Zurich University of Applied Sciences (ZHAW)Switzerland
Prof. Dr. Roland Büchi is a Professor at the School of Engineering, Zurich University of Applied Sciences (ZHAW), specializing in control systems engineering and applied AI. His affiliation includes leadership roles in research projects such as the ongoing 'Digital Bridge to Computer Science' initiative. With a career spanning decades, he maintains active research output and industrial collaborations. Research Interests: Büchi focuses on control systems optimization , particularly PID controller tuning using AI methods, system identification, and applications in robotics and drone technology. His work bridges theoretical control theory with practical implementations in mechatronics. Recent efforts explore machine learning for hysteresis modeling and swarm optimization for control systems. Publication Trends: His 15 most recent works (2018-2024) emphasize AI-driven control optimization , drone technology advancements, and engineering education. Dominant themes include PID parameter tuning for time-delayed systems, drone telemetry, and adaptive learning algorithms. A notable shift toward AI/ML applications in control engineering emerged post-2020. Labs and Teams: Büchi collaborates with researchers like Lukas Gruber and has historical ties to ETH Zurich’s robotics projects. His work involves experimental validation at ZHAW’s engineering facilities, with patents in turbocharger magnetic bearing systems.
Gert Cauwenberghs is a Professor of Bioengineering at the University of California San Diego (UCSD), affiliated with the Jacobs School of Engineering. He co-directs the Institute for Neural Computation and holds a visiting professorship at MIT. His research focuses on neuromorphic engineering, energy-efficient neural interfaces, and wearable biosensors. Key contributions include silicon-based adaptive neural circuits, implantable neural recording systems, and in-ear biosensing devices. Education: M.Eng. in Applied Physics (University of Brussels, 1988), M.S. and Ph.D. in Electrical Engineering (Caltech, 1989–1994). Prior roles include Professorships at Johns Hopkins University and Visiting Professor at MIT. Research Interests: Biomedical integrated circuits, neuromorphic computing, brain-machine interfaces, and energy-efficient neural systems. His work bridges neuroengineering and clinical applications, emphasizing adaptive intelligence and low-power designs. Recent Work: Development of femtojoule-efficient neural chips, high-density neural interfaces, and closed-loop wearable systems. Projects include neurobench benchmarking frameworks and RRAM-based neuromorphic hardware. Awards: NSF Career Award (1997), ONR Young Investigator (1999), PECASE (2000), IEEE Distinguished Lecturer (2003–2004). Grants & Labs: Active in NIH and DoD-funded projects, co-directs the UCSD Institute for Neural Computation. Collaborates with industry on neural interface technologies. Labs/Teams: Cauwenberghs Lab at UCSD focuses on integrated neuroengineering systems, including neural recording systems and neuromorphic computing architectures.
Michael Baldea is an Associate Professor in the Department of Chemical Engineering at the University of Texas at Austin . He holds a Ph.D. in Chemical Engineering from the University of Minnesota (2006), with prior degrees from 'Babeş-Bolyai' University in Romania (M.Sc. 2001, Diploma 2000). His research group develops theoretical and computational methods for Process and Energy Systems Engineering , focusing on integrated decision-making, performance optimization, and process intensification with industrial validation. Education: Ph.D., Chemical Engineering, University of Minnesota (2006) M.Sc., Interface Process Engineering, 'Babeş-Bolyai' University (2001) Diploma, Chemical Engineering, 'Babeş-Bolyai' University (2000) Research Thrusts: Integrated decision-making in chemical/energy supply chains Process performance monitoring and optimization Process integration and intensification Key applications include grid-responsive chemical plants, intensified distillation/column designs, and renewable energy integration for building systems. Scientific Awards: Frank A. Liddell, Jr. Fellowship NSF CAREER Award (2015-2020) Moncrief Grand Challenges Faculty Award (2014) AIChE Outstanding Young Researcher Award (2017) Implementation : His group has translated research into commercial tools through partnerships with industrial test beds and is working to integrate methods into commercial simulators. They explore predictive approaches for building energy management and strategic capital investment analysis in next-generation energy systems.
Amir-massoud Farahmand is an Associate Professor at the Polytechnique Montréal (Department of Computer and Software Engineering) and a Status-Only Associate Professor at the University of Toronto (Department of Computer Science). He is also a Core Academic Member at Mila (Quebec AI Institute). His research focuses on computational and statistical mechanisms for designing efficient reinforcement learning (RL) agents and adaptive algorithms. Dr. Farahmand's research spans reinforcement learning, optimal transport, adversarial robustness, and model-based methods. He has extensively studied regularization in RL, distributional approaches, and algorithm design for stability and convergence. His textbook Lecture Notes on Reinforcement Learning (2021) emphasizes mathematical intuition over algorithmic collections. Recent publications highlight trends in high-update-ratio RL, distributional equivalence, and self-prediction for task understanding. He is actively involved in teaching, having previously instructed courses on machine learning, neural networks, and RL at the University of Toronto. Scientific Awards : Ontario Early Researcher Award (2024) for Accelerated Reinforcement Learning Algorithms Dr. Farahmand has mentored numerous students, including his first PhD graduate Yangchen Pan (now at Oxford) and MSc students like Allen Bao (AMD) and Farnam Mansouri (University of Waterloo). He is currently recruiting graduate students at Polytechnique Montréal and Mila for 2025 admissions.
Virginia Polytechnic Institute and State UniversityUnited States
Rohit Kannan is an Assistant Professor in the Grado Department of Industrial and Systems Engineering at Virginia Tech. He holds a Ph.D. and M.S. in Chemical Engineering from MIT and a B.Tech. from IIT Madras. His research focuses on integrating machine learning with global optimization and optimization under uncertainty, emphasizing energy systems applications. Previous roles include postdoc positions at Los Alamos National Laboratory and the Wisconsin Institute for Discovery. Education: Ph.D., Chemical Engineering, Massachusetts Institute of Technology, 2018 M.S., Chemical Engineering Practice, MIT, 2014 B.Tech., Chemical Engineering, IIT Madras, 2012 Research Interests: Global optimization, optimization under uncertainty, computational optimization, energy systems, and machine learning integration. Recent Highlights: Recipient of the Excellence in Teaching Spotlight Award (2024) Lead researcher in stochastic optimization and energy systems (e.g., hybrid polygeneration systems) Developed algorithms for chance-constrained nonlinear programs and distributionally robust optimization Service & Leadership: Elected Vice-Chair of Global Optimization, INFORMS Optimization Society (2025–2026) Reviewer for top journals like Operations Research and Mathematical Programming Advisor to ISE InclusiveVT and Graduate Admissions Committee Labs & Collaborations: Directs a research group advancing optimization and machine learning for energy and engineering systems. Active in interdisciplinary projects with LANL and UW-Madison.
Jordi Perelló Muntan is an Associate Professor in the Department of Computer Architecture at the Universitat Politècnica de Catalunya (UPC), Barcelona, Spain, where he is also affiliated with the Escola Tècnica Superior d'Enginyeria de Telecomunicació de Barcelona (ETSETB). He is a member of the Broadband Communications Systems and Architectures (CBA) and IDEAI-UPC research groups, focusing on advanced optical and future internet networking technologies. Research Interests: His research spans telecommunications networks, optical fiber and optical networking, resource optimization, network architectures, and the Future Internet. He investigates performance optimization in 5G transport networks, Spatial Division Multiplexing (SDM), Recursive Inter-Network Architecture (RINA), elastic optical networks, and cognitive networking. His work integrates SDN, network virtualization, and green networking principles for scalable and efficient infrastructures. Publication Trends: His recent publications focus on probabilistic constellation shaping in multicore fiber networks, cognitive strategies for optical margin reduction, RINA-based QoS assurance, and migration planning toward spectrally-spatially flexible optical networks. These reflect a strong trend toward intelligent, adaptive, and energy-efficient network design for future communication systems. Scientific Awards: Co-recipient of the 2020 Fabio Neri Best Paper Award Runner-up (Elsevier Journal of Optical Switching and Networking) Co-recipient of the ONDM 2021 Best Paper Award Co-recipient of the 2019 IEEE Communications Society Charles Kao Award Co-recipient of the ONDM 2012 Best Student Paper Award Advising and Grants: He has advised multiple PhD students on topics including RINA, optical network planning, and virtual provisioning. He has led or participated in major European (H2020, FP7) and national (PID, TEC) research projects such as SLICENET, PRISTINE, TRAINER, and ALLIANCE, focusing on 5G, RINA, and sustainable network infrastructures. Labs and Teams: He is an active member of the CBA research group at UPC, contributing to experimental and theoretical advancements in optical and programmable networks. His team collaborates internationally on testbed development and standardization efforts in next-generation networking.
John Bagterp Jørgensen is a Professor at the Department of Applied Mathematics and Computer Science, Technical University of Denmark (DTU). His research focuses on computational methods for Model Predictive Control (MPC), numerical optimization, and dynamic optimization, with applications in industrial processes, biomedical systems, and sustainable energy. He holds leadership roles in 2-control ApS, a company developing advanced control solutions for industries such as cement production and oil recovery. Education: PhD and M.Sc. in Technical Sciences from DTU (1997–2005 and 1991–1997). Professional experience includes roles as an Assistant Professor at DTU and CTO/CEO at 2-control ApS. Research interests span MPC algorithms, numerical methods for differential equations, and system identification. His work bridges academia and industry, addressing challenges in energy efficiency, vaccine manufacturing, diabetes treatment, and cement production processes. His recent articles emphasize industrial applications of control systems, including cement rotary kiln dynamics, vaccine production optimization, and dual-hormone artificial pancreas development. He has received the Nordic Energy Research Award (1994) and contributed to UN Sustainable Development Goals related to affordable energy and industrial innovation. Advising and grants: Supervises multiple PhD projects on topics like electrification of industrial processes and sustainable SCP production. Collaborates with global institutions on energy and biomedical research. Labs/teams: Leads teams in DTU’s Scientific Computing and Center for Energy Resources Engineering, with active partnerships in industry and academia.
Dr. Alessandro Ottaviano is a Researcher affiliated with the Department of Digital Integrated Circuits and Systems at ETH Zürich. His work focuses on advanced computer architecture and embedded systems, particularly in the domains of RISC-V processors, real-time systems, and heterogeneous computing. He contributes to the design of time-predictable virtual memory solutions, mixed-criticality systems, and energy-efficient hardware-software interfaces. Key research areas include modular processor architectures, hardware monitoring for safety-critical applications, and power management in high-performance computing (HPC) systems. His recent projects involve developing controllers for 2.5D systems-in-package and creating open-source networking solutions for mixed-criticality environments. Ottaviano’s work often emphasizes open-source hardware design and scalable system-level approaches to address challenges in autonomous systems and edge computing. He collaborates on advancements in interrupt handling for virtualized systems, peripheral event linking for IoT devices, and FPGA-based thermal emulation for many-core processors. His research bridges theoretical computer architecture principles with practical implementations in embedded and real-time systems.
Chao Liu is a Research Scientist at CNRS (French National Center for Scientific Research) since 2008, affiliated with the DEXTER team and the Department of Robotics, LIRMM at University of Montpellier, France. He earned his Ph.D. in Electrical & Electronic Engineering from Nanyang Technological University, Singapore (2006). Current research focuses on surgical robotics , haptics , teleoperation , and nonlinear control theory with applications in computer vision. His work addresses challenges in robotic-assisted telesurgery, including: Stable and transparent human-robot interaction through wave variable compensators and passivity filters Physiological motion compensation using spatio-temporal LSTM and dual Kalman filters EMG-based motion recognition for surgical skill assessment 3D soft-tissue reconstruction with stereo-endoscopes and deep learning Dr. Liu leads European and French projects like: TS2RT (CNRS-funded): Safer teleoperation with motion compensation ROBACUS (ANR-funded): Needle positioning with MPC control HaTUMoCo (CNRS-funded): Haptic teleoperation with uncertainty handling ARAKNES (EU-funded): Microrobotic systems for endoluminal surgery Scientific honors include Senior Member of IEEE and Member of Sigma Xi . He supervises Ph.D. and Master's students working on topics such as concentric tube robot optimization, haptic teleoperation, and EMG-based force estimation. Dr. Liu serves on IEEE Technical Committees for Telerobotics and Haptics , and as Technical Editor of IEEE/ASME Transactions on Mechatronics.
Navid Bayati is an Associate Professor at the University of Southern Denmark, affiliated with the Institute of Mechanical and Electrical Engineering and the Centre for Industrial Electronics. He leads the Control and Protection of Smart Grids (CAP-SG) group and focuses on renewable/hybrid power systems, microgrid protection, and grid code compliance. Education: Ph.D. in Power Systems & Microgrid Protection (2020, Aalborg University); M.Sc. in Power Systems (2017, Amirkabir University of Technology) His research spans renewable energy integration , transient analysis , grid interconnection , and digital twin applications . Recent work includes machine learning for carbon emission prediction, fault localization in DC microgrids, and supercapacitor resilience in hybrid systems. Collaborations include projects like IEA Wind Task 50 and RePoSys , addressing grid renovation, life cycle assessment, and digital twin resilience. His teaching portfolio covers power electronics , energy management , and microgrid control .
Max Planck Institute for Security and PrivacyGermany
Xin Peng is a Professor and Deputy Dean at the School of Computer Science, Fudan University, China. He leads the CodeWisdom research team focusing on intelligent software engineering techniques for development, maintenance, and operation of software systems. His educational background includes a PhD in Computer Science (2001-2006) and Bachelor's degree in Computer Science (1997-2001), both from Fudan University. He progressed through the academic ranks from Assistant Professor (2006-2010) to Associate Professor (2010-2015) and finally to Professor (2015-present). Professor Peng's research interests span Software Analytics, Intelligent Software Development, Microservice systems, and AIOps. His work leverages AI technologies including deep learning and knowledge graphs to develop intelligent software engineering techniques. A significant portion of his recent work focuses on applying Large Language Models to various software engineering tasks, including vulnerability detection, API usage analysis, and test automation. His publication record shows a clear trend toward increasingly sophisticated applications of AI in software engineering, with recent work heavily featuring LLMs for tasks ranging from vulnerability patch porting to resource leak detection. The research spans multiple domains including microservice systems, automotive software, and Web of Things security. Best Paper Award of ICSM 2011 ACM SIGSOFT Distinguished Paper Award of ASE 2018 and 2021 IEEE TCSE Distinguished Paper Award of ICSME 2018, 2019, and 2020 IEEE Transactions on Software Engineering Best Paper award for 2018 Professor Peng serves in numerous leadership roles including Deputy Director of CCF Technical Committee on Software Engineering, Co-Editor-in-Chief of Journal of Software: Evolution and Process, and Associate Editor for ACM Transactions on Software Engineering and Methodology. He has been actively involved in program committees for major software engineering conferences including ICSE, ASE, ESEC/FSE, and ICSME. He leads the CodeWisdom research team at Fudan University, which has developed several benchmark systems including TrainTicket for microservice research. The team's work bridges academic research with industrial applications, particularly in microservice systems analysis and intelligent software development tools.
Reza Fotouhi is a Professor in the Department of Mechanical Engineering at the University of Saskatchewan, specializing in Robotics (Dynamics and Control), Structural Dynamics and Vibrations, Computational Mechanics, and Biomechanics. He leads research in agricultural robotics, finite element modeling, and teleoperated systems. Education: BSc, MSc (Iran) MSc, PhD (University of Saskatchewan) His research focuses on human-robot interaction, vibration control for flexible manipulators, composite material applications, and teleultrasound systems. He has developed mobile platforms for crop phenotyping and optimized control strategies for robotic arms. Recent Article Trends: His work spans telerobotic medical systems, agricultural automation, advanced control algorithms (e.g., Particle Swarm Optimization), and structural analysis of composite materials. Key subfields include musculoskeletal imaging, path planning, and dynamic modeling of flexible systems. Research Groups: Applied Mechanics and Machine Design Control Systems, Robotics and Fluid Power Engineering for Agriculture
Higher Institute of Education and SciencesPortugal
Nuno Miguel Fonseca Ferreira is a Full Professor at the Instituto Superior de Engenharia de Coimbra (ISEC), part of the Polytechnic of Coimbra, where he currently serves as President of the Scientific Council. His academic career spans over 25 years at ISEC, progressing from Assistant to Professor Coordenador Principal. He has held significant leadership positions including Vice-President of ISEC (2001-2005), Pro-President of the Polytechnic of Coimbra (2009-2010), President of ISEC (2010-2013), and Vice-President of the Polytechnic of Coimbra (2013-2017), where he was responsible for internationalization initiatives. His educational background includes a degree in Electrical Engineering from the University of Porto (1996), a Doctorate in Electrical Engineering from the University of Trás-os-Montes and Alto Douro (2006), and a Habilitation Title (Aggregation) from the same institution (2020). His research focuses on Robotic Systems, with specialization in cooperative robotic systems as evidenced by his Habilitation work. Professor Ferreira's research spans multiple domains of robotics and intelligent systems, with particular emphasis on multi-robot coordination, environmental applications, and medical robotics. His work bridges theoretical control systems with practical applications across diverse fields including forestry, healthcare, manufacturing, and education. He has developed innovative approaches to robotic manipulation, sensor integration, and human-robot interaction, often incorporating advanced techniques from artificial intelligence and machine learning. His recent publications demonstrate a strong trend toward practical applications of robotics in real-world environments, particularly in forestry maintenance, industrial automation, and medical applications. The research shows progression from theoretical control systems to applied robotics in challenging environments, with increasing integration of computer vision, deep learning, and collaborative systems. His work spans both fundamental robotics research and immediate industrial applications, reflecting a balance between academic inquiry and practical implementation. Professor Ferreira has supervised two doctoral theses and participated in numerous research projects with substantial funding. His leadership extends to coordinating 15 of the 33 national and international R&D projects he has participated in, demonstrating significant grant acquisition and management capabilities. His international collaborations through Erasmus+ and other European programs highlight his role in fostering global research partnerships. He is an integrated member of GECAD (Research Group in Engineering and Intelligent Computing for Innovation and Advanced Development), a Portuguese R&D unit classified as Excellent by the Portuguese Science and Technology Foundation. Additionally, he is a member of LASI (Associated Laboratory for Intelligent Systems), the Portuguese laboratory associated with Artificial Intelligence, connecting him to a broader national research ecosystem.
Dragan S. Antic is a Professor at the Department of Automation, Faculty of Electronics, University of Niš. He earned his PhD in Automation from the same institution in 1994, following a Master's (1991) and Bachelor's (1987) degree in the same field. His research focuses on control systems, signal processing, and neuro-fuzzy systems, with a strong emphasis on sliding mode control, orthogonal function-based modeling, and adaptive neural networks. He has published 51 papers in journals with impact factors and currently participates in 2 national and 8 international research projects. Key research areas include dynamic system modeling, quasi-orthogonal filters, and finite-time stability analysis of time-delay systems. His work often integrates mathematical techniques with engineering applications, such as anti-lock braking systems and PID control optimization. Contact details include his office at Aleksandra Medvedeva 4, Niš, and the email dragan.antic@elfak.ni.ac.rs. Professional contributions include foundational research in endocrine neural networks and low-pass filter design, with notable publications in journals like Electronics Letters and Journal of the Franklin Institute . His efforts bridge theoretical advancements with practical engineering solutions in automation and control.
Imraan Faruque is an Associate Professor in the Department of Mechanical and Aerospace Engineering at Oklahoma State University (OSU), part of the College of Engineering, Architecture and Technology (CEAT). His research focuses on biologically-inspired flight control systems, engineered autonomy for unmanned aerial vehicles (UAVs), and the integration of sensory feedback mechanisms in autonomous systems. Education: Faruque holds a Ph.D. and M.S. in Aerospace Engineering from the University of Maryland (2011 and 2010) and a B.S. in Aerospace Engineering from Virginia Tech (2006). Research Interests: His work emphasizes bio-inspired solutions for aerial autonomy, including swarm coordination, gust-aware flight control, and human-autonomy interaction. Key areas include unmanned systems design, visual feedback algorithms, and adaptive control strategies derived from insect flight dynamics. Awards: He has received notable accolades such as the ONR Young Investigator Award (2019), AIAA Hal Andrews Young Engineer/Scientist Award (2017), and multiple 'Best in Session' recognitions at major conferences. His team also secured 1st Place in the International Aerial Robotics Championship (2005). Publications: Faruque’s research spans topics like orbital debris management, swarm intelligence, and tornado sensing with UAVs. His work bridges biological principles and engineering, with applications in aerospace, robotics, and environmental monitoring.
