Valeriy Vyatkin is a Professor at the Department of Electrical Engineering and Automation, Aalto University. His research focuses on advancing industrial automation, control systems, and their integration with emerging technologies like machine learning and digital twins. He specializes in standards such as IEC 61499, addressing interoperability, formal verification, and performance optimization in distributed automation systems. Key research interests include: Physics-informed machine learning for industrial processes (e.g., steel rolling, reservoir engineering) Formal methods for control system validation and safety-critical applications Development of adaptive automation frameworks for Industry 5.0 challenges, including human-robot collaboration and energy systems Interoperability between legacy and modern industrial standards (OPAS, OPC UA) Recent work emphasizes real-time simulation, FPGA-based control prototyping, and AI-driven solutions for energy efficiency and sustainability in manufacturing, horticulture, and process industries. Publications span topics like robotic walker design, probabilistic model checking, and decentralized learning management systems. He collaborates on EU and industry-funded projects, focusing on digital twin implementation, edge computing, and virtual commissioning. His team develops tools for automated code generation, system migration, and anomaly detection in complex industrial settings.
Miguel Ángel Sotelo Vázquez is a full Professor at the University of Alcalá, leading the INVETT Research Group (Intelligent Vehicles and Traffic Technologies). He holds the Department of Automatic Control and specializes in autonomous systems, particularly in path planning, sensor fusion, and human-vehicle interaction. His research integrates machine learning, robotics, and control theory to address challenges in intelligent transportation systems. He earned his Ph.D. in 2001 with a thesis on autonomous vehicle navigation in partially known environments. His work emphasizes real-world deployment, explainable AI, and safety-critical systems. Recent projects focus on lane change prediction, pedestrian behavior modeling, and cybersecurity for autonomous systems. Key contributions include neuro-symbolic frameworks for decision-making, real-time multi-physics field reconstruction, and cross-cultural studies of pedestrian interactions. He collaborates internationally on urban mobility resilience and hydrogen refueling infrastructure. Research Highlights : Development of knowledge graph-based prediction architectures Experimental validation of human-vehicle interaction in VR environments Creation of the SCOUT trajectory prediction framework
Professor Nicholas Carah is a leading academic at the School of Communication and Arts , The University of Queensland , and serves as Director of the Centre for Digital Cultures & Societies . He is also an Associate Investigator in the ARC Centre of Excellence for Automated Decision-Making and Society . His research focuses on the algorithmic and participatory advertising model of digital media platforms , with a sustained investigation into digital alcohol marketing . His research spans digital media, algorithmic culture, and affective capitalism , examining how platforms like Instagram, Snapchat, and Facebook shape promotional practices and user behavior. He has led major ARC-funded projects and co-edited works such as Digital Intimate Publics and Social Media (2018) and Conflict in My Outlook (2022). As Deputy Chair of the Foundation for Alcohol Research and Education , he contributes to policy development on unhealthy product marketing. His recent publications highlight trends in social media advertising, algorithmic transparency, and health-risk marketing . Supervision roles include Principal Advisor for PhD projects on topics like digital labor, FemTech, and augmented reality , and Associate Advisor for cross-cultural studies in media and communication. He advocates for citizen science approaches to monitor digital marketing and its societal impacts.
Anna Dawid-Lekowska is an Assistant Professor at the Leiden Institute of Advanced Computer Science (LIACS) and affiliated with the Leiden Institute of Physics (LION) at Leiden University, Netherlands. She leads a research group within the aQa group, focusing on the intersection of machine learning and quantum physics. Previously, she was a Research Fellow at the Center for Computational Quantum Physics, Flatiron Institute, New York. PhD in Physics and Photonics (joint, cotutelle), University of Warsaw & ICFO, Spain MSc in Quantum Chemistry, University of Warsaw BSc in Biotechnology, University of Warsaw Anna's research centers on interpretable machine learning for scientific discovery, particularly in quantum systems. She investigates how overparametrized models generalize, the role of loss landscape flatness, and double descent phenomena. Her work bridges deep learning with quantum simulations, aiming to detect quantum phase transitions and extract physical insights from trained models. She also explores ultracold molecules and novel quantum phases using simulation platforms. Her recent publications demonstrate a strong trend in applying machine learning to automate and interpret quantum experiments, such as detecting laser cooling schemes and understanding neural network initialization. The work emphasizes interpretability, aiming to make AI a transparent scientific tool rather than a black box. Anna has received significant recognition, including: 2022 FNP START laureate Participant in the 2024 Lindau Nobel Laureate Meeting She is actively mentoring and expanding her group, currently recruiting PhD students and postdoctoral researchers. Her work is supported by institutional affiliations with leading research centers and collaborations across Europe and the US. Anna also engages in science communication and education, having lectured at the Nordita Winter School on Machine Learning and Physics. She is involved in the aQa research group, which focuses on quantum algorithms and AI, fostering interdisciplinary collaboration between computer science and physics. Her lab integrates theoretical modeling, algorithm development, and applications to quantum experiments.
Lisa Ollinger is a Professor of Production Automation at Ulm University of Applied Sciences (Technische Hochschule Ulm), where she has been serving since October 2019. She teaches courses in Automation Technology 1 and 2 for the Business Engineering program, Industrial Automation for the Digital Production program, and Flexible Automation for the Systems Engineering and Management Master's program. Her educational and professional background includes: Technology Leader for Engineering Projects in Automation and Digitalization at Procter & Gamble GmbH (2014-2019) Researcher at the German Research Center for Artificial Intelligence (DFKI) in the Innovative Factory Systems research area (2012-2014) Research Assistant at TU Kaiserslautern in the Production Automation department (2009-2011) Professor Ollinger's research focuses on the intersection of industrial automation and digital transformation. Her work explores how emerging technologies like Industrial Internet of Things, cyber-physical systems, and digital twins can revolutionize manufacturing and logistics processes. She investigates flexible production systems that can adapt to changing requirements through skill-based engineering approaches and novel communication architectures using OPC UA standards. Her research also extends to robotics applications, particularly industrial robotics and autonomous mobile robots, often leveraging ROS (Robot Operating System) frameworks. Her recent publications demonstrate a strong focus on practical implementations of Industry 4.0 concepts, particularly in warehouse management and production systems. She examines how digital twin technology can enhance logistics operations and how agent-based systems can improve manufacturing resilience. A common thread in her work is the application of OPC UA communication standards to create more flexible, interoperable industrial systems. Professor Ollinger holds significant administrative roles at THU: Dean of the Master's program in Systems Engineering and Management Member of the University Council Member of the Institute for Manufacturing Technology and Materials Testing (IFW) Founding Ambassador for Startup South She maintains active professional connections through ResearchGate, LinkedIn, and ORCID, reflecting her commitment to academic collaboration and knowledge sharing in the field of industrial automation and digital manufacturing.
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.
Professor Imre S. Szalai is a leading scholar in arbitration law and dispute resolution, currently serving as Professor of Law at the Elisabeth Haub School of Law at Pace University. His expertise spans arbitration, litigation, professional responsibility, and legal ethics. He is nationally recognized for his work on the Federal Arbitration Act and its impact on civil justice. University: Pace University School: Elisabeth Haub School of Law Department: Dispute Resolution Academic Rank: Professor Education: Bachelor of Arts (BA), Yale University – Double Major in Economics and Classical Civilizations JD, Columbia University School of Law – Harlan Fiske Stone Scholar Research Interests: Professor Szalai specializes in the legal, historical, and ethical dimensions of arbitration. His work critically examines the expansion of forced arbitration in consumer and employment contexts, the erosion of trial rights, and the role of arbitration in undermining accountability. He explores the historical roots of the Federal Arbitration Act and advocates for reforms that restore fairness and consent in dispute resolution. His recent scholarship delves into emerging issues such as artificial intelligence in arbitration and the impact of pop culture on legal interpretation. Publication Trends: His recent publications (2022–2025) reflect a strong focus on contemporary challenges in arbitration law, including the #MeToo movement's influence on forced arbitration, AI-driven dispute resolution, the role of the Supreme Court in shaping arbitration policy, and high-profile cases involving Ticketmaster and Taylor Swift. His work combines doctrinal analysis, historical inquiry, and policy critique, often drawing from media and cultural references to make complex legal issues accessible. Scientific Awards: Harlan Fiske Stone Scholar, Columbia Law School Advising and Grants: Professor Szalai actively collaborates with students on amicus briefs and scholarly work. While specific grant funding is not detailed in the provided text, his extensive publication record and media engagements suggest sustained scholarly support. He mentors students in legal research, writing, and advocacy, particularly in the areas of arbitration and civil rights. Labs, Teams, and Initiatives: He maintains a public blog on arbitration law developments, where he analyzes recent court decisions, legislative changes, and policy debates. This platform serves as an educational resource and contributes to public discourse on arbitration reform. He also participates in academic and professional networks focused on dispute resolution and legal ethics.
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.
Maurizio MUZZUPAPPA is a Full Professor at the Department of Mechanical, Energy and Management Engineering (University of Calabria) since 2018. His roles include Rector's Delegate for Technology Transfer, Academic Delegate for Education at DIMEG, and Head of the Physical Prototyping Laboratory at the MaTeRiA Center (UNICAL-CNISM collaboration). He supervises the Unical Racing Team in Formula SAE competitions and co-founded three university spin-offs: 3DResearch, Tech4Sea, and Q-BOT. As Scientific Director of projects like TECH4YOU (climate change adaptation technologies) and GROWN IN THE BLUE (Mediterranean reef conservation), he integrates research in industrial design, augmented reality, and underwater cultural heritage. He has authored over 200 publications (h-index 27) and holds 10 patents. His teaching includes Tools and Methods for Industrial Design and Formula SAE LAB . His research focuses on: Industrial design methodologies with parametric and sustainable approaches 3D prototyping and additive manufacturing User-Centered Design for product ergonomics Virtual/Augmented Reality applications in engineering and cultural heritage Underwater robotics and artifact restoration Recent publications highlight trends in AR for industrial maintenance, generative design tools, and mechatronic solutions for underwater heritage. He has supervised over 300 theses and 10 Ph.D. students while leading technology transfer initiatives.
Peng Zhou is an Assistant Professor at the School of Advanced Engineering, The Great Bay University , and the Principal Investigator of the Embodied Manipulation Intelligence (EMAIL) Robotics Lab . His research integrates robotics, machine learning, and computer vision, with a strong focus on deformable object manipulation, robot perception, and task-motion planning. Education: Ph.D. in Robotics, The Hong Kong Polytechnic University (Supervised by Dr. David Navarro-Alarcon) Postdoctoral Research Fellow, The University of Hong Kong (Advised by Dr. Pan Jia) Exchange Ph.D. Student, KTH Royal Institute of Technology (Supervised by Prof. Danica Kragic) Research Interests: Dr. Zhou's work spans robotics , machine learning , and computer vision , with specialized expertise in deformable object manipulation , robot perception and learning , and task and motion planning . His lab, EMAIL, pioneers solutions for robotic manipulation of soft and deformable materials. Scientific Awards & Honors: 2024 : Track 3 Champion, Zhuhai International Dexterous Manipulation Challenge 2023 : IEEE R10 Outstanding Volunteer Award 2022 : Outstanding Young Researcher Award, National Engineering Research Center 2022 : Best AI Implementation Award, Hong Kong AI Open Competition 2022 : IEEE MGA Young Professional Achievement Award Editorial & Leadership Roles: Dr. Zhou serves as an Associate Editor for IEEE Robotics and Automation Letters and has organized key workshops like the IROS 2025 Workshop on Contact and Impact-aware Manipulation . He is also a Guest Editor for special issues in Electronics and Frontiers in Robotics and AI .
Alexis Lussier Desbiens is an Associate Professor at the Université de Sherbrooke in the Department of Mechanical Engineering , Faculty of Engineering. He co-founded several research labs and initiatives including NSERC CREATE CoRoM (Collaborative Robotics in Manufacturing) and NSERC CREATE UTILI (Uninhabited Aircraft Systems Training). His work bridges robotics, mechanical design, and sports equipment innovation. PhD in Mechanical Engineering (Stanford University, 2012) BEng in Mechanical Engineering (Université de Sherbrooke, 2005) Postdoctoral Research (Harvard University, 2013) His research focuses on robotics and automation , particularly unmanned aerial vehicles (UAVs) with bioinspired design principles for mechanical intelligence. Key areas include: Autonomous UAV perching and climbing Hybrid locomotion systems Sports equipment dynamics (skis, hockey sticks) Magnetorheological actuator applications Conservation biology tools via aerial sampling Recent publications highlight interdisciplinary work in robotics , sports engineering , and ecological monitoring . His projects often integrate mechanical design with environmental or human-centric applications. Scientific recognition includes: Best Student Paper (IEEE SMC, 2021) Best Poster (ISEA, 2020) National Geographic Explorer (2020) CSME Gold Medal (2005) Current grants (2020-2023) include: $339,000 - High-performance UAV for power line interactions $1.65M - NSERC CREATE UTILI training program $120,000 - Intelligent hockey stick development He also leads the CREATEK Research Lab and maintains global collaborations with institutions like Harvard University, Stanford University, MIT, and EPFL.
Jing Jiang is a Professor in the Department of Electrical and Computer Engineering at the University of Western Ontario (Western University), where he holds the NSERC/UNENE Senior Industrial Research Chair in Nuclear Instrumentation and Control since 2003. He is a registered professional engineer (P. Eng.) in Ontario and a Fellow of multiple prestigious organizations including the Canadian Academy of Engineering, IEEE, and the Engineering Institute of Canada. Dr. Jiang's research focuses on: Fault-tolerant control of safety-critical systems Advanced control of nuclear power plants Integration of renewable energy resources in microgrids Instrumentation and control systems Advanced signal processing for fault diagnosis Industrial wireless sensor networks His educational background includes a Ph.D. from the University of New Brunswick (1989), MESc. from the University of New Brunswick (1984), and BESc. from Jiaotong University in Xi'an, China (1982). Dr. Jiang has been with Western University since 1991, progressing from Assistant Professor to Full Professor in 1999. Dr. Jiang has established the Control, Instrumentation and Electrical Systems (CIES) research group and two state-of-the-art laboratories: the NPP I/C research lab and the distributed generation (DG) research lab. His research has significant industry impact, particularly in nuclear power plant instrumentation and control systems, with collaborations with the Canadian nuclear industry and the International Atomic Energy Agency (IAEA). Professional Recognition Professional Engineers Gold Medal (2025, 2021) Canadian Association for Graduate Studies Award for Outstanding Graduate Mentorship (2021) Distinguished University Professor (2018) Fellow of IEEE (2017) RBC Top 25 Canadian Immigrant Award (2016) Fellow of Canadian Academy of Engineering (2010) Dr. Jiang has served on numerous technical committees including ISA 100.11a (Wireless Systems for Automation), ISA 67 (Nuclear Power Plant Standards), and IEC SC 45A (Nuclear Instrumentation). He has been actively involved with the IAEA as an expert consultant on various nuclear-related technical matters. He leads a research team consisting of post-doctoral fellows, research engineers, and graduate students. His NPP I/C lab includes a CANDU NPP simulator, physical simulators, various control systems (ABB, Siemens, DeltaV, Honeywell), safety PLC systems, and industrial wireless sensor networks. The DG lab features a reconfigurable microgrid with renewable energy sources, energy storage devices, and advanced power electronics control systems.
Mikael Johansson is a Professor at Kungliga Tekniska Högskolan (KTH), specializing in Control Technology . He teaches and coordinates courses such as Distributed Optimization (FEL3311) and various advanced-level degree projects in computer science, electrical engineering, and systems engineering. His research spans Control Systems , Machine Learning , and Optimization , with a focus on asynchronous algorithms, federated learning, and applications in energy systems and construction. His work includes 15 recent publications on topics like neural networks, distributed optimization, and battery technology. Notable areas of contribution are in asynchronous learning, federated learning with privacy constraints, and quasi-Newton methods for optimization. His research bridges theoretical advancements with practical applications in urban design, healthcare, and autonomous systems.
Sean Welleck is an Assistant Professor at Carnegie Mellon University's School of Computer Science, specifically within the Language Technologies Institute (LTI). He leads the L3 Lab and serves as an advisor for the AI for Math Fund. His academic journey includes a PhD from New York University under Kyunghyun Cho and postdoctoral positions at the Allen Institute for Artificial Intelligence and the University of Washington with Yejin Choi. Dr. Welleck's educational background shows a strong foundation in computer science. He earned his PhD in Computer Science from New York University, where he worked under the mentorship of Kyunghyun Cho and Zheng Zhang. Prior to this, he completed his MSE and BSE in Computer Science from the University of Pennsylvania, demonstrating a long-standing commitment to the field. Dr. Welleck's research focuses on bridging informal and formal reasoning with AI, with particular emphasis on developing learning, inference, and evaluation algorithms for large language models. His work spans multiple cutting-edge areas including mathematical reasoning , code generation , inference algorithms , and AI reasoning agents . A significant portion of his recent work involves combining AI with formal methods for mathematics, where he has developed frameworks like Llemma (an open-source language model for mathematical reasoning) and meta-generation (for inference-time algorithms). His research is characterized by a strong theoretical foundation coupled with practical applications that push the boundaries of what AI systems can achieve in formal reasoning domains. Analysis of Dr. Welleck's recent publications reveals a clear research trajectory focused on enhancing language models' capabilities in formal reasoning and mathematical problem-solving. His work demonstrates an evolution from foundational research in neural text generation to increasingly sophisticated approaches that integrate formal methods with deep learning. Key trends include the development of inference-time algorithms that improve model performance without additional training, frameworks for mathematical reasoning that connect informal and formal proofs, and novel evaluation methodologies for language models. His publications consistently appear in top-tier conferences including NeurIPS, ICLR, ICML, and ACL, reflecting the high impact of his contributions to the field. Dr. Welleck's scientific achievements have been recognized with several prestigious awards: NAACL 2025 Best Paper Award ICLR 2025 Oral Presentation (Top 2%) ICLR 2025 Spotlight Presentation (Top 5%) NeurIPS 2021 Outstanding Paper Award (Top 0.1%) for MAUVE NVIDIA AI Labs Pioneering Research Award (2017 and 2018) As an educator and mentor, Dr. Welleck actively guides the next generation of AI researchers. He currently advises multiple PhD students including Pranjal Aggarwal, Weihua Du, Andre He, and Seungone Kim (some co-advised with other faculty), along with MS students Riyaz Ahuja, Jiewen Hu, Qinyue Tan, and Thomas Zhu, and undergraduate Tate Rowney. At CMU, he teaches advanced courses such as Neural Code Generation and Advanced NLP, and has previously taught at New York University and the University of Washington. His commitment to education extends to creating resources like the Thesis Review Podcast and developing tutorials on neural theorem proving that have been presented at major conferences. Dr. Welleck leads the L3 Lab at CMU, which focuses on the intersection of language, learning, and logic. The lab brings together students and researchers to tackle challenging problems in AI reasoning, with particular emphasis on mathematical reasoning and code generation. Recent initiatives include the development of Llemma, an open-source language model specialized for mathematical reasoning, and work on inference-time algorithms that enable language models to improve their performance through additional computation during inference rather than through additional training.
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.
