Find Academic Supervisors

• Systems Engineering
• Control Systems Design
• Thermal Management Systems
• +5 more

James Allison is an Associate Professor in both the Industrial and Enterprise Systems Engineering and Aerospace Engineering departments at the University of Illinois at Urbana-Champaign. He is also affiliated with the Carl R. Woese Institute for Genomic Biology. His research focuses on systems engineering, control systems design, thermal management systems, and optimization methodologies. Dr. Allison has contributed to advancements in fluid-based thermal management systems, floating offshore wind turbine control co-design, and AI-driven design optimization. His work emphasizes interdisciplinary approaches, blending mechanical, aerospace, and computational engineering principles. Key research areas include design automation, graph neural networks for system architecture exploration, and reliability-based co-design of complex systems. He has pioneered methodologies for extracting design knowledge from optimization data and advancing multifunctional structures for attitude control in aerospace systems. Awards: NSF CAREER Award (2017) Labs/Groups: Involved in the development of tools like LGR-MPC and SS-MPC for Model Predictive Control, and the WEIS toolset for offshore wind turbine analysis. Dr. Allison’s recent publications highlight contributions to thermal management system configurations, control strategies for offshore renewable energy systems, and topology optimization techniques. His research bridges theoretical advancements with practical applications in aerospace, energy, and manufacturing sectors.

Venkatesh SaligramaView profile

Professor

• Machine Learning
• Video Analysis
• Statistical Signal Processing
• +3 more

Venkatesh Saligrama is a Professor in the Department of Electrical and Computer Engineering at Boston University, with affiliations in Systems Engineering, the Center for Information and Systems Engineering, and the College of Arts & Sciences’ Computer Science division. He holds a PhD from MIT (1997) and leads the Data Science & Machine Learning Lab. His research focuses on Machine Learning, Video Analysis, Statistical Signal Processing, and Network Science, with notable contributions to vision-language models, bandit algorithms, and synthetic data strategies. He has received prestigious awards like the NSF CAREER Award (2005) and Presidential Early Career Award (2003). His teaching includes courses on signals, stochastic processes, and digital communication systems. Saligrama’s work bridges theoretical foundations and applied systems, emphasizing efficient learning under constraints. His recent publications explore zero-shot detection, data-efficient pretraining, and safe optimization techniques. Beyond academia, his research impacts domains like autonomous systems, healthcare prediction, and cybersecurity. He actively contributes to editorial roles, conferences, and interdisciplinary initiatives, fostering collaborations across engineering and computer science.

• Robotics
• Control Systems
• Sensor Technology
• +3 more

Kartik Ariyur is a Lecturer in the Department of Mechanical Engineering at Purdue University, based remotely. His research focuses on autonomous systems, control systems, sensor technology, and energy systems. His work spans applications in robotics, navigation, and environmental sustainability. Key projects include improving UAV navigation accuracy, developing LiDAR-based traffic monitoring systems, and optimizing wireless networks through adaptive control techniques. Education details are not explicitly listed, but his publications suggest expertise in control systems, robotics, and interdisciplinary engineering. His research interests emphasize practical applications of advanced control theory in real-world systems, including autonomous vehicles and renewable energy systems. Recent publications highlight contributions to geolocation using celestial and magnetic sensing, multi-object tracking with LiDAR, and adaptive control algorithms for hypersonic vehicles. While no specific awards are listed, his extensive publication record reflects active engagement in cutting-edge engineering research. Advising and grants: No formal advisees are listed, but his research collaborations likely involve graduate students and interdisciplinary teams. His work aligns with initiatives in smart infrastructure, autonomous systems, and sustainable energy. No specific grants are mentioned, but his projects suggest funding from sources like the USDOT Regional University Transportation Center. Research activities include contributions to labs focused on autonomous systems, sensor networks, and control systems engineering. His work often integrates hardware and software solutions for real-world challenges in transportation and environmental monitoring.

• Quantum Chemistry
• Uncertainty Quantification
• Numerical Analysis
• +5 more

Virginie Ehrlacher Galland is a Professor at CERMICS (Centre d'Enseignement et de Recherche en Mathématiques et Calcul Scientifique) within École des Ponts ParisTech. Her expertise lies in applied mathematics, numerical analysis, and computational physics, with a focus on multiscale problems, quantum chemistry, and uncertainty quantification. She holds a PhD from CERMICS (2012) and a Habilitation (2020) from Université Paris-Dauphine. Her research interests include cross-diffusion systems, reduced basis methods, and optimal transport applications. Key contributions involve numerical methods for electronic structure calculations, homogenization techniques, and adaptive algorithms. She leads the ERC Starting Grant HighLEAP (2023-2028) and contributes to major projects like the ERC Synergy project EMC². Awards: Irène Joliot-Curie Prize (2023), Chevalier de l’Ordre National du Mérite (2025). Grants/Projects: ERC Starting Grant HighLEAP (PI), ERC Synergy EMC² (Member), ANR JCJC COMODO (PI). Her work bridges theoretical analysis and computational methods, addressing challenges in materials science, fluid dynamics, and machine learning applications.

• Artificial Intelligence
• Collaborative Robotics
• Human-Machine Interaction
• +3 more

Madeleine EL ZAHER is a Researcher-Lecturer at CESI, affiliated with the Engineering and Numerical Tools research team. Her work focuses on Artificial Intelligence, Collaborative Robotics, Human-Machine Interaction, and Multi-Agent Systems. She holds a PhD in Computer Sciences from the University of Technology of Belfort-Montbéliard (2013) and a Master’s degree in Computer Sciences and Telecommunications from Paul Sabatier University (2010). Teaching responsibilities include Computer Sciences and Electronics at the Engineering program level, emphasizing project-based learning and training through research. She co-supervises PhD students in industrial robotics and cyber-physical systems, including Abdessalem ACHOUR (defending in 2024) and Badra Souhila GUENDOUZI (defending in 2025). Her research spans semantic mapping in mobile robotics, federated learning for industrial systems, and platooning algorithms for autonomous vehicles. Notable publications include work on semantic mapping with 3D models (2024), federated learning frameworks using genetic algorithms (2023), and verification of platooning systems (2012–2015). No scientific awards are explicitly listed, but her contributions reflect impactful work in autonomous systems and robotics. Grants and lab affiliations are not detailed in the provided text, though her team’s research aligns with CESI’s focus on engineering and numerical tools.

Tarunraj SinghView profile

Professor

• Target tracking
• Human-vehicle control
• Optimal control
• +7 more

Tarunraj Singh is a Professor in the Department of Mechanical and Aerospace Engineering at the University at Buffalo, part of the School of Engineering and Applied Sciences. His research focuses on control systems, robotics, and dynamics, with emphasis on target tracking, optimal control, and vibration mitigation in complex systems. He holds a PhD from the University of Waterloo (1991) and earlier degrees from Indian educational institutions. His work integrates advanced mathematical techniques like polynomial chaos expansions and differential flatness with practical applications in aerospace, robotics, and biomedical engineering. Education : PhD, Mechanical Engineering, University of Waterloo, 1991 ME, Mechanical Engineering, Indian Institute of Science, 1988 BE, Mechanical Engineering, Bangalore University, 1986 Research Interests : Dr. Singh specializes in developing robust control strategies for nonlinear systems, with notable contributions to: Reference shaping for precision motion control Uncertainty quantification in dynamical systems Vibration suppression in robotic systems Bioengineering applications including blood glucose control for diabetics Space systems and tethered satellite dynamics His lab focuses on bridging theoretical advancements with real-world implementation through experiments involving UAVs, robotic manipulators, and biomedical devices. Research Trends : Recent work emphasizes energy-efficient trajectory planning, probabilistic control under uncertainty, and real-time sensor integration. His publications (2020–2025) consistently address challenges in optimal control with practical applications across robotics, aerospace, and biomedical systems. Lab Activities : Directs the Control, Dynamics and Estimation Laboratory, which develops novel algorithms for motion control, system identification, and safety-critical applications. Current projects include UAV payload stabilization, autonomous vehicle navigation, and smart agricultural robotics.

Rajasekhar AnguluriView profile

Assistant Professor

• Control Systems
• Power Systems
• Machine Learning
• +5 more

Rajasekhar Anguluri is an Assistant Professor in the Department of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County (UMBC). His research focuses on estimation, identification, and control in large-scale systems, particularly in energy systems and cyber-physical networks. He holds a Ph.D. and M.S. from the University of California, Riverside (2019), and a B.Tech. from National Institute of Technology, Warangal, India (2013). Research interests include sparse state estimation, structured system identification, statistical inference in engineering systems, and security of networked cyber-physical systems. He has contributed to applications in power systems, including parameter estimation in low-inertia grids and topology learning in distribution networks. His work also addresses cyber-attack detection and mitigation in bulk power systems. Anguluri has received the Mistletoe Research Fellowship (2022-2023) from the Momental Foundation. His academic background includes a postdoctoral position at Arizona State University's School of Electrical, Computer, and Energy Engineering, where he collaborated with experts in control theory and machine learning. Publications highlight advancements in network analysis, such as grid topology identification with hidden nodes and structure learning in conservation networks. His interdisciplinary approach integrates systems theory, statistics, and data science to solve real-world engineering challenges.

Umberto MichelucciView profile

Professor

• Machine Learning
• Artificial Intelligence
• Astrophysics
• +5 more

Umberto Michelucci is a Professor of Scientific Machine Learning at Lucerne University of Applied Sciences and Arts (HSLU), Switzerland. He holds a PhD in Machine Learning applied to Physics and has over 20 years of industry experience. He is the Subject Head of Applied Data Intelligence in Continuing and Executive Education, Head of Certificates in Machine Learning/Data Engineering, and founder of TOELT LLC and the AI Center of Excellence at Helsana Versicherung AG. His research focuses on machine learning applications in science, astrophysics, uncertainty quantification, and sensor technology. Education PhD in Machine Learning applied to Physics (Portsmouth University) Master in Theoretical Physics (University of Florence) Postgraduate Certificate in Higher Education (Open University, UK) Research Interests Michelucci’s work bridges machine learning and scientific disciplines. Key areas include: Machine learning for astrophysics (INAF collaborations) Uncertainty analysis in high-stakes ML systems Deep learning for optical sensing (e.g., olive oil quality analysis) Foundational mathematical concepts for ML in science Awards & Recognition World’s Top 2% Scientists (Stanford List) Google Developer Expert in Machine Learning AI Global Ambassador (2022) TOP AI Influencer in Switzerland (2021) Grants & Collaborations He collaborates with institutions like INAF (Italy) and NVIDIA/Google, leading projects on AI for agrifood, medical imaging, and astrophysics. His work includes $multi-million industry partnerships and EU-funded research. Labs & Teams Director of the TOELT AI Lab and oversees HSLU’s Applied Data Intelligence programs. Active in open-source initiatives and global AI standardization efforts.

David B. BrownView profile

Professor

• Stochastic models
• Mathematical optimization
• Energy transition
• +3 more

David B. Brown is the Snow Family Business Distinguished Professor in Decision Sciences and Faculty Director of the Center for Energy, Development and Global Environment (EDGE) at Duke University's Fuqua School of Business. He holds a Ph.D. from MIT (2006). His research focuses on algorithm design for decision problems under uncertainty, with applications in energy systems, sustainability, and stochastic modeling. Key contributions include dynamic resource allocation frameworks, stochastic dynamic programming relaxations, and robust optimization techniques. Professor Brown leads the GRACE project (funded by the Department of Energy, 2020–2025), exploring risk-aware grid management for clean energy systems. His work bridges theoretical advancements with practical applications in energy transition, climate mitigation, and mathematical optimization. He has received recognition including the Snow Family Distinguished Professorship (2024). Recent publications emphasize dynamic frameworks for integrated energy systems, fluid policies in resource allocation, and sequential decision-making under uncertainty. His research also addresses pricing strategies in shared resource networks and asymptotic optimization techniques. Education: Ph.D., Massachusetts Institute of Technology (2006) Grants: DOE-funded GRACE Project, Duke EDGE Faculty Seed Grant (2024) Affiliations: Center for Energy, Development and Global Environment (EDGE)

Rishikesh YadavView profile

Researcher

• Approximation Theory
• Functional Analysis
• Dynamical Systems
• +3 more

Rishikesh Yadav is a postdoctoral researcher at the Department of Mathematics and Mathematical Statistics, Umeå University, Sweden , and previously held a postdoctoral position at the Namur Institute for Complex Systems (naXys), University of Namur, Belgium . He earned his Ph.D. in Approximation Theory from Sardar Vallabhbhai National Institute of Technology Surat, India. His research focuses on Approximation Theory, Functional Analysis, Dynamical Systems, Operator Theory, and Optimization Theory. Notable contributions include work on Koopman operators, control theory, and compressed sensing. He has received awards such as the V. M. Shah Prize (2020) and Best Oral Presentation Award (2019). Recent research emphasizes developing algorithms for convex optimization on measure spaces, supported by the Kempe Foundation (2024–2026). His work bridges approximation theory with dynamical systems, including publications on Szász-Mirakjan operators, statistical convergence, and Koopman operator approximations via Bernstein polynomials. Grants/Fellowships: Kempe Foundation (2024–2026) Labs/Teams: naXys Institute (Belgium), Umeå University's Department of Mathematics

Feiran ZhaoView profile

Researcher

• Data-Driven Control
• Adaptive Control
• Reinforcement Learning
• +2 more

Feiran Zhao is a Researcher at the Institute of Automatic Control, part of the Department of Mechanical and Process Engineering at ETH Zürich. He holds a B.S. in Control Science and Engineering from Harbin Institute of Technology (2018) and a Ph.D. from Tsinghua University (2024). His research focuses on data-driven control, adaptive control, reinforcement learning, and their applications in engineering systems. Zhao is currently a postdoc under Prof. Florian Dorfler at ETH's Automatic Control Lab. Research interests span topics like policy optimization for LQR systems, quantized feedback control, and model predictive control acceleration. His work bridges machine learning and classical control theory, with applications in robotics, power systems, and aerospace engineering. His publications (2019–2025) explore theoretical foundations of policy gradient methods, convergence analysis, and practical implementations in autonomous systems. Though no awards are explicitly listed, his active research in high-impact areas suggests potential recognition. As part of the Automatic Control Lab, Zhao collaborates on projects involving data-enabled control strategies and real-world system applications. No student advisees are currently listed.

Kexue ZhangView profile

Assistant Professor

• Hybrid Dynamical Systems and Control Theory
• Time-Delay Systems
• Nonlinear and Hybrid Control Systems
• +7 more

Kexue Zhang is an Assistant Professor and Verner Family Faculty Fellow at Queen's University, holding a joint appointment in the Department of Mathematics and Statistics within the Faculty of Arts and Science and the Department of Electrical and Computer Engineering in the Stephen J.R. Smith Faculty of Engineering and Applied Science . He earned dual PhDs in Applied Mathematics (University of Waterloo) and Control Theory and Control Engineering (Shandong University), followed by postdoctoral positions at Queen's University, the University of Calgary, and the Pacific Institute for the Mathematical Sciences. His research focuses on hybrid dynamical systems , control theory , and applications in time-delay systems , nonlinear control , and dynamical systems on time scales . Key interests include event-triggered control, distributed convex optimization, and stability analysis of complex systems. Research Trends in Publications: Recent work emphasizes event-triggered stabilization, time-delay systems, and impulsive control strategies. His 2020–2024 articles highlight advancements in Zeno-behavior exclusion, synchronization of dynamical networks, and Lyapunov-based stability criteria for hybrid systems. Awards: Verner Family Faculty Fellow, Queen's University Advising and grants: Currently mentoring students in hybrid systems and time-delay control. Active in collaborative research projects across engineering and applied mathematics disciplines. Labs/Teams: Engaged in interdisciplinary research groups focused on systems control, automation, and mathematical modeling at Queen's University.

SEHER ASLANCIView profile

Professor

• Geometry
• Differential Geometry
• Riemannian Geometry
• +5 more

Prof. SEHER ASLANCI is a Professor of Mathematics Education at ALANYA ALAADDİN KEYKUBAT UNIVERSITY, Faculty of Education. Her academic journey includes roles such as Vice Dean (2022–2023) and Department Head (2017–2018). She holds a Doctorate in Geometry from Atatürk University (2011). Research focuses on differential geometry (tensors, Riemannian structures) and mathematics education (bibliometric analyses of pedagogical methods like inquiry-based learning and realistic mathematics education). Publications span 20+ years, emphasizing geometric structures and educational methodologies. Awards include TUBITAK's UBYT grants (2009, 2011, 2014). Administrative roles include Scientific Research Commission membership (2020–2021) and Mevlana Exchange Program coordination (2016–2017). Education: Integrated PhD (Geometry), Atatürk University, 2011 Mathematics Teaching Programme, Atatürk University, 1999–2004 Awards: Encouragement of International Scientific Publications (TUBITAK UBYT-2014) Encouragement of International Scientific Publications (TUBITAK UBYT-2011) Encouragement of International Scientific Publications (TUBITAK UBYT-2009) Research themes blend pure geometry (tensor bundles, complex structures) with applied educational studies (bibliometric trends in tech-enhanced learning). Recent work explores heat flux control systems and deformations in geometric structures.

Sevda AkarView profile

Professor

• Public Finance
• Fiscal Policy
• Health Expenditures
• +6 more

Sevda Akar is a Professor in the Department of Budget and Financial Planning at Bandırma Onyedi Eylül University, Faculty of Economics and Administrative Sciences. She currently serves as the Head of the Department and has held roles as Deputy Head and Faculty Board Member. Her research focuses on public finance, fiscal policy, health expenditures, and the impact of emerging technologies like AI on public sectors. She has authored/co-authored over 30 books and numerous articles, contributing to global debates on political economy, green development, and migration. Her work emphasizes evidence-based policymaking, particularly in crisis management and sustainable development. Education and Academic Journey: PhD studies and Research Assistant roles from 2009-2015 Academic appointments progressing from Research Assistant (2009-2016) to Associate Professor (2018) and Professor (2023) Research Interests: Public finance mechanisms and fiscal consolidation Healthcare financing and pandemic responses Gender equity in maritime industries Syrian refugee integration challenges AI's role in public administration and healthcare Publications: Her recent articles address AI-driven public sector innovation, post-disaster fiscal recovery (e.g., Kahramanmaraş earthquakes), and gender barriers in male-dominated sectors. She frequently publishes in indexed journals like Journal of Finance Studies and contributes to international conferences on political economy. Awards: Recipient of the Best Paper Award in international academic competitions. Advising & Grants: Supervised master's theses on topics like digital economy budget policies and maritime industry challenges. Lead researcher in projects analyzing female officers' workplace issues. Labs/Teams: Active in editorial boards of journals like International Journal of Business and Economics Research and collaborates with teams on global carbon budget modeling and smart city strategies.

Matthew HaleView profile

Associate Professor

• Control Systems
• Optimization
• Multi-Agent Systems
• +3 more

Matthew Hale is an Associate Professor in the Department of Electrical and Computer Engineering at Georgia Tech. He holds affiliations with the CORE Lab and focuses on control systems, optimization, robotics, and privacy-preserving algorithms. His work emphasizes multi-agent systems and practical applications in autonomous systems. Education: BSE in Electrical and Computer Engineering from the University of Pennsylvania (2012), PhD in Electrical and Computer Engineering from Georgia Tech (2017). Research Interests: His research bridges theoretical control systems with applied robotics, emphasizing distributed optimization, privacy in networked systems, and deception-resistant decision frameworks. Key areas include multi-agent coordination, privacy-aware control, and safe autonomous operations. Publications Trends: Recent work focuses on differential privacy in optimization, hybrid systems models for control, and scalable algorithms for multi-agent systems. Notable topics include privacy-preserving epidemic modeling and autonomous satellite control. Awards: AFOSR YIP (2023), ONR YIP (2022), AFRL Summer Faculty Fellowship (2020), NSF CAREER Award (2019). Advising & Grants: Leads research teams in multi-agent robotics and privacy engineering. Active in securing grants for foundational control theory and applied robotics projects. Labs/Teams: Core contributor to Georgia Tech’s CORE Lab , advancing interdisciplinary work in control systems and robotics.