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Keivan AhmadiView profile

Associate Professor

• Structural dynamics
• Mechanical vibrations
• Machine tool dynamics
• +5 more

Dr. Keivan Ahmadi is an Associate Professor in the Department of Mechanical Engineering at the University of Victoria (UVic), serving as Graduate Program Director. He holds a PhD from the University of Waterloo (2012), followed by postdoctoral positions at UBC and Pratt & Whitney Canada. His research focuses on dynamics and vibrations in machining processes, robotic manufacturing, and advanced manufacturing systems. Education: BSc (Tehran Polytechnic), MSc (IUST), PhD (Waterloo) Affiliations: Dynamics and Digital Manufacturing Lab (DDML), UVic Mechanical Engineering Research interests include vibration suppression in machining, chatter prediction, robotic milling dynamics, and high-speed manufacturing systems. His work combines experimental modal analysis, Bayesian modeling, and data-driven approaches to enhance manufacturing precision and sustainability. Key projects include vibration compensation in 3D printing, dynamic modeling of robotic arms for milling, and optimization of thin-walled structure machining. Over 20 peer-reviewed articles showcase his contributions to machining stability, FRF estimation, and additive manufacturing. Advised 19 graduate students (9 alumni, 10 current) Collaborations with industries like GM, Linamar, and CanEV Labs/Teams: Leads the Dynamics and Digital Manufacturing Lab (DDML), focused on sustainable manufacturing through dynamic systems innovation. Hosts a diverse team prioritizing underrepresented groups in engineering.

Mingzhou JinView profile

Professor

• Sustainability
• Climate Change
• Optimization
• +9 more

Mingzhou Jin is a Professor and Department Head in the Department of Industrial and Systems Engineering at the University of Tennessee, Knoxville, within the Tickle College of Engineering. He also directs the Institute for a Secure and Sustainable Environment (ISSE) and the FERSC Center, a DOT/UTC Tier-1 Center. Holding the John D. Tickle Professorship, he is a recognized leader in sustainability, optimization, logistics, and smart manufacturing. PhD, Industrial and Systems Engineering, Lehigh University, 2001 MS, Management Science, Zhejiang University, 1998 BS, Electrical Engineering and Mixed Class, Zhejiang University, 1995 Dr. Jin's research focuses on sustainability, climate change, transportation and logistics, supply chain engineering, additive and smart manufacturing, and energy efficiency. His work integrates operations research and systems engineering to solve complex environmental and industrial challenges. He has secured over $19 million in research funding from agencies including NSF, DOE, DOT, DHS, and industry partners like FedEx, Boeing, and Schneider Electric. His recent publications span high-impact journals such as Nature , Nature Communications , and European Journal of Operational Research , with themes in net-zero strategies, wildfire modeling, smart manufacturing, and sustainable supply chains. The research demonstrates a strong trend toward interdisciplinary, data-driven solutions for global environmental and industrial systems. 2023 Dr. Kenneth Kirby Endowed Faculty Award 2021 UTK Award for Success in Multidisciplinary Research 2020 UTK Chancellor’s Research and Creative Achievement Award 2020 TCE Research Achievement Award IISE Fellow (2018) Multiple teaching, advising, and service awards from TCE and UTK Dr. Jin has advised numerous graduate students and led large-scale research initiatives. He has served as Editor-in-Chief of Cleaner and Circular Bioeconomy , Executive Editor of Journal of Cleaner Production , and held leadership roles in IISE. His research has been supported by extensive grants from federal agencies and industry, reflecting strong collaboration and real-world impact. He also held adjunct professorships at Zhejiang University and Central South University of Forestry and Technology. He leads the Institute for a Secure and Sustainable Environment (ISSE) and the FERSC Center, fostering interdisciplinary research in sustainability, energy, and resilient infrastructure. These centers bring together experts from engineering, environmental science, and policy to address pressing global challenges through systems-level innovation.

• Database Systems
• Distributed Databases
• Transaction Processing
• +5 more

Daniel J. Abadi is a prominent researcher in database systems at Yale University. With over two decades of impactful research, he has made significant contributions to the fields of distributed databases, transaction processing, and column-oriented database systems. His work bridges theoretical foundations with practical implementations that have influenced both academia and industry. Dr. Abadi's research primarily focuses on database system architecture, with particular emphasis on: Distributed and geo-replicated database systems High-performance transaction processing Column-oriented and analytical database systems Stream processing and real-time analytics Cloud and serverless database technologies Integration of machine learning with database systems His recent work shows a continued focus on addressing scalability challenges in modern database systems, with particular attention to multi-region transaction processing, automated data management, and the integration of machine learning techniques. The trend in his publications indicates a strong emphasis on practical, deployable systems that solve real-world problems faced by industry. Dr. Abadi has been instrumental in several major research initiatives and reports that have shaped the direction of database research, including the Seattle Report and the Cambridge Report on Database Research. Throughout his career, Dr. Abadi has mentored numerous students and collaborated extensively with leading researchers in the field. His work has received significant recognition through widespread citations and adoption of his ideas in both academic and industrial database systems.

Alejandro VillaverdeView profile

Research Professor

• Systems Biology
• Control Theory
• Mathematical Modeling
• +5 more

Alejandro F. Villaverde is a Ramón y Cajal research fellow in the Department of Systems & Control Engineering at the School of Industrial Engineering, University of Vigo, Spain. He also serves as a Research fellow at CITMAga since 2022. Previously, he worked as a postdoctoral researcher at IIM-CSIC from 2016-2020. His research focuses on the modeling of dynamical systems with particular emphasis on biological applications. Villaverde earned his PhD in Systems and Control Engineering from University of Vigo between 2005 and 2009. His academic career has centered at Spanish institutions with a strong interdisciplinary approach bridging engineering, mathematics, and biology. His primary research interests include systems biology, control theory, and mathematical modeling, with specialized expertise in structural identifiability, observability analysis, and computational tools for dynamic modeling of biological systems. Villaverde's work addresses fundamental challenges in building reliable mathematical models of complex biological processes, with applications spanning immunology to microbial communities. His theoretical contributions have practical implications for improving model reliability and predictive power in biological research. Villaverde has published extensively in top journals including PLOS Computational Biology, Bioinformatics, and IEEE/ACM Transactions on Computational Biology. His recent publications (2023-2025) reveal a consistent research trajectory focused on developing theoretical frameworks for biological model analysis, creating practical software tools, and applying these methods to cutting-edge problems. His work shows particular strength in identifying and addressing fundamental limitations in modeling approaches, especially regarding parameter identifiability and model observability constraints. Among the top 2% Scientists Worldwide 2024 (Stanford University list) Recognition as one of the EEI's top valued instructors at University of Vigo's School of Industrial Engineering Villaverde leads multiple significant research projects including DYNAMO-bio (funded by Ministry of Science, Innovation and Universities), SICOMORO (focusing on symmetries in biological communities), and PREDYCTBIO. His group actively develops open-source software tools such as STRIKE-GOLDD for structural identifiability and observability analysis. The laboratory, part of the BICO research group, includes several researchers and students working on various aspects of dynamic modeling in biology, with recent additions including Mahmoud Shams Falavarjani, Adriana González Vázquez, and multiple interns working on specialized projects.

• Information Systems
• Model-Driven Engineering
• Requirements Engineering
• +2 more

Alberto Rodrigues da Silva is a Professor at the Institute Superior Técnico , part of the University of Lisbon . He teaches Fundamentals of Information Systems , primarily during the 1st Semester of the 2025/2026 academic year. His scientific interests revolve around Information Systems , Model-Driven Engineering (MDE), Requirements Engineering (RE), Social Computing , and Software Engineering . He has extensively contributed to the development of rigorous requirements specification languages like RSL (Requirements Specification Language) and its extensions (e.g., RSL-IL4Privacy for privacy policies). His collaborative work spans automated acceptance testing, GDPR compliance, and domain-specific languages (DSLs) for applications such as mobile development , digital twins , and legal contexts (e.g., LegalLanguage ). His research trends focus on integrating model-driven engineering with privacy policies , IoT applications , and low-code platforms . He has also explored tools like Maestro for data classification and usability testing, and RiverCure for flood simulation. Email: alberto.silva@tecnico.ulisboa.pt .

Sebastian RisiView profile

Professor

• Neuroevolution
• Evolutionary Robotics
• Self-Organizing AI
• +9 more

Sebastian Risi is a Professor at the IT University of Copenhagen , where he directs the Creative AI Lab and co-directs the Robotics, Evolution and Art Lab (REAL) . His work bridges computational evolution, deep learning, and collective intelligence for applications in robotics, art, and video game design. His research focuses on self-organizing AI systems that grow or assemble through local interactions, inspired by biological development. Key areas include neuroevolution , neural cellular automata , and generative modeling , with applications in adaptive robotics, game content creation, and damage-resilient AI. Recent publications highlight trends in self-assembling neural architectures (NDPs) and 3D functional machine generation (Minecraft experiments). Awards include ERC Consolidator Grant (2022), Best Paper at FDG’21 , and Google Faculty Award (2019). Scientific Awards : ERC Consolidator Grant (GROW-AI), Best Paper FDG’21, Runner-Up IEEE Games’20, GECCO 2017 Competition Winner, Sapere Aude Grant, Amazon/Google Faculty Awards He advises on projects like GROW-AI (EU-funded), AI-TESTER (game testing), and C2SIM (military systems). Media coverage includes Science , Wired , and Popular Science .

• Control and Systems Engineering
• Automotive Engineering
• Model Predictive Control
• +10 more

Massimo Canale is a Tenured Associate Professor at the Department of Electronics and Telecommunications (DET) of Politecnico di Torino , and a member of the CARS@PoliTO Interdepartmental Center for Automotive Research and Sustainable Mobility. His academic career spans over two decades, focusing on control systems engineering with applications in automotive technology. Scientific Branch: Systems and Control Engineering (IINF-04/A) ERC Sectors: Automotive Engineering, Control Engineering, Control Theory Dr. Canale's research bridges theoretical advancements in Model Predictive Control (MPC) with practical applications in autonomous vehicles , hybrid/electric propulsion , and active suspension systems . His work integrates reinforcement learning and dynamic programming for optimizing vehicle performance and energy efficiency. Recent publications demonstrate trends in autonomous driving architectures (2024), sliding mode control for highway scenarios (2024), and energy management for sustainable mobility (2023-2024). He has developed patented solutions for semi-active suspension control and autonomous vehicle guidance. Award: IEEE Transactions on Control Systems Technology Outstanding Paper Award (2011) Editorial Roles: Associate Editor, IEEE Open Journal of Control Systems (2022–present) Dr. Canale supervises PhD students like Francesco Cerrito and teaches courses on digital control technologies , automatic control , and reinforcement learning at Politecnico di Torino. His research is funded through competitive grants (e.g., MPC4AVP 2021-2022) and commercial contracts (AD Shuttle 2024).

Matthew James KeelingView profile

Professor

• Infectious Disease Modeling
• Mathematical Epidemiology
• Network Theory in Epidemiology
• +5 more

Professor Matthew James Keeling is a distinguished academic at the University of Warwick, where he serves as Director of the Zeeman Institute for Systems Biology & Infectious Disease Epidemiology Research (SBIDER). He holds a professorship in the Department of Mathematics within the School of Science, specializing in mathematical modeling of infectious disease dynamics. His work bridges theoretical mathematics with practical public health applications, making significant contributions to epidemic prediction and control strategies. Professor Keeling's educational background includes a B.A. in Mathematics (1991), Master in Mathematics (1992), and PhD in Mathematical Modelling (1995). His career progression at Warwick shows steady advancement from Lecturer (2002) to Reader (2005) and finally to Professor (2007), supported by prestigious fellowships including the Royal Society University Research Fellowship (1998-2006) and Wellcome Trust Fellowship (1995-1998). His research focuses on mathematical models for infectious disease transmission, with particular expertise in network-based approaches to understanding how diseases spread through populations. Keeling has applied his modeling expertise to numerous outbreaks including the 2001 Foot-and-Mouth Outbreak, 2009 Swine flu pandemic, and most notably the COVID-19 pandemic. His work spans both human and animal diseases, covering pathogens such as influenza, measles, HPV, and various livestock infections. Analysis of Professor Keeling's publications reveals a strong emphasis on network theory applied to epidemiology, with significant contributions to understanding cattle movement networks, human social contact patterns, and spatial disease dynamics. His work consistently bridges theoretical mathematics with practical disease control applications, demonstrating how mathematical models can inform real-world policy decisions regarding vaccination strategies and outbreak control measures. Scientific Awards and Recognitions: Royal Society University Research Fellowship (1998-2006) Wellcome Trust Fellowship (1995-1998) Professor Keeling has been actively involved in advising government agencies during major disease outbreaks, including providing critical modeling input during the COVID-19 pandemic. His work on HPV vaccination cost-effectiveness directly influenced policy decisions regarding gender-neutral vaccination programs. He maintains an active research group focused on developing novel mathematical approaches to infectious disease modeling, with particular interest in household transmission dynamics and the impact of network structure on disease spread. The Zeeman Institute (SBIDER), which he directs, serves as a hub for interdisciplinary research connecting mathematical sciences with biological and medical applications.

• dc-dc converters
• multi-level converters
• electric machines
• +4 more

Dr. Ahmed Badawy is a Lecturer in Electrical Engineering at Lancaster University's School of Engineering, where he has been serving since 2017. His academic journey includes advanced degrees in electrical engineering and significant research experience in power electronics and renewable energy systems. Dr. Badawy's educational background includes: B.Sc. in Electrical Engineering from the Faculty of Engineering, Alexandria University, Egypt (2008) M.Sc. in Electrical Engineering from the Faculty of Engineering, Alexandria University, Egypt (2012) Ph.D. in Electrical Engineering from the Electric and Electronic Engineering Department at the University of Strathclyde, Glasgow, U.K. (2016) Dr. Badawy's research focuses on power electronics and energy conversion systems, with particular expertise in DC-DC converters, multi-level converters, and electric machines. His work emphasizes digital control of power electronic systems for applications in renewable energy integration, electric vehicles, and power quality improvement. His research has significant implications for sustainable energy systems and transportation electrification. His recent publication record demonstrates a strong focus on electric vehicle power systems, particularly modular converter topologies for on-board charging applications. There's a clear trend toward integrated solutions for EV charging that combine renewable energy sources with grid connectivity. His work also shows significant contributions to control methodologies, including model predictive control and hierarchical control systems for power electronic converters. Dr. Badawy mentors several PhD students working on cutting-edge research in power electronics and renewable energy systems. His research group is actively involved in projects related to sustainable energy conversion and electric transportation. Dr. Badawy is affiliated with Energy Lancaster and the TALOS research group at Lancaster University, where he contributes to interdisciplinary research on sustainable energy systems and power electronics applications.

• Robotics
• Control Systems
• Multi-Agent Systems
• +6 more

Herman Bruyninckx is a Part-Time Full Professor at Eindhoven University of Technology (TU/e) in the Mechanical Engineering department, specifically within the Control Systems Technology group and EAISI High Tech Systems initiative. He also serves as a professor (Hoogleraar) at KU Leuven in Belgium. Academic focus on robotics, control systems, and multi-agent coordination Active research in model predictive control , semantic mapping , and dynamic constraint algorithms Recent publications address industrial automation , agro-food robotics , and haptic technology Research Highlights : Developed hybrid decision-making frameworks for multi-agent navigation Innovated swing-free control methods for robotic pick-and-place operations Formulated constrained dynamics algorithms with LQR-Gauss principle integration Created ExoTen-Glove for haptic feedback in virtual environments Collaborative Projects : Coordinated with researchers like René van de Molengraft , Elena Torta , and Koen de Vos Contributed to NWO/TTW FlexCRAFT project for cognitive robotics in agro-food technology

• Automated Transportation Systems
• Connected Vehicles
• Electric Vehicles
• +3 more

Dr. Xiaopeng Li is the Harvey D. Spangler Professor in the Department of Civil and Environmental Engineering at the University of Wisconsin-Madison, with an affiliation in the Department of Electrical and Computer Engineering. He leads the USDOT Rural Autonomous Vehicle Program and previously directed the National Institute for Congestion Reduction. He earned his B.S. in Civil Engineering from Tsinghua University (2006), M.S. in Civil Engineering (2007), M.S. in Applied Mathematics (2010), and Ph.D. in Civil Engineering (2011) from the University of Illinois at Urbana-Champaign. His research focuses on modeling and field experiments for connected, electric, and automated vehicles (CAVs), infrastructure systems analysis, and interdependent network modeling. He has pioneered physics-enhanced machine learning frameworks for vehicle control and developed simulation tools for CAV deployment. His 2025-2024 publications highlight advancements in Connected vehicle trajectory modeling Energy consumption optimization Edge computing for autonomous operations Residual learning control systems Equity analysis in AV deployment Communication technologies for V2X Awards include: TRB Best Paper Award (2025) NSF CAREER (2015) ASCE Fellow (2024) IEEE Senior Member (2022) Multiple institution-specific fellowships He has advised 15+ graduate students, secured $35M+ in grants from NSF, USDOT, and industry partners, and chairs the IEEE ITSS Emerging Transportation Technology Testing committee. His work addresses real-world AV implementation, safety validation, and sustainable transportation systems.

Hwan-Sik YoonView profile

Associate Professor

• Advanced Manufacturing
• Artificial Intelligence
• Computer Vision
• +8 more

Dr. Hwan-Sik Yoon is an Associate Professor in the Department of Mechanical Engineering at The University of Alabama, where he focuses on applying Artificial Intelligence (AI) and Machine Learning (ML) to automotive, transportation, and manufacturing systems. His research spans modeling, simulation, and control of dynamic systems, with a strong emphasis on connected and automated vehicles (CAVs), energy-efficient routing, and sensor fusion technologies. Ph.D., Mechanical Engineering, Ohio State University, 2002 M.S., Mechanical Engineering, Ohio State University, 1998 B.S., Physics Education, Seoul National University, Korea, 1994 Dr. Yoon’s research integrates AI/ML into applications such as traffic signal control , excavator manipulator pose estimation , hybrid electric vehicle powertrain control , and factory floor safety monitoring . He is also involved in additive manufacturing , vision-based control systems , and reinforcement learning -driven automotive innovations. Recent publications highlight trends in deep reinforcement learning for vehicle energy efficiency, sensor fusion for traffic surveillance, and neural networks for dynamic system control. His work addresses challenges in multi-component failure analysis and real-time edge computing platforms . NSF Outstanding Faculty Advisor Award (2019) College of Engineering Faculty Productivity Award, Tennessee Tech University (2012) Dr. Yoon leads the Intelligent Structures and Systems Laboratory and serves as the lead CAVs faculty advisor for the University of Alabama’s EcoCAR student team, which has achieved national recognition in advanced vehicle technology competitions.

Yuanyuan ShiView profile

Assistant Professor

• Energy Systems
• Cyber-Physical Systems
• Machine Learning for Energy Management
• +6 more

Yuanyuan Shi is an Assistant Professor in the Electrical and Computer Engineering Department at the University of California, San Diego (UCSD), with affiliations at the Center for Energy Research and the MICS. Her research integrates machine learning with control theory, focusing on energy systems, cyber-physical systems, and PDE-governed systems, aiming to provide reliable and efficient decision-making in complex environments like power grids and buildings. Assistant Professor, UCSD (2021–present) Postdoctoral Fellow, Caltech (2020–2021) Ph.D., Electrical and Computer Engineering, University of Washington (2020) M.Sc., Electrical Engineering and Statistics, University of Washington B.Eng., Nanjing University, China Her work spans machine learning, optimization, and control theory, with applications in power systems, PDEs, and intelligent systems. She develops algorithms that combine learning with control guarantees, enabling robust solutions for energy management and grid stability. Recent publications highlight her focus on neural operators for PDE and delay systems, stability-constrained reinforcement learning, and multi-agent control in sustainability contexts. These works advance physics-informed models, grid frequency regulation, and commercialized energy storage integration. She has received prestigious awards, including: NSF CAREER Award (2025) Schmidt Sciences AI2050 Early Career Fellowship (2025) Hellman Fellowship (2023) Jacobs School Early-Career Faculty Acceleration Award (2024) MIT Rising Star in EECS (2018) Clean Energy Institute Scientific Achievement Award (2020) At UCSD, her lab collaborates on projects like FedNeMO (federated neural operators) and BEAR-Data (multi-zone building dataset). She co-organized Control Meets Learning seminars and serves as guest co-editor for the Applied Energy special issue on Trustworthy Machine Learning.

Chengguo ZhangView profile

Associate Professor

• Mining Engineering
• Geomechanics
• Numerical Analysis
• +6 more

Associate Professor Chengguo Zhang is a researcher at the University of New South Wales (UNSW Sydney) specializing in Mining Engineering and Geomechanics . His work focuses on improving mining safety and sustainability through fundamental and applied research on dynamic rock mass failures , groundwater-mining interactions , and data-driven visualization technologies . He currently serves as the Postgraduate Research Coordinator for the School of Mining Engineering. PhD in Mining Engineering from UNSW Sydney (2015) Coordinates postgraduate research programs Recipient of multiple teaching and research awards Research Interests: Zhang's work addresses critical mining industry challenges through: Quantification of energy sources and dissipation in rock masses for rockburst management Integration of AI data analytics and 3D visualization for geotechnical risk assessment Mine subsidence and coupled hydro-mechanical behavior of rock discontinuities Development of digital ground control management systems Article Trends: His recent publications demonstrate expertise in: Numerical modeling of rock fracturing mechanisms Nonlinear fluid flow analysis in fractured rock masses Shotcrete and ground support system evaluation Hydro-mechanical coupling during shear processes Energy-based coal burst risk classification Scientific Awards: Tim Shaw Award for Innovation in Teaching (2024) International Outstanding Young Scholar Award (2023) UNSW Education Excellence Award (2021) UNSW Research Excellence Award (2018) Research Supervision: Supervises 12 active PhD students (9 as primary/joint supervisor) and has guided 11 PhD completions (7 as primary/joint supervisor), including 3 Dean's Award recipients. Focuses on numerical modeling, data visualization, and machine learning applications in mining geomechanics.

Amir JavedView profile

Lecturer

• Cybersecurity
• Machine Learning
• IoT Security
• +5 more

Dr. Amir Javed is a Lecturer in the School of Computer Science and Informatics at Cardiff University, where he has been employed since 2019. Previously, he served as a Research Associate at the same institution from 2015 to 2019, working on projects including WEFO collaboration with Airbus, the EPSRC Ebb and Flow Energy Systems project, and the ESRC HateLab project. His research spans cybersecurity, machine learning, and IoT security, with particular focus on intrusion detection systems for in-vehicle networks, adversarial machine learning, cloud security, and cybersecurity education. He investigates malware propagation on social networks, drive-by download attacks on Twitter, and the application of machine learning for real-time cyberattack forecasting. His recent work explores generative AI integration in cybersecurity education and adversarial attacks on autonomous vehicle security systems. Dr. Javed's publication trends reveal a strong emphasis on automotive cybersecurity (particularly intrusion detection for connected vehicles), adversarial machine learning techniques, and innovative approaches to cybersecurity education. His work increasingly focuses on federated learning applications for vehicle security and the educational challenges of integrating generative AI into cybersecurity curricula. He leads the Social Data Science Lab (ESRC-funded, £1.5 million, 2020-2022) and teaches the CMT116 Cyber Security and Risk course. His supervisory portfolio includes doctoral research on in-vehicle network security, cloud service abuse detection, and adversarial attacks in intrusion detection systems.