Jari Vepsäläinen is an Assistant Professor at Aalto University's Department of Energy and Mechanical Engineering under the College of Engineering. He serves as Director of the Fluid Power group and specializes in mechatronics design, energy efficiency, and generative design methodologies. Research focuses on physics-based modeling for energy recovery AI/ML applications in electromechanical system design Applications in robotics, heavy machinery, and sustainable transportation His recent publications demonstrate expertise in hybrid systems, fluid power optimization, and AI-driven engineering, with a strong emphasis on electrification and efficiency across automotive, maritime, and industrial domains. Key areas: Mechatronics, Energy Systems, Generative Design Technologies: Digital Twins, IoT, Machine Learning Current projects involve thermal energy systems, electric motor optimization, and advanced control algorithms for mobile machinery.
Professor Aruna Prasad Seneviratne serves as the Foundation Professor of Telecommunications at the University of New South Wales (Australia), where he holds the prestigious Mahanakorn Chair of Telecommunications. He is currently the Research Director for the Cyber Physical Systems Research Program within Data61, following the merger of NICTA with CSIRO. Previously, he directed the Australian Technology Park Laboratory of NICTA and led their Networked Systems research activities. Professor Seneviratne's research focuses on physical analytics - technologies enabling applications to interact intelligently and securely with their environment in real time. His recent work includes behavioral biometrics, wearable device optimization, and IoT system verification. His extensive publication record spans cybersecurity, artificial intelligence, communications engineering, and mobile technologies, with particular emphasis on integrated communications and sensing systems. His scholarly contributions include over 180 refereed technical papers and book chapters, reflecting his leadership in telecommunications and networked systems research. Professor Seneviratne's work demonstrates a consistent trajectory toward developing practical solutions for next-generation digital services and security frameworks. His scientific recognition includes prestigious fellowships at British Telecom and Telecom Australia Research Labs, underscoring his industry impact alongside academic contributions. Professor Seneviratne has supervised 30 PhD dissertations throughout his career, mentoring the next generation of telecommunications researchers. His leadership extends to directing major research initiatives at NICTA and Data61, where he has guided the development of new technologies for establishing trust, energy-efficient content storage, search, and distribution within digital economies. His laboratory work centers on the Cyber Physical Systems Research Program at Data61, where his team develops innovative approaches to secure and intelligent interaction between digital systems and physical environments.
Maurice Gagnaire is a Full Professor at Télécom Paris in the Computer Science and Networks (Infres) department, affiliated with the Networks, Mobility and Services (RMS) research team and the Information Processing and Communication Laboratory (LTCI). He has contributed extensively to optical network design, cloud computing, and network virtualization. Education: Engineering degree from Télécom SudParis, Master's in Computer Systems (Paris VI), Ph.D. (Télécom ParisTech), HDR (University of Versailles) Research Interests : His work focuses on translucent WDM networks , green networking , dynamic resource allocation , and physical layer impairments . Key projects include traffic grooming , failure detection , and energy-aware routing . Publications & Awards : He co-authored Springer's Traffic Grooming for Optical Networks and received the IBM Faculty Award 2014 . His research spans optical access systems , cloud brokering , and multi-layer traffic engineering . Scientific Honors: IBM Faculty award, Chevalier de l'Ordre des Palmes Académiques Academic Service : He served as expert for NSF (USA), IEEE, and ARCEP. His leadership includes coordinating the RMS research group and leading the Optimization and Networking Cluster.
Amir Taherkordi is a Professor in the Networks and Distributed Systems group at the Department of Informatics, University of Oslo, Norway. His research focuses on resource-efficiency, scalability, adaptability, and mobility in distributed systems for emerging technologies like IoT, Fog/Edge/Cloud Computing, and Cyber-Physical Systems (CPS). University: University of Oslo Department: Informatics Academic Rank: Professor His research spans IoT, Edge/Fog Computing, and Cyber-Physical Systems, emphasizing energy efficiency, privacy preservation, and self-adaptive architectures. Key areas include network traffic classification, computation offloading, and federated learning applications in vehicular systems. Recent publications highlight advances in latency-aware IoT data transmission , federated vehicular networks , energy-efficient wireless charging , and privacy-preserving data integration . These works often integrate machine learning with network optimization. Projects include the CPS Lab at UiO, DILUTE (Fluid Service Abstraction), and the Gemini Centre on IoT . He collaborates on initiatives like PACE for energy informatics curricula development.
Yafeng Yin is Professor of Civil and Environmental Engineering and Professor of Industrial and Operations Engineering at the University of Michigan, College of Engineering, where he serves as Donald Malloure Department Chair of Civil and Environmental Engineering and holds the Donald Cleveland Collegiate Professorship in Engineering. His educational background includes: PhD in Civil Engineering from University of Tokyo (2002) ME in Civil Engineering from Tsinghua University (1996) BE in Environmental Engineering from Tsinghua University (1994) BE in Structural Engineering from Tsinghua University (1994) Dr. Yin's research centers on developing sustainable and economically efficient transportation systems through analysis, modeling, design, and optimization. He investigates how emerging technologies—including connected/automated vehicles, electric vehicles, drones, and mobile sensing—impact transportation demand and supply. His work extends to interdependencies between transportation, power, and communications networks in urban infrastructure systems. Key focus areas include mobility services, ride-sourcing markets, traffic management, and integration of artificial intelligence in transportation. His recent publications (2023-2025) demonstrate a pronounced shift toward leveraging large language models and agent-based frameworks for transportation analysis, with significant emphasis on on-demand mobility services (ride-sourcing, food delivery), traffic control with connected vehicles, and economic implications of emerging technologies. The research spans theoretical foundations in game theory and optimization to practical applications in urban settings. As director of the Lab for Innovative Mobility Systems, Dr. Yin leads interdisciplinary research developing solutions that enhance transportation efficiency, reliability, safety, and service diversity through technological integration. His work bridges theoretical modeling with real-world implementation challenges in evolving transportation ecosystems.
Vishesh Mishra is a Prime Minister's Research Fellow at the Department of Computer Science and Engineering, Indian Institute of Technology Kanpur. He concurrently serves as a Visiting Research Fellow at INRIA Centre, University of Rennes, France, and an External Research Collaborator at CANDLE LAB, IIT Roorkee. His research centers on hardware security vulnerabilities in approximate computing systems, with focus areas including hardware trojan detection in approximate circuits, energy-efficient error-resilient architectures, and side-channel attack mitigation. He develops novel methodologies for securing IoT devices and blockchain implementations through circuit-level innovations and floating-point approximation techniques. Analysis of his 15 most recent publications reveals dominant themes in approximate arithmetic unit design (adders/multipliers), hardware trojan countermeasures, and floating-point resilience. His work bridges theoretical security models with practical VLSI implementations, consistently targeting energy efficiency without compromising critical functionality in error-tolerant applications. Scientific recognition includes: Prime Minister's Research Fellowship (India's premier PhD fellowship) Collège doctoral de Bretagne international mobility grant (€9600 for 6-month INRIA research) His research is supported through competitive fellowships rather than traditional grants, with no student advising roles documented. Current collaborations span IIT Kanpur's C3i Center, IIT Roorkee's CANDLE LAB, and INRIA's Rennes research unit, focusing on cross-institutional hardware security projects.
Dr. Yulin Hu serves as a Visiting Professor at RWTH Aachen University, holding the Chair of Information Theory and Data Analytics. His research program bridges theoretical foundations with practical implementations in next-generation wireless systems, with particular emphasis on UAV-aided networks and information-theoretic approaches to communication challenges. His core research interests span multiple interconnected domains: Wireless Communications (especially finite blocklength regimes) Information Theory applications in network design UAV trajectory optimization and network integration Wireless power transfer with nonlinear energy harvesting Edge computing and distributed learning systems Data analytics for network performance optimization Analysis of Dr. Hu's 2025 publication record reveals a concentrated research thrust on UAV trajectory design, where he develops joint optimization frameworks addressing energy efficiency, security, and reliability constraints. His work consistently integrates information-theoretic principles—particularly finite blocklength analysis—to solve practical challenges in ultra-reliable low-latency communications (URLLC) and wireless power transfer. A distinctive feature of his approach is the fusion of deep reinforcement learning with traditional optimization methods for dynamic network scenarios, including no-fly zone constraints and covert operations. While no specific scientific awards are documented in the available materials, his prolific output across top-tier venues demonstrates significant scholarly impact. Details regarding graduate student mentoring and research funding mechanisms remain unspecified in the current documentation. The Chair of Information Theory and Data Analytics, which Dr. Hu leads, functions as a specialized research unit focused on theoretical rigor and algorithmic innovation for wireless systems, though specific laboratory infrastructure or team composition details are not provided.
Yuvraj Agarwal is a Professor at Carnegie Mellon University's School of Computer Science, where he founded and directs the SYNERGY Lab. He previously served as Executive Director of the NSF Expeditions in Variability (2010-2013) and was affiliated with UCSD's Microelectronic Embedded Systems Lab (MESL) and Systems and Networking Group (SysNet). Research Themes : Systems & Networking, Embedded Systems, Mobile Computing, with focus on energy efficiency and privacy. Leadership : Director of SYNERGY Lab, Co-PI in NSF CoDec expedition, Brick Consortium member. His research bridges hardware-software systems with societal impact, including smart building energy optimization using occupancy sensing, mobile privacy tools like ProtectMyPrivacy, and IoT security labeling frameworks . Recent work includes the Computational Decarbonization NSF expedition ($12M over 5 years) to reduce carbon footprints in societal infrastructure. Scientific Awards : 2024: Promoted to Full Professor 2012: UCSD Outstanding Faculty Award for Sustainability 2016: Google Faculty Research Award Advising : Guided 11 PhD students to completion (now in academia/startups) and mentored 10+ MS/undergraduate researchers. Current advisees include 4 PhD students in Carnegie Mellon's Societal Computing program. Grants : NSF Expeditions in Variability (2010-2013), NSF #1564009 (2016), NSF #1526237 (2015), NSF #1513957 (2015), DARPA BRANDEIS program (2015), and Google research funding (2015). Labs & Collaborations : SYNERGY Lab at CMU focusing on IoT and smart campus systems. Key collaborations with the Brick Consortium (Johnson Controls, Schneider Electric), Microsoft Research, Intel Research, and academic institutions including UCSD and UMass Amherst.
Dr. Kalikinkar Mandal is an Associate Professor in the Faculty of Computer Science at the University of New Brunswick (UNB), Fredericton, Canada. He holds the prestigious NB Power Cybersecurity Research Chair for smart grid security and privacy, a position supported by $500,000 in funding from NB Power for a five-year term. Dr. Mandal is also a member of the Canadian Institute for Cybersecurity (CIC), an ACM member, and a member of the International Association for Cryptologic Research (IACR). Education: PhD in Electrical and Computer Engineering from the University of Waterloo (2013) MTech in Computer Science from the Indian Statistical Institute, Kolkata (2009) Additional Master's degree in Mathematics Dr. Mandal's research broadly focuses on cryptography, cybersecurity, and privacy, with specific expertise in lightweight cryptography, privacy-preserving computation, security and privacy in smart grids and Internet of Things (IoT), trusted computing, and high-speed cryptography. His work addresses critical challenges in securing emerging technologies, particularly in energy infrastructure where cybersecurity threats can have severe consequences for essential services. His research bridges theoretical cryptography with practical applications in real-world systems. Analysis of Dr. Mandal's recent publications reveals a consistent focus on cryptographic techniques for resource-constrained environments, particularly for smart grid and IoT applications. His work spans theoretical foundations of cryptographic primitives, practical implementations of lightweight ciphers, and innovative privacy-preserving protocols for emerging technologies. A notable trend in his research is the development of efficient cryptographic solutions that balance security requirements with performance constraints in critical infrastructure systems. Scientific Awards: NB Power Cybersecurity Research Chair ($500,000 funding) Contributor to multiple cryptographic algorithms (ACE, SPIX, SpoC, WAGE) that reached Round 2 of NIST Lightweight Cryptography standardization Dr. Mandal actively mentors graduate students in cybersecurity research, currently supervising three students working on cryptographic protocols for cyber-physical systems, cybersecurity in advanced metering infrastructure, and privacy for electric vehicles. His NB Power Cybersecurity Research Chair supports research that provides training opportunities for both graduate and undergraduate students, preparing them as future cybersecurity leaders. Through direct applied research, knowledge dissemination, and student training, his work addresses critical challenges in power and security infrastructure. Dr. Mandal is actively involved in several research initiatives related to lightweight cryptography. He is part of the development teams for ACE, SPIX, SpoC, and WAGE - all of which were Round 2 candidates in the NIST Lightweight Cryptography standardization project. His GitHub repository (comsec-lwc) contains reference and optimized implementations of these cryptographic algorithms. He also contributes to the Canadian Institute for Cybersecurity at UNB, focusing on practical applications of cryptographic techniques in critical infrastructure security.
Professor James Taylor is a distinguished academic at Lancaster University where he holds a Personal Chair in Control Engineering within the School of Engineering . As Impact Champion for the School of Engineering and lead for Robotics & Control, he has been instrumental in advancing control engineering research. Previously, he served as group lead for Nuclear Science & Engineering (2019-24) and held senior administrative roles including Director of Teaching and Deputy Head of Engineering (2005-2018). Professor Taylor's research spans data-driven modelling and automatic control for challenging, uncertain systems with applications in Energy systems Healthcare Robotics Environmental monitoring His work has attracted over £10m in UK research council funding as co-investigator across 10+ projects. He has made significant contributions through his research on topics such as Electricity theft detection using machine learning Nuclear fuel analysis with hyperspectral imaging Digital twins for nuclear manufacturing Adaptive medical treatment systems Robotic plant phenotyping platforms Scientific recognition includes: Fellow of the Institution of Engineering & Technology (FIET) Member of IET Academic Accreditation Committee Editorial board member for three Elsevier journals Active participation in UK Automatic Control Council Professor Taylor has supervised numerous PhD students to successful completion and co-develops the internationally used CAPTAIN Toolbox (MATLAB) for system identification and control. He has been involved in developing control systems for Hydraulically actuated dual-arm robots Wave energy converters Assisted tele-operation systems Grow-cell agricultural facilities Motion planning algorithms
Sangtae Ha is an Associate Professor in the Computer Science Department at the University of Colorado Boulder. His research focuses on building practical computer systems spanning multiple disciplines, including machine learning/deep learning systems, networks and distributed systems, internet protocols, wireless networks, video streaming, storage systems, and security. He specializes in creating efficient and scalable solutions for real-world computing challenges. His work emphasizes interdisciplinary approaches, bridging theoretical computer science with practical system design. Key areas of exploration include optimizing neural network execution for edge computing, developing adaptive streaming protocols, and enhancing wireless network performance through novel signal processing and spectrum management techniques. Recent projects include frameworks for semantic offloading in neural networks and reinforcement learning-based cloud scheduling. No scientific awards or notable grants are explicitly mentioned in the provided materials. While no formal advisees are listed, his research team likely involves graduate students and collaborators. No dedicated labs or teams are named, though his work aligns with broader university initiatives in computer systems and networking.
Bradley Denby is an Assistant Professor in the Department of Aerospace & Ocean Engineering at Virginia Tech, holding the Marty and Anna Irvine AOE Faculty Fellow position. He specializes in cyber-physical systems, computational nanosatellite constellations, and machine learning for autonomy, with a focus on orbital edge computing. Denby leads the Starbelt Lab, focusing on advanced air mobility and satellite systems research. Education: PhD in Electrical and Computer Engineering, Carnegie Mellon University (20XX) MS in Computer Engineering, Air Force Institute of Technology (AFIT) BS in Physics, Southern Illinois University Research Interests: Denby’s work bridges computer science and aerospace engineering, addressing challenges in computational satellite systems, edge computing in space, and autonomous decision-making. His projects include batteryless satellites (Tartan Artibeus) and high-coverage nanosatellite constellations (EagleEye). He explores orbital edge computing frameworks to enable real-time data processing directly on satellites. Recent Research Trends: His publications emphasize scalable nanosatellite constellations, overcoming computational bottlenecks in space, and integrating machine learning for on-orbit inference. Themes include energy-efficient systems, inter-satellite communication, and mission-critical autonomy. Awards: Marty and Anna Irvine AOE Faculty Fellow (Virginia Tech) Teaching & Labs: Instructs courses on space engineering, satellite design, and advanced air mobility. Runs the Starbelt Lab (website/GitHub linked).
Thivya KANDAPPU is an Assistant Professor at the School of Computing and Information Systems, Singapore Management University (SMU). Her research focuses on mobile and wearable computing systems for human cognition monitoring, memory modeling, and technology-enhanced learning. Education: PhD in Computer Science, University of New South Wales (2014) Research Interests span: Human-Centric Wearable Systems Cognitive State Analysis Event-Based Vision & Sensing Privacy in Pervasive Systems Health & Wellbeing Applications Smart City Mobility Analytics Selected Publications demonstrate expertise in eye tracking, cognitive monitoring, and privacy-preserving wearables, with recent work appearing in NeurIPS and ACM IMWUT. Research Grants include projects funded by MOE and A*STAR on cognitive dynamics, privacy-aware systems, and multimodal travel analytics. Professional Service involves organizing AutoMLPerSys 2025, and serving on TPCs for ACM MobiSys 25, IEEE PerCom 25, and ICDCN 25.
Prof. Ferdinand Grozema is a Full Professor at Delft University of Technology, Faculty of Applied Sciences, Department of Chemical Engineering. His research focuses on the dynamics of charges and excited states using ultrafast spectroscopy, microwave conductivity techniques, computational chemistry, and materials synthesis. Research Interests: Charge transport in molecular wires, DNA charge transfer, quantum interference effects, singlet exciton fission, and photochemical upconversion. Teaching: Leads courses in Numerical Methods for Chemical Engineers and Computational Materials Science at TU Delft. Group Leadership: Head of the Grozema Group, supervising PhD students like María C. Gélvez-Rueda and Damla Inan. Publication Trends: Recent work emphasizes perovskite materials, molecular electronics, and charge/exciton dynamics. Keywords from 15 most recent articles include Materials Science , Computational Chemistry , and Ultrafast Spectroscopy , with subtopics like 2D Perovskites , Quantum Interference , and Exciton Spin Dynamics .
Emmanuel Cecchet is a Senior Research Fellow at the University of Massachusetts Amherst's Department of Computer Science. He is affiliated with multiple research groups including the Laboratory for Advanced System Software (LASS), the Commonwealth Center for Forensics & Society, and the Advanced Networked Systems Research Group. His research focuses on distributed systems, dependability, high availability, databases, and benchmarking. Cecchet has held roles as a postdoctoral researcher at Rice University, a Research Scientist at INRIA, and Chief Architect at Continuent. He has received numerous awards, including the Best Paper Award at IWQoS 2013 and the Best PhD Thesis Award from Institut National Polytechnique de Grenoble (2004). His work includes projects like BenchLab, Open Cloud Testbed (OCT), and contributions to middleware systems such as C-JDBC and Sequoia. Education: PhD in Distributed Systems from Institut National Polytechnique de Grenoble (2001). Research Interests: Operating Systems, Distributed Systems, Dependable Systems, Databases, Virtualization, Networking, and Open Source Software. He also contributes to data forensics and leads the Frog Racing foundation as an amateur car racer. Key Projects: BenchLab for realistic benchmarking, CloudLab for cloud infrastructure research, and WiFiMon for mobility analytics using WiFi sensing. He has served on program committees for Eurosys, SRDS, and other conferences.
