Find Academic Supervisors

Magdalini EirinakiView profile

Professor

• Recommender Systems
• Machine Learning
• Data Mining
• +5 more

Magdalini Eirinaki is a Professor and Academic Program Coordinator for the MS in Artificial Intelligence at San José State University's Charles W. Davidson College of Engineering. With a career spanning two decades, her work bridges recommender systems , machine learning , and smart city applications . PhD in Computer Science (2006), Athens University of Economics and Business MSc in Advanced Computing (2000), Imperial College London BSc in Computer Science (1998), University of Piraeus Her research focuses on machine learning and recommender systems with extensions to generative AI , privacy-sensitive algorithms , and social network analysis . Recent publications explore federated learning , multi-resolution diffusion models , and autonomous network defense using reinforcement learning. Current projects include NSF-funded CollaborAIte (2024) EU Horizon/Marie Sklodowska-Curie's MUSIT (2024) IBM SkillsBuild Cloud Credits for Sustainability (2024) She has received multiple teaching and mentorship awards including: Newnan Brothers Award (2019) Applied Materials Award (2017) 5-time SJSU Distinguished Faculty Mentor Award Dr. Eirinaki advises students in AI , ML , and smart city projects, with recent graduates presenting at IEEE CAI (2025) and CSU Conference (2025).

• Signal Processing
• Electronics
• Biomedical Engineering
• +5 more

Marek Miśkowicz serves as a Professor at the Department of Metrology and Electronics within the Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering at AGH University of Science and Technology in Kraków, Poland. His primary institutional contact is miskow@agh.edu.pl, with physical location in building B-1, room 212. His research spans signal processing, biomedical engineering, and electronics, specializing in event-based sampling methodologies, time-to-digital conversion techniques, and reconstruction of bandlimited signals from nonuniform samples. Key contributions include QRS detection algorithms for ECG monitoring, POCS-based reconstruction frameworks, and event-driven control systems for industrial IoT applications. His work emphasizes resource efficiency in embedded systems and mobile health monitoring through approximate computing and adaptive sampling strategies. Recent publications (2022-2025) demonstrate consistent focus on signal reconstruction from irregular samples, with growing emphasis on spiking neural networks for event classification and industrial IoT optimization. Biomedical applications (particularly ECG analysis) and industrial control systems represent dominant application domains, while methodological innovations center on iterative reconstruction algorithms and temporal accuracy evaluation in noisy environments. No scientific awards were referenced in the source materials. No information regarding student advising or research grants was available in the provided documentation. The source texts contained no details about laboratory facilities, research teams, or collaborative groups associated with Professor Miśkowicz.

Daniel Hernández de la IglesiaView profile

Researcher

• Internet of Things (IoT)
• Multi-Agent Systems
• Artificial Intelligence
• +3 more

Daniel Hernández de la Iglesia is a researcher at the University of Salamanca , affiliated with the School of Informatics and the Department of Computer Science and Artificial Intelligence . He completed his PhD at the University of Salamanca in 2018 with the thesis titled "Embedding smart software agents in resource constrained internet of things devices." Research Focus: Specializes in integrating Multi-Agent Systems and IoT for sustainable applications, including electric vehicle optimization, battery reuse, and smart urban solutions. Academic Contributions: Published extensively on AI-driven energy management, smart mobility, and ethical implications of technology. Key Trends in Publications (2022-2025): His work spans IoT , Artificial Intelligence , and Sustainable Mobility , with a focus on electric vehicle battery health, strategic management in digitalization, and ethical considerations in technology. Collaboration: Supervised by Dr. Gabriel Villarrubia González and Dr. Juan Francisco de Paz Santana , his research bridges academic rigor with real-world applications in smart systems and renewable energy.

Sanjay JayaramView profile

Associate Professor

• Bio-inspired aerodynamics
• Fluid-structure-controls interaction
• Advanced control systems design
• +5 more

Sanjay Jayaram is an Associate Professor in the Department of Aerospace and Mechanical Engineering at Saint Louis University's School of Science and Engineering, where he has served since 2005 and became Aerospace Engineering Coordinator in 2013. His academic foundation includes a Ph.D. and M.S. in Mechanical Engineering from the University of Central Florida and a B.S. from R.V. College of Engineering in India. His educational background: Ph.D. Mechanical Engineering, University of Central Florida M.S. Mechanical Engineering, University of Central Florida B.S. Mechanical Engineering, R.V. College of Engineering, Bangalore, India Dr. Jayaram's research pioneers bio-inspired aerospace systems through five interconnected thrusts: (1) Bio-AIMS for fluid-structure-control morphing using shape memory alloys; (2) Tubercle-inspired vertical tail drag reduction via biomimetic flow control; (3) Intelligent Learning Control (ILC) for autonomous morphing wing systems; (4) Multirotor drone turbulence mitigation and noise reduction for Urban Air Mobility; and (5) Distributed guidance/navigation for multi-UAV teams. His work integrates wind tunnel experimentation , computational fluid dynamics , and adaptive control theory to solve real-world aerospace challenges. Analysis of his recent publications reveals dual emphasis on cutting-edge aerospace research (morphing structures, drone stability, spacecraft systems) and transformative engineering education , with 60% of his 2013-2025 publications focusing on student-led projects in rocketry, satellite design, and curriculum innovation. His publications span spacecraft engineering, control systems, and sustainable propulsion technologies. As an educator, Dr. Jayaram mentors multiple student spacecraft teams including SLUCube, BillikenSat, and high-powered rocket projects for Spaceport America Cup. He serves as Treasurer for the ASEE Aerospace Division and Committee Chair for the AIAA/ASEE Leland Atwood Awards Committee, demonstrating leadership in aerospace education communities. His laboratory infrastructure supports wind tunnel testing for bio-inspired morphing structures, drone aerodynamics characterization, and full spacecraft integration/test facilities for student satellite programs, enabling hands-on research from concept to flight validation.

Giorgio ScorzelliView profile

Researcher

• Extreme Data Management
• Scientific Visualization
• Computational Topology
• +6 more

Giorgio Scorzelli is a researcher at the University of Utah, serving as Director of Software Development for the Center for Extreme Data Management, Analysis, and Visualization (CEDMAV) and the National Science Data Fabric (NSDF) . He specializes in extreme data management, scientific visualization, and computational topology, with a focus on scalable solutions for climate science, materials science, and neuroscience datasets. His work emphasizes democratizing data access through platforms like OpenVisus , enabling efficient analysis of petascale and exascale data. Key contributions include orchestrating cyberinfrastructure, optimizing parallel I/O, and developing real-time visualization systems for heterogeneous resources. Notable scientific contributions include the NSF Grant #2127548 for NSDF development . His projects integrate cloud computing, geo-distributed storage, and FAIR digital objects to lower barriers to data democratization. Giorgio's research spans multi-resolution algorithms , computational topology , and 3D geometric modeling , with applications in infrastructure security, archaeological reconstruction, and biomedical imaging. His work bridges abstract mathematical frameworks (e.g., Boolean algebras, chain complexes) with practical software solutions.

• Radar Systems
• Radar Waveform Design
• Signal Processing
• +3 more

Eunhee Kim is a Professor in the Department of Defense Systems Engineering at Sejong University. She holds a Ph.D. in Mechanical Engineering from KAIST and has extensive industry experience in radar systems development. 1995 B.S. in Precision Engineering, KAIST 1997 M.S. in Mechanical Engineering, KAIST 2004 Ph.D. in Mechanical Engineering, KAIST Her research focuses on Radar Systems , Waveform Design , and MIMO Radar signal processing. She has contributed to projects involving space object tracking, airborne radar systems, and automotive radar optimization. Recent publications highlight her work on Machine Learning integration for Energy Forecasting and advanced MIMO Array Designs for improved radar resolution. She leads the Defense Radar Technology Laboratory, specializing in Phased Array Radar and Broadband Noise Radar systems. Patents include vehicle camouflage netting and RF-based positioning systems. Collaborations with agencies like Agency for Defense Development and companies such as LIG Nex1 and Hanwha Systems are prominent in her career.

Gérard BerryView profile

Professor

• Computer Science
• Lambda Calculus
• Functional Programming
• +5 more

Gérard Berry (born December 25, 1948) is a distinguished French computer scientist currently serving as Professor at the Collège de France, holding the permanent chair Algorithmes, machines et langages (Algorithms, Machines, and Languages) since 2012. He previously held the Informatique et sciences numériques chair (2009-2010) and the Technological Innovation Liliane Bettencourt chair (2007-2008) at the same institution. Before joining Collège de France full-time, he served as Director of Research at INRIA Sophia Antipolis (2009-2012) and at École des Mines de Paris (1977-2001). His research spans over 30 years in three main fields: lambda calculus and functional programming, parallel and real-time programming languages, and design automation for synchronous digital circuits. He is particularly renowned for developing the Esterel programming language. His work bridges theoretical computer science with practical industrial applications. Berry's research has evolved to include current work in Hop and HipHop for Web programming, formal verification of compilers, and languages for computer music. His publications demonstrate consistent contributions to programming language theory, formal methods, and their applications in hardware and software systems. Gold Medal of CNRS (2014) Chevalier de l'Ordre de la Légion d'Honneur (2012) Member of French Academy of Sciences (2002) Member of Academia Europaea (1993) Monpetit Prize of Académie des sciences (1990) Berry has advised 17 PhD students and reviewed numerous theses. His industrial experience includes serving as Chief Scientist Officer of Esterel Technologies (2000-2009), where he directed the implementation of the Esterel v7 compiler. He has also held significant leadership roles including President of the Scientific Council of IRCAM and membership on the Scientific Council of the National Education. His teaching at Collège de France has covered topics ranging from the foundations of computation to the societal impact of digital technology, with courses including The Informatics of Time and Events and Proving Programs: Why? When? How? His laboratory work has focused on developing practical applications of theoretical computer science concepts.

• Human-robot physical interaction
• Elderly people
• Smart-walker
• +3 more

Ludovic Saint-Bauzel is a lecturer at Sorbonne University and head of the IRIS team at the Institute of Intelligent Systems and Robotics (ISIR). His work focuses on improving physical human-robot interaction for individuals with autonomy loss through disability or aging. Specializes in computational models of disabilities Develops user intent detection through sensor fusion Active in IFRH and Fedrha federations IEEE and True Life Lab member Research interests His research centers on Human-robot physical interaction with applications in Elderly care , Smart-walker development, and Walking exoskeleton systems. Key methodologies include: Sensor fusion (depth cameras, force sensors, IMUs) Adaptive robot control systems Pathological movement modeling Motor intent prediction algorithms Article trends show consistent focus on haptic communication (6/15), assistive robotics (9/15), and sensorimotor interaction (11/15) across 2013-2022 publications. Laboratory involvement : Leads the IRIS team at ISIR, part of Fedrha (Federation for Research on Disability and Autonomy) with 50+ research teams.

Zhiyuan LiView profile

Professor

• Program Analysis
• Compiler Design
• Parallel Computing
• +6 more

Zhiyuan Li is a Professor in the Department of Computer Sciences at Purdue University's College of Engineering. His primary research and teaching focus on program analysis, transformation, and run-time management for high-performance computing and multicore systems, as well as reliable software for networked embedded systems. Professor Li teaches graduate-level courses including CS502: Compiling and Programming Systems and CS591RS1: Research Seminar for First-year Graduate Students. Office: LWSN 3154H Contact: li@cs.purdue.edu Phone: +1 765-494-7822 Professor Li's research spans multiple areas within computer science, with particular emphasis on compiler design, program analysis, and parallel computing. His work addresses fundamental challenges in enabling efficient execution of applications on modern parallel architectures, including multicore processors and large-scale distributed systems. He has made significant contributions to techniques for data dependence analysis, loop parallelization, array privatization, and memory optimization in compilers. His research also extends to reliable software development for embedded and sensor network systems, where resource constraints and reliability requirements present unique challenges. Professor Li's publication record demonstrates consistent contributions to top-tier conferences and journals in computer science, particularly in the areas of parallel computing, compiler optimization, and high-performance numerical methods. His work shows a progression from foundational compiler techniques to applications in scientific computing domains such as computational fluid dynamics for jet engine noise simulation. This interdisciplinary approach connects low-level program analysis with real-world engineering applications requiring petascale computing resources. Principal Investigator for NSF/PetaApps project on jet engine noise simulation Principal Investigator for Intel-sponsored research on data dependence profiling Extensive service on program committees for major conferences including ICS, PPoPP, and LCTES Professor Li has been actively involved in mentoring graduate students through research projects and course instruction. His jet engine noise simulation project specifically mentions training three Ph.D. graduate students and involving undergraduate research assistants. As coordinator for the first-year graduate research seminar, he plays a significant role in guiding new students through the transition to graduate research work in computer science. His laboratory work focuses on developing compiler techniques and runtime systems for parallel and high-performance computing. The research infrastructure includes implementations in GCC for fast data dependence profiling and support for SIMD/SSE instructions, demonstrating practical applications of theoretical compiler techniques.

• Electronic Design Automation
• VLSI Design
• Computer Systems
• +7 more

Shinji Kimura is a Professor at Waseda University's Faculty of Science and Engineering, specializing in VLSI design and electronic systems. He holds a Doctor of Engineering from Kyoto University and has been with Waseda since 2002, previously serving as Associate Professor at Nara Institute of Science and Technology (1993–2002) and Assistant Professor at Kobe University (1985–1993). Kimura's research spans low-power circuit design , approximate computing , FPGA optimization , video coding (HEVC) , and hardware acceleration for AI . His work focuses on energy-efficient architectures for applications like neural networks, computer vision, and ultra-high-definition video processing. Recent publications emphasize hardware-efficient multipliers, neural network compression, and 3D-stacked memory systems. Awards include the LSI IP Design Award (2000, 1999) and the Information Processing Society of Japan Encouragement Award (1993). He leads projects on HEVC encoding/decoding, non-volatile memory optimization, and 3D integrated circuits, with VLSI implementations achieving real-time 8K video processing.

Vinh NguyenView profile

Assistant Professor

• Advanced Manufacturing
• Industry 4.0
• Human-Robot-Machine Interaction
• +5 more

Vinh Nguyen is an Assistant Professor in the Department of Mechanical and Aerospace Engineering at Michigan Technological University, where he directs the Michigan Tech Center for AI and coordinates the NIST-PREP program. His research focuses on advanced manufacturing through Industry 4.0, human-robot-machine interaction, and physics-based/data-driven modeling. He has developed solutions for machining, additive manufacturing, metal forming, and robotic assembly to promote smart and sustainable manufacturing. Prior to joining Michigan Tech in 2022, he was a National Research Council Postdoctoral Fellow at NIST (2020–2022). Dr. Nguyen earned his PhD (2020), MS in Mechanical Engineering (2017), and MS in Electrical & Computer Engineering (2017) from Georgia Institute of Technology. He received dual bachelor’s degrees in Electrical and Mechanical Engineering from Rensselaer Polytechnic Institute (2014). His research portfolio spans Advanced Manufacturing Industry 4.0 and 5.0 Human-Robot Interaction Physics-Based/Data-Driven Modeling Industrial Automation based on his lab’s interdisciplinary focus on human-centric, resilient solutions. His recent publications address trends in Machine Learning for Manufacturing Autonomous Vehicle Sensors Hybrid Additive/Subtractive Manufacturing Augmented/Mixed Reality Interfaces Industrial Robot Diagnostics Material-Specific Machining with keywords spanning Robotics, Data Science, and Industrial Engineering.

Changhee JungView profile

Associate Professor

• Compilers
• Computer Architecture
• Performance Optimization
• +8 more

Changhee Jung is the Samuel D. Conte Associate Professor in the Department of Computer Science at Purdue University. His research focuses on compilers and computer architecture with an emphasis on performance, reliability, and security. His educational background includes a Ph.D. from Georgia Tech (2013) under the supervision of Prof. Santosh Pande. Professor Jung's research spans compilers and computer architecture with a focus on performance, reliability, and security. He has developed program analysis and microarchitecture optimization techniques for soft error resilience, concurrency bug detection, and system security such as memory safety and Linux kernel permission check. Currently, he is working on energy-efficient intermittent computation and nonvolatile memory crash consistency. He often leverages compiler-architecture codesign and repurposes existing hardware features to develop cost-effective solutions for complex computing challenges. His recent publications demonstrate a clear trajectory toward intermittent computing systems, nonvolatile memory architectures, and security mechanisms for energy-constrained environments. His work shows innovative approaches to power failure recovery, capacitor vulnerability exploitation, and EMI attack defense in intermittent systems, with significant contributions to whole-system persistence, cache design, and prefetching techniques for low-power computing. His notable scientific achievements include: NSF CAREER Award (2018) Inducted into MICRO Hall of Fame (2021) Best Paper Honorable Mention in ISCA 2025 Memorable Paper Award Finalist in NVMW 2024 Dissertation Advisor of 2023 ACM SIGBED Paul Caspi Memorial Dissertation Award winner Jongouk Choi 2017 AMD Faculty Research Award Professor Jung has successfully advised numerous graduate students, many of whom have secured prominent positions at leading technology companies including Google, Intel, and Samsung Electronics. His lab, the CompArch (Compiler and Architecture) research group, was formed in 2013 at Virginia Tech and continues at Purdue, focusing on compiler-architecture cooperation to address cross-cutting concerns involving performance, reliability, and security. The lab has received significant research funding, including an NSF CAREER Award in 2018 and an AMD Faculty Research Award in 2017.

James ShacklefordView profile

Associate Professor

• Medical Image Processing
• High Performance Computing
• Embedded Systems
• +2 more

James Shackleford serves as Associate Professor and Interim Associate Dean for Enrollment Management and Graduate Education in the Department of Electrical and Computer Engineering at Drexel University. His research bridges medical image processing, high performance computing, and emerging neuromorphic architectures with significant contributions to radiation therapy applications. Education: PhD in Electrical Engineering, Drexel University, 2011 MS in Electrical Engineering, Drexel University BS in Electrical Engineering, Drexel University Research Focus: Professor Shackleford's work centers on GPU-accelerated medical image registration (forming the core of the open-source Plastimatch software), real-time tumor motion management for radiation therapy, and digital spiking neuromorphic systems . His research integrates computer vision, machine learning, and embedded systems to solve clinical imaging challenges. Publication Trends: Recent work (2020-2024) reveals dual research trajectories: (1) advancing deformable image registration through CycleGAN-based domain adaptation for CT auto-segmentation in radiation oncology, and (2) pioneering neuromorphic computing with configurable hardware architectures, dataflow-based compilers, and resource-aware neural network mapping. These streams converge on high-performance solutions for medical imaging and efficient neural processing.

Wilfried KubingerView profile

Professor

• Embedded Systems
• Machine Vision
• Autonomous Robotics
• +3 more

Prof. Wilfried Kubinger serves as the Head of the Department of Electronic Engineering at the University of Applied Sciences Technikum Wien, Austria. He holds a PhD in Technical Sciences from the Technical University of Vienna (1999) and has extensive experience in research and industry. His academic roles include leading the 'Automation & Sensor Technology' competence field and managing the 'Automation & Robotics' research area. **Research Focus:** His work centers on embedded systems, machine vision, autonomous robotics, and real-time control systems. Notable projects include obstacle detection for autonomous vehicles, stereo vision algorithms, and agricultural robotics applications. He actively contributes to IEEE and OVE engineering associations. **Professional Journey:** Prior to academia, he worked at Siemens Austria (2000–2003) as a software developer and project manager, and at AIT Austrian Institute of Technology (2003–2010) managing research projects in autonomous systems. He participated in DARPA Grand Challenge/Urban Challenge as a principal scientist for vision-based obstacle detection. **Publications:** His research spans embedded vision systems, FPGA implementations, and autonomous vehicle technologies. Recent work emphasizes agricultural robotics and Industry 4.0 applications. He also leads R&D project acquisition and implementation for Technikum Wien.

• Human-Computer Interaction
• Augmented Reality
• Computer Vision
• +4 more

Karan Ahuja is the Lisa Wissner-Slivka & Benjamin Slivka Assistant Professor in Computer Science at Northwestern University, where he directs the Sensing, Perception, Interactive Computing & Experiences (SPICE) Lab. He will begin this position in September 2024, transitioning from his role as a Visiting Research Scientist at Google where he led Augmented Reality initiatives. Education: Ph.D. in Human-Computer Interaction from Carnegie Mellon University (2023) B.Tech. in Computer Science (2017) His research creates cutting-edge technologies that sense, track, and understand humans to augment daily interactions. The SPICE Lab tackles high-impact problems in: Mobile health sensing : Developing unobtrusive monitoring systems Extended reality : Creating immersive AR/VR experiences Embodied perception : Advancing full-body motion capture Natural interfaces : Designing intuitive interaction paradigms The lab leverages expertise in novel sensors, embedded systems, computer vision, and on-device machine learning to deploy real-world solutions. Ahuja's publications demonstrate strong focus on human sensing technologies, particularly in motion capture (full-body and hand pose estimation), eye/gaze tracking, and multimodal interaction systems. Recent work shows increasing emphasis on practical applications of extended reality and wearable computing. Awards and honors: Forbes 30 Under 30 - Asia, Healthcare & Science (2024) MIT 35 Innovators Under 35 Asia Pacific ACM SIGCHI Special Recognition ACM SIGCHI Outstanding Dissertation Award He actively recruits post-docs, PhD students, and researchers for the SPICE Lab. His industry experience includes research positions at Apple, Microsoft Research, Meta Reality Labs, and IBM Research, with technologies shipped to over 100 million users.