Gökhan Alcan is an Assistant Professor in Robotics and Machine Learning at the Automation Technology and Mechanical Engineering Unit of Tampere University, Finland. He leads the Advanced Learning, Control and AutomatioN (ALCAN) Research Group, focusing on safe model predictive control, constrained optimal control theory, reinforcement learning, and their applications to dynamical systems. His research addresses challenges in robotic manipulation, safe navigation, and human-robot collaboration through projects like the Aurora initiative on automated and connected machines. Education: B.Sc., M.Sc., and Ph.D. in Mechatronics Engineering from Sabanci University (2008–2019). Postdoctoral research at Sabanci University (2019) and Aalto University (2020–2024). Research Interests: Robotics, control theory, system identification, autonomous systems, and machine learning applied to robotic manipulation, autonomous vehicles, and safety-critical systems. Notable work includes trajectory optimization for hybrid systems, magnetic manipulation for medical applications, and sim-to-real gap analysis in cloth manipulation. Awards: Third Place in Aalto Open Science Award 2023, Elginkan Foundation Technology Award (2016), and multiple scholarships. Advised Ph.D. student David Blanco Mulero, who defended his thesis on robotic manipulation of deformable objects. Labs/Teams: ALCAN Research Group, former roles in Aalto University's Intelligent Robotics Group and Sabanci University's Control, Vision, and Robotics (CVR) Group.
Hamed Badihi is an Assistant Professor in Automation Technology and Dependable Systems at Tampere University , part of the Faculty of Engineering and Natural Sciences. He leads the Dependability and Automation Research in Cyber-Physical Systems (DARES) Group within the Dependable Systems Cyber Laboratories . His research focuses on critical aspects of condition monitoring, fault-tolerant control, and attack-resilient control to advance sustainable, dependable cyber-physical systems. Research Interests include: Cybersecurity for industrial control systems Fault-tolerant control mechanisms Resilient control strategies for renewable energy systems Condition monitoring of wind turbines and microgrids Recent Contributions emphasize hybrid approaches combining machine learning and control theory for cyber-attack detection and system resilience in wind farms and microgrids. His work addresses challenges like real-time fault diagnosis and adaptive control under adversarial or environmental perturbations. Awards & Roles : Senior Member of IEEE, editor for International Transactions on Electrical Energy Systems , Advances in Fuzzy Systems , and Processes journals. Active in EU projects like StreamSTEP . Labs & Teams : Directs the DARES Group, collaborating on initiatives like the Dependable Systems Cyber Laboratories to pioneer innovations in cyber-physical system dependability.
Alex Cresswell is a Reader in the Department of Chemistry at the University of Bath, where he leads research in synthetic organic chemistry. His work focuses on catalysis (metal and metal-free), C–H bond functionalization, and the use of reactive intermediates for sustainable synthesis. He is affiliated with the Institute of Sustainability and Climate Change and collaborates on projects like the Sustainable Chemicals and Materials Manufacturing Hub. M.Chem, University of Oxford (2008) D.Phil., University of Oxford (2012) Postdoctoral Research Associate, University of Illinois at Urbana-Champaign (2012–2014) Postdoctoral Research Associate, University of Edinburgh (2014–2016) His research interests center on developing new synthetic methods for organic chemistry, with an emphasis on photocatalysis, radical chemistry, and sustainable approaches. Key themes include photoredox-hydrogen atom transfer (HAT), gold-catalyzed C–H arylation, and iron-mediated cross-coupling reactions. His work contributes to UN Sustainable Development Goals like responsible consumption and climate action. Recent publications highlight advancements in photoredox-HAT catalysis, automated synthesis of spirocycles, and mechanistic studies using transient absorption spectroscopy. Collaborative projects involve optimizing radical C–C bond formation and exploring paired electrosynthesis for green chemistry. Scientific Awards Royal Society University Research Fellowship (2016) As an advisor, Alex has mentored doctoral students including Hannah Askey, Jacob Turner-Dore, Alison Ryder, and Anna Kinsella, with a focus on training the next generation of chemists in sustainable methodologies. His group also collaborates with institutions like the University of Illinois, University of Edinburgh, and the Royal Society.
Ramses Martinez is an Assistant Professor in the Department of Industrial Engineering and Biomedical Engineering at Purdue University . He holds a B.A. in Applied Physics from Universidad Autonoma de Madrid (2004) and a Ph.D. in Physics and Materials Science from the Spanish National Research Council (CSIC) in 2009. Prior to joining Purdue, he conducted postdoctoral research in the lab of Prof. George M. Whitesides at Harvard University, focusing on nanofabrication, microfluidics, and soft robotics. Education B.A. in Applied Physics, Universidad Autonoma de Madrid (2004) Ph.D. in Physics and Materials Science, Spanish National Research Council (CSIC) (2009) His research bridges soft robotics , flexible electronics , and nanofabrication , with a focus on creating self-powered e-textiles , omniphobic paper-based devices , and programmable mechanical metamaterials . His work has led to over 25 publications and 9 patents, emphasizing practical applications in health monitoring and industrial automation . Notable projects include waterproof electronic decals for biofluid monitoring, smart bandages for chronic wound detection, and laser nanoforming methods for scalable metallic structures. His research has been recognized through the Fulbright Fellowship and the Marie Curie IOF Grant .
Avi Goldfarb is Professor of Marketing at the Joseph L. Rotman School of Management, University of Toronto, where he holds the Rotman Chair in Artificial Intelligence and Healthcare. He serves as Chief Data Scientist at the Creative Destruction Lab and Research Associate at the National Bureau of Economic Research (NBER). His academic career spans 2002-2012 as Assistant and Associate Professor, becoming Professor of Marketing in 2012. Rotman Chair in Artificial Intelligence and Healthcare (2018-present) Professor of Marketing (2012-present) Research Associate, NBER (2014-present) Chief Data Scientist, Creative Destruction Lab (2015-present) Research Interests : Specializing in digital economics and the economics of artificial intelligence, his work bridges technology, marketing, and public policy. Key focus areas include: Digital economics and platform behavior AI adoption in healthcare systems Quantum computing economic implications Privacy regulation and data markets AI's impact on labor and inequality General purpose technology frameworks Publication Trends : His recent work spans AI implementation strategies across industries, digital behavior economics, and emerging technology commercialization. Key subfields include AI adoption patterns, digital privacy frameworks, quantum computing applications, and workplace health safety analytics. Scientific Recognition : INFORMS Society of Marketing Science Long Term Impact Award (2018) Roger Martin Award for Excellence in Teaching (2017) Leadership Roles : Former Senior Editor at Marketing Science (2016-2021), with editorial board memberships at Management Science, Journal of Marketing Research, and Journal of Economics and Management Strategy. He contributes to the Acceleration Consortium and Schwartz Reisman Institute for Technology and Society.
Sergei Lebedev is a Professor of Geophysics at the University of Cambridge's Department of Earth Sciences and holds an adjunct professorship at Dublin Institute for Advanced Studies. His primary affiliation is with the Bullard Laboratories. He specializes in seismology, seismic tomography, and the structural evolution of the Earth's crust and upper mantle, with a focus on regions like the North Atlantic, Africa, and Tibet. Lebedev leads major projects such as SEA-SEIS and Ireland Array, combining research with public engagement. His work integrates seismic data with geodynamic models to understand tectonic processes, volcanic systems, and resource exploration. PhD: Princeton University (2000) MSc: Moscow Institute of Physics and Technology (1991) Research interests include seismic imaging methods, lithospheric dynamics, and the interplay between seismicity and tectonic evolution. He has contributed to studies on cratonic lithosphere structure, mantle plumes, and crustal deformation mechanisms.
Chiara Natali is a PhD Student in Computer Science at University of Milan-Bicocca (2022-present) and a Visiting Research Fellow at the Dalle Molle Institute for Artificial Intelligence USI-SUPSI, supported by a Swiss Government Excellence Research Fellowship. She serves as a Lecturer for Interaction Design Lab and Human-Computer Interaction courses at University of Milano-Bicocca, and as a Tutor for Advanced Data Management and Decision Support Systems and Human-System Interaction courses across multiple Italian universities. Her educational background includes: MA in Politics, Philosophy and Public Affairs at University of Milan (2020-2022) Master's in Digital Communication Strategy at IED, Milan (2019-2020) BSc in International Politics and Government at Bocconi University, Milan (2016-2019) Natali's research centers on the complex relationship between humans and AI systems, with particular focus on Human-AI Interaction, Explainable AI (XAI), Ethical AI, and her signature concept of Frictional AI. She investigates the multidirectional effects of AI on human cognitive faculties, examining the tension between Augmentation and Deskilling. Her work explores how intentional design friction can serve as a debiasing strategy against Automation Bias, promoting more thoughtful human-AI collaboration while preserving human agency and critical thinking. Her publication record reveals a strong emphasis on practical applications of XAI in high-stakes domains like healthcare, with particular attention to medical decision-making processes. Her research consistently addresses the challenge of designing AI systems that support rather than replace human expertise, exploring how explanations impact accuracy in hybrid decision-making and how to measure technology dominance in AI-supported environments. Her significant contributions have been recognized with: Best Paper Award at the World Conference on Explainable Artificial Intelligence (2024) Best Doctoral Consortium Award at CEUR Workshop Proceedings (2023) Natali actively shapes her field through academic service, serving as PUBLICITY & PROCEEDINGS CHAIR for HHAI 2025 and organizing multiple workshops on Human-Centred Machine Learning, Algorithmic Authority, and Frictional AI. She also contributes to gender equality in STEM as a Science Ambassador for her institution's Gender Equality Plan and previously served as PhD co-representative at the Department Board. Her interdisciplinary approach extends into creative domains, where she is developing an Interactive AI Opera on 'The Garden of (Un)Earthly AI's' funded by the University of Edinburgh's Generative AI Laboratory, and has curated projects exploring Human-AI Music Co-Creation and live-coding music performances.
Prof. Julio Lloret-Fillol is a Group Leader at the Institute of Chemical Research of Catalonia (ICIQ) and an ICREA Research Professor since 2015. His work bridges homogeneous catalysis , material science , and automation to develop sustainable chemical processes and solar fuels. He earned his PhD in 2006 from Universidad de Valencia under Prof. Lahuerta and J. Pérez-Prieto, followed by postdoctoral research at University of Heidelberg (MEyC and Marie Curie Fellowships). Awards: 2024 RSEQ-GEQO Award on Excellence 2023 Fellow of the Royal Society of Chemistry 2022 Ramón Areces Grant 2019 Thieme Chemistry Journals Award 2017 Young Academy of Europe 2015 Young Researcher RSEQ Award Research Interests: Water oxidation catalysis CO₂ reduction to value-added chemicals Artificial photosynthesis Electrocatalytic hydrogen generation Spin-off technologies for green hydrogen and photoreactors Notable Publications: 2024: Angew. Chem. Int. Ed. (electrocatalytic ketones from CO₂) 2024: ACS Catal. (Fe-doped NiO for OER) 2023: ACS Catal. (COF-based cobalt catalysts) 2022: JACS (OER mechanism with cobalt complexes) 2022: Angew. Chem. Int. Ed. (chloroalkane activation) Spin-offs: Treellum Technologies (photoreactors) JOLT Solutions (electrodes for hydrogen production)
Aurélien Bornet is a Lecturer at École Polytechnique Fédérale de Lausanne (EPFL) in the School of Basic Sciences (SB), specifically within the Institute of Chemical Sciences and Engineering (ISIC). He serves as the Platform Leader for the Nuclear Magnetic Resonance Platform at EPFL, where he oversees advanced NMR facilities and research. Dr. Bornet's research focuses on Nuclear Magnetic Resonance (NMR) and Dynamic Nuclear Polarization (DNP) techniques. His work spans several key areas including hyperpolarization methodologies, development of NMR instrumentation, and applications in both chemistry and biomedical fields. His research has led to significant advancements in dissolution DNP, long-lived nuclear spin states, and hyperpolarized metabolite imaging. His recent publication record demonstrates strong activity in developing new NMR techniques and applications, with particular emphasis on hyperpolarization methods that dramatically enhance NMR sensitivity. His work bridges fundamental physics with practical applications in medical imaging and materials science. The research outputs include numerous high-impact publications in journals like Nature Communications, Journal of the American Chemical Society, and Physical Chemistry Chemical Physics, as well as several patents related to NMR technology. Dr. Bornet has received recognition through multiple patents for his innovations in NMR technology, including patents related to polarizing agents, dissolution DNP methods, and NMR instrumentation. His work has important implications for biomedical imaging, particularly in the development of hyperpolarized metabolic imaging for cancer diagnostics and other medical applications. As an educator, Dr. Bornet teaches courses on Basic and Advanced NMR at multiple levels (Level 1 A, Level 1 B, and Level 2) at EPFL and in Sion. His teaching focuses on both theoretical and experimental aspects of NMR, providing students with hands-on experience with modern NMR spectrometers. His academic journey includes completing his PhD at EPFL in 2015 with a thesis on hyperpolarized protons for enhancing NMR sensitivity, advised by G. Bodenhausen and S. Jannin. Prior to this, he completed earlier research on long-lived states as probes of protein stability in 2010 under the supervision of G. Bodenhausen and P. Vasos.
Marina Milovanović is a Professor at the University of Singidunum, Faculty of Informatics and Computing, Department of Mathematics. She holds dual doctoral degrees from the Faculty of Science, University of Kragujevac (Department of Mathematics, 2014) and Faculty of Entrepreneurial Business, Union University (2008), along with Master's and Bachelor's degrees from the Faculty of Mathematics, University of Belgrade (2000-2005 and 1995-2000 respectively). Faculty of Science, University of Kragujevac, Department of Mathematics (PhD, 2014) Faculty of Entrepreneurial Business, Union University (PhD, 2008) Faculty of Mathematics, University of Belgrade (Master's, 2000-2005) Faculty of Mathematics, University of Belgrade (Bachelor's, 1995-2000) Svetozar Marković High School, science and mathematics major (1991-1995) Professor Milovanović specializes in Mathematics Education and Educational Technology, with particular expertise in interactive multimedia applications for teaching mathematics. Her research consistently bridges theoretical mathematics with practical educational technology solutions, evolving from traditional multimedia approaches to incorporating cutting-edge AI and machine learning techniques. She has authored multiple books including 'Interactive multimedia in mathematics teaching' (2015) and collections of solved mathematics problems for entrance exams. Her recent publication record through 2025 demonstrates active engagement in interdisciplinary research, particularly at the intersection of educational technology, artificial intelligence, and practical applications in fields ranging from software engineering to medical diagnostics. Her work shows a clear trajectory from foundational educational technology research toward more sophisticated AI-enhanced learning systems. Professor Milovanović has made significant contributions to semantic web applications in education, particularly through Moodle LMS enhancements, and has explored SCADA applications in industrial contexts. Her collaborative research spans multiple countries and institutions, reflecting an international scholarly network. She has extensive experience developing computer tools for engineering education and has published on diverse topics including petroleum industry processes, environmental management, and financial mathematics. Her work demonstrates consistent application of computational approaches to solve domain-specific problems across multiple disciplines.
Bradley Schmerl serves as a Principal Systems Scientist in the Software and Societal Systems Department (S3D) within Carnegie Mellon University's School of Computer Science. His research advances software engineering practices for modern challenges in distributed heterogeneous systems, self-adaptation, and cyber-physical integration. He leads the ABLE research group and actively mentors students in the Masters in Software Engineering program while teaching core courses like Software Architecture and Software Engineering Practicum. Dr. Schmerl's work addresses critical challenges in composing continuously evolving software systems, including components from untrusted third parties and on-the-fly recomposition for environmental changes. His research develops reusable, analyzable tools for software composition with emphasis on model-based adaptation, uncertainty management, and cross-language integration. Key projects include Rainbow for runtime architecture reflection, Acme for formal architectural foundations, and Cyber-physical Systems research linking software models with physical dynamics. Analysis of his 2023-2025 publications reveals intensifying focus on robotics software architecture (particularly ROS-based systems), explainable AI for architectural tradeoff analysis, and configuration management in adaptive systems. Trends show growing integration of machine learning for auto-tuning, empirical studies of misconfigurations, and dimensionality reduction techniques for visualizing design spaces—consistently bridging theoretical rigor with practical tool development for real-world applications. Scientific Awards: No specific awards were documented in the source materials. Dr. Schmerl serves as Practice Area Lead and mentor in CMU's Masters in Software Engineering program, guiding client projects including Rainbow UI for self-adaptive framework interfaces, CoBot UI for telepresence robots, and Educational Telepresence Tasking Language development. His research receives support through ABLE group projects funded by grants targeting software architecture foundations, adaptation mechanisms, and cyber-physical system validation. As a core member of the ABLE research group, he directs investigations into architecture-based self-adaptation with active projects spanning Rainbow (runtime architecture models for dynamic adaptation), Acme (formal architectural styles and tools), and Cyber-physical Systems (software-physical model integration). The group also maintains legacy work in End-User Architecting, Architecture Evolution, and service-oriented platforms for intelligence analysis through SORASCS.
Stephen Turner is an Associate Professor of Data Science and Assistant Dean for Research at the University of Virginia School of Data Science . His work bridges genomics, data science, and national security , focusing on biosecurity, synthetic biology, conservation, and bioinformatics applications in human health . Previously, he was a faculty member in the UVA School of Medicine’s Department of Public Health Sciences (2011–2019) and directed the UVA Bioinformatics Core . Ph.D., Human Genetics, Vanderbilt University M.S., Applied Statistics, Vanderbilt University B.S., Biology, James Madison University Turner’s research spans computational approaches to biosecurity, biodiversity conservation, and human health . Recent publications highlight tools like the qqman and kgp R packages, PLANES for epidemiological modeling, and biorecap for bioRxiv preprint summarization. His work integrates large-scale sequencing, genome editing, and machine learning in conservation biotechnology and public health forecasting. Scientific contributions include applications in infectious disease forecasting , forensic genomics , and maternal-fetal biology . He has mentored interdisciplinary students and collaborated on NIH-funded research , while advising biotech startups at the intersection of academia, industry, government, and policy .
Markus Lange-Hegermann serves as Professor of Mathematics and Data Science at Ostwestfalen-Lippe University of Applied Sciences (TH OWL) since 2018 and holds a board position at the Institute for Industrial Information Technology (inIT). His career bridges academic research and industrial applications, with expertise in translating machine learning theory into practical engineering solutions for automation and manufacturing sectors. His educational foundation includes a Diplom (Master equivalent) in Computer Mathematics from RWTH Aachen University (2004-2008) followed by a Dr. rer. nat. (PhD equivalent) in algorithmic differential algebra (2008-2014). Prior to academia, he gained industry experience at FEV GmbH as an R&D engineer (2014-2017) and P3 automotive GmbH as a Data Science Consultant (2017-2018). Lange-Hegermann's research centers on probabilistic machine learning with distinctive emphasis on physics-informed approaches. He develops Gaussian process methodologies that incorporate differential equations to model time dependencies, uncertainties, and physical constraints in industrial systems. His work enables robust data-based modeling and optimization for cyber-physical systems, with applications spanning predictive maintenance, process control, and quality assurance in manufacturing. Analysis of his 15 most recent publications (2024-2025) reveals consistent innovation in physics-integrated machine learning, particularly using Gaussian processes to solve partial differential equations and optimal control problems. The research demonstrates strong industrial applicability across domains including medical imaging, material science, automotive engineering, and brewing processes, with recurring themes of anomaly detection in time-series data and uncertainty-aware decision making. His scientific contributions have earned significant recognition: Forschungspreis TH OWL (2024) Top reviewer award at NeurIPS (2023) Outstanding reviewer award at NeurIPS (2021) Best poster award at Bosch AI CON (2019) Borchers Plakette for outstanding dissertation (2014) Springorum Denkmünze for outstanding diploma (2009) As chairman of the Data Science study program and vice chairman of undergraduate examination boards, Lange-Hegermann actively shapes academic curricula while supervising graduate theses. His governance roles include serving on professorship search committees at multiple institutions and contributing to examination regulations. He maintains active research funding through collaborations with industrial partners and reviews proposals for initiatives like It's OWL and 3IA Côte d’Azur. Lange-Hegermann leads the Mathematics and Data Sciences research group within inIT, fostering collaboration between theoretical machine learning and industrial automation. He co-founded AICOmmunityOWL and the Informatics Europe working group on Data Analysis and Reporting, while organizing machine learning reading groups and data science hackathons to bridge academic research with industrial problem-solving.
Alastair F. Donaldson is a Professor and Director of Research in the Department of Computing at Imperial College London, where he leads the FastPL research group. His academic career spans over a decade at Imperial, progressing from Lecturer (2011-2014) to Senior Lecturer (2014-2017), Reader (2017-2020), and Professor (2020-present). He has also held significant industry positions, including Founder and Director of GraphicsFuzz Ltd. (acquired by Google in 2018), Senior Software Engineer at Google (2018-2021), and Visiting Researcher at both Google and Microsoft Research Redmond. Donaldson earned his PhD from the University of Glasgow under Alice Miller, following a BSc (hons, First Class) in Computing Science and Mathematics. His postdoctoral work included an EPSRC Postdoctoral Research Fellowship at the University of Oxford and a Research Fellowship at Wolfson College Oxford. His research focuses on formal analysis, software testing and programming languages techniques for improving software reliability, with special emphasis on high-performance systems. Donaldson's work bridges theoretical foundations with practical applications, particularly in compiler testing, GPU programming verification, and metamorphic testing. His research has significantly influenced both academia and industry, as evidenced by the acquisition of his startup GraphicsFuzz by Google. Analysis of his recent publications reveals a strong focus on fuzz testing techniques applied across diverse domains including compilers, GPUs, cryptographic protocols, and large language models. His work consistently combines formal methods with practical testing approaches, addressing challenges in compiler correctness, memory models, and API verification across multiple platforms. 2017 BCS Roger Needham Award EPSRC Early Career Fellowship Best Paper Award, EuroSys 2024 Best Paper Award, MET 2021 Best Paper Award, IWOCL 2019 Best Paper Award, IISWC 2019 Best Paper Award, ICST 2016 ACM SIGSOFT Distinguished Paper Award, ISSTA 2023 ACM SIGSOFT Distinguished Paper Award, FSE 2017 ACM SIGPLAN Most Influential OOPSLA Paper Award, 2022 (for GPUVerify) As Director of Research in the Department of Computing, Donaldson oversees research strategy and development. His FastPL research group investigates novel techniques for programming, testing and reasoning about high performance systems. He has served on numerous program committees and held leadership roles including PLDI Steering Committee Chair (2022-2025) and PACM-PL Advisory Board member. His industry engagement includes testifying as an Expert Witness in the IBM UK Ltd v LzLabs GmbH & Ors case. The FastPL research group, which Donaldson leads, focuses on formal analysis, software testing and programming languages. The group has made significant contributions to compiler testing, GPU verification, and metamorphic testing techniques, with practical impact demonstrated by the acquisition of GraphicsFuzz. Current research directions include fuzzing for zero-knowledge proof circuits, randomized testing of decompilers, and systematic testing of large language models for code generation.
Dr. Giancarlo Pascali is a Conjoint Associate Professor at the School of Chemistry, UNSW Sydney , and Radiochemistry Team Leader at ANSTO's Camperdown cyclotron site. With a PhD in "Innovative Biomedical Technologies" from the University of Lecce (2004), he has held research positions at IFC-CNR , NIH , and GMP facilities in Milan and Pisa. His expertise spans radiochemical methods , radiopharmaceutical development , and microfluidic automation for nuclear medicine production. Education: PhD in Innovative Biomedical Technologies, University of Lecce (2004) BSc in Chemistry, University of Pisa (2001) Research interests focus on M 3 : Molecules, Methods, Machines . In Molecules , he designs radiopharmaceuticals for cancer , dementia , and inflammatory diseases . For Methods , his work explores photochemistry , electrochemistry , and mechanochemistry to label biomolecules with 18 F and other isotopes. Under Machines , he pioneers microfluidic systems for automated radiochemistry, emphasizing safety and process reliability . Editorial & Leadership Roles: Editorial Board Member of Nuclear Medicine and Biology , Contrast Media & Molecular Imaging , and Current Radiopharmaceuticals Executive Board of ANZSNM , ARTnet , and ASMI Asia-Oceania Director and iSRS2025 Chair for SRS
