Find Academic Supervisors

Rahul MangharamView profile

Professor

• Formal Methods
• Machine Learning
• Control Systems
• +3 more

Rahul Mangharam is a Professor in the Department of Electrical and Systems Engineering at the University of Pennsylvania's School of Engineering and Applied Science, with a secondary appointment in Computer and Information Science. He directs the Safe Autonomous Systems Lab (mLAB) and is a founding member of the PRECISE Center. Mangharam serves as Penn Director for the Safety21 DoT National University Transportation Center ($20MM), Director of the Autoware Center of Excellence, and leads the F1Tenth Autonomous Racing Community. Education: Ph.D. in Electrical & Computer Engineering, Carnegie Mellon University M.S. in Electrical & Computer Engineering, Carnegie Mellon University B.S. in Electrical & Computer Engineering, Carnegie Mellon University His research bridges formal methods, machine learning, and control systems with applications in medical devices, autonomous systems, and energy-efficient buildings. Key focus areas include safety verification for autonomous vehicles, real-time control systems, and patient-specific cardiac modeling for clinical applications. Recent work explores conformal prediction for safe perception, differentiable control barrier functions, and explainable autonomous systems. Mangharam's publication trends show strong emphasis on autonomous systems safety (control synthesis, uncertainty quantification) and biomedical applications (cardiac modeling, clinical decision support). His 2022-2023 publications demonstrate cross-disciplinary approaches combining control theory, machine learning, and formal methods for robust autonomous systems. Awards and Honors: Presidential Early Career Award (PECASE) 2016 IEEE Benjamin Franklin Key Award 2014 NSF CAREER Award 2013 Intel Early Faculty Career Award 2012 National Academy of Engineers US Frontiers of Engineering (2012, 2018) Stephen J. Angelo Term Chair (2008-2013) He leads multiple major grants including NSF CAREER, DoT Safety21 Center ($20MM), DoE Energy-Efficient Building Hub ($160MM), and DARPA HACMS. Current PhD students include Zirui Zang. Mangharam founded the F1Tenth autonomous racing platform used globally for education and hosts international competitions through the Autoware Center of Excellence.

Betsy StovallView profile

Professor

• Harmonic Analysis
• Fourier Analysis
• Partial Differential Equations
• +2 more

Betsy Stovall is a Professor of Mathematics at the University of Wisconsin–Madison and holds the Letters and Science Mary Herman Rubenstein Professor chair. She serves as the AMS Associate Secretary for the Central Section . Education : Not explicitly stated in provided text. Appointments : Regular faculty at UW–Madison since at least 2012 Organizer of graduate analysis seminars Research Interests : Stovall specializes in harmonic analysis , focusing on operators involving curvature, oscillatory integrals, and Fourier restriction phenomena. Her work intersects with partial differential equations (PDEs) through the study of dispersive equations and geometric analysis problems. Teaching : Complex Analysis (Math 623) - Fall 2021 Calculus III (Math 234) - Fall 2020 Graduate Analysis Seminar - Spring 2022 Organized UW Madison undergraduate summer school in Analysis (2018) Scientific Contributions : Sole or joint author of 15+ publications NSF RTG grant in Analysis and PDE Active in harmonic analysis seminars and educational initiatives Administrative Roles : AMS Associate Secretary Co-organizer of RTG/Student seminars Summer school director

• Machine Learning
• Dynamical Systems
• Control Theory
• +4 more

Michael Mühlebach is a Research Group Leader at the Max Planck Institute for Intelligent Systems in Tübingen, Germany, leading the independent Learning and Dynamical Systems group. His academic journey began at ETH Zurich where he earned his B.Sc. (2010) and M.Sc. (2013) in mechanical engineering, specializing in robotics, systems, and control. He completed his Ph.D. at ETH Zurich in 2018 under Prof. R. D'Andrea, followed by postdoctoral research at UC Berkeley with Prof. Michael I. Jordan. Dr. Mühlebach's research spans machine learning, dynamical systems, control theory, and optimization . His work bridges theoretical foundations with practical applications in robotics, developing methods that incorporate physical constraints and system dynamics into learning frameworks. His group focuses on online learning, physics-informed machine learning, and large-scale optimization for cyber-physical systems, with applications in electromagnetic navigation, robotic table tennis, and energy-efficient flight systems like the shape-changing robot Floaty . His publication record shows a strong focus on constrained optimization, with recent work exploring decision-dependent stochastic optimization, nonlinear feedback, and the theoretical foundations of reinforcement learning. His research integrates perspectives from control theory, dynamical systems, and optimization to develop algorithms with strong theoretical guarantees and practical performance. Outstanding D-MAVT Bachelor Award Willi-Studer prize for best Master's degree ETH Medal and HILTI prize for doctoral thesis Branco Weiss Fellow (2018) Emmy Noether Fellowship (2020) Amazon Fellowship (2024) Dr. Mühlebach actively mentors doctoral researchers and is seeking talented students for PhD and Master's projects. His research group has received funding from multiple prestigious fellowships and maintains collaborations across institutions including ETH Zurich, UC Berkeley, and various Max Planck research units. The group's work spans theoretical developments to practical implementations on robotic systems, demonstrating strong connections between mathematical theory and physical realization.

• Wireless Systems
• Network Security
• Computational Trust
• +8 more

Steve Hailes is a Professor of Wireless Systems at the Department of Computer Science, University College London. He has served as Head of Department since 2019 and Deputy Head from 2005. His research spans wireless networks, computational trust, AI, and sensor systems for health, ecological, and environmental applications. Education: PhD and undergraduate degree from Cambridge University Appointment: Joined UCL in 1991 (postdoc), Lecturer in 1992 Research interests include: Trust and security in networked systems (co-founder of computational trust) Security of industrial control systems AI/ML applications in security and causal discovery Multi-agent reinforcement learning and moral behavior modeling Gas sensor fabrication and deployment for diverse applications Recent publications focus on feature selection for cybersecurity , moral alignment in LLM agents , trust-based consensus algorithms , and causal discovery using reinforcement learning . Collaborations include co-authors like Westphal, Musolesi, and Tennant. Applications span healthcare (dementia, JIA), ecology (endangered species in Botswana), and environmental monitoring (CO distribution, meth lab detection).

• Algebraic Geometry
• Symplectic Geometry
• Mathematical Physics
• +1 more

Kazushi Ueda is an Associate Professor at the Graduate School of Mathematical Sciences, The University of Tokyo, where he has been since April 2015. His research spans algebraic geometry, symplectic geometry, and mathematical physics, with a focus on homological mirror symmetry and its applications to moduli spaces, Calabi-Yau manifolds, and singularities. He previously held academic positions at Osaka University from 2006 to 2015, including roles as Assistant Professor and Associate Professor. Ueda has also had visiting appointments at institutions such as the University of Oxford, Max Planck Institute for Mathematics, and Korea Institute for Advanced Study. Bachelor of Science, Kyoto University (1997-2001) Master of Science, Kyoto University (2001-2003) Doctor of Science, Kyoto University (2003-2006) Ueda's research explores the deep interplay between complex and symplectic geometry through mirror symmetry, particularly in the context of Calabi-Yau varieties, toric degenerations, and dimer models. His work addresses derived categories, stability conditions, and moduli problems, with recent contributions to noncommutative algebraic geometry and applications in mathematical physics. He has collaborated extensively with researchers like Akira Ishii, Masahiro Futaki, and Shinnosuke Okawa. His publications highlight homological mirror symmetry for K3 surfaces, Grassmannians, and singularities, as well as studies on modular forms, cluster transformations, and the Grothendieck ring. Ueda is a member of the Mathematical Society of Japan and has contributed to educational programs, including graduate lectures on mirror symmetry and symplectic geometry.

Dieter BraunView profile

Professor

• Origins of Life
• Prebiotic Chemistry
• Molecular Evolution
• +5 more

Dieter Braun is a Professor in the Faculty of Physics at Ludwig Maximilian University of Munich (LMU), leading the Functional NanoSystems research group. He serves as speaker of the CRC 235 Emergence of Life and coordinates the Molecular Origins component of the Origins Cluster. Dr. Braun holds an ERC Synergy Grant (starting April 2025), leads the CRC 392 Molecular Evolution (starting April 2024), and is a Fellow in the Max Planck School Matter to Life (since October 2023). His research focuses on understanding the physical mechanisms that could have led to the emergence of Darwinian evolution from prebiotic molecules on early Earth. Braun's laboratory investigates non-equilibrium settings, particularly asymmetrically heated open cracks in rocks, which create intricate wet-dry cycles, temperature gradients, and fluidic effects that could drive molecular evolution. His work bridges physics, chemistry, and biology to explore how dead molecules might combine through physical forces into autonomous mechanisms of evolution. Analysis of Braun's recent publications reveals a strong focus on thermal gradients and non-equilibrium physics in prebiotic environments. His research demonstrates how heat flows can concentrate molecules, drive polymerization, create pH gradients, and enable non-enzymatic replication of nucleic acids. The publications span high-impact journals including Nature, Nature Physics, and Nature Chemistry, showing interdisciplinary work connecting physics, chemistry, geology, and biology in the context of life's origins. Klung-Wilhelmy Weberbank Price (2011) Technology Transfer Price of the DPG (with LMU and NanoTemper) Deutscher Innovationspreis (2012) Step Award (2012) Dr. Braun has successfully mentored numerous PhD students, including Stefan Duhr and Philipp Baaske who founded the award-winning startup NanoTemper Technologies. His research is supported by multiple prestigious grants including ERC Starting, Advanced, and Synergy Grants, as well as funding from the Simons Collaboration on the Origins of Life. His laboratory collaborates extensively with other researchers across disciplines and institutions, particularly with Hannes Mutschler in the new ERC Synergy project. The Braun laboratory operates within the CRC 235 Emergence of Life and the Origins Cluster at LMU Munich, with strong connections to the Max Planck Society through the Max Planck School Matter to Life. The research group maintains active collaborations with geochemists, biophysicists, and molecular biologists to create comprehensive experimental models of prebiotic environments.

• Nuclear Engineering
• Reactor Physics
• Thermal Hydraulics
• +3 more

Alessandro Petruzzi is an Industry Professor in the Department of Engineering Physics at McMaster University's College of Engineering. His research focuses on nuclear engineering and reactor physics, particularly in thermal hydraulics and uncertainty analysis for nuclear installations. McMaster University, College of Engineering, Department of Engineering Physics Academic Rank: Industry Professor Petruzzi's scholarly work includes contributions to nuclear reactor safety analysis and computational methods. His 2014 publication on OECD-NEA PSBT benchmarks and 2009 study on void fraction uncertainty in BWR fuel bundles highlight expertise in nuclear reactor design and thermal-hydraulic modeling. Research trends emphasize computational reactor physics, safety assessment methodologies (e.g., CIAU), and uncertainty quantification in nuclear systems. While no specific awards are listed, his work aligns with advanced nuclear engineering practices.

• Cosmology
• Particle Physics
• Astrophysics
• +6 more

Prof. Dr. Dominik Schwarz is a faculty member at the Faculty of Physics , Bielefeld University. His research focuses on Cosmology and Particle Physics , particularly in the areas of Dark Energy , Dark Matter , Cosmological Inflation , and Large-Scale Structure Formation . He contributes to projects like the International LOFAR Telescope Consortium and the SFB-TRR 211 on strongly interacting matter. APART Fellow of Austrian Academy of Sciences Humboldt Fellow CERN Fellow His recent work explores the cosmic dipole anisotropy , axion density perturbations , and multi-wavelength cosmic web mapping . He also advances data science infrastructure through the PUNCH4NFDI consortium.

• Machine Learning
• Artificial Intelligence
• Distributed Systems
• +3 more

Ameet Talwalkar is an Associate Professor in the Machine Learning Department at Carnegie Mellon University and Chief Scientist at Datadog. He holds a PhD from the Courant Institute at NYU (2010) where he received the Janet Fabri Prize for Best Thesis. His professional achievements include co-founding Determined AI (acquired by HPE), creating MLlib in Apache Spark, co-authoring the textbook 'Foundations of Machine Learning,' and spearheading the MLSys conference. Talwalkar's research focuses on fundamental challenges in machine learning systems, including distributed ML, federated learning, neural architecture search, and human-AI interaction. His work bridges theoretical foundations with practical applications across domains like computational biology, PDE solving, and code generation. Current interests include AI for science, specialized model development, and agent-based systems. His publications demonstrate strong focus on ML systems optimization, foundation model evaluation, and interpretable AI. Recent works investigate specialized foundation models, PDE-solving frameworks, code generation tools, and human-AI interaction paradigms. The research consistently targets efficiency, scalability, and practical deployment challenges. Best Paper Award at EAAMO 2023 Best Student Paper at NYAS ML Symposium 2009 Runner-up for Best Real-world Application at Socal ML Symposium 2017 Janet Fabri Prize for Best PhD Thesis (2010) Talwalkar leads the CMU MLSys Lab focused on scalable ML systems and has served as Board President for the MLSys conference series. His educational contributions include developing courses like 'Machine Learning with Large Datasets' and creating the LEAF benchmark for federated learning and NAS-Bench-360 for neural architecture search.

Chitraleema ChakrabortyView profile

Assistant Professor

• Quantum Emitters
• 2D Materials
• Quantum Optics
• +5 more

Dr. Chitraleema Chakraborty is an Assistant Professor in the Departments of Materials Science and Engineering and Physics and Astronomy at the University of Delaware. Her research focuses on solid-state quantum emitters, 2D materials, and quantum optics, aiming to develop quantum technologies for computing, communication, and sensing. She combines computational and experimental approaches to predict, fabricate, and image quantum emitters integrated with photonic devices. Education: Ph.D. in Materials Science, University of Rochester (2018) Dual MS and MTech in Nanophysics and Nanostructures, University of Delhi and Joseph Fourier University, Grenoble (France) BSc Honors in Physics, Jadavpur University, Kolkata (2009) Research interests include quantum emitters in 2D materials, integrated photonics, and hybrid quantum systems. Her work bridges theoretical predictions and experimental validation to advance applications in quantum information and nanoscale sensing. Publications highlight advancements in strain-tunable quantum emitters, on-chip photonic integration, and room-temperature ferromagnetism in van der Waals materials. Recent trends emphasize scalable synthesis methods and defect engineering for quantum technologies. Awards: Carl E. Anderson Outstanding Doctoral Thesis Finalist (2019) Rising Star in EECS (2019) Outstanding Dissertation Award, University of Rochester (2018) Best Student Speaker at MRS Fall Meeting (2017) Egide Scholar, France (2010-2011) Her research group actively explores nanoscale confinement effects and electrical tuning of quantum systems. Collaborations focus on integrating 2D materials with photonic platforms for scalable quantum devices. Ongoing projects aim to develop robust quantum emitters for real-world applications.

Ovijit ChaudhuriView profile

Associate Professor

• Mechanobiology
• Cellular Mechanics
• Extracellular Matrix
• +5 more

Ovijit Chaudhuri is an Associate Professor of Mechanical Engineering at Stanford University, with a courtesy appointment in Bioengineering. He leads research at the interface of mechanics and biology, focusing on how cellular and extracellular mechanical properties influence biological processes like cancer progression and tissue formation. His work employs advanced tools such as atomic force microscopy and 3D cell culture systems. Education: Ph.D., University of California, Berkeley/San Francisco (Bioengineering, 2009) B.S., University of California, Berkeley (Engineering Physics, 2003) Postdoctoral Fellow, Harvard University (Biomaterials, 2013) Research Interests: His lab explores molecular mechanisms behind cellular mechanics, extracellular matrix dynamics, and how mechanical cues regulate cell behavior. Key areas include cancer metastasis, mechanotransduction, and engineered biomaterials for 3D cell culture. Publications Trends: Recent work emphasizes viscoelastic hydrogels, matrix mechanics in cancer progression, and mechanistic insights into cell migration. Over 50 publications since 2015 highlight interdisciplinary approaches in biomaterials and mechanobiology. Awards: Not explicitly listed in provided materials. Advising & Labs: No specific advisee names listed, but his lab focuses on collaborative projects in mechano-biology. Active in developing biomaterial systems for drug discovery and tissue engineering applications. Labs/Teams: Leads the Chaudhuri Lab at Stanford, which integrates engineering principles with biological systems to address complex disease mechanisms and therapeutic strategies.

• Inverse Problems
• Applied Harmonic Analysis
• Machine Learning
• +3 more

Rima Alaifari is an Assistant Professor for Applied Mathematics at ETH Zürich, specializing in inverse problems, applied harmonic analysis, and scientific machine learning. She is an associated member of the ETH AI Center and will assume a full professorship at RWTH Aachen University in 2025. Her work focuses on stability analysis, regularization, and operator learning, with applications in phase retrieval and robustness of neural networks. Alaifari holds a Ph.D. in Mathematics from Vrije Universiteit Brussel (2014), where she studied under Prof. Ingrid Daubechies and Prof. Michel Defrise. She completed her M.Sc. in Applied and Industrial Mathematics at Johannes Kepler University, Linz (2010). Her academic career includes postdoctoral fellowships at ETH Zurich (2014–2016) and a Marie Curie-funded position (2016). Her research interests span inverse problems, phase retrieval, stability in machine learning, and operator learning. Notable projects include SNF-funded work on phase retrieval (2019) and collaborations on adversarial robustness in medical imaging (e.g., CT reconstruction). She has mentored PhD students Tandri Gauksson and Matthias Wellershoff, and postdocs Francesca Bartolucci (now at TU Delft) and Jesse Railo (Finnish Inverse Prize winner). Teaching includes courses on inverse problems, time-frequency analysis, and robustness of deep neural networks. She actively participates in international conferences, delivering plenary talks at venues like the International Conference on Computational Harmonic Analysis (2022) and ICERM (2023). Her work bridges mathematical foundations and practical applications, emphasizing stability and robustness in computational methods.

• Engineering sciences
• Materials science
• Aerospace
• +11 more

Ares J. Rosakis is the Theodore von Kármán Professor of Aeronautics and Mechanical Engineering at the California Institute of Technology (Caltech), where he served as Chair of the Division of Engineering and Applied Science from 2009-2015 and previously as Director of the Graduate Aerospace Laboratories (GALCIT). He has held numerous prestigious visiting professorships including at Nanyang Technological University, Northwestern University, Columbia University, Oxford University, and École Normale Supérieure in Paris. Rosakis earned his B.A. and M.A. in Engineering Science from Oxford University in 1978, followed by his Sc.M. (1980) and Ph.D. (1982) in Engineering (Solid Mechanics) from Brown University. He joined Caltech as an Assistant Professor in 1982, was promoted to Associate Professor in 1988, and to full Professor in 1993. In 2004, he was named the Theodore von Kármán Professor, one of Caltech's most distinguished named chairs. Rosakis is globally recognized as the foremost expert in dynamic failure mechanics of solid materials. His pioneering contributions span the dynamic failure of metals, composites, and interfaces. He invented Coherent Gradient Sensing (CGS) interferometry, a novel optical method sensitive to gradients of optical path differences that has been widely adopted in fracture mechanics and thin film stress measurements. His research encompasses dynamic shear-dominated rupture of heterogeneous materials, rupture mechanics of crustal earthquakes (where he experimentally discovered 'intersonic' or 'supershear' ruptures), and reliability of thin films and in-situ wafer level metrology. His work bridges engineering science, materials mechanics, and geophysics with remarkable interdisciplinary impact. His recent publications demonstrate a strong focus on earthquake mechanics and laboratory simulations of seismic events, particularly supershear earthquake ruptures. The research connects fundamental fracture mechanics with real-world geophysical phenomena, revealing how laboratory-scale experiments can illuminate the physics of large-scale earthquakes. His work has established critical links between theoretical models, experimental observations, and geological field evidence. Rosakis has received numerous prestigious awards including: 2024 Foreign Member of the Royal Society, UK 2023 Honorary PhD from National Technical University of Athens 2023 Honorary Degree of Doctor of Engineering from University of Illinois 2021 Zdeněk P. Bažant Medal for Failure and Damage Prevention 2018 Timoshenko Medal from ASME 2016 Elected to the National Academy of Sciences 2011 Elected to the National Academy of Engineering Throughout his distinguished career at Caltech, Rosakis has mentored numerous graduate students and postdoctoral researchers, many of whom have become leaders in their fields. His research has been continuously supported by major grants from the National Science Foundation, Department of Energy, and other federal agencies, focusing on dynamic fracture, earthquake mechanics, and advanced optical measurement techniques. He has served on numerous editorial boards and advisory committees for major scientific organizations. At Caltech, Rosakis leads research in the Graduate Aerospace Laboratories (GALCIT), where he has established world-class experimental facilities for studying dynamic fracture and earthquake mechanics. His laboratory features high-speed imaging systems capable of millions of frames per second, infrared diagnostics for temperature field measurements, and specialized equipment for simulating earthquake ruptures at laboratory scale. His research group combines experimental, theoretical, and computational approaches to address fundamental questions in solid mechanics and their applications to geophysics and materials engineering.

Björn BrandenburgView profile

Researcher

• Real-Time Systems
• Scheduling Algorithms
• Real-Time Operating Systems
• +5 more

Björn Brandenburg is a researcher at the Max Planck Institute for Software Systems (MPI-SWS) in Kaiserslautern, Germany. His work focuses on real-time systems, scheduling algorithms, and operating system design, with a particular emphasis on predictable resource allocation and performance guarantees in multiprocessor and cyber-physical environments. His research interests include real-time response-time analysis (e.g., PROSA ), locking protocols for multiprocessor systems, side-channel mitigation in cloud environments, and the verification of real-time scheduling policies. He has contributed to foundational studies on deadline failure probabilities, self-suspending tasks, and predictable real-time Linux implementations. Scientific awards include recognition for outstanding papers on TimerShield (2017) Offline Equivalence (2017) . His work intersects with practical systems like LITMUSRT and ROS 2, aiming to bridge theoretical guarantees with real-world applications in safety-critical and distributed real-time systems.

Xugui ZhouView profile

Assistant Professor

• Cyber-Physical Systems
• Security
• Dependability
• +7 more

Xugui Zhou is an Assistant Professor jointly appointed in the Department of Electrical and Computer Engineering and the Department of Computer Science at Louisiana State University (LSU). He holds a Ph.D. in Electrical and Computer Engineering from the University of Virginia (2024), where he worked under Prof. Homa Alemzadeh. Prior to his Ph.D., he earned an M.Eng in Control Science and Engineering and a B.Eng in Automation from Shandong University. Before joining UVA, he was a Senior Engineer at NR Research Institute, State Grid of China. His research focuses on the intersection of system dependability, security, and control theory, with applications in autonomous systems and healthcare. Key interests include engineering resilient autonomous vehicles, mitigating adversarial attacks on cyber-physical systems (CPS), and enhancing safety through formal methods and machine learning. He leads a research lab with openings for Ph.D. students and interns. Zhou is actively involved in academic service, serving on program committees for DSN'26, AsiaCCS'26, and ICCPS'25, among others. He also chairs workshops like DSML'25 and organizes conferences such as ICCPS'24. His recent awards include the UVA Engineering Endowed Graduate Fellowship (2023) and selection as a Rising Star in Cyber-Physical Systems (2023). His teaching includes courses like AI-Driven Autonomous Driving Systems and ML for CPS Security at LSU. He has extensive experience in teaching and research supervision, emphasizing hands-on projects and interdisciplinary collaboration.