جست‌وجوی استاد و پژوهشگر

Jingjie Yeoمشاهده پروفایل

استادیار

• Computational Materials Science
• Engineered Living Materials
• Multiscale Modeling
• +۱۷ مورد دیگر

Prof. Jingjie Yeo is an Assistant Professor in the Sibley School of Mechanical and Aerospace Engineering at Cornell University and the Principal Investigator of the J² Lab for Engineering Living Materials. His research integrates computational modeling, machine learning, and biomaterials to design sustainable, adaptive materials for healthcare and environmental applications. He is also a co-instructor at Station1, a nonprofit advancing socially-directed STEM education. Ph.D. and B.Eng., Nanyang Technological University, Singapore Postdoctoral training, Tufts University and Massachusetts Institute of Technology Research Scientist, Institute of High Performance Computing, Singapore Prof. Yeo’s research focuses on computational design of engineered living materials , particularly bacteria-based systems and biopolymers. His lab employs multiscale simulations —from quantum mechanics to continuum modeling—and integrates machine learning and materials informatics to accelerate discovery. Key areas include nanomechanics of mucus-bacteria interactions, mucoadhesive biomaterials, sustainable materials, and living materials for gut health and infrastructure. The lab pioneers AI-driven frameworks for materials-by-design, aiming to advance a "Materials 4.0" paradigm. The lab’s recent publications reveal a strong trend in AI-enhanced materials discovery , bio-inspired soft materials , and biomechanical modeling of biological systems . Themes include antimicrobial surfaces, solid electrolytes, hydrogels, and sustainable carbon materials. The work is highly interdisciplinary, combining physics, biology, and computation. Notable scientific awards include: NSF CAREER Award (2024) Dennis G. Shepherd Excellence in Teaching Award, Cornell College of Engineering (2023) ASME Rising Star in Mechanical Engineering Emerging Investigator, Journal of Materials Chemistry B (2020) Multiple NSF grants including EFRI and Convergence Accelerator awards Prof. Yeo has advised numerous PhD, MS, and undergraduate students , including the lab’s first PhD graduates, Drs. Haoyuan Shi and Tianjiao Li. His research is supported by NSF, Cornell, and international seed grants . He serves on editorial boards for STEM Education and International Journal of AI for Materials and Design , reflecting his dual commitment to research and education innovation. The J² Lab is part of the Cornell Engineered Living Materials Institute and collaborates with researchers at MIT, Montana State University, Zhejiang University, and others. The lab emphasizes interdisciplinary teamwork, computational rigor, and socially impactful science.

Christoph Goebelمشاهده پروفایل

استاد

• Energy Management Systems
• Renewable Energy Integration
• Smart Grids
• +۴ مورد دیگر

Prof. Christoph Goebel is a Professor of Energy Management Technologies at the TUM School of Engineering and Design, Technical University of Munich. His research focuses on optimizing energy systems through advanced algorithms, machine learning, and data-driven approaches. Key areas include renewable energy integration, smart grid technologies, electric vehicle charging optimization, and the application of graph neural networks in power systems. His work bridges theoretical advancements with practical implementation, such as developing platforms like e-SparX for collaborative machine learning in energy research and frameworks like EnergyOS for modular energy management systems. Recent studies explore thermal energy storage models, cost-effective CO2 reduction strategies via heat pumps, and the economic incentives of wind energy data sharing. Goebel's publications (2016–2025) consistently emphasize predictive control, optimization under market constraints, and the integration of ICT innovations into energy infrastructure. Notable contributions include transfer learning models for building thermal dynamics and benchmarking frameworks for energy hardware-software systems.

Sasani Jayawardhanaمشاهده پروفایل

مدرس

• Materials Science
• Machine Learning
• Nanotechnology
• +۳ مورد دیگر

Dr. Sasani Jayawardhana is a full-time Lecturer at the University of Colombo, where she co-founded the ALOeKA research facility to integrate artificial intelligence with spectroscopic techniques in materials science. Her academic journey includes a BSc in Physics from the University of Colombo, a PhD in nanostructured sensors from Swinburne University of Technology, and postdoctoral research in optical instrumentation at the University of Sri Jayewardenepura. She has pioneered low-cost experimental setups, including optical fiber sensors and Raman spectrometers. Education: BSc (Physics, University of Colombo), PhD (Swinburne University), Postdoc (University of Sri Jayewardenepura) Her research focuses on leveraging machine learning to accelerate materials discovery, particularly in nanotechnology and optical sensing. Key areas include SERS (Surface-Enhanced Raman Scattering), biomimetic surface engineering, and energy harvesting systems. Her work bridges experimental physics with computational tools, emphasizing cost-effective instrumentation for global accessibility. Publications highlight innovations like solar radio burst analysis, gemstone identification via Raman-ML hybrid systems, and low-cost lab equipment. These advancements underscore her commitment to both theoretical and applied materials science. No scientific awards are explicitly noted, but her contributions to accessible research tools and interdisciplinary collaboration are notable. She has advised no listed students but has mentored teams in lab development. The ALOeKA facility exemplifies her vision for integrative research frameworks.

• Artificial Intelligence (AI)
• Machine Learning (ML)
• Algorithms and Computational Complexity
• +۵ مورد دیگر

Abhishek K. Umrawal is a Teaching Assistant Professor at the University of Illinois Urbana-Champaign, affiliated with the Department of Electrical and Computer Engineering and the Coordinated Science Lab. He holds a Ph.D. in Industrial Engineering from Purdue University, an MS in Economics, and an MS in Statistics from IIT Kanpur. His research focuses on AI/ML fundamentals (Generative AI, Causal AI, Reinforcement Learning), algorithmic theory (Combinatorial Optimization, Game Theory), and applications in social networks, transportation systems, and conversational AI. Umrawal teaches core courses like ECE 313 (Probability), ECE 120 (Intro to Computing), and CS/ECE 374 (Algorithms). He has published 10+ peer-reviewed articles in top venues (IEEE TETCI, ACM TDS, AAAI), focusing on influence maximization, reinforcement learning, and causal graph applications. His work bridges theoretical foundations with real-world problems in transportation and social networks. Recognitions include the Bilsland Dissertation Fellowship (2022), multiple mentorship awards, and research travel grants. He actively reviews for NeurIPS, ICML, and journals like IEEE T-ITS. Service roles include Scholarship Committees in Grainger College of Engineering and leadership in ACM/IEEE societies.

Pu Wangمشاهده پروفایل

دانشیار

• Computer Networking
• Cyber-Physical Systems
• Internet of Things
• +۴ مورد دیگر

Pu Wang is an Associate Professor in the Department of Computer Science at the University of North Carolina at Charlotte (UNC Charlotte), part of the College of Computing and Informatics (CCI). His research focuses on networked systems, generative AI, computer vision, and computational biomechanics. He holds a Ph.D. from Georgia Institute of Technology (2013) and previously served as an Assistant Professor at Wichita State University (2013–2016). Education: B.E. in Electrical Engineering, Beijing Institute of Technology, 2003 M.E. in Electrical and Computer Engineering, Memorial University of Newfoundland, 2008 Ph.D. in Electrical and Computer Engineering, Georgia Tech, 2013 Research interests include modeling of networked systems (e.g., Software Defined Networking, IoT), AI-driven human mesh recovery, and biomechanically accurate 3D pose estimation. His work is supported by NSF grants and has led to advancements in federated learning, motion synthesis, and underwater communication systems. Awards and Honors: John A. See Innovation Award (Wichita State, 2016) BWN Lab Researcher of the Year (Georgia Tech, 2012) Best Student Paper Runner-Up (IEEE SCC 2016) Fellow of Memorial University’s School of Graduate Studies (2008) Key publications span top venues like ICCV, CVPR, AAAI, and IEEE journals, addressing topics such as masked motion control, vision-language models, and federated learning optimization. He actively mentors graduate students in AI/ML, computer vision, and network systems.

Charles Eesleyمشاهده پروفایل

دانشیار

• Entrepreneurship
• Institutional Economics
• Technology Policy
• +۴ مورد دیگر

Chuck Eesley is an Associate Professor in Management Science and Engineering at Stanford University, holding roles such as Faculty Director of the Stanford Technology Ventures Program and affiliate at the Stanford Center for AI Safety. His research focuses on how institutions, digital platforms, and AI shape entrepreneurship, with global fieldwork spanning Asia, Africa, and the Americas. He teaches courses on entrepreneurship and innovation, and has pioneered methodological innovations like randomized controlled trials in entrepreneurship studies. Education: Ph.D., MIT Sloan School of Management (2009) B.S., Duke University, Biological Basis of Behavior (2002) Research Interests: Eesley explores institutional barriers to entrepreneurship, platform governance, and the impact of AI/ML algorithms on venture opportunities. His work emphasizes methodological rigor, including experiments on mentorship effects and refugee entrepreneurship training in Sub-Saharan Africa. Key themes include bridging education and policy to support high-growth ventures, and addressing misinformation financing through platform accountability. Recent Contributions: His 2024 Nature study revealed how companies inadvertently fund misinformation via ads, while his 2025 work on return migration and platform-enabled entrepreneurship highlights rural-urban entrepreneurial linkages. He has advised global initiatives like Chile’s Startup Program and Taiwan’s ITRI-Stanford collaboration. Awards & Recognition: 2024 Stanford Social Impact Labs Fellowship 2022 Teagle Fellow in Liberal Education 2018 TUM Research Excellence Award Advising & Grants: Supervises PhD and Masters students on topics like AI ethics and refugee entrepreneurship. Leads RCTs in Uganda and has secured grants from Kauffman, Schulze, and the NSF. Labs & Teams: Co-founded NovoEd (an edtech platform) and collaborates with the Stanford Center for AI Safety to address algorithmic governance challenges.

Pierluigi Nuzzoمشاهده پروفایل

دانشیار

• Cyber-Physical Systems
• Artificial Intelligence
• Control Systems
• +۵ مورد دیگر

Pierluigi Nuzzo is an Associate Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. His research focuses on high-assurance design of cyber-physical systems, including methodologies for AI, autonomous systems, secure hardware, and electronic design automation. He holds faculty positions at Berkeley and previously at the University of Southern California, where he co-directed the Center for Autonomy and Artificial Intelligence. Education: Ph.D. (UC Berkeley, 2015), M.S./B.S. (University of Pisa and Sant'Anna School) Affiliations: Berkeley Artificial Intelligence Research Lab (BAIR), Berkeley Wireless Research Center (BWRC), Industrial Cyber-Physical Systems Center (iCyPhy) Research interests span compositional methods, system design, and certification frameworks. His work integrates formal verification, optimization, and machine learning to ensure safety and trustworthiness in complex systems. Recent projects include DeFacto (cyber-physical production systems) and secure logic locking for ICs. Awards include the NSF CAREER Award, DARPA Young Faculty Award, and multiple best paper awards. His teaching includes EECS C249B on cyber-physical system design principles.

• Computer Networking
• Telecommunications
• Internet of Things (IoT)
• +۷ مورد دیگر

Thomas Heide Clausen is a Professor at École Polytechnique (France) and leads its computer networking research group. He holds the Cisco Endowed Chair in 'Internet of Everything' and serves as Academic Director for the interdisciplinary Master's program 'IoT: Innovation and Management'. His work focuses on protocols and architectures for maintaining internet connectivity in dynamic environments, particularly through the development of the OLSR and LOADng routing protocols. Education: M.Sc. and PhD from Aalborg University (Denmark) Research: IoT networking, Smart Grid communication, wireless mesh networks, routing protocol design, network load balancing, and security in MANETs Recent Article Trends: Explored machine learning for load balancing (2022), zero-trust architectures (2023), and wireless optimization for IoT/SmarGrid (2016-2022) Scientific Awards: IEEE Senior Member (2016), IEEE Computer Society Distinguished Contributor (2021) Industrial Collaborations: Hitachi (Japan), Fujitsu (USA), Toyota (Japan), Cisco (USA/France), EDF (France), and others

• Systems Engineering
• Automation Engineering
• Industrial Informatics
• +۵ مورد دیگر

Antonio Javier Díaz Longueira is a researcher at the Department of Industrial Engineering, University of A Coruña. He belongs to the Cyber Science and Technology research group, focusing on systems and automation engineering. His academic activities are centered at the College of Engineering, specifically the Ferrol campus. Systems Engineering Automation Engineering Industrial Informatics Robotics Control Systems IoT Applications Power Electronics Digital Electronics His research interests span industrial automation systems, IoT applications in water management, and control engineering. He has contributed to fields like predictive maintenance through machine learning, smart irrigation systems, and FPGA-based industrial control implementations. Recent publications in journals such as Neurocomputing , Applied Intelligence , and Energies demonstrate his expertise in integrating machine learning with industrial automation, particularly in energy management and water systems optimization. Active in teaching since 2022, he has instructed courses in Advanced Automation , Digital Electronics , and Power Electronics across both undergraduate and master's programs in Industrial and Automatic Electronic Engineering. Current research funding includes projects from: NUCLEAR SAFETY COUNCIL (2024-2027) European Union (2024-2026) EMALCSA (2024) Universidad de Castilla-La Mancha (2023-2025) TELEFÓNICA DE ESPAÑA (2023) NAVANTIA (2023-2025) Instituto Tecnológico de Castilla y León (2023-2024) CONSELLERIA DE EDUCACIÓN (2023-2025) European Commission (2023-2025) Instituto Nacional de Ciberseguridad (2023-2025) He provided tutorials at the Ferrol School of Engineering (2024/2025) and directed multiple final degree projects since 2013.

Shantenu Jhaمشاهده پروفایل

استاد

• High-Performance Computing
• Distributed Computing
• Computational Science
• +۳ مورد دیگر

Shantenu Jha is a Professor of Computer Engineering at Rutgers University and Chair of the Department (Center) for Data Driven Discovery at Brookhaven National Laboratory. He leads the RADICAL Lab and the RADICAL-Cybertools project, focusing on middleware for large-scale scientific applications. His research bridges high-performance computing, data-driven science, and health informatics, collaborating with diverse fields like molecular sciences and high-energy physics. Education: Ph.D., Syracuse University (2004); M.Sc., IIT Delhi (1995). Research Interests : High-Performance and Distributed Computing Data-Intensive Science & Engineering Cyberinfrastructure for Science Health Computing (Personalized Medicine) Awards : NSF CAREER Award (2013) Chancellor’s Excellence in Research (2016) Rutgers Board of Trustees Fellowship (2014) Multiple best paper awards at SC/ISC conferences Grants & Funding : Supported by NSF, U.S. DOE, NIH, and UK EPSRC. Current projects include ExaWorks and quantum-HPC middleware development. Labs/Teams: PI of the RADICAL Lab, leading initiatives in FAIR principles, quantum computing integration, and autonomous laboratories.

• Internet Computing
• Network-Centric Systems
• Cloud Computing
• +۳ مورد دیگر

Professor Marios Dikaiakos is a full-time faculty member at the University of Cyprus (UCY), Department of Informatics. He holds a Ph.D. in Computer Science from Princeton University (1994) and has been at UCY since 1998. His roles include serving as Chair of the Department of Informatics (2010-2014) and Founding Director of the Entrepreneurship Center (2014-2021). He leads the Internet Computing Laboratory, focusing on Network-Centric Systems, Cloud Computing, and Social Media Analysis. His research has resulted in over 160 publications and 22 funded projects. Research interests include edge computing, cloud systems, IoT energy modeling, and polarization detection in news media. He has developed open-source tools like Fogify and BenchPilot for edge computing emulation. Active in academic governance, he has reviewed proposals for EU, national, and Chinese institutions. His work bridges theoretical computer science with practical applications in telemedicine, smart grids, and entrepreneurship ecosystems.

Dustin Richmondمشاهده پروفایل

استادیار

• Computer Architecture
• FPGA
• RISC-V
• +۴ مورد دیگر

Dustin Richmond is an Assistant Professor in the Department of Computer Science and Engineering at the Baskin School of Engineering, University of California, Santa Cruz. His work focuses on secure, usable hardware systems with applications in FPGA acceleration, RISC-V architectures, and side-channel analysis. Email: drichmond@ucsc Office: Engineering 2, Room 221 Research Interests: Secure hardware systems FPGA-based computing Manycore processors High-level synthesis Side-channel vulnerabilities Notable Article Trends: Recent publications emphasize cloud FPGA security, manycore design optimization, and hardware security. Earlier works focus on RISC-V acceleration, OpenCL compiler enhancements, and heterogeneous computing systems. GitHub Contributions: Maintains open-source projects like RISC-V-On-PYNQ and PYNQ-HLS, addressing FPGA programming challenges and RISC-V integration. Active in resolving community issues related to toolchain compatibility and hardware-software interfaces.

Dimitrios Soudrisمشاهده پروفایل

استاد

• Computer Engineering
• Embedded Systems
• Reconfigurable Architectures
• +۵ مورد دیگر

Dimitrios Soudris is a Professor at the School of Electrical and Computer Engineering, National Technical University of Athens (NTUA), leading the Microprocessor and Digital Systems Lab (MicroLab). Previously, he served as Lecturer, Assistant, and Associate Professor at Democritus University of Thrace from 1995 to 2008. Diploma in Electrical Engineering (1987), University of Patras PhD in Electrical Engineering (1992), University of Patras His research focuses on Embedded Systems , Reconfigurable Architectures (FPGAs) , Hardware Accelerators for data centers/space/cloud, Edge Computing , and Low Power VLSI Design . Recent publications highlight trends in: AI/ML acceleration for edge and space applications Secure FPGA architectures for 6G networks Energy-efficient heterogeneous memory systems Approximate computing techniques Transformer optimization for low-power contexts Scientific Awards: INTEL and IBM awards (project LPGD #25256) HiPEAC, DAC, and ISCA awards (2010–2024) XILINX Open Hardware Design Contest (2017, 2019, 2021) He has coordinated >70 R&D projects funded by the European Commission, ENIAC-JU, ESA, and industry partners. As Associate Editor of ACM TODAES and conference chair (PATMOS, VLSI-SOC), he contributes to academic leadership. His lab, MicroLab, specializes in hardware-software co-design for emerging computing paradigms.

Luca Fanucciمشاهده پروفایل

استاد

• Integrated Circuit Design
• Embedded Systems
• System-Level Design
• +۷ مورد دیگر

Professor Luca Fanucci is a Full Professor of Electronics at the Department of Information Engineering , University of Pisa. He serves as Rector's Delegate for Inclusion of Students with Disabilities and leads research in integrated circuits, embedded systems, and assistive technologies . Institutional roles include membership in the National University Conference of Delegates for Disability (CNUDD) and leadership in the PhD program in Information Engineering. Born: Montecatini Terme (1965) Education: Laurea in Electronic Engineering (1992), PhD in Information Engineering (1996), University of Pisa Professional Journey: ESA research (1992-1996), CNR researcher (1996-2004), University of Pisa faculty (2004-present) His research focuses on: System-level design of integrated circuits and embedded systems Hardware-software co-design for low power consumption Spacecraft and satellite communication systems Medical devices and telemonitoring platforms Assistive technologies for disabilities Recent publications highlight AI in space applications and telemedicine systems . Key projects include the Ingeniars spin-off for satellite communications and the AsTech National Laboratory for assistive technologies. Scientific Recognition : IEEE Fellow (2019) 40+ patents H-index 34 (5000+ citations) He coordinates international conferences (DATE, HiPEAC, Spacewire) and serves as Associate Editor for Technology and Disability and Microprocessors and Microsystems journals.

• Database Systems
• In-Database Machine Learning
• GPU Acceleration for Databases
• +۳ مورد دیگر

Maximilian Schüle serves as Assistant Professor in the Department of Data Engineering at the University of Bamberg's Faculty of Information Systems and Applied Computer Sciences since October 2022. Previously, he held research positions at Technical University of Munich (2017-2022). His research bridges database systems and machine learning through compiler-based approaches. His research focuses on in-database machine learning , GPU-accelerated query processing , and recursive SQL extensions . Key contributions include: Developing MLIR-based compilers for automatic differentiation in SQL (DuoLingo-AutoDiff) Creating GPU code generators for database kernels using NVRTC Designing higher-order lambda functions for expressive query languages Implementing end-to-end neural network training within database engines His recent publications (2023-2025) demonstrate consistent output in top venues including ICDE, VLDB workshops, and BTW conferences, with growing emphasis on hardware-aware optimization and compiler techniques for analytical workloads. He currently leads a DFG-funded project on elastic memory hierarchies for memory-intensive applications (2025-2028), supporting multiple PhD researchers. His supervision emphasizes open-source contributions to database systems like Umbra and practical implementation skills alongside theoretical foundations. As an active member of the database community, he serves as workshop chair for BTW 2025 and regularly reviews for ACM TODS, VLDB Journal, and Information Systems. His work on public transport analytics demonstrates real-world impact through collaborations with urban mobility initiatives in Bamberg.