جست‌وجوی استاد و پژوهشگر

• Wireless Communication Systems and Technologies
• Signal Processing
• Full Duplex Communications
• +۳ مورد دیگر

Prof. Xiaojing Huang is a Professor of Information and Communications Technology at the University of Technology Sydney (UTS), serving as Head of Discipline for SEDE Communications and Electronics within the School of Electrical and Data Engineering. He leads the Mobile Sensing and Communications program at the Global Big Data Technologies Centre. With over 30 years of experience, he has authored over 300 publications and 31 patents, focusing on wireless communications, signal processing, and antenna technologies. Education: PhD (Electrical Engineering, Shanghai Jiao Tong University, 1989). Previous roles include Principal Research Scientist at CSIRO (2009-2014), Associate Professor at University of Wollongong (2004-2009), and key industry roles at Motorola and Shanghai Yang Tian Science and Technology Corporation. Research interests include full-duplex wireless systems, millimeter-wave and terahertz communications, massive antenna arrays, and mixed-signal processing platforms. His work on the CSIRO Ngara backhaul system earned multiple awards, including the 2012 CSIRO Chairman's Medal and Australian Engineering Innovation Award. Recent grants include $4.2M (AUD) for projects like 'Radio Frequency Camera for Radar Imaging' (ARC DP220101158) and 'Terabit mm-Wave Backbones for Integrated Space Networks' (ARC DP200101532). He has supervised numerous students in high-speed communication systems and full-duplex technologies. Awards include: 2013 CSIRO Leadership Achievement Award, 2012 Australian Engineering Innovation Award, and IEEE Sumner Award (nominee). Active in IEEE standards (802.11/802.15) and collaborations with institutions like Tsinghua University.

• Data Mining
• Artificial Intelligence
• Text Mining
• +۳ مورد دیگر

Jiawei Han is the Michael Aiken Chair Professor at the University of Illinois at Urbana-Champaign (UIUC), affiliated with the Siebel School of Computing and Data Science and the Department of Computer Science. He holds a Ph.D. in Computer Science from the University of Wisconsin-Madison (1985). His research focuses on Data Mining, Text Mining, and Intelligent Systems, with notable contributions to knowledge hypercubes, molecular discovery, and geospatial understanding. Key affiliations include leading the Data Mining Research Group (DMG) and the Data and Information Systems Research Laboratory (DAIS). He is also involved in major initiatives like the NSF AI Institute for Molecular Discovery (Molecule Maker Lab) and the DARPA INCAS project. Recent work emphasizes large language models (LLMs), scientific knowledge integration, and graph-based reasoning. Notable achievements include an ICLR 2024 Outstanding Paper Honorable Mention (co-authored with Suyu Ge) and mentoring Yu Meng, recipient of the ACM SIGKDD 2024 Dissertation Award. Teaching includes courses such as CS 412 (Data Mining), CS 512 (Data Mining Principles), and specialized topics like Text Mining with Large Language Models (Fall 2024). He has authored/co-authored numerous books, including editions of *Data Mining: Concepts and Techniques* and works on taxonomy discovery and text mining. His research spans interdisciplinary areas such as bioinformatics, geospatial analytics, and molecular innovation, with a focus on practical applications and foundational theory.

Zhong-Ping Jiangمشاهده پروفایل

استاد

• Nonlinear Control
• Adaptive Dynamic Programming
• Learning-Based Control
• +۵ مورد دیگر

Zhong-Ping Jiang is an Institute Professor at New York University Tandon School of Engineering, affiliated with the Department of Electrical and Computer Engineering, and holds cross appointments in Civil and Urban Engineering. He leads the Control and Network (CAN) Lab and contributes to research centers like the Center for Advanced Technology in Telecommunications (CATT) and C2SMARTER. His work focuses on nonlinear control, adaptive dynamic programming, and learning-based control with applications to autonomous systems, urban mobility, and computational neuroscience. He serves as Deputy Editor-in-Chief of the IEEE/CAA Journal of Automatica Sinica and has held editorial roles in multiple journals. Research interests include model-based and learning-based control for network systems, with emphasis on robotics, connected vehicles, and urban infrastructure. His contributions to nonlinear small-gain theory and robust reinforcement learning have advanced control methodologies for complex systems. Recent publications highlight trends in resilient control under cyberattacks, data-driven optimal control, and reinforcement learning applications in traffic signal optimization and autonomous driving. His work bridges theoretical advancements with real-world challenges in transportation and cyber-physical systems. Awards: Elected to the European Academy of Sciences and Arts (2024). Grants/Projects: Includes RAPID-funded studies on high-resolution agent-based modeling of epidemic spread and NSF-supported research on urban traffic networks. Labs/Teams: CAN Lab (focusing on control theory and networked systems), CATT (telecommunications innovations), and C2SMARTER (urban mobility solutions).

Dino Pedreschiمشاهده پروفایل

استاد

• Big Data Analytics
• Social Network Analysis and Mining
• Analysis of Human Mobility
• +۱۷ مورد دیگر

Dino Pedreschi is a Full Professor of Computer Science at the University of Pisa, affiliated with the Department of Computer Science (DI-UNIPI). He co-leads the Pisa KDD Lab, a joint research initiative between the University of Pisa and the Italian National Research Council’s Institute of Information Science and Technology, one of the earliest labs focused on data mining and knowledge discovery. His research spans Big Data Analytics , Social Network Analysis , Human Mobility Analysis , Privacy-by-Design , Explainable AI (XAI) , and ethical data mining . He is a pioneer in privacy-preserving data mining and has contributed significantly to understanding societal impacts of AI and big data. Recent publications highlight trends in explainable AI , fairness-aware data mining , urban mobility modeling , and socio-economic nowcasting , reflecting a strong interdisciplinary focus combining computer science, social science, and policy. Notable scientific awards include: Google Research Award on Privacy (2009) University of Pisa Ordine del Cherubino (2017) Pedreschi has played leadership roles in major conferences such as ECML/PKDD (Co-Chair 2004), ICDM (Vice-Chair 2005), and ICDE (Vice-Chair 2014). He founded the Business Informatics MSc program at the University of Pisa to train interdisciplinary data scientists. He has been a visiting scientist at the University of Texas at Austin, CWI Amsterdam, UCLA, and the Barabási Lab at Northeastern University. He actively contributes to European research initiatives including SoBigData++, FAIR, and TAILOR, and has advised on AI policy, including testimony before the Italian Parliament on AI and labor markets. He is a key member of the Pisa KDD Lab, a leading research group in data science and AI ethics, fostering collaboration between academia and public institutions.

• Algorithms
• Game Theory
• Automata Theory
• +۷ مورد دیگر

Marcin Jurdzinski is an Associate Professor (Reader) in the Department of Computer Science at the University of Warwick , UK. He has been a faculty member since 2004 and is a core member of the Foundations of Computer Science and Discrete Mathematics and its Applications research groups. University: University of Warwick School: Faculty of Science Department: Department of Computer Science Position: Associate Professor (Reader) Email: Marcin.Jurdzinski@warwick.ac.uk Office: CS2.19 His research lies at the intersection of algorithms, game theory, automata, and logic , with a strong emphasis on formal verification , model checking , and theoretical computer science . He is best known for his foundational work on parity games , including the development of small progress measures and discrete strategy improvement algorithms. The recent publications reveal a consistent focus on computational complexity in games and verification. Key themes include stochastic games, timed automata, bisimilarity, and quantitative analysis . His work often bridges theoretical insights with practical verification challenges, especially in real-time and probabilistic systems. He has supervised several PhD students and hosted postdoctoral researchers such as Laure Daviaud and Alexander Kozachinskiy. He has led EPSRC-funded projects including Solving Parity Games in Theory and Practice and Counter Automata: Verification and Synthesis . PhD Students: Aditya Prakash, Thejaswini K. S., Michail Fasoulakis, John Fearnley, Michal Rutkowski, Ashutosh Trivedi Postdocs: Laure Daviaud, Alexander Kozachinskiy He is actively involved in the academic community, serving on the steering committee of the Highlights of Logic, Games and Automata conference and on program committees for major venues such as CONCUR, ICALP, and LICS. He has also organized workshops including FORMATS and ICALP co-located events.

Samuel Chengمشاهده پروفایل

دانشیار

• Information Theory
• Signal and Image Processing
• Remote Sensing
• +۴ مورد دیگر

Dr. Samuel Cheng is an Associate Professor at the Gallogly College of Engineering , University of Oklahoma , specializing in Electrical and Computer Engineering . He holds a Ph.D. in Electrical Engineering from Texas A&M University (2004), preceded by M.S. and M.Phil. degrees from the University of Hawaii and Hong Kong University of Science and Technology. Education: B.S. (University of Hong Kong, 1995), M.Phil. (HKUST, 1997), M.S. (University of Hawaii, 2000), Ph.D. (Texas A&M, 2004) Professional Experience: Senior Research Engineer at Advanced Digital Imaging Research (2004-2005), prior internships at Microsoft Asia and Panasonic Technologies His research focuses on Information Theory , Signal and Image Processing , and Pattern Recognition , with applications in remote sensing, urbanization analysis, and disaster monitoring. His publications span topics including urban impervious surface mapping , nighttime light analysis , and machine learning for environmental data . His work often integrates multi-source datasets (e.g., Landsat, LiDAR, social media) for spatiotemporal modeling. Technical Expertise: Spectral unmixing, machine learning, thermal remote sensing, GIS integration Key Applications: Power outage detection, vegetation-crime correlation, PM2.5 estimation, smart meter data fusion Dr. Cheng holds three US patents in digital watermarking and is affiliated with IEEE, Sigma Xi, and AAAS. His recent articles demonstrate a trend toward leveraging AI for remote sensing challenges and analyzing urbanization impacts on ecosystems.

Brandon Graingerمشاهده پروفایل

دانشیار

• Electric Power Conversion
• Medium- and High-Voltage Power Electronics
• HVDC & STATCOM Systems
• +۶ مورد دیگر

Dr Brandon M Grainger is an Eaton Faculty Fellow and Associate Professor of Electrical and Computer Engineering at the University of Pittsburgh’s Swanson School of Engineering, where he also directs the Electric Power Technologies Laboratory, serves as Associate Director of the Energy GRID Institute, and co-directs Pitt AMPED. A key architect of Pitt’s electric power program since 2008, he focuses on advanced power conversion, high-voltage electronics, wide-band-gap semiconductors, and aerospace power systems. Education PhD, Electrical Engineering (Power Conversion), University of Pittsburgh, 2014 MS, Electrical Engineering, University of Pittsburgh, 2011 BS, Mechanical Engineering & Minor in Electrical Engineering, University of Pittsburgh, 2007 Executive Education Certificate, Tepper School of Business, Carnegie Mellon University, 2019 Research Focus Dr Grainger’s work lies at the intersection of power electronics, high-voltage engineering, and sustainable energy systems. He specializes in medium- and high-voltage power electronics (HVDC, STATCOM), resonant converters, and ultra-high-power-density designs leveraging SiC and GaN semiconductors. His investigations extend to electric-vehicle traction drives, solid-state transformers, optimized magnetics for aerospace applications, and resilient microgrids. He and his students routinely collaborate with NASA JPL, Johns Hopkins APL, Honeywell Aerospace, and the Naval Research Laboratory, leveraging Pitt’s NSF SHREC center to push the boundaries of power conversion in space and defense systems. Selected Research Themes High-frequency, high-density DC/DC converters for satellite power systems Radiation-tolerant GaN converters and point-of-load power stages Medium-voltage testbed development (13.8 kV, 5 MVA) Rare-earth-free permanent-magnet machine topologies Model-predictive control of multi-phase drives and microgrids Honors & Awards 2024 IEEE Region 2 Outstanding Educator Award 2024 Pitt STRIVE Outstanding DEI Service Award 2019 ESWP Engineer of the Year 2019 ASEE 2nd Place Best Paper Award 2019 SRI Undergraduate Best Mentor Award Richard K. Mellon Endowed Graduate Fellowship National Academies of Science & Engineering Ambassador Senior Member, IEEE Grants & Industry Partnerships Dr Grainger’s research has been continuously funded by federal agencies and industry partners including NASA JPL, Johns Hopkins APL, Honeywell Aerospace, the Naval Research Laboratory, Eaton, and the National Science Foundation through the SHREC Center. These awards support graduate students and post-docs working on next-generation power systems for aerospace, naval, and terrestrial applications. Laboratories & Teams Director, Electric Power Technologies Laboratory (EPTL) Associate Director, Energy GRID Institute Co-Director, Pitt AMPED (Advanced Multimodal Power and Energy Development) Faculty Affiliate, NSF SHREC Center

Mikkel N. Schmidtمشاهده پروفایل

دانشیار

• Statistical Modeling
• Machine Learning
• Bayesian Methods
• +۳ مورد دیگر

Mikkel N. Schmidt is an Associate Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU). His research focuses on statistical modeling, Bayesian methods, and their applications in science and industry. He has held visiting roles at Columbia University (2007) and Cambridge University (2008-2009). His work integrates probabilistic modeling with computational inference to address complex problems in diverse fields such as molecular discovery, optical communication, and brain connectivity analysis. Education highlights include visiting scholar and postdoctoral experiences at top-tier institutions. Research interests span statistical methodology development, machine learning applications, and interdisciplinary problem-solving. Current projects involve Bayesian neural networks for molecular discovery and federated learning optimization. Advising efforts include supervising multiple PhD students in areas like molecular discovery and denoising diffusion models. Notable collaborations involve work on materials science, quantum communication, and medical signal processing. His contributions bridge theoretical advancements with practical industrial applications, emphasizing interdisciplinary innovation.

John Bagterp Jørgensenمشاهده پروفایل

استاد

• Model Predictive Control
• Numerical Optimization
• Process Control
• +۵ مورد دیگر

John Bagterp Jørgensen is a Professor at the Department of Applied Mathematics and Computer Science, Technical University of Denmark (DTU). His research focuses on computational methods for Model Predictive Control (MPC), numerical optimization, and dynamic optimization, with applications in industrial processes, biomedical systems, and sustainable energy. He holds leadership roles in 2-control ApS, a company developing advanced control solutions for industries such as cement production and oil recovery. Education: PhD and M.Sc. in Technical Sciences from DTU (1997–2005 and 1991–1997). Professional experience includes roles as an Assistant Professor at DTU and CTO/CEO at 2-control ApS. Research interests span MPC algorithms, numerical methods for differential equations, and system identification. His work bridges academia and industry, addressing challenges in energy efficiency, vaccine manufacturing, diabetes treatment, and cement production processes. His recent articles emphasize industrial applications of control systems, including cement rotary kiln dynamics, vaccine production optimization, and dual-hormone artificial pancreas development. He has received the Nordic Energy Research Award (1994) and contributed to UN Sustainable Development Goals related to affordable energy and industrial innovation. Advising and grants: Supervises multiple PhD projects on topics like electrification of industrial processes and sustainable SCP production. Collaborates with global institutions on energy and biomedical research. Labs/teams: Leads teams in DTU’s Scientific Computing and Center for Energy Resources Engineering, with active partnerships in industry and academia.

Hamid Krimمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Signal Processing
• +۵ مورد دیگر

Hamid Krim is a Professor in the Department of Electrical and Computer Engineering at North Carolina State University. He leads the Vision, Information and Statistical Signal Theories and Applications (VISSTA) group, focusing on statistical signal/image analysis, data science, and machine learning. His prior roles include Research Scientist at MIT’s Laboratory for Information and Decision Systems and Member of Technical Staff at AT&T Bell Labs. He holds a Ph.D. in Electrical Engineering from Northeastern University, and degrees from the University of Washington and University of Southern California. Education: Ph.D., Electrical Engineering, Northeastern University (MA), 1990s Master's, Electrical Engineering, University of Washington Bachelor's, Electrical Engineering, University of Southern California and University of Washington Research Interests: Machine Learning, AI, Signal Processing, Communications, and Control Systems . His work bridges formal mathematical frameworks with applied problems, emphasizing generative AI, adversarial robustness, and subspace-driven data analysis. Recent innovations include Volterra neural networks and expansive synthesis techniques for data generation. Awards & Recognition: 2000 NSF CAREER Award 2008 IEEE Fellow 2019 IEEE SPS Sustained Impact Paper Award Multiple extended research invitations at top institutions globally Grants & Advising: Leads the VISSTA Lab, collaborating on projects like medical algorithm development (e.g., lung wheeze analysis) and hurricane activity prediction. His work spans interdisciplinary applications in healthcare, robotics, and defense systems. Labs & Teams: Director of the VISSTA Lab, fostering research in signal theory and machine intelligence. Collaborates with academia and industry on cutting-edge AI and sensor fusion technologies.

Ghassan AlRegibمشاهده پروفایل

استاد

• Machine Learning
• Image and Video Processing
• Computer Vision
• +۷ مورد دیگر

Ghassan AlRegib is the John and Marilu McCarty Chair Professor in the School of Electrical and Computer Engineering at Georgia Institute of Technology. He directs the Omni Lab for Intelligent Visual Engineering and Science (OLIVES), the Center for Energy and Geo Processing (CeGP), and previously led Georgia Tech's MENA initiatives (2015-2018). His research spans machine learning, image processing, and seismic interpretation with real-world applications in autonomous vehicles, medical imaging, and subsurface analysis. His research focuses on trustworthy AI systems through three pillars: enhancing interpretability, improving robustness/generalizability, and tackling domain-specific challenges. Key interests include human-in-the-loop frameworks, uncertainty quantification, explainable AI, and physics-driven learning. The OLIVES lab pioneered modern machine learning applications in seismic interpretation and developed open-source datasets for geological fault analysis. Dr. AlRegib's scientific contributions include over 270 publications, multiple U.S. patents, and leadership roles as Technical Program co-Chair for ICIP 2020/2024. His work demonstrates significant impact through awards like the IEEE Fellow designation (2022) and multiple best paper awards at premier conferences. IEEE Fellow (2022) 2023 EURASIP Best Paper Award 2019 ICIP Best Paper Award 2017 Denning Faculty Award for Global Engagement CSIP Research & Service Awards (2003) He has advised numerous PhD students including Dr. Ashraf Alattar (now Auburn professor) and Dr. Zhiling Long (Kennesaw State faculty). His lab structure emphasizes collaborative teams comprising postdocs, senior/junior PhD students, and undergraduates working on high-impact problems from autonomous systems to medical diagnostics. Current research thrusts include trustworthy neural networks, human-in-the-loop frameworks, and deployment of machine learning in seismic interpretation and ophthalmology.

Francesca Grisoniمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Drug Discovery
• +۸ مورد دیگر

Francesca Grisoni serves as an Assistant Professor in the Department of Biomedical Engineering at Eindhoven University of Technology (TU/e), where she currently leads the Molecular Machine Learning team. She additionally holds appointments as an ICMS Core member and Associate Professor at EAISI (Eindhoven Artificial Intelligence Systems Institute), reflecting her cross-disciplinary role at the intersection of computational science and biomedical applications. Academic Background : Grisoni completed her Environmental Sciences degree and earned a Ph.D. in 2016 from the University of Milano-Bicocca, where her dissertation focused on interpretable machine learning for molecular property prediction. During doctoral studies, she conducted research at ETH Zurich's Department of Chemistry and Applied Biosciences and the U.S. EPA's National Center for Computational Toxicology. Ph.D., University of Milano-Bicocca, 2016 (Dissertation: Interpretable machine learning for molecular property prediction) Environmental Sciences, University of Milano-Bicocca Her research integrates artificial intelligence, chemistry, and biology to develop computational methods for drug discovery, emphasizing wet-lab experimental validation alongside algorithmic innovation. Key focus areas include overcoming activity cliffs in molecular machine learning, generative modeling for scaffold hopping, and AI-augmented decision-making in therapeutic development, with the ultimate goal of achieving 'better decisions faster' in drug discovery pipelines. Analysis of her recent 2025 publications reveals a concentrated trend toward chemical language models and generative deep learning frameworks, specifically addressing low-data drug discovery challenges through active learning and neural network architectures. These works bridge computer science with pharmacology, targeting bioactivity prediction, molecular representation, and enzyme design while maintaining strong ties to experimental validation. Scientific Awards : Lush Young Researcher Prize Early Career Award 2022 from the Dutch Royal Netherlands Academy of Arts and Sciences (KNAW) ERC Starting Grant (2022) Grants and Supervision : Dr. Grisoni secured the prestigious ERC Starting Grant in 2022 to advance her molecular machine learning research. Institutional records indicate she has supervised 7 students (as shown in TU/e's 'Supervised Work (7)' repository section), though specific names aren't provided in the source material. Her group maintains active industry collaborations, including past engagement with Bracco Pharmaceuticals. Laboratory and Team : The Molecular Machine Learning team operates under the ICMS and EAISI frameworks, merging computational AI development with experimental wet-lab validation. This collaborative unit focuses on fragment-based molecular design, chirality representation (evidenced by fragSMILES work), and high-throughput nanoparticle identification using machine learning, as highlighted in recent press coverage and datasets.

• Robotics
• Artificial Intelligence
• Space Robotics
• +۵ مورد دیگر

M.Sc. Maximilian Mühlbauer is a researcher at the Chair of Sensor-Based Robot Systems and Intelligent Assistance Systems at Technische Universität München (TUM), part of the Faculty of Computer Science. His work focuses on robotics, artificial intelligence, and space robotics, particularly in areas like in-orbit manufacturing, virtual fixtures, and human-robot interaction. He contributes to projects such as the ACOR initiative and the AI-In-Orbit-Factory, exploring fault-tolerant processes and adaptive robotic systems for space applications. Research Interests: Maximilian’s research emphasizes AI-driven robotics , space robotics , and control systems . He develops methodologies for virtual fixtures , reconfigurable robotic systems , and teleoperation with shared control . His work integrates probabilistic models and machine learning for resilient systems in challenging environments like space. Publications: His recent work spans topics from in-orbit manufacturing and force-sensitive space manipulators to multi-modal haptic teleoperation , reflecting a focus on practical robotic applications in aerospace and industry. Grants/Advising: Maximilian oversees available theses on topics like mixture of experts fixture learning and virtual fixture adaptation , inviting collaboration on AI-driven robotics projects. He collaborates with Prof. Alin Albu-Schäffer and contributes to TUM’s research initiatives in autonomous systems. Labs: He is part of the Sensor-Based Robot Systems lab, advancing robotics for human-centric and space-oriented applications.

• Behavioral modeling
• Electromagnetic compatibility
• Macromodeling
• +۷ مورد دیگر

Stefano Grivet-Talocia is a Full Professor at the Department of Electronics and Telecommunications at the Polytechnic University of Turin, where he also serves as Director of the Doctoral School and President of the Doctoral School Council. He is a member of the Interdepartmental Center SmartData@PoliTO - Big Data and Data Science Laboratory, the University Committee for Research, Technology Transfer and Services to the Territory, and the Commission for the Promotion of Library, Archive and Museum Heritage. His academic career spans over two decades at Politecnico di Torino, where he has established himself as a leading researcher in electromagnetic modeling and signal integrity. Grivet-Talocia earned his Laurea degree (summa cum laude) in Electronic Engineering in 1994 and his Ph.D. in Electronic and Communication Engineering in 1998, both from the Polytechnic University of Turin. Between 1994 and 1996, he conducted research at NASA/Goddard Space Flight Center in Greenbelt, Maryland. His educational background laid the foundation for his expertise in electromagnetic modeling, wavelet analysis, and signal processing. His research focuses on behavioral modeling, electromagnetic compatibility, macromodeling, model order reduction, numerical modeling, passivity, power integrity, signal integrity, transmission lines, and wavelets . Grivet-Talocia is particularly renowned for his work on passive macromodeling of interconnect structures, development of the TOPLine technique for transmission line simulation, and pioneering contributions to passivity enforcement algorithms. He has co-authored the first book entirely dedicated to Macromodeling (2016) and developed innovative approaches to waveform relaxation and wavelet-based signal processing. His recent publications (2024-2025) demonstrate continued leadership in model order reduction, with significant contributions to data-driven modeling of linear and nonlinear systems, power integrity analysis, and electromagnetic compatibility. His work spans both theoretical advances in numerical methods and practical applications in circuit design, with strong industry relevance particularly for semiconductor and electronic design automation companies. IEEE Fellow (2018-present) Three Intel SRS Grants (2022-2024) Three IBM SUR Grant Awards (2007-2009) Best Associate Editor Award - IEEE Transactions on Components, Packaging and Manufacturing Technology (2020) Multiple Best Conference Paper Awards (2006-2020) URSI Young Scientist Awards (1999) Ranked among the "top 2% worldwide researchers" (Stanford) since 2019 Grivet-Talocia actively supervises doctoral students including Michele Cusano, Sara Paknezhad Panahi, Antonio Carlucci, and Kun Zhao. He has secured numerous research grants from competitive national calls (PRIN) and commercial contracts with industry partners including Intel, IBM, Nokia, Hitachi, Infineon, and Cadence. His technology transfer activities include co-founding the spin-off IdemWorks (2007-2016), which was acquired by CST in 2016. He also developed the autoCircuits web service for automated circuit problem generation, widely used in electrical engineering education. He leads the EMC Group (Electromagnetic Compatibility) at DET and has been instrumental in establishing the Compact Dynamical Modeling research area. His work has practical applications in high-speed electronics design, with algorithms embedded in commercial tools like IBM PowerSPICE. Grivet-Talocia maintains strong industry connections through his research projects and serves as Associate Editor for IEEE Transactions on Components, Packaging and Manufacturing Technology.

• Artificial Intelligence
• Legal Technology
• Dispute Resolution
• +۳ مورد دیگر

Professor John Zeleznikow is an Honorary Associate at La Trobe University's Law School, with a distinguished career spanning 49 years across multiple institutions including the University of Edinburgh and Victoria Business School. His research focuses on AI applications in legal decision-making, dispute resolution, and autonomous vehicle technology. He has secured over $8M in research grants and supervised 20 PhD graduates. Key projects include the Split-Up system (used in high-profile divorce cases like Prince Charles and Lady Di) and Family-Winner software, which won an ABC TV innovation award. His work bridges law, technology, and ethics, with publications in leading journals like the Harvard Negotiation Law Review and Artificial Intelligence and Law . Recent research explores AI-driven online dispute resolution (ODR), autonomous vehicle regulation, and the ethical implications of technology in legal systems. He advocates for transparent, user-centric legal tech solutions to enhance access to justice and improve decision-making processes.