary economics, Joachim Herz Foundation (2024) PhD funding from the Foundation of German Business (Stiftung der deutschen Wirtschaft) Research grant from the Danish Data Science Academy His recent article trends emphasize ensembling techniques that leverage complementary human and AI errors, algorithmic fairness, and practical heuristics like Hybrid Confirmation Trees. These works demonstrate significant improvements in diagnostic accuracy and decision cost-efficiency. Beyond academia, Berger works as a consultant and ML engineer with Simply Rational , focusing on interpretable models for financial and sports analytics. His work bridges theoretical research with real-world applications, prioritizing fairness, transparency, and human accountability in AI systems.
Joss Wright is an Associate Professor and Senior Research Fellow at the Oxford Internet Institute , University of Oxford. He co-directs the Oxford EPSRC Cybersecurity Doctoral Training Centre and the Oxford Martin Programme on the Wildlife Trade, focusing on computational approaches to social science questions about information control and privacy. Education : PhD in Computer Science from the University of York (research on anonymous communication systems), postdoctoral work at the University of Siegen (cloud computing security). His research spans internet censorship , privacy-enhancing technologies , and cyber-enabled crime (notably the online illegal wildlife trade ). He bridges technical analyses of security systems with their social and political implications, advising the European Commission and UK Parliamentary Science Committee on digital policy. Recent work includes machine learning applications to detect patent filing trends related to wildlife trade and analyzing Chinese smart city surveillance for human rights risks. He has contributed to media outlets like the Guardian and New Scientist. Notable projects include the Oxford Martin Programme on Wildlife Trade and studies on discriminatory effects of internet filtering . He supervises students like William Lugoloobi (DPhil in Social Data Science) and former advisee Samantha Bradshaw (now Assistant Professor at American University).
Professor Peter Watkinson serves as Professor of Intensive Care Medicine at the University of Oxford and is an NHS consultant in intensive care at the Oxford University Hospitals NHS Foundation Trust. He leads the Critical Care Research Group based at the Kadoorie Centre for Critical Care Research & Education at the John Radcliffe Hospital, Oxford. His work bridges clinical practice with academic research in the field of critical care medicine through the Nuffield Department of Clinical Neurosciences. Professor Watkinson's research primarily focuses on the identification of deteriorating patients in hospital settings. His work encompasses: Design and implementation of studies on wearable monitoring devices Exploration of non-contact monitoring technologies Analysis of standard electronically-recorded patient descriptors Pattern recognition in vital signs data to predict clinical deterioration Development of electronic monitoring systems Application of human factors techniques for technology integration in healthcare Assessment of long-term effects of critical illnesses on patient quality of life The Critical Care Research Group maintains a strong collaborative link with the University of Oxford Institute of Biomedical Engineering. Using data collected from thousands of patients' vital signs both in Oxford and elsewhere, the multi-disciplinary team investigates patterns that precede and predict clinical deterioration in hospitalized patients. Recent publications indicate a strong focus on early warning scores, patient monitoring technologies, and the application of machine learning approaches to critical care data. Professor Watkinson's research output demonstrates consistent productivity with numerous 2024-2025 publications spanning systematic reviews of early warning systems, development of novel monitoring technologies, and analytical approaches to predicting patient deterioration. His work frequently employs rigorous methodology including systematic reviews, meta-analyses, and innovative study designs to address critical questions in intensive care medicine. As leader of the Critical Care Research Group, Professor Watkinson oversees a multi-disciplinary team investigating vital sign patterns and developing predictive algorithms that have direct clinical applications. The group's research has significant implications for improving patient safety through earlier recognition of clinical deterioration and more effective resource allocation in hospital settings.
