Find Academic Supervisors

• Quantum Chromodynamics
• Nuclear Physics
• Particle Physics
• +3 more

Zhoudunming (Kong) Tu is an Associate Adjunct Professor in the Department of Physics and Astronomy at Stony Brook University and Associate Physicist at Brookhaven National Laboratory's Physics Department, where he leads the EIC Group. As an experimental nuclear and particle physicist, he investigates fundamental Quantum Chromodynamics (QCD) questions, particularly color confinement—the mechanism binding quarks and gluons into hadrons. His educational background includes: M.S & Ph.D in high energy nuclear physics, Rice University, 2018 B.S. in Physics & Mathematics, University of Kansas, 2013 Dr. Tu's research spans both 'hot' QCD (studying quark-gluon plasma via heavy-ion collisions at RHIC) and 'cold' QCD (probing proton structure through deep inelastic scattering at the EIC). His work addresses the color confinement problem through quantum entanglement studies, gluonic structure measurements in nuclei, and investigations of the Chiral Magnetic Effect. He actively contributes to the EPIC experiment at the EIC, STAR experiment at RHIC, and previously participated in CMS at the LHC (2012-2018) and H1 at HERA. Analysis of his 11 publications (2017-2024) reveals a distinct trend toward applying quantum information science to particle physics, particularly through entanglement entropy in QCD. His experimental focus on J/ψ photoproduction has yielded breakthroughs in mapping gluonic nuclear structure, while his precision studies of azimuthal correlations have reshaped understanding of topological effects in heavy-ion collisions. Dr. Tu's scientific contributions have been recognized with: ElCUG award for early career scientists (2024) Inter-American Network of QCD Challenges exchange award (2022) Brookhaven LDRD Award with $400,000 funding (2022-2024) Goldhaber Distinguished Fellowship (2018) Quark Matter Conference Young Scientist Award (2017) Committed to scientific development, Dr. Tu mentors students through the DOE SULI program and actively recruits diverse talent into his research group. He serves as a peer reviewer for Physical Review Letters and grant reviewer for the US Department of Energy and National Science Center of Poland, emphasizing his dedication to advancing both research and scientific community standards. As a core member of Brookhaven's EIC Group, Dr. Tu collaborates across international teams including STAR and EPIC, leveraging major facilities like RHIC and the future EIC. His work integrates theoretical frameworks with cutting-edge experimental techniques to address foundational questions about matter's structure in the universe.

Daniel SchneiderView profile

Researcher

• Materials Science
• Computational Mechanics
• Phase-field Modeling
• +3 more

Dr. Daniel Schneider is a Scientist at the Karlsruhe Institute of Technology (KIT) within the Institute of Nanotechnology, specifically working in the Microstructure Simulations research unit. His office is located in Building 30.48, Room 110.1 at KIT's Eggenstein-Leopoldshafen campus. He is actively engaged in the Multiphysics Materials Modeling: Microstructure Mechanics research group, focusing on computational approaches to materials science problems. Dr. Schneider's research centers on the interactions between microstructural and mechanical influencing factors at the mesoscopic length scale of materials. His work investigates how grain and domain evolution, along with resulting heterogeneous microstructures, affect material properties. He primarily employs the phase field method coupled with numerical algorithms to study these phenomena. This computational approach allows for optimization of process parameters, reduction of production costs, and development of new materials with tailored properties. His current research portfolio spans several key areas including recrystallization processes, solid-solid phase transformations, electrochemical processes, and crack propagation. The materials systems he investigates are diverse, ranging from metals and fiber composites to lithium-ion batteries and piezo crystals. His extensive publication record demonstrates particular expertise in modeling phase transformations with mechanical driving forces, crack propagation in various materials, and the influence of microstructure on mechanical properties. The integration of chemical, thermal, and electromagnetic driving forces in his models represents a sophisticated multiphysics approach to materials simulation. Dr. Schneider's work has significant practical applications in virtual material design, where computational models can predict material behavior before physical production. His research contributes to optimizing manufacturing processes and developing advanced materials with specific performance characteristics. The breadth of his publications across journals in materials science, computational mechanics, geoscience, and electrochemistry highlights the interdisciplinary nature of his work and its relevance across multiple scientific domains.

• Machine Learning
• Deep Learning on Graphs
• Trustworthy Artificial Intelligence
• +5 more

Xiaorui Liu is an Assistant Professor in the Department of Computer Science at North Carolina State University's College of Engineering, where he joined the faculty in August 2022. He also holds a courtesy appointment in the Department of Electrical and Computer Engineering. His research focuses on developing scalable and trustworthy machine learning systems with applications in data science, AI, and intelligent computing. Dr. Liu earned his Ph.D. in Computer Science from Michigan State University in 2022 under the supervision of Dr. Jiliang Tang. He completed both his Master's and Bachelor's degrees at South China University of Technology. His academic journey reflects a strong foundation in computer science with specialized expertise in machine learning and graph-based systems. Liu's research spans trustworthy artificial intelligence, large-scale machine learning, deep learning on graphs/language/vision, and generative AI. His work addresses fundamental challenges in graph neural networks, adversarial robustness, and scalable AI systems. He has published extensively in top-tier conferences including NeurIPS, ICML, ICLR, KDD, AISTATS, and SIGIR, demonstrating both theoretical depth and practical applications across multiple domains. His publications reveal a strong focus on making AI systems more robust, efficient, and applicable to real-world problems. Recent work explores the intersection of large language models with graph structures, adversarial robustness in deep learning systems, and scalable approaches to graph neural networks that can handle industrial-scale data. NSF Career Award (2025) AAAI-2025 New Faculty Highlights National AI Research Resource Pilot Award (2024) ACM SIGKDD Outstanding Dissertation Award (Runner-up, 2023) Amazon Research Award (2023) NCSU Data Science Academy Award (2023) Dr. Liu actively mentors multiple PhD and Master's students including Weizhi Gao, Zhichao Hou, Xingyue Shi, and Daniel Buchanan. His group has secured research funding from Amazon, Snap Research, and the National Science Foundation. He regularly serves as an organizer, senior program committee member, and reviewer for major venues including ICML, NeurIPS, ICLR, KDD, AAAI, and IJCAI. He has co-organized tutorials at SDM 2024, AAAI 2024, KDD 2023, and other leading conferences, helping to disseminate knowledge about large-scale graph neural networks and trustworthy AI. His research group, the Network and Data Science Lab, focuses on developing techniques for large-scale machine learning on graphs with applications in cybersecurity, manufacturing, biology, and healthcare. The group has established collaborations with Oak Ridge National Laboratory, Amazon, and other industry partners to address real-world challenges in AI systems.

Soheil BehnezhadView profile

Assistant Professor

• Theoretical Computer Science
• Algorithms for Big Data
• Sublinear Time Algorithms
• +7 more

Soheil Behnezhad is an Assistant Professor in the Department of Computer Science at Khoury College of Computer Sciences, Northeastern University. He previously served as a Motwani Postdoctoral Fellow at Stanford University and received his PhD from the University of Maryland (UMD) and BSc from Sharif University. Research Interests: His work centers on theoretical computer science, particularly the design and analysis of algorithms for large-scale data. Key areas include sublinear-time algorithms, dynamic algorithms, streaming algorithms, parallel computation (MPC), and graph sparsification, with a strong emphasis on matching, coloring, and clustering problems. Recent Trends in Publications: His recent publications (2023–2025) highlight groundbreaking work in dynamic and sublinear algorithms. He has made significant advances in maximum matching, edge coloring, and correlation clustering, often providing tight bounds or breaking longstanding approximation barriers. His work connects theoretical models with combinatorial structures such as Ruzsa-Szemerédi graphs and explores practical implications in massive data settings. Scientific Awards: NSF CAREER Award Google Research Award Best Paper Award at STOC'25 Best Paper Award at SODA'23 Charles A. Caramello Distinguished Dissertation Award Larry S. Davis Doctoral Dissertation Award Multiple invitations to TALG and HALG Advising and Grants: He advises multiple PhD students including Amir Azarmehr, Mohammad Saneian, and Alma Ghafari. His research is supported by an NSF CAREER Award and a Google Research Award, reflecting both federal and industry recognition of his impactful work. Labs and Teams: He co-organizes the Graph Simplification reading group with Ronitt Rubinfeld and Madhu Sudan and is a key member of the Theory Group at Northeastern University, fostering a collaborative environment for theoretical research.

• Lithium-ion battery modeling
• Electrochemical-mechanical modeling
• Battery diagnostics
• +3 more

Davide Clerici is a Fixed-term Assistant Professor at the Department of Mechanical and Aerospace Engineering (DIMEAS) at Politecnico di Torino. He serves as an invited member of both the College of Chemical and Materials Engineering and the College of Mechanical, Aerospace, and Automotive Engineering. His teaching responsibilities span multiple academic years (2021/22 through 2025/26), including courses on Materials and Production Process Simulation for the Materials Engineering for Industry 4.0 program and Product and Process Design with Numerical Methods for the Mechanical Engineering program. Dr. Clerici's research focuses on the intersection of mechanical engineering and electrochemistry, particularly in lithium-ion battery technology. His work centers on developing advanced models that couple electrochemical processes with mechanical behavior to understand battery degradation mechanisms. He investigates how mechanical stresses develop during battery operation and contribute to performance degradation, employing both analytical solutions and multi-scale modeling approaches. His research spans from fundamental fracture mechanics in battery materials to practical applications in battery diagnostics and state-of-charge estimation. His publication record reveals a strong trend toward increasingly sophisticated multi-physics modeling, with recent work (2023-2025) developing diagnostic methodologies based on mechanical measurements, innovative modeling frameworks like Polidemo for predicting battery degradation, and hybrid state-of-charge estimation algorithms that leverage both electrical and mechanical measurements. These advancements demonstrate a clear progression from theoretical modeling toward practical engineering applications in energy storage technology. Dr. Clerici actively participates in significant research projects, including the EU-funded LIFE LAERTHES project (2025-2027) focused on sustainable non-road mobile machinery, where he contributes as a research group member. He also leads commercial research initiatives, serving as Scientific Director for the 'Functional and performance evaluation of a sensorized battery pack' project (2025) and participating in MEMS sensor applications research. He currently supervises PhD student Salvatore Scalzo in the Mechanical Engineering Program (40th cycle, 2024-present), guiding research in the mechanical aspects of battery technology. His work bridges theoretical modeling and practical applications, contributing significantly to the advancement of safer, more reliable, and longer-lasting lithium-ion battery systems.

• Energy transition
• Energy efficiency and management
• Smart Building technologies
• +8 more

Jean-Philippe Bacher is a Full Professor at the University of Applied Sciences and Arts Western Switzerland (HES-SO), specifically affiliated with the Fribourg School of Engineering and Architecture. He serves as a key researcher at the Institute for Applied Research in Energy Systems, where he leads multiple significant research initiatives focused on energy transition and building performance optimization. His work bridges academic research with practical applications in the energy sector, particularly in the context of Switzerland's Energy Strategy 2050. Professor Bacher's research interests span the critical domains of energy transition, energy efficiency and management, Smart Building technologies, and building performance analysis. His expertise extends to photovoltaic systems, energy storage solutions, Building Information Modeling (BIM), and the intersection of Internet of Things (IoT) technologies with building management. He has developed a particular focus on understanding human behavior in built environments and its impact on energy consumption patterns. His work often addresses the complex challenges of district energy systems and innovative approaches to building decarbonization strategies, with numerous publications demonstrating his leadership in these fields. An analysis of his recent publications reveals a strong trajectory toward integrated solutions for energy transition in the built environment. His work increasingly focuses on the convergence of multiple technologies - particularly the combination of BIM, IoT, and virtual reality for facility management, as well as the integration of photovoltaics with hydrogen-based storage systems for seasonal energy balancing. His research demonstrates a methodological shift toward data-driven approaches using machine learning techniques to optimize energy systems at multiple scales, from individual buildings to district-level infrastructure. The consistent theme across his publications is the practical application of research to address Switzerland's and Europe's energy transition challenges, with particular attention to grid stability issues arising from decentralized renewable energy production. EXPOSITION project (2024-2025): Experimentation Platform for Smart Districts focusing on grid stability issues ProRen project (2019-2021): Interventions on existing building stock for large-scale energy optimization Multi-Confort project (2011-2020): Comprehensive approach to workplace comfort considering physiological, psychological, and functional components CityPulse project (2017-2020): Sensor platform for urban quality of life monitoring in Fribourg's BlueFactory district Professor Bacher leads a substantial research team within HES-SO, collaborating with numerous colleagues across multiple disciplines. His work demonstrates strong industry engagement through practical applications of research findings and technology transfer initiatives. He serves as a bridge between academic research and practical implementation in the energy sector, particularly in the context of Switzerland's Energy Strategy 2050 and the challenges of integrating renewable energy sources into existing infrastructure.

Arthur J. Lembo, Jr.View profile

Professor

• Geographic Information Systems (GIS)
• Geo-processing and spatial analysis
• Enterprise GIS implementation
• +7 more

Arthur J. Lembo, Jr. is Professor of Geography and Geoscience in the Henson School of Science and Technology at Salisbury University, where he has taught since 2007. He also serves as Technical Director of the Eastern Shore Regional GIS Cooperative, leading regional geospatial initiatives and enterprise GIS projects. Education B.S. Geography, State University of New York, College at Oneonta (1987) M.A. Geography, State University of New York at Buffalo (1989) Ph.D. Environmental Resource Engineering, State University of New York, College of Environmental Science and Forestry (1997) Research Interests Dr. Lembo’s research centers on the innovative application of advanced information technologies to geographic problem-solving. His work explores high-performance GIS through massively-parallel algorithms executed on consumer-grade gaming GPUs, enterprise-scale spatial database design, and the integration of smartphone sensors into real-time mapping workflows. Additional emphases include spatial decision support for post-disaster infrastructure assessment, crime analysis, and sustainable local food systems. Selected Research Sponsors National Science Foundation (multiple awards) W. K. Kellogg Foundation Maryland Governor’s Office of Crime Control and Prevention Maryland Department of Natural Resources USDA and NASA Scientific Awards 2018 State of Maryland Board of Regents Award for Teaching Excellence 2006 SUNY Chancellor’s Award for Excellence in Teaching 2005 NACTA Teacher of Merit 2003 CALS Faculty Innovation in Teaching Award, Cornell University 2002 & 2003 Master Teacher recognition, University of Mississippi & NASA Teaching & Outreach Dr. Lembo teaches core GIS programming and spatial-analysis courses (GEOG 204, 415, 419, 435, 619) and maintains an active extension program that provides GIS solutions for municipalities, first responders, and environmental NGOs such as The Nature Conservancy and the City of Havre de Grace. Lab & Teams He directs the Eastern Shore Regional GIS Cooperative, a university-based center that supports multi-agency geospatial data sharing and application development across Maryland’s Eastern Shore.

Asoke NandiView profile

Professor

• Signal Processing
• Machine Learning
• Wireless Communications
• +7 more

Professor Asoke Nandi serves as Professor of Electronic & Computer Engineering at Brunel University of London, where he has been Head of Electronic and Computer Engineering since April 2013. Previously, he held the David Jardine Chair of Signal Processing at the University of Liverpool, where he established and led the Signal Processing and Communications Research Group. His academic journey began with a PhD from the University of Cambridge, followed by positions at prestigious institutions including Rutherford Appleton Laboratory, CERN, Queen Mary University of London, the University of Oxford, Imperial College London, and the University of Strathclyde. Professor Nandi's research spans theoretical developments in signal processing and machine learning, with applied contributions across multiple domains. His work encompasses wireless communications (including automatic modulation recognition and equalization), biomedical signal processing (breast cancer detection, electrocardiogram extraction, retinal image processing), machine condition monitoring, image forensics, time series predictions, genomic signal processing (gene clustering), brain signal processing (music, EEG, and fMRI data processing), and Big Data analytics. These diverse research interests demonstrate his interdisciplinary approach to information engineering problems. His recent publications reveal a strong focus on deep learning applications across multiple domains, with emphasis on medical imaging (particularly cancer diagnosis and segmentation), remote sensing change detection, fault diagnosis in mechanical systems, and innovative approaches to image processing. The research shows a clear trajectory toward more sophisticated neural network architectures including attention mechanisms, diffusion models, and multimodal fusion techniques applied to real-world engineering and medical challenges. IEEE Transactions on Radiation and Plasma Medical Sciences Best Paper Award (2025) Foreign Member of Chinese Society for Vibration Engineering (2025) Member of Academia Scientiarum et Artium Europaea (2023) Member of Academia Europaea (2023) Fellow of Royal Academy of Engineering, U.K. (2014) IEEE Distinguished Lecturer (EMBS, 2018-2019) Finland Distinguished Professor Award (2010-2014) Co-discoverer of W+, W-, and Z0 particles (1983) Professor Nandi has supervised numerous PhD students and research projects across his career, though specific student names aren't listed in the provided materials. His work has been supported by various research grants that have enabled his prolific output of over 650 technical papers, including more than 320 in quality international journals, with an impressive h-index of 92 according to Google Scholar. His research group IEHS at Brunel focuses on information engineering and health systems, reflecting his interdisciplinary approach. Professor Nandi maintains active collaborations through his visiting professorships at institutions including Xi'an Jiatong University (China), Universite d'Orleans (France), and Tongji University (China), as well as his previous adjunct professorship at the University of Calgary (Canada). These international connections enrich his research environment and provide opportunities for cross-cultural scientific exchange.

• Optimization
• Cryptography
• Artificial Intelligence
• +4 more

Chu Min LI is a University Professor at the University of Picardy Jules Verne (UPJV), working in the Modélisation, Information et Systèmes (MIS) laboratory (UR UPJV 4290). Their research focuses on Optimization, Cryptography, and Artificial Intelligence, with particular expertise in MaxSAT (Maximum Satisfiability), Constraint Programming, and Combinatorial Optimization. Professor LI's research spans theoretical and applied computational problem solving. Key research areas include: MaxSAT and MinSAT solving techniques Constraint programming and satisfaction Combinatorial optimization algorithms Geometric packing problems AI-driven optimization methods Theoretical foundations of satisfiability Analysis of Professor LI's publications from 2023-2025 reveals a strong focus on advancing MaxSAT solving techniques with diverse applications including conference scheduling, assembly line balancing, and geometric packing problems. Their work consistently bridges theoretical insights with practical algorithmic improvements, demonstrating both depth in theoretical understanding and relevance to real-world problems across multiple domains. Professor LI has received significant recognition for their contributions, including a Best Paper Award at CP 2021 (27th International Conference on Principles and Practice of Constraint Programming). Professor LI leads substantial research projects including 'BforSAT' (Branching for SAT and beyond) and 'Massal'IA' (Propositional reasoning for large-scale optimization), indicating significant research funding and leadership. Their extensive collaborative network includes researchers such as Felip Manyà, Kun He, Jiongzhi Zheng, and Sami Cherif, suggesting an active research group and international connections.

Steve M. RacitiView profile

Associate Professor

• Urban Ecology
• Plant-Soil Interactions
• Biogeochemistry
• +7 more

Steve M. Raciti is Associate Professor of Biology and Director of Graduate Studies in Biology and Urban Ecology at Hofstra University , where he is based in the Department of Biology. Operating out of 227 Gittleson Hall on the Hempstead, NY campus, he leads an active research program examining plant-soil interactions under urbanization and global change. Education: Ph.D., Cornell University M.S., Cornell University B.A., Vassar College Research Interests: Raciti’s overarching goal is to understand how human activities influence ecosystems and to translate this knowledge into actionable strategies for mitigating environmental problems. His work is organized around three intertwined themes: The role of plants and soils in mediating air and water quality. Urbanization-driven changes to carbon and nitrogen cycling and their consequences from local to global scales. The fate of natural and semi-natural ecosystems embedded within urban and suburban landscapes. Current projects span monitoring nitrogen pollution in the Hempstead Bay estuary, managing Long-Island grasslands for species diversity, evaluating urban-tree site quality across U.S. cities, tracking carbon and nitrogen stock changes along urbanization chronosequences, conserving the threatened Seabeach Amaranth, and using remote sensing to quantify storm- and land-use-driven shifts in tree canopy cover across Long Island and New York City. Teaching & Mentorship: At Hofstra, Raciti teaches undergraduate courses including General Biology I, Biostatistics, and Independent/Honors Research, as well as graduate seminars and statistical-analysis modules. He has mentored graduate students such as Polly Weigand (grassland ecology) and Chris Smith (coastal plant conservation), integrating them into large collaborative research initiatives with agencies such as the Long Island Regional Planning Council, the Town of Hempstead, and the New York State Department of Environmental Conservation. Laboratory & Collaborations: The Raciti Lab operates within Hofstra University’s Department of Biology and frequently partners with the National Center for Suburban Studies. External collaborators include Bryant Scharenbroch (Morton Arboretum & University of Wisconsin–Stevens Point) on urban-tree site indexing, and multiple municipal and state agencies on Long-Island-wide water-quality and canopy-monitoring programs.

• Computer Architecture
• Programming Education
• Hardware Security
• +3 more

Andrew Hilton is a Professor in the Electrical and Computer Engineering Department at Duke University's Pratt School of Engineering. His primary academic role focuses on teaching computer engineering courses specializing in programming, software engineering, architecture, and system software. His research spans multiple areas of computer architecture with particular emphasis on security, performance optimization, and energy efficiency. Hilton has developed innovative approaches in core micro-architecture, architectural support for security, simulation methodologies, and micro-architectural applications for emerging technologies. His work on programming education has led to the development of the "Seven Steps" methodology for translating problems to code, which forms the foundation of his educational materials. Hilton's recent publications demonstrate consistent contributions to the field, with research trends showing increasing focus on hardware security mechanisms alongside traditional architecture optimization. His 2018 paper "MAPS: Understanding Metadata Access Patterns in Secure Memory" received the Best Paper award at ISPASS 2018, highlighting his significant contributions to secure memory systems. Best paper award at ISPASS 2018 for 'MAPS: Understanding Metadata Access Patterns in Secure Memory' Hilton actively mentors students through Duke's academic programs and has developed extensive educational materials including the "All of Programming" textbook and multiple Coursera specializations. His teaching philosophy emphasizes mastery learning and flipped classroom approaches, particularly evident in ECE 551 where students must demonstrate proficiency before advancing. He maintains an active research laboratory focused on computer architecture innovation and collaborates with students on cutting-edge research in processor design and security.

Yangyang XuView profile

Associate Professor

• Stochastic Optimization
• Nonlinear Programming
• Computational Optimization
• +4 more

Yangyang Xu is an Associate Professor in the Department of Mathematical Sciences at Rensselaer Polytechnic Institute (RPI), where he conducts research at the intersection of optimization theory and computational methods. His work bridges theoretical foundations with practical applications in machine learning and data science. Dr. Xu's research focuses on several interconnected areas: Continuous optimization : specializing in stochastic programming, distributed optimization, and tensor optimization Optimization methods : developing stochastic approximation techniques, first-order methods, block update methods, and operator splitting approaches Computational frameworks : advancing parallel and distributed computing implementations for large-scale optimization problems Applications : applying optimization techniques to machine learning, signal processing, compressed sensing, and data mining challenges His recent publications (2023-2025) demonstrate a clear progression toward increasingly sophisticated optimization frameworks, particularly in decentralized and stochastic settings. Xu has made significant contributions to nonconvex optimization, minimax problems, and constrained optimization, with his work showing strong theoretical foundations coupled with practical implementations. Dr. Xu serves on the editorial board of Mathematics of Operations Research (since January 2023), reflecting his standing in the optimization community. He actively mentors six PhD students and has successfully guided multiple students to completion, with graduates moving to positions at organizations including Evonik, SAS, and UT Austin. His teaching includes graduate courses such as Stochastic Optimization and Reinforcement Learning and Introduction to Optimization. Dr. Xu leads a research group focused on advancing optimization theory while developing practical algorithms for real-world applications, particularly in distributed computing environments where communication efficiency is critical.

• Learning Classifier Systems
• Evolutionary Computation
• Machine Learning
• +7 more

Will N. Browne is a Professor specializing in Artificial Intelligence with extensive contributions to Learning Classifier Systems, Evolutionary Computation, and Machine Learning. His research spans multiple disciplines including Robotics, Computer Vision, and Explainable AI, with publications in top-tier conferences and journals across these fields. Dr. Browne's research interests primarily center around Learning Classifier Systems, which are rule-based machine learning systems combining reinforcement learning, supervised learning, and evolutionary algorithms. His work has significantly advanced the field by developing methods to scale these systems for complex problems, addressing perceptual aliasing through lateralized learning approaches, and extending them to handle continuous features. He has pioneered the integration of attention mechanisms with rule-based learning, creating more robust systems for applications like emotion recognition from partially covered faces. His recent research strongly emphasizes interpretable and explainable AI, developing evolutionary methods that maintain model transparency while achieving high performance. His scientific contributions show a clear progression from theoretical foundations to practical applications. Early work focused on core Learning Classifier System algorithms and their application to Boolean problems, while recent publications demonstrate successful applications in multi-robot systems, emotion recognition, and human-robot interaction. His publications in IEEE Robotics and Automation Letters, Evolutionary Computation, and Neurocomputing reflect the interdisciplinary nature of his work. Dr. Browne has mentored numerous researchers, with frequent collaborations indicating his role in guiding students and postdocs. His work often bridges theoretical advances with practical implementations, as evidenced by applications ranging from robot navigation and collision avoidance to smart home technology adoption frameworks. He has been instrumental in developing Learning Classifier Systems for real-world problems, particularly focusing on making these systems applicable to continuous domains and enhancing their interpretability. His laboratory appears to focus on creating AI systems that can be understood by humans, which addresses a critical need in the deployment of AI technologies across various domains.

Giovanni ChiorboliView profile

Associate Professor

• Electronic Instrumentation
• Sensors
• Analog/Digital Integrated Circuits
• +2 more

Giovanni Chiorboli serves as Associate Professor of Electrical and Electronic Measurements in the Department of Engineering and Architecture at the University of Parma, Italy. Affiliated with the university since 1990, he progressed from Technician to Researcher before his 2001 appointment to Associate Professor. He teaches Electronic Measurements for undergraduate Computer, Electronic and Telecommunications Engineering students and Electronic Instrumentation and Sensors for Master's in Electronic Engineering students across multiple academic cohorts from 2013/2014 through 2025/2026. He earned his honors degree in Electronic Engineering from the University of Bologna in 1987. His research centers on electronic instrumentation and sensors , with specialized expertise in mixed analog/digital integrated circuit characterization and analog signal processing . Key application areas include cryogenic electronics for operational amplifiers, thermal measurement systems for materials like wetsuits, and electrical tomography for biomedical and geophysical imaging. His work bridges theoretical circuit design with practical industrial sensor applications. Analysis of his 2025 publications reveals strong thematic continuity in measurement science: four papers address cryogenic electronics challenges (particularly operational amplifier behavior), while others explore machine learning uncertainty in activity recognition and novel imaging systems. This demonstrates his sustained focus on precision measurement techniques across extreme environments and emerging computational paradigms. Professor Chiorboli has secured significant research funding as Principal Investigator for European and national projects including the CENELEC-supported "DYNAD" standardization initiative (2006-2007), PRIN 2005 on non-conventional Si-based sensor technologies, and PRIN 2009 on Electric Impedance Tomography for hemostasis imaging. He maintains active industrial research agreements with Italian sensor manufacturers and contributes to the SITEIA Regional Laboratory's agri-food safety initiatives. He serves as both tutor and reference faculty for the Electronic Engineering Master's program (2013/2014-2025/2026) and previously chaired the program (2018/2019-2019/2020), demonstrating sustained leadership in academic administration alongside his research and teaching duties.

• Optimization
• Data Analytics
• Metaheuristic Methods
• +10 more

Professor Regina Berretta is an Honorary Professor at the University of Newcastle's School of Information and Physical Sciences, specializing in Computing and Information Technology. With expertise spanning computer science, mathematics, and applied optimization, she has established herself as a leading researcher in metaheuristic methods for solving complex combinatorial problems. Currently serving as a Chief Investigator at the ARC Training Centre for Food and Beverage Supply Chain Optimisation, she applies her mathematical modeling skills to address critical challenges in food industry supply chains, with the Centre receiving over $2 million in funding to train next-generation researchers. Professor Berretta earned her PhD, Master of Engineering (Electrical), Bachelor of Mathematics, and Teachers Certificate from Universidade Estadual de Campinas in Brazil. Her educational background in computational and applied mathematics has provided the foundation for her extensive research career focused on integer programming and metaheuristic approaches to optimization problems. Her academic journey includes progression from Lecturer at the University of Newcastle (2003-2007) to various leadership positions including Head of Discipline of Computer Science and Software Engineering (2011-2014) and Assistant Dean of Equity, Diversity and Inclusion (2019-2022). Her research expertise centers on the design of mathematical models and development of efficient computational techniques to tackle large and complex combinatorial optimization problems across diverse application areas. Professor Berretta has made significant contributions to bioinformatics through her work on genetic signature identification from gene expression datasets, and to supply chain optimization through her research on perishable food inventory management, lot sizing, and scheduling problems. Her methodological specialties include memetic algorithms, evolutionary computation, and integer programming approaches that have proven effective for problems that are otherwise computationally intractable. Analysis of her recent publications reveals a strong interdisciplinary focus, with optimization techniques increasingly applied to food supply chain challenges while maintaining connections to bioinformatics applications. Her work demonstrates a consistent pattern of translating theoretical optimization methods into practical solutions for industry problems, particularly in the agricultural sector. Additionally, her more recent publications show growing engagement with gender equity issues in STEM fields, reflecting her leadership in relevant initiatives. Co-founder of HunterWISE, promoting girls and women in STEM Leader of Google CS4HS project for five consecutive years Recipient of over $4 million in research funding through 35 grants Author of more than 80 papers and book chapters Chief Investigator at ARC Training Centre for Food and Beverage Supply Chain Optimisation Professor Berretta has demonstrated exceptional leadership through her administrative roles and community initiatives. As co-founder of HunterWISE, she has developed a comprehensive approach to increasing female participation in STEM through school programs and professional networking events. Her leadership of the Google CS4HS project has directly impacted high school computer science education in the Hunter region. Her research collaborations span multiple disciplines and institutions, reflecting her ability to bridge theoretical computer science with practical industry applications, particularly in the food supply chain sector where her optimization models have demonstrated significant cost and waste reduction potential.