Find Academic Supervisors

Eric F. LockView profile

Associate Professor

• Statistical genomics
• High-dimensional data analysis
• Machine learning
• +7 more

Eric F. Lock is an Associate Professor in the Division of Biostatistics & Health Data Science at the University of Minnesota's School of Public Health. He is also a Member of the Masonic Cancer Center (MCC) and has been at the University of Minnesota since 2014, after completing his PhD in Statistics from the University of North Carolina in 2012 and a postdoctoral fellowship in Statistical Genomics at Duke University in 2014. Lock's research focuses on developing methods for the analysis of multi-faceted high-dimensional data, particularly in "omics" fields such as genomics, metabolomics, and proteomics. His work emphasizes the integrated analysis of data from multiple sources (e.g., gene expression, metabolomics, imaging) or measured in multiple dimensions (e.g., multiple tissue types or body regions). He also specializes in exploratory factorization and clustering methods, and Bayesian nonparametric inference. His recent publications demonstrate significant contributions to tensor data imputation (BAMITA), matrix decomposition (EV-BIDIFAC), and methods for handling complex genomic data. His work bridges statistical theory with practical applications in molecular biology, addressing challenges in data integration across multiple biological measurement platforms. Delta Omega, Honorary Society in Public Health (2019) As an active researcher and educator, Lock serves on dissertation committees, including for Mykhaylo M. Malakhov who recently defended his PhD at the University of Minnesota School of Public Health. He is involved in organizing and participating in major conferences such as STATGEN 2025, demonstrating his leadership in the biostatistics community.

Marc ReichenbachView profile

Professor

• Integrated Circuits
• Embedded Systems
• VLSI Design
• +7 more

Prof. Dr.-Ing. Marc Reichenbach serves as the Chair of Integrated Systems at the Institute for Applied Microelectronics and Data Technology at the University of Rostock. His office is located at Albert-Einstein-Straße 26, 18059 Rostock, Room 102 (1st floor), with contact information including telephone (0381) 498 7270 and email marc.reichenbach@uni-rostock.de. Professor Reichenbach's research focuses on the intersection of hardware design and artificial intelligence, with particular expertise in memory technologies and computing architectures. His work spans several key areas: Development of specialized computer architectures for deep learning applications Advanced VLSI design and CPU architecture Emerging memory technologies, particularly RRAM (Resistive Random-Access Memory) FPGA-based acceleration systems Hardware implementations for neural networks and AI applications Analysis of Professor Reichenbach's recent publications (2023-2025) reveals a strong focus on memory computing technologies, particularly RRAM-based systems. His work demonstrates expertise across multiple dimensions of computer architecture including ASIC design, FPGA acceleration, and novel memory systems. The publications show a clear trajectory toward implementing AI and machine learning capabilities directly in hardware, with applications ranging from edge computing to satellite systems. A significant portion of his recent work addresses the challenges of implementing neural networks using emerging memory technologies, focusing on efficiency, reliability, and performance optimization. Professor Reichenbach teaches several advanced courses including: Computer architectures for deep learning applications Project seminar Embedded Systems Advanced VLSI Design (Advanced CPU Design) His research group appears to be actively engaged in several cutting-edge projects related to hardware acceleration for AI applications, memory computing, and embedded systems design. The group collaborates on projects involving digital twins for hardware systems, real-time operating systems for heterogeneous architectures, and specialized computing systems for various applications from medical devices to drone technology.

• String Theory
• Quantum Field Theory
• Integrable Systems
• +2 more

Konstantin Zarembo serves as a Professor within the Theoretical high energy, astroparticle and gravitational physics group at the Niels Bohr Institute, University of Copenhagen, focusing on fundamental theoretical physics research. His research program centers on String Theory and Quantum Field Theory , with particular emphasis on integrable structures in supersymmetric gauge theories. Key contributions include groundbreaking work on AdS/CFT correspondence, where he explores exact solutions through Bethe ansatz techniques, domain walls in ABJM theory, and boundary states in holographic dualities. His methodology combines advanced mathematical physics with quantum integrability to address non-perturbative phenomena in high-energy systems. Analysis of his 2021-2025 publications reveals a cohesive research trajectory centered on integrability applications across quantum field theory and string theory, with significant focus on 't Hooft loops, Gross-Neveu models, and eigenvalue systems. These works consistently appear in premier journals like Journal of High Energy Physics and Physical Review Letters, demonstrating sustained impact in theoretical physics. No scientific awards were documented in the source materials. Information regarding student supervision, grant funding, or collaborative projects beyond co-authorship was not provided in the available texts. Professor Zarembo operates within the Theoretical high energy, astroparticle and gravitational physics research group at the Niels Bohr Institute, contributing to Copenhagen's prominence in fundamental theoretical physics through his specialized expertise in integrable systems and holographic dualities.

Patrick DesrosiersView profile

Adjunct Professor

• Dimensionality Reduction
• Dynamical Systems
• Mathematical and Computational Neuroscience
• +8 more

Patrick Desrosiers serves as an Adjunct Professor in the Department of Physics, Physical Engineering and Optics within Université Laval's Faculty of Science and Engineering, while conducting neuroscience research at the CERVO Brain Research Center. He co-directs Dynamica, a multidisciplinary complex systems research group, and participates in UNIQUE (neuroscience-AI integration) and CIMMUL (mathematical modeling applications). His academic training spans physics and mathematics at Université Laval, the University of Melbourne, and CEA-Saclay. Dr. Desrosiers' research centers on mathematical and computational neuroscience , with signature contributions in dimensionality reduction and network resilience analysis . His work bridges biological and artificial neural networks , zebrafish brain mapping , and neurovascular coupling using advanced techniques from spectral graph theory , random matrix theory , and dynamical systems . Current investigations focus on neural decoding under chronic stress and structural-functional relationships in brain networks. Analysis of his 2023-2025 publications reveals three dominant trajectories: (1) Low-dimensional representations for predicting cognitive decline and neural dynamics, (2) Network reconstruction methodologies applied to neuroscience and biodiversity, and (3) Development of computational tools like NeuroTorch for neural data analysis. His work consistently integrates mathematical rigor with biological relevance across species and scales. His recognition includes: Professeur étoile prize for exceptional teaching (Faculty of Science and Engineering, Université Laval, 2018) As Dynamica co-director, he mentors a research team comprising Antoine Légaré, Arthur Légaré, Benjamin Claveau, Jordan Charest, Marziyeh Pourmousavi, Pierre-Luc Larouche, Vincent Savard, Vincent Thibeault, and Zahra Yazdani. His collaborative framework connects physics, mathematics, and neuroscience to address fundamental questions in neural network organization, with funding evident through sustained publication output and lab operations. Dynamica Lab ( https://dynamicalab.github.io/ ) serves as the operational hub for his interdisciplinary research, maintaining active collaboration with CERVO Brain Research Center and international institutions.

Mingda LiView profile

Associate Professor

• Quantum Materials
• Topological Quantum Matter
• Nanoscale Thermal Transport
• +4 more

Mingda Li is an Associate Professor in the Department of Nuclear Science and Engineering at the Massachusetts Institute of Technology (MIT), holding the Class of 1947 Career Development Professorship. His research spans quantum materials, nanoscale energy transport, and AI-driven materials discovery, utilizing neutron/X-ray scattering techniques and machine learning to address challenges in quantum computing, thermal management, and energy conversion. He leads the Quantum Measurement Group and teaches graduate courses including Quantum Theory of Materials Characterization. Education: Bachelor of Science in Engineering Physics, Tsinghua University, 2009 Doctor of Philosophy in Nuclear Science and Engineering, MIT, 2015 Postdoctoral Research, MIT Mechanical Engineering Department Research Interests: Dr. Li's quantum research develops theoretical frameworks for topological order and defect-engineered quantum materials, with applications in microelectronics and quantum computing. His energy transport studies investigate phonon/electron dynamics at interfaces under non-equilibrium conditions to design materials for thermal management in electronics. The AI program creates symmetry-aware generative models that integrate ab initio calculations with experimental data, enabling closed-loop materials discovery for quantum and energy technologies. Publication Trends: Analysis of 15 recent 2025 publications reveals dominant themes in quantum materials (topological semimetals, 2D magnets), AI-driven design (generative models, symmetry-equivariant networks), and advanced characterization (neutron/X-ray spectroscopy). Key innovations include defect engineering for thermal transport, machine learning for spectroscopic data interpretation, and quantum phenomenon discovery in complex materials, reflecting strong interdisciplinary integration. Scientific Awards: No scientific awards were mentioned in the provided text. Advising and Grants: Dr. Li mentors graduate students in the Quantum Measurement Group, guiding research in quantum materials characterization and AI applications. He has taught core courses including Applied Nuclear Physics and Machine Learning in Nuclear Science and Engineering. His research is supported by grants focused on quantum engineering and nuclear materials, with collaborations spanning national laboratories and industry partners for quantum computing and energy applications. Labs and Teams: The Quantum Measurement Group operates at the intersection of experimental physics and computational science, utilizing neutron scattering facilities (including Spallation Neutron Source) and ultrafast X-ray techniques. The team develops custom software for data analysis and collaborates with institutions like MIT.nano for materials synthesis, maintaining a pipeline from theoretical prediction to device-level validation for quantum and thermoelectric materials.

Dharanidhar DangView profile

Assistant Professor

• Photonic Computing
• AI Hardware Acceleration
• Memristor-based Computing
• +3 more

Dharanidhar Dang serves as Assistant Professor in the Department of Computer Engineering at the College of AI, Cyber and Computing, The University of Texas at San Antonio (UTSA), where he advances hardware-centric artificial intelligence solutions through photonic and memristor technologies. Education Ph.D., Texas A&M University His research program bridges hardware innovation and biomedical applications, with primary focus on photonic computing architectures for real-time AI acceleration and memristor-based neuromorphic systems. He investigates critical challenges in hardware reliability (particularly degradation in memristor crossbars), energy efficiency in photonic accelerators, and co-design methodologies that optimize both algorithms and physical implementations. His biomedical work applies machine learning to macrophage biology, identifying predictive signatures for inflammatory diseases through computational immunology approaches. Analysis of his 2020-2025 publications reveals three dominant research trajectories: 1) Silicon photonic accelerators (P-ReTI, P-ReTiNA, SOFTONIC) targeting real-time and energy-efficient AI, 2) Memristor reliability frameworks addressing aging effects in deep learning hardware, and 3) Translational biomedical applications where machine learning deciphers macrophage behavior in inflammatory bowel disease and preterm infant lung conditions. This tripartite focus demonstrates exceptional versatility across hardware engineering and life sciences.

Lieven VandenbergheView profile

Professor

• Convex Optimization
• Semidefinite Programming
• System Identification
• +6 more

Lieven Vandenberghe is a Professor in the Electrical and Computer Engineering Department and Department of Mathematics at the University of California, Los Angeles (UCLA). His research focuses on convex optimization, semidefinite programming, and applications in signal processing, system identification, and control theory. Books: Co-author of Convex Optimization (2004) and Introduction to Applied Linear Algebra (2018) Courses: Teaches graduate-level courses in linear programming, convex optimization, and numerical computing (ECE236A/B/C, ECE133A/B) Software: Developer of CVXOPT, CHOMPACK, and SMCP for optimization algorithms His research group has produced significant work in sparse matrix computations, operator splitting methods, and applications to machine learning and control systems. Publications span topics like Bregman splitting, proximal gradient methods, and semidefinite programming for signal processing. His advisees include PhD students in optimization and postdoctoral researchers in applied mathematics.

Alexander MogilnerView profile

Professor

• Computational Biology
• Cell Biophysics
• Mathematical Biology
• +7 more

Alexander Mogilner is a Professor of Mathematics and Biology at New York University, with dual appointments in the Courant Institute of Mathematical Sciences and the Department of Biology within the Faculty of Arts & Science. His interdisciplinary research bridges mathematics and cell biology, focusing on computational approaches to understanding cellular mechanics. New York University, Faculty of Arts & Science Courant Institute of Mathematical Sciences Department of Biology Mogilner's educational background includes a Ph.D. in Applied Mathematics from the University of British Columbia (1995), a Ph.D. in Physics from the USSR Academy of Sciences (1990), and an M.Eng. in Engineering Physics from Ural Polytechnic Institute (1985). His research focuses on the mathematical modeling of cellular processes, particularly how cells use self-assembling molecular machines for movement and division. His work examines mechanochemical systems involving cytoskeletal polymers, molecular motors, and biochemical regulators. His publication record shows a strong focus on computational approaches to cell motility, mitosis, and cytoskeletal dynamics. Over the past decade, his work has increasingly incorporated advanced computational methods including partial differential equations, free boundary simulations, and stochastic processes to model complex cellular phenomena. His research spans both theoretical developments in mathematical biology and close collaborations with experimental cell biologists. Mogilner maintains an active laboratory focused on computational cell biology, with research projects spanning cell polarization, mitotic spindle assembly, actin-myosin contraction mechanisms, and cellular responses to electric fields. His work demonstrates how mathematical approaches can provide deep insights into complex biological systems.

• Neuroscience
• Neurology
• Psychiatry
• +4 more

Dr. Mazen Kheirbek is a Professor in Psychiatry at the University of California, San Francisco (UCSF) School of Medicine. He leads research at the Kheirbek Lab, focusing on hippocampal circuits in emotional behavior and psychiatric disorders. Education : BA (Washington University), PhD (University of Chicago), Postdoc (Columbia University) Research Interests center on: Neural circuits in anxiety and depression Hippocampal-prefrontal pathways Adult neurogenesis mechanisms Neuroplasticity in mood disorders Optogenetics and circuit mapping Translational psychiatry Publication Trends show consistent contributions to neuroscience journals, particularly in hippocampal function, neural circuits, and psychiatric disorders. His work often employs animal models and advanced imaging techniques. Scientific Awards : McKnight Foundation Memory & Cognitive Disorders Award (2020) Pew Scholar in Biomedical Sciences (2019) Klingenstein-Simons Fellowship (2019) Human Frontier Science Program Young Investigator (2019) NARSAD Young Investigator Award (2012) Grants include multiple NIH-funded projects: NIH/NIDA 1R01DA062018 (2025-2030): Dopaminergic circuits in insight learning NIH/NIMH 1R01MH136270 (2025-2030): Hippocampal stimulus processing NIH/NIDCD R01DC19813 (2021-2026): Dentate gyrus associative learning

• Additive Manufacturing
• Rapid Prototyping
• Control Systems
• +3 more

Joseph J. Beaman is Professor and Cockrell Family Dean's Chair in Engineering Excellence in the Walker Department of Mechanical Engineering at The University of Texas at Austin, where he has served since 1979 and chaired the department from 2000-2011. A pioneer in additive manufacturing, he coined the term Solid Freeform Fabrication (SFF) in 1987 and initiated academic research in the field in 1985, developing the foundational Selective Laser Sintering (SLS) process in his laboratory. His educational background includes: B.S.M.E. with high honors from The University of Texas at Austin (1972) Sc.D. in nonlinear control from Massachusetts Institute of Technology Professor Beaman's research spans additive manufacturing, control systems, and materials engineering, with seminal contributions to SFF technology that enabled rapid prototyping and manufacturing across medical, automotive, and industrial applications. His work encompasses materials processing, laser scanning, thermal control, direct metal fabrication, and biomedical applications, driving the emergence of a global industry. Analysis of his publications (2004-2012) reveals evolving focus from core SLS development to cross-disciplinary applications in metallurgical process control (electroslag remelting) and energy systems (fuel cell membrane modeling), while maintaining additive manufacturing as the central theme. Keywords consistently include materials science, control theory, and advanced manufacturing across diverse subfields. His distinguished honors include: National Science Foundation Presidential Young Investigator Award (1984, inaugural year) Distinguished Mechanical Engineer (2011) DuPont Young Faculty Award and Engineering Foundation Awards (1984, 1988) Multiple Best Paper Awards across engineering journals As an academic entrepreneur, Beaman co-founded DTM Corporation (now 3D Systems) and served as Advanced Development head (1990-1992), mentoring graduate students who became key inventors in SLS commercialization. His research has been supported by NSF and industry grants, while his leadership extended to chairing the World Technology Evaluation Center panel on Additive/Subtractive Manufacturing (2003) and serving on international assessment panels. His laboratory established UT Austin as the birthplace of academic SLS research, fostering industry-academia collaboration that directly enabled commercialization of rapid manufacturing systems now used globally for complex part production impossible with conventional methods.

• Machine Learning
• Statistical Methodology
• Computational Biology
• +5 more

Eric P. Xing is a Professor at the Language Technologies Institute of Carnegie Mellon University , and currently serves as President of the Mohamed bin Zayed University of Artificial Intelligence . His work bridges machine learning methodology with computational biology and large-scale AI systems . Research Focus: Developing machine learning theory for high-dimensional, dynamic data Building foundation models for biology (AIDO, scLong, ProteinAligner) Designing scalable AI architectures (Pollux, LLM360, PAN) Advancing interpretable and controllable NLP systems Scientific Leadership: Founded the SAILING Lab at CMU Co-chaired ICML 2014 and ICML 2019 Recipient of the Jay Lepreau Best Paper Award (OSDI 2021) Education & Mentorship: Advises PhD students across machine learning and computational biology Alumni include faculty at ETH Zurich, University of Chicago, and UC San Diego

• Control Systems
• Robotics
• Marine Robotics
• +3 more

Kristin Y. Pettersen is a Professor at the Department of Technical Cybernetics, Norwegian University of Science and Technology (NTNU), and a Professor II at the Norwegian Defence Research Institute (FFI). She is a co-founder of Eelume AS, a company specializing in underwater robotics solutions. Education: Civil Engineering and PhD in Technical Cybernetics from NTNU Her research focuses on advanced control systems for marine and underwater vehicles, particularly snake robots and autonomous underwater vehicles (AUVs). Key areas include formation control, path following, adaptive guidance algorithms, and safety-critical control in dynamic environments. Recent work explores machine learning integration and energy-shaping techniques for robust locomotion. Publications highlight trends in Model Predictive Control (MPC) , Collision Avoidance , and Task-Priority Operational Space Control for redundant and underactuated systems. Her work bridges theoretical control theory with practical applications in marine robotics, including autonomous inspections and cooperative transport. Labs/Teams: Collaborates with NTNU's Faculty of Information Technology and Electrical Engineering and co-founded Eelume AS, advancing subsea robotic manipulation technologies.

Andreas HoneckerView profile

Professor

• Condensed Matter Physics
• Strongly Correlated Electron Systems
• Magnetocaloric Materials
• +4 more

Andreas Honecker is a Professor at the Theoretical Physics and Modeling Laboratory (CNRS UMR 8089) of CY Cergy Paris Université, where he has been employed since September 2014. He currently serves as Adjoint Director of the Institut des Sciences et Techniques (since April 2023) and was previously Director of the Physics Department (October 2020-April 2023). He also co-directs the Master Program in Physics at CY Cergy Paris Université. Professor Honecker's research focuses on condensed matter physics, particularly strongly correlated electron systems, quantum magnetism, and magnetocaloric materials. His work bridges theoretical physics with practical applications in quantum information and low-temperature refrigeration. He has made significant contributions to understanding quantum phase transitions, frustrated spin systems, and the magnetocaloric effect, with a notable Nature publication in 2021 on critical points in SrCu 2 (BO 3 ) 2 . His research activities are characterized by a strong emphasis on numerical methods for many-body systems, including quantum Monte Carlo techniques, density matrix renormalization group approaches, and advanced diagonalization methods. He has organized numerous international workshops on quantum materials, magnetocaloric effects, and quantum information, reflecting the interdisciplinary nature of his work. Among his notable recognitions are the APS Outstanding Referee award (2019) and being named a distinguished referee of The European Physical Journal (2015). He previously held a prestigious Heisenberg fellowship from the Deutsche Forschungsgemeinschaft (2007-2011). Honecker has extensive experience in academic service, including membership in the Conseil National des Universités (section 29, 2017-2023) and the Commission de la formation et de la vie universitaire at Université de Cergy-Pontoise (2016-2019). His collaborative work spans institutions across Europe, including previous positions at Göttingen University, ETH Zürich, and TU Braunschweig.

Haipeng LuoView profile

Associate Professor

• Machine Learning
• Online Learning
• Reinforcement Learning
• +3 more

Haipeng Luo is an Associate Professor at the Thomas Lord Department of Computer Science, University of Southern California, holding the IBM Early Career Chair. He previously worked as a Postdoctoral Researcher at Microsoft Research, NYC, and has held visiting roles at Google and Amazon. His research focuses on developing practical machine learning algorithms with strong theoretical guarantees, particularly in online learning, bandit problems, reinforcement learning, and game theory. PhD in Computer Science, Princeton University (2011–2016) BSc in Computer Science, Peking University (2007–2011) His work spans adversarial and stochastic environments, addressing challenges in reinforcement learning, game dynamics, calibration, and omniprediction. Recent publications highlight advancements in regret minimization, game equilibrium computation, and robust optimization frameworks. Key contributions include algorithms for zero-sum games, bandit problems with feedback graphs, and theoretical analyses of convergence properties in multi-agent systems. Scientific accolades include Best Paper Awards at COLT 2021, COLT 2018, NeurIPS 2015, and ICML 2015. He has received prestigious grants such as the NSF CAREER Award (2020), Google Faculty Research Award (2020), and NSF CRII Award (2018). His students have secured academic and industry positions, and he actively teaches graduate courses in machine learning and online optimization.

• Model Predictive Control
• Power Electronics
• Renewable Energy Integration
• +5 more

Ricardo Aguilera Echeverria is an Associate Professor at the University of Technology Sydney (UTS), School of Electrical and Data Engineering . With a Ph.D. in Electrical Engineering from the University of Newcastle (2012), he has held academic positions at UNSW Australia (2014-2016) and UTS since 2016. His research focuses on model predictive control (MPC) applied to power electronics , renewable energy integration , and microgrid control systems . He actively supervises Masters and PhD students and has developed courses such as Control Studio A and Control Studio B . Education: PhD in Electrical Engineering (University of Newcastle, 2012) MSc in Electronics Engineering (Universidad Tecnica Federico Santa Maria, 2007) BSc in Electrical Engineering (Universidad de Antofagasta, 2003) Research Interests: Model Predictive Control (MPC) for power converters Microgrid stability and cybersecurity Second-life battery integration Hybrid DC-AC microgrid solutions Recent Research Trends: Advancements in modular multilevel matrix converters (M3C) for LFAC systems Development of per-phase instantaneous power theories for LVRT compensation Sliding mode observers (SMO) for cyberattack mitigation in AC microgrids Optimal control strategies for delta-connected CHB converters in energy storage Grants & Projects: Lead investigator in HORIZON Europe (2024-2027) on digital solutions for renewable energy systems ARC Discovery Project (DP240102646) on extending second-life battery life (2024-2026) Collaborative grants with Sovereign Propulsion Systems Pty Ltd and NSW Department of Industry for hybrid-electric vehicle control