جست‌وجوی استاد و پژوهشگر

Souradeep Duttaمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Reinforcement Learning
• +۴ مورد دیگر

Souradeep Dutta is an Assistant Professor in the Department of Electrical and Computer Engineering within the Faculty of Applied Science at the University of British Columbia (UBC), joining in Fall 2024 after postdoctoral research at the PRECISE center, University of Pennsylvania. His academic credentials include a PhD in Electrical and Computer Engineering from the University of Colorado Boulder and a BE in Instrumentation and Electronics Engineering from Jadavpur University, India. Education: PhD, Electrical and Computer Engineering, University of Colorado Boulder BE, Instrumentation and Electronics Engineering, Jadavpur University, India Dr. Dutta's research centers on artificial intelligence with emphasis on reinforcement learning, cyber-physical systems, and formal methods. He investigates fundamental challenges in efficient data-driven learning and assurance techniques for learned models, targeting applications in robotics and medical devices. His work bridges theoretical guarantees with practical implementations for safe human-machine knowledge transfer. Analysis of his 15 most recent publications reveals dominant trends in robustness verification for learning-enabled systems, memory-based adaptation techniques, and distribution shift handling. His research consistently addresses safety-critical applications, particularly in medical diagnostics (e.g., ECG analysis, acne grading) and autonomous control systems, while maintaining strong theoretical foundations in formal methods. Awards: Recognition at top-tier conferences including ICLR, CORL, HSCC, ICCPS, ICAPS, NFM, L4DC, CHASE, and ADHS Dr. Dutta actively seeks graduate students for Fall 2025 and welcomes interdisciplinary collaborations. He serves on program committees for AAAI, ICCPS, ICML, and NeurIPS, and is available for undergraduate research supervision. His advising philosophy emphasizes safe and efficient transfer of human expertise to machine systems. He leads a research group at UBC focused on developing verifiable AI frameworks for cyber-physical applications, with current projects spanning medical device assurance and adaptive robotics control systems.

Nader Sadeghمشاهده پروفایل

استاد

• Robotics
• Controls
• Automation
• +۸ مورد دیگر

Nader Sadegh is a Professor in the Woodruff School of Mechanical Engineering at the Georgia Institute of Technology's College of Engineering, where he also serves as Associate Director and Education Director of the Robotics Ph.D. Program. His research spans robotics, control theory, and artificial intelligence with applications in industrial automation and public health. Dr. Sadegh's educational background includes: B.S. from University of California, Santa Barbara (1982) M.S. from University of California, Berkeley (1984) Ph.D. from University of California, Berkeley (1987) His research evolved from pioneering work on adaptive learning controllers for robotic manipulators—which enable robots to learn repetitive tasks without precise models—to neural network applications and nonlinear system identification. Current work focuses on barrier state theory for safety-critical control systems, safe trajectory optimization in robotics, and epidemiological modeling for disease transmission control. His methodologies consistently bridge theoretical control frameworks with industrial implementations to enhance system accuracy and autonomy while reducing hardware complexity. Analysis of his recent publications reveals a dominant trend toward safety-critical control architectures using barrier states and functions, with expanding applications in quadrotor navigation, agricultural robotics, and pandemic response systems. The interdisciplinary nature of his work connects control theory with machine learning, epidemiology, and industrial automation. Scientific distinctions include: Associate Editor, Journal of Dynamic Systems, Measurement, and Control (1993-1997) Registered Professional Engineer in Georgia U.S. Patent 5,946,449 for precision apparatus with non-rigid structures Dr. Sadegh has secured significant industry-sponsored research including Xerox Corporation projects on photoreceptor speed regulation, Ford Motor Company collaborations on assembly operations and continuously variable transmissions, and Visteon-funded work on high-precision manufacturing systems. His grants consistently target practical implementations where theoretical control methods solve real-world problems in automotive systems, electro-hydraulic valves, and glass forming processes. Based at the Georgia Tech Manufacturing Institute (GTMI), his lab develops integrated control solutions for complex mechanical systems, with recent emphasis on safety-guaranteed autonomous operations in unstructured environments and data-driven modeling for biological processes.

Saibal Mukhopadhyayمشاهده پروفایل

استاد

• VLSI Systems
• Nanotechnology
• Low-Power Electronics
• +۵ مورد دیگر

Dr. Saibal Mukhopadhyay is a Professor in the Department of Electrical and Computer Engineering at the Georgia Institute of Technology, where he joined in 2007. He holds the Joseph M. Pettit Professorship and is recognized as an IEEE Fellow for his contributions to low-power and reliable VLSI systems. Education: BEng (Jadavpur University, India), Ph.D. (Purdue University) Labs: Gigascale Reliable Energy Efficient Nanosystem (GREEN) Lab His research focuses on VLSI Systems , Nanotechnology , and Low-Power Electronics , with emphasis on technology-circuit co-design for energy-efficient computing. Recent work explores Compute-in-Memory (CIM) architectures and Spiking Neural Networks for edge AI. Key article themes include Transformer Model Acceleration , Quantum Computing Calibration , 3D Object Detection , and Device Aging Analysis , reflecting his interdisciplinary approach bridging hardware design and machine learning. Scientific Awards IEEE Fellow (2018) ONR Young Investigator (2012) NSF CAREER Award (2011) IBM Faculty Awards (2009, 2010) Best Paper Awards (IEEE-Nano 2003, ICCD 2004)

Stefanos Nikolaidisمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Human-Robot Interaction
• Procedural Content Generation
• +۳ مورد دیگر

Stefanos Nikolaidis is a tenured Associate Professor in the Department of Computer Science at the University of Southern California (USC), where he directs the Interactive and Collaborative Autonomous Robotic Systems (ICAROS) Lab. His research focuses on enabling robots to interact robustly with humans in dynamic environments through advancements in artificial intelligence, human-robot interaction, procedural content generation, and quality diversity optimization. Education: PhD in Robotics from Carnegie Mellon University (CMU) and MS in Computer Science from MIT. The ICAROS Lab develops interactive agents for real-world tasks while creating diverse testing scenarios to enhance system robustness. Research integrates AI techniques with human-centric design principles across applications like rehabilitation robotics, collaborative manufacturing, and socially interactive embodiments. Recent publications demonstrate trends in quality diversity optimization, human-aware planning, and policy adaptation. Notable works include AutoQD for behavior discovery, CMA-MA for multi-objective optimization, and applications in hair manipulation, rehabilitation personalization, and large language model integration. Scientific Awards: NSF CAREER Award (2022). He actively shares research updates via Twitter and has contributed software tools like pyribs for quality diversity optimization. His work spans theoretical advancements in optimization algorithms and practical implementations in assistive robotics.

David A. Goldbergمشاهده پروفایل

دانشیار

• Applied Probability
• Stochastic Processes
• Queueing Theory
• +۶ مورد دیگر

David A. Goldberg is an Associate Professor and Director of Undergraduate Studies in the Operations Research and Information Engineering (ORIE) department at Cornell University's College of Engineering. His research bridges theoretical probability with practical applications in operations management, inventory systems, and queueing networks. Education: Ph.D. in Operations Research, MIT, 2011 B.S. in Computer Science, minors in Applied Math and Industrial Engineering / Operations Research, Columbia University SEAS, 2006 Professor Goldberg's research focuses on advancing theoretical understanding of stochastic systems while developing practical insights for operations management. His work spans applied probability, stochastic processes, queueing theory, inventory models, distributionally robust optimization, and combinatorial optimization. He has made significant contributions to understanding the behavior of complex systems under uncertainty, particularly in many-server queues and inventory management under demand variability. His publications reveal strong trends in asymptotic analysis of stochastic systems, particularly in the Halfin-Whitt regime for queueing systems and in inventory models with large lead times. His work consistently bridges theoretical probability with practical operations management applications, with a strong emphasis on developing models that account for real-world uncertainties while maintaining mathematical tractability. His recent work shows increasing focus on distributionally robust approaches that require minimal assumptions about underlying distributions. Scientific Awards: INFORMS Applied Probability Society Best Publication Award (2019) INFORMS Nicholson student paper competition first place (2019) INFORMS Nicholson student paper competition first place (2015) INFORMS Junior Faculty Interest Group paper competition second place (2015) Professor Goldberg has successfully advised multiple Ph.D. students who have gone on to prestigious academic and industry positions. His research is supported by significant NSF funding, including a CAREER award and a grant for stochastic comparison approaches to parallel server queues. He serves on editorial boards for leading journals including Operations Research and Stochastic Models, and has held leadership positions in the INFORMS Applied Probability Society including Vice-chair (2020-2022) and Council member (2015-2017).

• Trustworthy Machine Learning
• Algorithmic Fairness
• Robust Distributed Learning
• +۵ مورد دیگر

Sanmi (Oluwasanmi) Koyejo is an Assistant Professor in the Department of Computer Science at Stanford University and an adjunct Associate Professor at the University of Illinois at Urbana-Champaign. He leads Stanford Trustworthy AI Research (STAIR), working to develop the principles and practice of trustworthy machine learning with applications to neuroscience and healthcare. Koyejo holds affiliations with multiple Stanford institutes including SAIL, HAI, CRFM, AIMI, AI Safety, Machine Learning Group, and Bio-X. Koyejo completed his Ph.D. at the University of Texas at Austin followed by postdoctoral research at Stanford University. His research bridges theoretical machine learning with practical healthcare applications, focusing on developing robust and fair AI systems that can be trusted in critical domains. His work spans algorithmic fairness, robust distributed learning, metric elicitation, and applications to medical imaging and neuroscience. His recent publications demonstrate a strong focus on emerging challenges in AI including emergent abilities in large language models, fairness in medical AI, federated learning, and robustness against adversarial attacks. His work has increasingly addressed real-world healthcare challenges through deep learning applications to medical imaging, particularly chest radiographs for disease detection. Scientific Awards: NSF CAREER Award 2021 Skip Ellis Early Career Award Sloan Research Fellowship Frederick E. Terman Faculty Fellow (2022) Best Paper Award from UAI Kavli Fellowship IJCAI Early Career Spotlight Koyejo actively mentors a large research group with numerous PhD students and postdocs. His research has been supported by significant grants including NSF funding for projects like 'Fair Federated Representation Learning for Breast Cancer Risk Scoring.' He serves in leadership roles including as General Co-chair for NeurIPS 2022 and President of the Black in AI organization. His STAIR research group focuses on developing trustworthy AI principles and practices, with applications to healthcare and neuroimaging. The group collaborates extensively with healthcare institutions including OSF Healthcare and participates in major initiatives like the NIH-funded MIDRC and the NSF AI research institute AIFARMS.

• Electrical Machines
• Energy Conversion
• Permanent Magnet Motors
• +۳ مورد دیگر

Lale Tükenmez Ergene is a Professor at Istanbul Technical University 's Department of Electrical Engineering, specializing in Electrical Machines and Energy Conversion . Her work bridges theoretical research and practical applications in motor design for electric vehicles and home appliances. Ph.D. in Electrical Engineering from Rensselaer Polytechnic Institute 20+ years of academic and administrative leadership Focus areas: Permanent Magnet Motors, Synchronous Reluctance Motors, and Sensorless Control Systems Her research explores: Optimization of traction motors for electric vehicles Advanced sensorless control algorithms for motor drives Reduction of voltage distortion in high-performance motors Integration of predictive diagnostics in motor systems Applications of neurofuzzy control systems in multicopters Recent publications highlight trends in PMaSynRM parameter estimation , flux weakening capabilities , and real-time motor diagnostics . Her work spans both traditional electrical engineering and cross-disciplinary innovations like VR-based language learning systems for EU workforce mobility. Scientific recognition includes: Best Poster Paper Award (2016) 2nd Prize in Graduation Design Competition (2015) Doctoral Thesis Excellence Award (2015) She leads projects such as: Pmasynrm's Innovative Real-Time Model Diagnostic System (2021-2024) Sensorless Magnet-Supported Motor Drive for Washing Machines (2019-2022) VR-based Business English Training for Engineers (2018-2022)

Angelos Georghiouمشاهده پروفایل

دانشیار

• Stochastic Optimization
• Robust Optimization
• Operations Management
• +۴ مورد دیگر

Angelos Georghiou is an Associate Professor of Operations Management at the Department of Business and Public Administration, University of Cyprus. He previously held academic positions as Assistant Professor at McGill University (2016–2019), Post-Doctoral Researcher at MIT (2012–2013), and Postdoctoral Researcher at ETH Zurich (2013–2016). His work bridges computational methods in stochastic and robust optimization with applications in healthcare, energy systems, and operations management. Ph.D. in Operations Research, Imperial College London (2012) M.Sci. in Mathematics, Imperial College London (2008) His research focuses on stochastic optimization , robust optimization , and decision rules , addressing challenges in healthcare analytics , energy-efficient control systems , and machine learning integration . Recent publications in Management Science and Operations Research highlight his contributions to decision-dependent information discovery and computational frameworks. Key trends in his 15 most recent articles include: Advancing robust optimization techniques for multistage problems Applications in healthcare (psychiatric risk prediction) and energy systems Integration of machine learning with stochastic programming Development of tractable algorithms for complex decision environments Scientific Awards : Esdras-Minville Best Student Paper Award (2023) He serves on editorial boards of Operations Research Letters (Area Editor) and Management Science , and collaborates with institutions like MIT, ETH Zurich, and McGill University. His work with undergraduate students includes a 2021 paper in SIAM Undergraduate Research Online .

• Wireless Communication
• Machine Learning
• Signal Processing
• +۴ مورد دیگر

Prof. Dr. Haris Gačanin is a faculty member at RWTH Aachen University, affiliated with the Institute for Distributed Signal Processing under the College of Electrical Engineering. His research focuses on integrating machine learning with wireless communication systems, particularly in industrial IoT, edge computing, and network optimization. Current academic rank: Professor Contact: harisg@dsp.rwth-aachen.de Research Interests: Wireless systems, machine learning, signal processing, and network optimization. Key contributions include: Adaptive resource allocation in IIoT and vehicular networks AI-driven channel estimation and feedback mechanisms Security-oriented emitter identification via metric learning Federated/transfer learning for edge environments Hardware-efficient deep learning models for mmWave and THz communications Methodological Focus: Combines reinforcement learning, attention mechanisms, and robust neural architectures with practical implementations on FPGA and vehicular systems.

Roberto Tronمشاهده پروفایل

استادیار

• Control Theory
• Robotics
• Computer Vision
• +۴ مورد دیگر

Roberto Tron is an Assistant Professor in the Mechanical Engineering and Systems Engineering departments at the Boston University College of Engineering , with his office located at 110 Cummington Mall. His research integrates control theory, robotics, and computer vision to solve complex multi-agent coordination problems. His primary research interests focus on Riemannian geometry applications , distributed multi-agent systems , and safety-critical control . Key methodologies include Control Barrier Functions (CBFs), Riemannian optimization, and distributed consensus algorithms, with applications spanning autonomous aerial vehicles, robotic manipulation, and multi-robot security systems. Analysis of his recent publications reveals a strong emphasis on safety verification and real-time optimization for autonomous systems. His work consistently bridges theoretical foundations in nonlinear control with practical implementations in robotics, particularly addressing challenges in limited sensor fields of view, distributed task allocation, and noise-robust navigation. The research shows increasing integration of formal methods like Signal Temporal Logic with learning-based approaches. Tron received his Ph.D. from The John Hopkins University and previously conducted post-doctoral research at the GRASP Lab, University of Pennsylvania. His work demonstrates significant contributions to provably safe autonomous systems through frameworks like the Control Barrier Function Toolbox.

Zahra Aminzareمشاهده پروفایل

دانشیار

• Mathematical Biology
• Dynamical Systems
• Nonlinear Dynamics
• +۳ مورد دیگر

Zahra Aminzare is an Associate Professor of Mathematics at the University of Iowa. She is affiliated with the Department of Mathematics within the College of Liberal Arts and Sciences. Her research focuses on Mathematical Biology and Dynamical Systems, with emphasis on modeling biological systems such as cellular homeostasis, insect locomotion, and neural oscillators. She holds a PhD from Rutgers University. Her work explores topics including synchronization in nonlinear networks, stochastic processes in biological systems, and the application of contraction theory to stability analysis. Notable contributions include studies on ion transport dynamics, bacterial chemotaxis, and phase reduction in noisy oscillators. Her research bridges mathematical methodologies with biological phenomena, addressing questions related to system robustness and emergent behaviors. Aminzare’s publications span across journals and conferences, with recent work addressing rhythmicity in insect locomotion, spike-generation mechanisms in multi-timescale systems, and stochastic synchronization in networked systems. She maintains an active research lab focused on interdisciplinary applications of dynamical systems theory.

• Computer Architecture
• Parallel Algorithms
• Optimization
• +۴ مورد دیگر

Betül Boz is an Assistant Professor at the Department of Computer Hardware, Faculty of Engineering, Marmara University. She holds a B.Sc. and M.Sc. in Computer Engineering from Marmara University, and a Ph.D. in Computer Engineering from Boğaziçi University. Her research focuses on computer architecture, optimization, and evolutionary computing. B.Sc., M.Sc., and Ph.D. in Computer Engineering Her research interests include computer architecture, parallel algorithms, optimization techniques, and evolutionary algorithms applied to graph coloring and scheduling. Recent work explores cloud computing scheduling, register allocation, and bioinformatics applications like circRNA-disease prediction. She has published extensively in these areas, utilizing evolutionary computing and machine learning. Key trends in her publications include evolutionary algorithms for graph coloring (2015–2025), register allocation (2004–2024), and cloud computing optimization (2023). She also investigates biomedical applications such as circRNA-disease association prediction. She has advised one thesis, managed one project, and her work aligns with UN Sustainable Development Goals. Her research outputs include 14 WoS-indexed publications, 11 WoS citations, and an h-index of 25 on WoS.

• Optimization
• Process Control
• Energy Systems
• +۴ مورد دیگر

Gabriel Patron is a Researcher in the Department of Computing at Imperial College London, part of the Faculty of Engineering. He holds a PhD from the University of Waterloo, an MSc from Imperial College London, and a BASc from the University of Toronto. His research focuses on optimization, control systems, and their applications in energy, carbon capture, and aquaculture systems, emphasizing economic incentives and sustainability within the water-energy-food nexus. Patron is affiliated with the Tsay Group and the Computational Optimization Group (COG), developing algorithms for fault-tolerant processes and sustainable resource management. His academic career includes roles as a Research Associate at Imperial College London (2024–present) and a Postdoctoral Fellow at the University of Waterloo (2023–2024). His work spans process control, real-time optimization, and economic modeling, addressing challenges in industrial systems. Notable contributions include advancing algorithms for CO₂ capture systems and recirculating aquaculture operations under uncertainty. Patron’s publications highlight interdisciplinary approaches to energy systems, environmental engineering, and sustainable technologies. His research integrates theoretical advancements with practical applications, aiming to enhance efficiency and resilience in critical infrastructure sectors. He collaborates extensively with industry and academic partners to bridge theoretical insights with real-world implementation.

Venkat N. Kroviمشاهده پروفایل

استاد

• Robotics
• Autonomous Vehicles
• Human-Robot Interaction
• +۵ مورد دیگر

Prof. Venkat N. Krovi serves as the Michelin Endowed Chair Professor of Vehicle Automation in the Departments of Automotive Engineering and Mechanical Engineering at Clemson University's College of Engineering, Computing and Applied Sciences (CECAS). He directs the Automation, Robotics and Mechatronics Laboratory (ARMLab) at the International Center for Automotive Research (CU-ICAR), focusing on smart embedded systems for autonomy in challenging environments. He earned his Ph.D. in Mechanical Engineering and Applied Mechanics from the University of Pennsylvania in 1998. His research leverages distributed autonomy and human-robot synergy to extend human capabilities, with applications spanning plant automation, consumer electronics, automobile, defense, and healthcare. The work emphasizes lifecycle treatment (design through verification) of robotic systems under uncertainty. Recent publications (2024-2025) demonstrate strong trends in digital twin frameworks for autonomous vehicle validation, sim2real transfer via reinforcement learning, and integration of large language models for editable simulations. Key themes include scalable cloud-based architectures, Koopman operator theory for robustness, and containerization for reproducible robotics development. His accolades include: National Science Foundation (NSF) CAREER Award Petro-Canada Young Innovator Award Multiple best paper awards at conferences and journals ASME Dedicated Service Award (2024) Prof. Krovi has advised doctoral students including Dr. Srivatsan Srinivasan (2024). His research receives substantial funding from NSF, DARPA, ARO, and industrial partners like Michelin. He leads the NSF I/UCRC RoSeHuB center and the AutoDRIVE ecosystem for autonomous driving education. As ARMLab director, he oversees projects including OpenCAV, the Robotics for AV Systems Bootcamp, and containerized terramechanics simulations. The lab specializes in mechatronic design, verification/validation frameworks, and human-autonomy coexistence studies for next-generation mobility solutions.

SangWoo Parkمشاهده پروفایل

استادیار

• Power Systems
• Supply Chain Resilience
• Optimization Algorithms
• +۳ مورد دیگر

SangWoo Park is an Assistant Professor in the Department of Mechanical and Industrial Engineering at the New Jersey Institute of Technology (NJIT). He holds a B.S. in Environmental Engineering from Johns Hopkins University (2016), and M.S. and Ph.D. in Industrial Engineering and Operations Research from the University of California, Berkeley (2017 and 2022, respectively). His research focuses on power systems optimization, supply chain resilience, and AI-driven engineering solutions. He is a recipient of the 2020 American Control Conference Best Student Paper Award. Education Background: Ph.D., Industrial Engineering and Operations Research, UC Berkeley (2022) M.S., Industrial Engineering and Operations Research, UC Berkeley (2017) B.S., Environmental Engineering, Johns Hopkins University (2016) Research interests include: Power grid operations and resiliency Circular supply chain modeling Advanced optimization algorithms Machine learning applications in energy systems His work bridges theoretical optimization with practical engineering challenges, such as anomaly detection in smart grids and post-disaster supply chain recovery. Awards: Best Student Paper Award, 2020 American Control Conference Professional Activities: Office Hours: Monday 10:30 AM–12:00 PM Website: https://sangwoopark-njit.github.io/