جستجوی استاد راهنمای دانشگاهی

• Big Data Management
• Data Mining
• Graph Learning
• +۹ مورد دیگر

Dr. YANG, Renchi is an Assistant Professor in the Department of Computer Science at Hong Kong Baptist University, Faculty of Science. He earned his BEng in Software Engineering from Beijing University of Posts and Telecommunications and his PhD in Computer Science from Nanyang Technological University, followed by a postdoctoral fellowship at the National University of Singapore. His research is centered on developing efficient algorithms and systems for large-scale data management and analysis. His research interests include: Big Data Management and Analysis Graph Learning and Network Embedding Databases and Data Management (especially graph query processing and similarity search) The Web and Information Retrieval (search, ranking, recommendation, web mining) Data Mining and Machine Learning (social network analysis, text mining, large language models) Dr. Yang’s recent publications span top conferences such as KDD, SIGMOD, WWW, ICDE, and AAAI, focusing on scalable graph clustering, network embedding, GNNs, and LLM integration. His work emphasizes algorithmic efficiency, scalability, and practical applications in real-world graph data. Scientific honors include: VLDB 2021 Best Research Paper Award 2022 ACM SIGMOD Research Highlight Award Best Paper Award Nominee in WWW 2022 Honorable mention as best PC member in WWW 2022 Dr. Yang actively mentors PhD and research students, currently supervising several RPg students including LIN Xiaoyang, LAI Yurui, and ZHENG Haoran. He has secured research funding enabling PhD scholarships and research assistant positions. He serves on the program committees of major conferences like VLDB, KDD, WWW, and SIGIR, and reviews for journals including TKDE and VLDBJ. He is a key member of the Database Research Group at HKBU, which has published extensively in top venues, including 8 papers at SIGMOD 2023. His research lab, the LAGAS Group, focuses on large-scale graph analytics and systems. The team is actively working on projects involving graph clustering, embedding, GNNs, and integration with large language models. Dr. Yang is currently recruiting PhD students for 2026 and research assistants for 2025, indicating active and expanding research operations.

• Stochastic Optimization
• Chance Constrained Optimization
• Distributionally Robust Optimization
• +۹ مورد دیگر

Professor Abdel Lisser is affiliated with CentraleSupélec, where he conducts research in Gif-sur-Yvette, France. His work spans multiple disciplines including stochastic optimization, game theory, and machine learning. Research Interests: Stochastic Optimization, Chance Constrained Optimization, Distributionally Robust Optimization, Stochastic Game Theory, Physics-Informed Neural Networks. His recent publications focus on integrating stochastic programming with deep learning frameworks to address complex optimization problems under uncertainty. Key areas include Markov Decision Processes, joint chance constraints, and applications in autonomous vehicle control and network design. In 2025, he published on single-controller stochastic games, convex approximations for Markov processes, and physics-informed neural networks for nonlinear equations. 2024 contributions include distributionally robust Markov decision processes, neurodynamic optimization, and CNN-based equilibrium prediction in games. Email: abdel.lisser@l2s.centralesupelec.fr Institution: L2S, CentraleSupélec Location: 3 rue Joliot Curie, 91190 Gif-sur-Yvette, France

• AI Agents
• Multi-Agent AI
• Artificial Intelligence
• +۱۴ مورد دیگر

Saptarashmi Bandyopadhyay is a Tenure-Track Assistant Professor of Computer Science at the City College of New York and the Graduate Center at the City University of New York (CUNY). Her research focuses on Artificial Intelligence Agents and Autonomous Decision Making, with special emphasis on Multi-Agent Reinforcement Learning, Multi-Agent Imitation Learning, and related paradigms. She has established significant collaborations with leading institutions including Google DeepMind, Carnegie Mellon University, Oxford University, and MIT. Dr. Bandyopadhyay received her PhD from the University of Maryland, College Park, where she was advised by Professor John Dickerson and Professor Tom Goldstein. Prior to that, she graduated from Penn State in 2020 with a thesis on Multimodal Computer Vision in Medical Domain advised by Prof. William Evan Higgins. Her research expertise spans multiple domains of AI including Multi-Agent Systems, Reinforcement Learning, Imitation Learning, and Multimodal Perception. She specializes in developing AI agents for applications in climate conservation, economic systems, and AI safety. Her work integrates techniques from computer vision, natural language processing, and robotics to create more robust and explainable AI systems that can operate effectively in complex, real-world scenarios. Current work includes improving explainable AI, developing Multimodal LLM/VLM/Robotic Agents, and creating libraries to speed up Multi-Agent evolutionary training with JAXMARL. Analysis of Dr. Bandyopadhyay's publication record reveals a clear progression from foundational work in medical imaging and natural language processing toward increasingly sophisticated multi-agent AI systems. Her recent publications demonstrate a strong focus on Multi-Agent Reinforcement Learning frameworks like JAXMARL, with applications spanning from supply chain orchestration to climate conservation. The interdisciplinary nature of her work is evident in publications spanning computer vision, NLP, and multi-agent systems conferences including AAAI, NeurIPS, AAMAS, EMNLP, and ACL. DoGood Fellow (2022) UMD Dean's Summer Fellow (2021) Dr. Bandyopadhyay has been actively involved in securing research funding from major agencies including NSF, NIH, DoD, and ARL. She served as the lead PhD student RA in a DoD project for Multi-Agent Explainable AI to improve AI trustworthiness. Her service to the academic community includes membership on program committees for major conferences including IJCAI 2024, KDD 2024, ACL 2024, and AAMAS 2023-2024. She has also created the AI Agents Seminar Series at UMD in 2022 with over 1,000 participants from six continents. Currently, Dr. Bandyopadhyay leads research on improving explainable AI, developing Multimodal LLM/VLM/Robotic Agents, and creating libraries to speed up Multi-Agent evolutionary training with JAXMARL. Her lab collaborates prominently with researchers from Google DeepMind, Carnegie Mellon University, Oxford University, University of Sheffield, Waymo, Meta AI, and MIT, with special focus on Dr. Jakob Foerster's and Dr. Robert Loftin's groups.

Bopaya Bidandaمشاهده پروفایل

استاد

• Manufacturing Systems
• Group Technology
• Reverse Engineering
• +۱۱ مورد دیگر

Bopaya Bidanda serves as the Ernest E. Roth Professor and Chairman of the Department of Industrial Engineering at the University of Pittsburgh, where he has led since joining the faculty in 1987 after completing his PhD at Penn State. His institutional impact includes founding the Manufacturing Assistance Center, Automated Data Collection Laboratory, and Swanson Center for New Product Innovation. His research expertise spans manufacturing systems with concentrated focus on Group Technology, Reverse Engineering, Cellular Manufacturing, Lean Manufacturing, and Human Issues in Manufacturing. This work bridges advanced computational methods with industrial applications to solve real-world production challenges. Publication trends from 2000-2002 reveal strong emphasis on human capital integration in cellular systems, reverse engineering applications in biomedical contexts, and virtual laboratory development for manufacturing education. Key thematic threads connect workforce skills assessment with lean transformation and advanced computing techniques for production optimization. Scientific recognition includes: Fellow of the Institute of Industrial Engineers His grant portfolio demonstrates exceptional funding diversity, securing support from NSF, Department of Commerce, W.M. Keck Foundation, and industry partners like FedEx Ground for initiatives in manufacturing modernization and workforce development. He actively mentors doctoral candidates through flexible international programs and industry-university collaborations. Leadership extends to operational hubs including the Manufacturing Assistance Center which provides direct technical support to regional manufacturers, and the Swanson Center driving product innovation through interdisciplinary industry partnerships.

• Theoretical Neuroscience
• Computational Neuroscience
• Behavioral Neuroscience
• +۴ مورد دیگر

Dr. Ann Kennedy is an Assistant Professor of Neuroscience at Northwestern University's Feinberg School of Medicine, where she leads the Kennedy Lab. Her research focuses on theoretical neuroscience, using mathematical modeling and computational approaches to understand how neural circuits produce adaptive behaviors. She investigates how the brain integrates sensory information with survival needs to drive behavior across multiple scales, from single neurons to social groups. Dr. Kennedy received her BS and BA in Biomedical Engineering from Johns Hopkins University in 2008, followed by a PhD in Neuroscience from Columbia University in 2014. She conducted postdoctoral research at the California Institute of Technology before establishing her independent laboratory at Northwestern in 2020. Her research interests center on understanding the computational principles of neural circuits, particularly in subcortical brain regions involved in survival behaviors. She develops theories for distributed behavior control by multiple neural populations, studies computation in heterogeneous neural populations, and constructs richer descriptions of animal behavior using pose estimation and machine learning tools. Her work blends dynamical systems, control theory, reinforcement learning, and machine learning to investigate neural dynamics across multiple scales and model organisms. Dr. Kennedy's recent publications reveal trends in understanding neural heterogeneity, early nervous system evolution, and the theoretical foundations of neuroscience. Her work spans from fundamental computational principles to applications in pain research and behavioral neuroscience, with increasing emphasis on translating theoretical insights to understand neurological conditions. Sloan Research Fellowship, Sloan Foundation (2023) Eppendorf and Science Prize in Neurobiology (2022) McKnight Scholars Award (2024) Pew Scholar in the Biomedical Sciences (2024) Thom Young Researcher Award (2025) Dr. Kennedy actively mentors postdoctoral researchers and collaborates with experimentalists both locally and internationally. Her lab has received significant grant support including the $75,000 Sloan Fellowship and the prestigious McKnight Scholars Award. She co-chairs the CAJAL summer school on quantitative approaches to behavior and serves as Associate Editor for Science Advances. The Kennedy Lab operates at the intersection of theory and experiment, developing community resources like the Mouse Action Recognition System (MARS) for behavioral analysis and contributing to major initiatives like the 2025 Multi-Agent Behavior Challenge on Kaggle. The Kennedy Lab maintains active collaborations with researchers across San Diego and beyond, focusing on theoretical approaches to behavior and neural circuit function. Current projects analyze data from multiple brain regions and organisms, including hypothalamus studies of social and defensive behavior, basal ganglia research on behavior-neural activity correlations, hippocampal studies of social bond formation, and whole-organism neural imaging in cnidarians. The lab values open science, clear communication, and community resource development.

• Scientific Computing
• Numerical Methods
• Computational Cardiology
• +۵ مورد دیگر

Luca Pavarino is a Professor at the Department of Mathematics, University of Pavia. His research focuses on scientific computing and numerical methods, particularly in the context of cardiac electrophysiology and multiphysics systems. He leads the Scientific Computing group, specializing in domain decomposition methods (BDDC/FETI-DP), isogeometric analysis, and parallel algorithms. His work integrates advanced numerical techniques with biomedical applications, including cardiac electromechanical coupling, drug testing on cardiac tissues, and modeling genetic cardiac disorders like LQT8 syndrome. Key contributions include scalable solvers for nonlinear systems, preconditioners for heterogeneous media, and operator learning for ionic dynamics. Research interests span computational cardiology, numerical analysis, and parallel computing, with applications to biophysics and drug discovery. His projects often involve interdisciplinary collaborations between mathematics, engineering, and medicine. Notable contributions include: Development of BDDC/FETI-DP preconditioners for cardiac models Integration of machine learning with cardiac electrophysiology High-performance computing for multiphysics systems (Biot’s consolidation, protein stability) Labs/Teams: Scientific Computing Group at the University of Pavia’s Department of Mathematics.

• Artificial Intelligence (AI)
• Unmanned Aerial Vehicles (UAVs)
• Fuzzy Logic Systems
• +۷ مورد دیگر

Dr. Kelly Cohen is a Professor and Brian H. Rowe Endowed Chair in the Department of Aerospace Engineering and Engineering Mechanics at the University of Cincinnati's College of Engineering and Applied Science. His career spans over two decades, marked by significant contributions in AI, UAV systems, and aerospace engineering. He has led numerous research initiatives, secured grants from agencies like NSF, NIH, and NASA, and developed innovative solutions in autonomous robotics and medical AI applications. Dr. Cohen is also recognized for his teaching excellence and mentorship, having advised 12 PhD and 33 MS students. Education: Ph.D. in Aerospace Engineering, Technion, Israel Institute of Technology (1999) M.Sc. in Aerospace Engineering, Technion (1991) B.Sc. in Aeronautical Engineering, Technion (1986) Research Interests: Focus on AI-driven systems for aerospace and biomedical applications, including trustworthy AI, UAV control, optimization, and safety-critical systems. Current projects involve assured autonomy, medical phenotypic plasticity modeling, and advanced air mobility. Scientific Awards: Over 20 awards, including the UC Dolly Cohen Award, AIAA Technical Contribution Award, and Greater Cincinnati Consortium Excellence in Teaching Award. Grants and Advising: Principal Investigator on over 100 grants totaling millions, including projects on UAV traffic systems, AI safety, and medical AI. His labs include the AI Bio Lab and collaborations with NASA, FAA, and industry partners.

Chad G. Roseمشاهده پروفایل

استادیار

• Robotics
• Human-Robot Interaction
• Robotic Rehabilitation
• +۳ مورد دیگر

Chad G. Rose is an Assistant Professor of Mechanical Engineering at Auburn University's College of Engineering. He holds a Ph.D. and M.S. from Rice University and a B.S. from Auburn University. His research focuses on robotics, particularly in human-robot interaction, rehabilitation robotics, and wearable devices. He actively collaborates on projects like robotic rehabilitation systems and virtual reality empathy training for healthcare professionals. Dr. Rose has received internal research funding from Auburn University and contributes to multidisciplinary initiatives with Toyota and European institutions. Education : Ph.D. Mechanical Engineering, Rice University M.S. Mechanical Engineering, Rice University B.S. Mechanical Engineering, Auburn University His research interests emphasize robotic rehabilitation , including exoskeleton design for functional tasks and neuromechanical modeling of injuries. He also explores virtual reality applications to enhance empathy in medical training and collaborative assembly frameworks for industrial robotics. His work bridges biomechanics, control systems, and human-centered design. Recent publications span topics like soft hand exoskeleton control, human-robot collaborative assembly, and predictive haptic feedback for autonomous systems. His research has been highlighted in interdisciplinary collaborations and competitions like the NASA Robotic Mining Challenge. Awards : Recipient of Auburn University's internal research funding (2023) Dr. Rose advises projects in robotic systems development and has contributed to Auburn's Robotic Mining Team. His lab focuses on wearable robotics, sensorimotor interfaces, and human-centered automation. He collaborates with industry partners like Toyota and international institutions to advance safety and accessibility in robotics.

Konrad Kowalczykمشاهده پروفایل

دانشیار

• Speech and Audio Signal Processing
• Machine Learning
• Deep Neural Networks
• +۶ مورد دیگر

Konrad Kowalczyk is an Associate Professor at AGH University of Science and Technology in Krakow, Poland, where he heads the Signal Processing Group within the Faculty of Computer Science, Electronics and Telecommunications. With extensive international experience from institutions including Queen's University Belfast, Stanford University, and Fraunhofer Institute, he has established himself as a leading researcher in audio and speech signal processing. His academic journey includes B.Eng. and M.Sc. degrees from AGH University (2005), a Ph.D. from Queen's University Belfast (2009), and a Habilitation in ICT from AGH University (2020). B.Eng. and M.Sc. in Electronics and Telecommunications, AGH University of Krakow (2005) Ph.D. in Electronics, Queen's University Belfast, UK (2009) Habilitation (D.Sc.) in Information and Communication Technology, AGH University of Krakow (2020) Kowalczyk's research spans multiple cutting-edge areas in audio processing, with particular focus on speech and audio signal processing enhanced by machine learning techniques. His work integrates deep neural networks with traditional signal processing methods to address challenges in array signal processing , speech enhancement , and speaker recognition . The research group he leads explores innovative applications in distributed signal processing for IoT , acoustic event detection , and spatial audio rendering , bridging theoretical advances with practical implementations. His recent publications demonstrate a clear trend toward integrating deep learning with traditional signal processing techniques, particularly in speaker diarization, source separation, and robust speech recognition. The research increasingly focuses on real-world applications requiring reverberation-robust processing , distributed microphone array systems , and end-to-end neural architectures that can operate in challenging acoustic environments. There's a noticeable shift toward more complex, integrated systems that combine multiple signal processing tasks. Stanislaw Staszic Medal for best graduate of AGH (2005) IEEE Best Student Paper Contest finalist (2007) AES Student Technical Paper Award winner (2008) Best Student Paper Award at IWAENC conference (2014) Best Paper Awards at IEEE SPA conferences (2016, 2019) Polish Ministry of Science Scholarship for Distinguished Young Scientists (2016-2019) Prime Minister Award for outstanding scientific achievements (2020) As Principal Investigator, Kowalczyk leads multiple significant research projects including "Acoustic Intelligence" (2024-2028) funded by National Science Center, and "Deep extraction for robust speech recognition" (2023-2028). He has successfully secured funding from prestigious programs including First TEAM from the Foundation for Polish Science, and EU FP7 projects. His research group actively supervises Ph.D., M.Sc., and B.Eng. students, with strong connections to international institutions including Aalto University and IEEE Signal Processing Society. The research output includes numerous journal publications, conference papers, patents, and software implementations that have advanced the field of audio signal processing. Kowalczyk leads the Signal Processing Group at AGH University, which focuses on developing innovative solutions for speech and audio processing challenges. The group maintains strong collaborations with international institutions including Aalto University (Finland), and participates in European research initiatives. Their work spans theoretical development through practical implementation, with applications ranging from medical voice assistants to distributed acoustic sensor networks.

• Wireless Sensor Networks
• Indoor/Outdoor Localization
• QoS/QoE in Vehicular Networks
• +۹ مورد دیگر

Mohammad Ali Salahuddin is a Research Assistant Professor at the David R. Cheriton School of Computer Science, University of Waterloo , specializing in networking and machine learning. He holds a Ph.D. in Computer Science from Western Michigan University (2014), with prior academic roles at Université du Québec à Montréal and Concordia University. His research spans 5G network slicing, vehicular networks, and secure content delivery systems. Education: Ph.D. (2014, Western Michigan University); M.S. (2003, Western Michigan University); M.S. (2001, SZABIST); B.S. (1999, FAST-NUCES) Dr. Salahuddin's research focuses on 5G/6G network softwarization , autonomous threat mitigation , and machine learning for network management . His work integrates reinforcement learning and federated learning for scalable solutions in SDN/NFV , IoT , and edge computing . Recent studies address data drift in encrypted traffic classification and DDoS detection using outlier exposure-based federated learning . He has received multiple best paper awards at IEEE/IFIP NOMS (2023, 2022), IEEE CNOM (2021), and Kenneth C. Sevcik Outstanding Student Paper Award (ACM SIGMETRICS, 2021). His NSF-funded projects include vehicular cloud resource management and localization techniques. Dr. Salahuddin actively contributes to academic service as Vice-Chair of IEEE KW Section's Communications Society and TPC member for top conferences.

Y. Charlie Huمشاهده پروفایل

استاد

• Mobile Systems
• Distributed Systems
• Operating Systems
• +۵ مورد دیگر

Y. Charlie Hu is the Michael and Katherine Birck Professor of Electrical and Computer Engineering and Professor of Computer Science (by courtesy) at Purdue University, where he leads the PurNET Lab and contributes to the Systems and Networking Group. His research spans Mobile Systems, Distributed Systems, Operating Systems, and Computer Networks , with a focus on energy-efficient AI systems and edge computing. His groundbreaking work on smartphone energy management has been widely adopted by the mobile industry and recognized with multiple test-of-time awards , including from ACM SIGOPS and ACM SIGMOBILE . He has received prestigious honors like the NSF CAREER Award , Honda Initiation Grant , and industry accolades from Google Research and Qualcomm . Notable Funded Projects: NSF's NeTS: Black-box Optimization of White-box Networks (2023-2026) Intel -NSF's SPLICE initiative His research has produced 15+ PhD graduates now in academia (University of Arizona, Virginia Tech) and industry (Google, Apple, Qualcomm). The articles reflect a career-long focus on edge computing , 5G network optimization , and energy-aware systems , with recurring themes in mobile AR/VR , video analytics , and network protocol design . Scientific Awards Honda Initiation Grant NSF CAREER Award Purdue Early Career Research Award Google Research Award Qualcomm Faculty Award ACM SIGOPS EuroSys Best Student Paper Award ACM MobiCom Best Community Paper Award IEEE Fellow ACM Distinguished Scientist Purdue PRF Innovator Hall of Fame

Dong S. Haمشاهده پروفایل

استاد

• Analog IC Design
• RF ICs
• Power Management Circuits
• +۷ مورد دیگر

Dong S. Ha is a Professor in the Bradley Department of Electrical and Computer Engineering at Virginia Tech. As Founding Director of the Multifunctional Integrated Circuits & Systems (MICS) Lab, he focuses on advanced circuit design for energy harvesting, RF systems, and high-temperature electronics. His work spans analog/RF ICs, power management circuits, and wireless IoT solutions with machine learning integration. Education: PhD (1986) and MS (1984) in Electrical and Computer Engineering from the University of Iowa; B.S. (1974) in Electrical Engineering from Seoul National University. Research interests include energy harvesting (piezoelectric, thermal, RF), high-temperature RF circuits for oil/gas/spacecraft applications, and smart IoT systems. He actively seeks students for projects in RF design, energy harvesting, and embedded systems. His lab develops cutting-edge solutions for harsh environment communication, sustainable energy systems, and smart agriculture monitoring. Awarded IEEE Fellow (2008) for contributions to VLSI design/test. Key publications span energy harvesting circuits, GaN-based high-temperature systems, and low-power IoT architectures. Current projects include self-sustaining smart farm networks using federated learning and attack-resistant sensor systems. Labs/Teams: Leads MICS Lab focusing on integrated circuits and systems. Collaborates on cross-disciplinary projects involving machine learning, embedded systems, and sustainable energy. Active in industry partnerships for aerospace and automotive applications.

• Cybersecurity
• Data Security and Privacy
• Context-Aware Access Control
• +۵ مورد دیگر

Dr ASM Kayes serves as Senior Lecturer in Cybersecurity and Cyber Curriculum Lead at La Trobe University's Department of Computer Science and Information Technology, where he shapes cybersecurity education programs including Master's, Bachelor's, and Double Degrees. His academic journey began with a PhD from Swinburne University of Technology in 2015, followed by postdoctoral research at La Trobe before joining as Lecturer in 2019 and promotion to Senior Lecturer in 2022. His research spans critical cybersecurity domains including data security, privacy preservation, context-aware access control, malware/ransomware defense, and IoT/fog/cloud security leveraging AI/ML techniques. Dr Kayes has established himself as a leading voice in blockchain security frameworks, privacy policy analysis, and cyber incident response through publications in top-tier venues like ACM Computing Surveys, IEEE Internet of Things Journal, and Computers & Security. His recent publications reveal a strong trajectory toward integrating AI with traditional security frameworks, particularly in blockchain risk assessment (2025), cross-domain access control (2025), and IoT behavior prediction (2024). The research demonstrates consistent focus on practical security solutions addressing ransomware mitigation, privacy breaches, and emerging threats in decentralized systems. Over $880,000 secured as Chief Investigator for cybersecurity projects Australian Government Department of Social Services grant (2023-2026) for cyberbullying prevention AustCyber research funds with industry partners (2020-2023) SmartSat CRC and ASCRIN PhD scholarship grants (2021) Dr Kayes has successfully supervised 5 PhD candidates to completion and currently mentors 5 doctoral students across diverse topics including AI-driven threat hunting, satellite network security, and blockchain risk frameworks. His collaborative network spans UK, USA, Europe, and Asia, with active industry partnerships through Westpac, BHP, and Quantum Victoria. He serves on editorial boards for leading cybersecurity journals and has examined HDR dissertations globally, reflecting his significant standing in the academic community.

Mohammed Khalidمشاهده پروفایل

استاد

• FPGA-based Systems
• Network-on-Chip (NoC) Architectures
• Signal Processing Hardware
• +۳ مورد دیگر

Prof. Mohammed Khalid is a Professor in the Department of Electrical and Computer Engineering at the University of Windsor. He specializes in FPGA-based systems, network-on-chip architectures, and hardware acceleration for signal processing applications. His leadership roles include serving on the executive committee of IEEE Canada. His research focuses on optimizing cryptographic hardware, automotive embedded systems, and efficient algorithm implementations on FPGAs. Key contributions include advancements in PUF-based security mechanisms, high-speed elliptic curve processors, and FPGA-accelerated machine learning algorithms. He has led projects in automotive radar systems and AUTOSAR configuration tools. His work emphasizes practical applications of hardware-software co-design principles. Research Highlights : Development of novel FPGA architectures for real-time signal processing Innovative approaches to resource-efficient cryptographic hardware Pioneering work on hybrid NoC architectures for multi-FPGA systems Awards : IEEE Windsor Section Award (2019) for group leadership Best Student Paper Award (2024) for supervised research by Mohit Sharma Prof. Khalid's 150+ publications span FPGA design methodologies, adaptive signal processing, and embedded systems security. His research group collaborates with industry partners to advance automotive electronics and IoT applications.

Davide Previtaliمشاهده پروفایل

استادیار

• Control Systems
• Black-Box Optimization
• Preference-Based Optimization
• +۴ مورد دیگر

Davide Previtali is an Assistant Professor at the University of Bergamo's Department of Engineering. His research focuses on control systems, black-box and preference-based optimization, and lithium-ion battery technologies. He teaches the 'Data science and automation' course (6 CFU). Education: Master’s in Computer Science Engineering (2019), PhD in Engineering and Applied Sciences (2022), both from University of Bergamo Research interests include model predictive control, thermal systems optimization, regenerative braking systems, and integration of biological models with neural networks. Recent work emphasizes energy-efficient industrial processes and algorithms for preference-based optimization with convergence guarantees. Publications span industrial oven modeling, shrink tunnel control systems, and advanced optimization algorithms. He leads the Control systems and Automation Laboratory, focusing on practical applications of control theory in manufacturing and energy systems. Labs/Teams: Control systems and Automation Laboratory