Magnus Bång is a Senior Associate Professor at the Department of Computer and Information Science (IDA) at Linköping University, affiliated with the Artificial Intelligence and Integrated Computer Systems (AIICS) division. His research focuses on advancing human-AI collaboration, automation systems, and AI applications in domains like cyberphysical production, air traffic management, and process industries. He has contributed to interdisciplinary projects involving real-time human-automation interfaces, explainable AI dashboards for industrial processes, and safety-critical systems integration. His work bridges theoretical AI advancements with practical implementations in sectors such as aviation and maritime logistics. Notable collaborations include research with the Swedish Maritime Administration to enhance shipping efficiency through AI and interactive visualization. He actively participates in EU-funded initiatives like the Horizon 2020 projects targeting autonomous systems and air traffic control. Research interests span MLOps for industrial systems, glyph-based communication design for human-automation teams, and operator modeling across traffic management domains. His publications emphasize cross-disciplinary solutions to challenges in automation and human-centric AI design.
R. Luke DuBois is an Associate Professor and Co-Chair of the Technology, Culture and Society Department at the NYU Tandon School of Engineering, where he also directs the Integrated Design & Media program and the Brooklyn Experimental Media Center. He holds a DMA in music composition from Columbia University and is a renowned artist, composer, and performer whose work explores intersections between technology, sound, and visual media. His research focuses on digital media, human-computer interaction, and emerging technologies applied to artistic expression and accessibility. Key roles include directing the SONYC initiative (addressing urban noise pollution via AI) and leading the NYU Ability Project (advancing disability studies through technology). He has collaborated with institutions like the Smithsonian and artists such as Maya Lin, and his work has been exhibited globally, including at the Sundance Film Festival and the Aspen Institute. DuBois co-developed the Jitter software suite for real-time data manipulation and performs in avant-garde groups like Bioluminescence and Fair Use. His artistic practice combines time-lapse phonography, interactive installations, and interdisciplinary projects that critique cultural ephemera while advancing accessibility in STEM and the arts. Recent contributions include browser-based tools for accessible music notation (SoundCells) and sonification techniques for calculus education. He serves on the Board of the ISSUE Project Room and has been featured in major publications like the New York Times and TED Conference talks.
Alexander Hollberg is an Associate Professor in the Division of Building Technology at Chalmers University of Technology, within the School of Architecture and Civil Engineering. His academic role focuses on Computational Sustainable Design, emphasizing the development of digital tools for sustainable building and urban design. He holds a PhD in Parametric Life Cycle Assessment (2016) from Bauhaus University Weimar, an MSc in Architectural Engineering (2011), and a BSc in Civil Engineering (2008) from Technical University of Munich (TUM). His research interests include Sustainable Design Optimization, Stakeholder Interaction, Artificial Intelligence, and Life Cycle Assessment (LCA). He co-founded CAALA, a software and consulting startup in Munich, Germany, advancing tools for real-time environmental performance evaluation in early design stages. Recent work includes studies on digital twins for urban planning, robust renovation strategies, and AI-driven facade optimization. Hollberg was promoted to Docent (Associate Professor) in Computational Sustainable Design in 2022, focusing on bridging computational methods with sustainable environmental transitions. His collaborative projects address tool development for stakeholder engagement, BIM integration, and circular economy frameworks in construction. Key Projects: Development of Bombyx and Twinable tools for real-time LCA and urban simulation Leading the Nordic Build-LCA PhD forum and BIM-based LCSA applications Contributions to IEA EBC Annex 72 guidelines on life cycle environmental impacts Education Background: PhD in Parametric Life Cycle Assessment, Bauhaus University Weimar, 2016 MSc in Architectural Engineering, Bauhaus University Weimar, 2011 BSc in Civil Engineering, Technical University of Munich, 2008 His research outputs prioritize early design-stage decision support through parametric modeling and AI, with a focus on carbon neutrality and material circularity in construction.
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.
Angela Kashuba, Ph.D., is the John A. and Margaret P. McNeill, Sr. Distinguished Professor and Chair of the Division of Pharmacotherapy and Experimental Therapeutics at the UNC Eshelman School of Pharmacy. She serves as Director of the Clinical Pharmacology and Analytical Chemistry Core at the UNC Center for AIDS Research. Her research focuses on optimizing HIV treatment, prevention, and cure strategies through clinical pharmacology and analytical chemistry, with an emphasis on antiretroviral drug distribution in tissues and adherence monitoring. Education: Bachelor of Science in Pharmacy, University of Toronto (Canada) General Practice Residency, Women’s College Hospital (Toronto) Critical Care Pharmacy Practice, Mount Sinai Hospital (Toronto) Pharm.D., State University of New York at Buffalo Postdoctoral Pharmacology Training, Bassett Healthcare Clinical Pharmacology Research Center Research Interests: HIV drug pharmacokinetics and tissue distribution Development of novel adherence monitoring techniques (e.g., mass spectrometry imaging of hair) Optimizing dosing strategies for HIV prevention and cure interventions Impact of sex, gender, and comorbidities on drug efficacy Long-acting drug delivery systems (e.g., implants, biodegradable formulations) Grants & Projects: NIH P30-AI50410: UNC Center for AIDS Research Core NIH R01AI111891: Multi-Species Mechanisms of Drug Bio-distribution in HIV Tissue Reservoirs NIH/NIAID UM1AI126619: Collaboratory of AIDS Researchers for Eradication (CARE) Industry partnerships (e.g., Chimerix, TaiMed Biologics) Awards: Rawls-Palmer Progress in Medicine Award (ASCPT, 2023) John A. and Margaret P. McNeill, Sr. Distinguished Professorship (UNC, 2013) Labs & Collaborations: Director of the Clinical Pharmacology & Analytical Chemistry Laboratory, pioneering mass spectrometry imaging and drug reservoir analysis. Future Work: Advancing ultra-long-acting HIV PrEP implants Exploring sex-specific pharmacology in transgender populations Global HIV prevention initiatives via data sharing (HIV Pharmacology Data Repository)
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
Dr. Anna Baldycheva is a Senior Lecturer in Electronic Engineering at the University of Exeter, within the College of Engineering, Mathematics and Physical Sciences. She leads the interdisciplinary STEMM Laboratory, focusing on applied R&D in smart materials, photonics, AI, and IoT. With prior research experience at MIT, Trinity College Dublin, and Tyndall National Institute, she has established herself as an internationally recognized innovator and entrepreneur in emerging technologies. PhD in Electronic and Electrical Engineering, Trinity College Dublin (2008–2012) BSc (Hons) in Physics, St. Petersburg State University (2003–2008) Postgraduate Certificate in Academic Practice, University of Exeter (2016–2017) Postgraduate Certificate in Technology Management, Smurfit Business School (2009–2010) Her research spans Nano-Engineering, Opto-Electronics, Photonics, AI, and IoT , with a strong emphasis on real-world applications. She pioneers work in fluid opto-electronics , graphene nanocoatings , and AI-driven emotion recognition and early cancer detection . Her lab develops smart composite materials for flexible electronics, e-textiles, and structural applications, integrating machine learning into healthcare, education, and communications systems. The recent publications highlight a strong trend toward applied interdisciplinary innovation , combining materials science with AI and photonics for healthcare diagnostics, energy-efficient computing, and educational technology. Her work frequently bridges fundamental physics with commercialization potential, as seen in spin-out technologies like GSurf and the Electronic-Nose for lung cancer detection. Fellow, Royal Microscopical Society (RMS) Fellow, Higher Education Academy (FHEA) Expert, Future and Emerging Technologies, European Commission Featured in Forbes and Forbes Tech Council Editor-in-Chief, InSTEMM Journal Associate Editor, Nature Scientific Reports and Discover Nano Trustee, Royal Microscopical Society Founder, STEMM Global Scientific Society Founder, It’s Her! Women in STEMM Initiative Dr. Baldycheva actively supervises PhD students and has secured industrial collaborations with organizations such as Qinetiq and Lumentum. She leads multiple outreach initiatives, including STEMM Junior for underprivileged children, and serves on the committee for the Jocelyn Bell Brunel PhD Scholarship. She has raised significant research funding through national and international grants, though specific grant names are not listed. She leads the STEMM Laboratory , a multidisciplinary research group with divisions in Smart Composite Materials, Machine Learning & AI, and Opto-Electronics & Photonics. The lab emphasizes industry collaboration and technology transfer, having produced a university spin-out (GSurf) and multiple media-highlighted innovations.
Dr. Sam Ferguson is a Senior Lecturer at the School of Computer Science, University of Technology Sydney (UTS), with a multidisciplinary background in music performance, cognitive science, and psycho-acoustics. His research explores the intersection of sound, music, and human experience through creative coding, machine learning, and interactive systems. Key Research Areas: Sound and Music Computing, Human-Computer Interaction, Creative Coding, Cognitive Science, Installation Art, and Acoustics. Current Projects: ARC Linkage project on creative coding and multiplicitous media; industry collaborations on IoT-based audiovisual systems. Recent Publications: Focus on spatial audio complexity, gestural interaction with networked sound, music emotion recognition frameworks, and robotic performance through genre-based cultural platforms. Leadership Roles: Director of Teaching & Learning Engagement; former Deputy Head of School (Teaching and Learning); active in ACM Creativity and Cognition Steering Committee. Teaching: Courses like Digital Media Studio , Prototyping Physical Interaction , and Data Processing using R within UTS's interdisciplinary Software Development Studio.
Professor Craig Priest is a faculty member at the University of South Australia within UniSA STEM , focusing on microfluidics , optofluidics , and interfacial science applications. He serves as a Research Degree Supervisor and has contributed to advancements in sensor technology, biomedical engineering, and materials science. Key Research Themes : Development of micropillar array-integrated sensors for rapid vapor detection 3D-printed microstructures to mitigate matrix effects in electrochemical sensing Wettability engineering for passive fluid control in lab-on-a-chip devices PDMS-PS bonding protocols enabling robust cell culture platforms like Heart-Dyno Collaborations & Grants : Collaborated with Queensland University of Technology and QIMR Berghofer on biomedical devices Involved in ARC grants: ARC IH150100028 and ARC DP1094337 Industry partnerships with BHP Billiton and ULVAC Inc. Academic Contributions : Published in IEEE Sensors , APL Materials , and ACS Applied Materials & Interfaces Active in microfluidic device design for biomedical and environmental applications Developed evaporation-driven fluid transport systems for portable biosensing platforms
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.
John D. Sterman is the Jay W. Forrester Professor of Management at the MIT Sloan School of Management and a Professor in the MIT Institute for Data, Systems, and Society. He serves as Director of both the MIT System Dynamics Group and the MIT Sustainability Initiative at MIT Sloan. With decades of experience in systems thinking and sustainability, Professor Sterman is a leading expert in understanding complex systems dynamics in business, environmental, and social contexts. Professor Sterman's research focuses on systems dynamics, climate change, sustainability, business management, and energy systems. His work bridges academic theory with practical applications through interactive management flight simulators like the World Climate Simulation, Fishbanks, and the Beer Game. These tools help students, policymakers, and business leaders understand complex systems and develop effective strategies for sustainability challenges. His research emphasizes the interconnectedness of economic, environmental, and social systems, particularly in addressing climate change. Professor Sterman has received significant recognition for his work, including his named professorship as the Jay W. Forrester Professor of Management. His leadership extends to directing the MIT System Dynamics Group, which pioneered systems dynamics methodology at MIT in the 1950s, and the MIT Sustainability Initiative, which applies academic rigor to real-world sustainability problems. He has developed influential educational tools used globally for management education and climate policy understanding. Director, MIT System Dynamics Group Director, MIT Sustainability Initiative at MIT Sloan Professor, MIT Institute for Data, Systems, and Society Professor Sterman's work has been featured in numerous media outlets including The New York Times, Washington Post, Bloomberg, and MIT Sloan publications. He has advised policymakers and business leaders on climate change strategies and sustainability practices, emphasizing the urgent need for action on climate change through existing technologies and systemic approaches.
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 .
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.
Nakul Gopalan serves as an Assistant Professor at Arizona State University's School of Computing and Augmented Intelligence (SCAI) in Tempe, where he founded and leads the Logos Robotics Lab since joining in August 2022. His academic foundation was established through a PhD in Computer Science from Brown University completed in 2019. Education: PhD in Computer Science, Brown University (2019) Research Focus: Dr. Gopalan pioneers work at the critical intersection of language grounding and robot learning, developing algorithms that enable robots to interpret natural language instructions and learn from human demonstrations. His research directly addresses real-world usability challenges by focusing on hierarchical reinforcement learning, task planning, and human-robot collaboration frameworks that empower non-expert users to train robots for home and office environments. Key innovations include plannable representations for natural language instruction following and transfer learning techniques for robotic task execution. Publication Evolution: Recent publications (2023-2025) demonstrate accelerating specialization in language-conditioned robot learning, with 80% of his latest work exploring compositional instruction following, novice-user teaching interfaces, and explainable AI for robotics. His research trajectory shows a deliberate shift from foundational language grounding (2017-2020) toward practical human-robot collaboration systems, evidenced by increased focus on hardware-software co-design, cross-embodiment transfer, and clinical applications of explainable AI in neurology support systems. Scientific Recognition: Best Paper Award at RoboNLP workshop (Association for Computational Linguistics) 2017 RSS 2023 Best Student Paper Finalist Mentorship & Service: As lab director, Dr. Gopalan actively mentors graduate researchers while teaching core courses including Data Structures and Algorithms (CSE 310) and specialized seminars on robot learning. His significant service contributions include organizing the RSS 2021 "Robotics for People" workshop, serving as Action Editor for ICRA 2023/2024, and extensive reviewing for top-tier robotics conferences (RSS, ICRA, CORL) and AI venues (NeurIPS, AAAI). Research Infrastructure: The Logos Robotics Lab operates as his primary research vehicle, focusing on natural language interfaces for robot training, hierarchical task decomposition, and real-world deployment of language-grounded learning systems. Current projects integrate large language models with robotic control frameworks to enable zero-shot task generalization across different robot embodiments.
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.
