Find Academic Supervisors

Jean-Paul KneibView profile

Professor

• Cosmology
• Gravitational Lensing
• Redshift Surveys
• +5 more

Jean-Paul Kneib is a Full Professor at the Laboratory of Astrophysics (LASTRO) within the School of Basic Sciences (SB) at École Polytechnique Fédérale de Lausanne (EPFL), Switzerland. He is also involved in the Space Sustainability initiative at EPFL and has held visiting positions at Caltech and CNRS institutions. His research focuses on cosmology, gravitational lensing, and next-generation spectroscopic surveys like DESI and Euclid. Education : PhD in Astrophysics (1993), Master in Astrophysics, Geophysics & Space Technics (1990), Engineer degree in Aerodynamics & Space Technics (1990), all from Toulouse University and École Nationale Supérieure de l'Aéronautique et de l'Espace. Research Interests : Kneib specializes in cosmology, using galaxy surveys and gravitational lensing to study dark matter and dark energy. His work includes strong lensing in galaxy clusters, redshift surveys for cosmic expansion analysis, and robotic fiber-positioner systems for spectroscopic instruments. He also contributes to space sustainability initiatives addressing orbital debris. Recent Publications emphasize deep learning for radio interferometry, Euclid mission early data, DESI BAO analysis, and novel methods for asteroid detection. Collaborative projects involve JWST observations of high-redshift galaxies and digital twins for CubeSats. Scientific Awards : ERC Advanced Laureate (for project "Light on the Dark") Marie Curie Fellowship Leadership Roles : Co-Lead of ESA Euclid "Strong Lensing" Working Group Co-Principal Investigator for CFHT-Stripe 82 project Developer of Fiber-Robot Positioner System for DESI collaboration

Mikhail ShapiroView profile

Professor

• Biomedical Engineering
• Synthetic Biology
• Neuroscience
• +3 more

Mikhail Shapiro is the Max Delbrück Professor of Chemical Engineering and Medical Engineering at the California Institute of Technology , and an Howard Hughes Medical Institute Investigator since 2021. His research laboratory develops acoustic and magnetic biomolecular tools for imaging and controlling cellular function in vivo. Chemical Engineering Faculty Medical Engineering Affiliation Howard Hughes Medical Institute Investigator Research focuses on noninvasive control of biological systems through: Acoustic reporter genes (ARG 2.0) for real-time tumor monitoring Ultrasound neuroimaging via acoustically transparent cranial windows Gas vesicle engineering for cellular manipulation Microrobotics with acoustic hydrogels His 2024-2025 publications demonstrate breakthroughs in: Nonlinear ultrasound for deep-tissue visualization Acoustic percolation switches for targeted drug delivery SEMPER systems for multi-gene expression control Scientific recognition includes: HHMI Investigator (2021-) Vilcek Foundation Prize in Biomedical Innovation Roger Tsien Award for Chemical Biology Packard Fellowship in Science and Engineering

• Control Systems
• Structural Systems Theory
• Biomedical Engineering
• +5 more

Sérgio Pequito is an Associate Professor in the Department of Electrical and Computer Engineering at Instituto Superior Técnico, University of Lisbon, and a principal investigator at the Evolutionary Systems and Biomedical Engineering Lab (Institute for Systems and Robotics). His research bridges structural control systems theory with applications in neuroscience and biomedicine. Education: PhD in Electrical and Computer Engineering from Carnegie Mellon University and Instituto Superior Técnico (2014); MSc (2009) and BSc (2007) in Mathematics from Instituto Superior Técnico Research focuses on understanding large-scale systems through structural/parametric analysis, with applications to brain dynamics, personalized medicine, and control of complex networks. Key themes include fractional-order systems, distributed consensus, and brain-machine interfaces. Recent publications analyze fractional-order dynamical networks, controllability metrics, and privacy-preserving control algorithms, reflecting his expertise in both theoretical and applied domains. Keywords: Control Systems, Structural Theory, Biomedical Engineering, Neuroscience. Scientific recognition includes the 2016 O. Hugo Schuck Award (Theory Category) and a 2009 IEEE Conference on Decision and Control Best Student Paper Finalist. He leads interdisciplinary research teams and collaborates on neural engineering projects, particularly in epilepsy treatment and cortical activity modeling.

Maximilian SchifferView profile

Associate Professor

• Operations Research
• Management Science
• Machine Learning
• +7 more

Prof. Dr. Maximilian Schiffer is an Associate Professor at the Professorship of Business Analytics & Intelligent Systems , TUM School of Management , Technical University of Munich. He holds a Ph.D. in Operations Research from RWTH Aachen University (2017) and has previously served as a visiting postdoctoral scholar at Stanford University and a postdoctoral scholar at RWTH Aachen. Education: Ph.D. in Operations Research, RWTH Aachen University (2017) Maximilian’s research focuses on developing operations research and prescriptive analytics methods to address societal mobility and transportation challenges, including electric vehicles , autonomous mobility-on-demand systems , and multi-modal transportation networks . His interdisciplinary work bridges robotics , computer science , and transport engineering . Recent publications highlight advancements in autonomous fleet control , electric vehicle charging infrastructure , and collusion detection in dynamic pricing . Scientific Awards: INFORMS TSL Dissertation Prize GOR Doctoral Dissertation Prize Best Paper Award, IEEE Conference on Intelligent Transportation Systems Maximilian is an associate member of the GERAD research group and serves on the editorial board of OR Spectrum and the TSL Dissertation Prize committee . His lab, the Professorship of Business Analytics & Intelligent Systems , trains students in machine learning, optimization, and future mobility systems.

Brenda L. Bartnik-OlsonView profile

Associate Professor

• Neuroimaging
• Traumatic Brain Injury
• Magnetic Resonance Spectroscopy
• +4 more

Brenda L. Bartnik-Olson, PhD is an Associate Professor in both the Radiology Department and the Basic Sciences, Biomed Engineering Science Division at Loma Linda University School of Medicine. She also serves as Co-Director of Research for the Radiology Department. With a PhD from the University of Saskatchewan (2002), Dr. Bartnik-Olson has established herself as a leading researcher in neuroimaging and traumatic brain injury, particularly in pediatric populations. Her educational background includes: Doctor of Philosophy, University of Saskatchewan, 2002 Dr. Bartnik-Olson's research program focuses on advanced neuroimaging techniques to study brain metabolism and structural changes following injury. Her work spans multiple areas including: Magnetic Resonance Spectroscopy (MRS) for measuring cerebral metabolism Diffusion Tensor Imaging (DTI) for assessing white matter integrity Hyperpolarized carbon-13 MRI for studying real-time brain metabolism Pediatric traumatic brain injury and concussion Neuroimaging biomarkers for predicting functional outcomes Quantitative susceptibility mapping for measuring cerebral oxygenation Her extensive publication record demonstrates a consistent focus on understanding the metabolic and structural changes following brain injury, with particular emphasis on pediatric populations. Her research often involves multimodal imaging approaches and large collaborative efforts through the ENIGMA Consortium, which has resulted in standardized methodologies for multi-scanner studies and identification of key biomarkers for injury and recovery. Dr. Bartnik-Olson has secured numerous research projects as Principal Investigator or co-investigator, including: Steerable Laser Interstitial Thermotherapy (SLIT) Robot for Brain Tumor Therapy (2023-2026) Personalized Profiles of Pathology in Pediatric Traumatic Brain Injury (2022-2026) Federal Pediatric TBI and DAI: Normal appearing brain is not normal (2024-2025) Neural basis of social cognition deficits in youth with autism and schizophrenia (2020) She serves on the MRI Safety Committee (2014-2021) and is a reviewer for Neurotrauma Reports. Her professional society memberships include the American Association of Physicists in Medicine (2015-2021), National Neurotrauma Society (2009-2021), and International Society for Magnetic Resonance in Medicine (2006-2021), where she has presented her research regularly. Dr. Bartnik-Olson teaches courses including Cross Sectional Radiographic Anatomy and Topics in Rehabilitation, demonstrating her commitment to education alongside her research endeavors.

Huan XuView profile

Associate Professor

• Control Systems
• Formal Methods
• Autonomous Systems
• +6 more

Huan Xu is an Associate Professor in the Department of Aerospace Engineering at the University of Maryland, College Park, with a joint appointment at the Institute for Systems Research. Her work focuses on formal methods for multi-agent autonomous systems, particularly in cyber-physical systems and UAVs. Ph.D., Mechanical Engineering, California Institute of Technology (2013) M.S., Mechanical and Civil Engineering (2008) S.B., Mechanical and Materials Sciences and Engineering, Harvard University (2007) Her research interests lie in control and dynamical systems, formal methods, and optimization, with applications in autonomy, planning, and system identification. She develops robust cyber-physical systems that perform complex tasks in dynamic environments, emphasizing rigorous theory and real-world applications. Her recent publications focus on collision avoidance algorithms, swarm robotics, and autonomy certification. These works integrate formal logic with practical implementations for UAVs and multi-agent systems. DARPA Cooperative Agreement (2018) AIAA Awards for UAS Collision Avoidance (2020) DoD SMART Scholarship (2023) She advises current Ph.D. and MS students in aerospace engineering and robotics, including John Schmidt, Sarah Wielgosz, and Estefany Carrillo. Her lab (MATRIX Lab) collaborates on autonomous drone racing, hazard mitigation, and swarm tactics projects. Facilities like the USMSM SMART Building and UROC support her research on autonomous aerial systems and flight software validation.

• Electrical and Computer Engineering
• Deep Learning
• Image Processing
• +5 more

Fatema Albalooshi is an Assistant Professor in the Computer Engineering department at the University of Bahrain since 2015. She holds a Ph.D. in Electrical and Computer Engineering from the University of Dayton (2015) and an MSc in Electrical Engineering from the University of Nottingham (2009). Education: PhD, Electrical and Computer Engineering, University of Dayton (2015) MSc, Electrical Engineering, University of Nottingham (2009) BSc, Electrical Engineering, University of Nottingham (2009) Research Interests: Deep Learning Image Processing Computer Vision Biometrics Machine Olfaction Environmental Remote Sensing Peer Review Contributions: Applied Intelligence (5 reviews) Signal, Image and Video Processing (16 reviews) Electronics (1 review) Computers in Biology and Medicine (1 review) Email: falbalooshi@uob.edu.bh

• Artificial Intelligence
• Robotics
• Motion Planning
• +8 more

Maxim Likhachev is a Professor at the Robotics Institute and National Robotics Engineering Center , both within the School of Computer Science at Carnegie Mellon University (CMU). His research focuses on Artificial Intelligence and Robotics , particularly in real-time graph search algorithms , planning under uncertainty , and high-dimensional mobile manipulation . He co-founded TravelWits.com and RobotWits (acquired by Waymo), and his lab develops the Search-based Planning Library (SBPL), integrated into ROS. Research Themes : Graph search-based planning for deterministic and probabilistic domains Decomposition of hard planning problems into sequential graph searches Incremental algorithms for dynamic environments Experience-driven heuristic optimization Scientific Awards : Gold Edison Award (2013) for industrial mobile manipulator project DARPA Urban Challenge Winner (2007) for autonomous SUV navigation Students : Supervises current PhD students including Itamar Mishani , Muhammad Suhail Saleem , and Rishi Veerapaneni , alongside Masters students like Gabriel Olin . Past advisees include Dhruv Saxena (now at Uber), Michael Phillips (now at Uber), and Shivam Vats (now at Waymo). Lab & Projects : Leads the Search-based Planning Laboratory at CMU, working on systems like K-MAX helicopter , PR2 robots , and industrial mobile manipulators for tasks including autonomous paint stripping and aerial navigation.

Swarun KumarView profile

Professor

• Low-Power Wide-Area Networks (LP-WANs)
• Battery-Free IoT
• Future Wi-Fi
• +7 more

Swarun Kumar is the Sathaye Family Foundation Professor at Carnegie Mellon University (CMU) in the Electrical and Computer Engineering (ECE) department, with affiliate appointments in the Computer Science (CS) department, Human-Computer Interaction (HCI) Institute, and CyLab. He leads the Emerging Wireless Technologies (WiTech) lab, focusing on next-generation wireless network protocols and services. Ph.D., Computer Science, MIT (George Sprowls Award winner) B.Tech., IIT Madras (President of India Gold Medal) His research spans Low-Power WANs , Battery-Free IoT , mmWave Sensing , and Soft Robotics . Key projects include MetaMorph (robotic signal enhancers), WASER (RF-powered soft robots), and Hydra (multi-bounce mmWave sensing). He pioneered wireless actuation, body-frame tracking, and whale rendezvous frameworks using autonomous robots. His recent work explores wireless systems for high-temperature materials (Viton), microwave energy optimization (MicroSurf), and dental health sensing (ToMoBrush). Articles reflect trends in Robotics , Material Science , and Wireless Systems . 2024 ACM SIGMOBILE Rockstar Award 2021 ACM SIGBED Early Career Researcher Award NSF CAREER Award George Sprowls Award (MIT Best Ph.D. Thesis) President of India Gold Medal (IIT Madras) He has advised numerous Ph.D. students, including alumni joining universities like University of Washington, UC San Diego, and NUS. His teaching includes courses on Computer Networks , Wireless Systems , and Telecommunication Networks .

• Neuromorphic Engineering
• Edge AI
• Computer Architecture
• +4 more

Dr. A. Yousefzadeh is an Assistant Professor in Edge AI at the University of Twente (joined February 2024), affiliated with the Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) within the Department of Computer Architecture Design and Test for Embedded Systems. He holds a Ph.D. in Neuromorphic Engineering from IMSE (Instituto de Microelectrónica de Sevilla), where his thesis focused on bio-inspired vision processing. His research specializes in neuromorphic computing systems, with emphasis on: Designing ultra-low-power AI processors and event-based vision systems Developing hardware accelerators for spiking neural networks (SNNs) Edge AI deployment for sensor-based applications Hardware-software co-design for energy-efficient computing His publication trends (2015-2025) reveal core foci on neuromorphic processor architectures (e.g., SENECA, NeuronFlow), event-based vision processing, hardware-aware neural network optimization, and 3D integration techniques. Recent work explores activation sparsification in transformers and hybrid analog-digital neuromorphic systems. Prior to academia, he contributed to industry neuromorphic projects: Architected the NeuronFlow processor at GrAI Matter Labs (acquired by Snap) Led SENECA processor development at imec's Hardware Efficient AI group He currently leads research on next-generation edge AI processors at UT's Embedded Systems lab.

Almuatazbellah BokerView profile

Assistant Professor

• Autonomous Systems
• Robotics
• Machine Learning
• +2 more

Almuatazbellah Boker is an Assistant Professor in the Bradley Department of Electrical and Computer Engineering at Virginia Tech. He earned his PhD in Electrical Engineering from Michigan State University in 2013. His research focuses on autonomous systems, machine learning for robotic decision-making, and distributed control optimization. Key research areas include Koopman operator-based control, sparse controller design, and human-supervised autonomous systems. His research explores: Nonlinear control strategies for robotic and motion systems Data-driven approaches for optimal control Human-robot interaction in autonomous navigation Publication analysis shows strong focus on Koopman operator applications for system modeling and control, with recent extensions to digital twins and fault detection. Work consistently addresses practical implementation challenges in robotic systems and industrial automation. Dr. Boker has supervised PhD candidate Muhammad Al Saaideh to graduation. His research receives support from the US Department of Energy and Siemens USA, including a $300,000 subaward for sensor placement optimization in solar systems (2024-2027).

Geng BaiView profile

Adjunct Professor

• Advanced Plant Phenotyping
• Precision Agriculture
• Remote Sensing in Agriculture
• +3 more

Geng Bai is an Adjunct Professor in the Department of Biological Systems Engineering at the University of Nebraska-Lincoln. He holds a Ph.D. in Environmental Science and Technology from Niigata University (Japan), alongside M.E. and B.E. degrees in Agricultural Engineering from China Agricultural University. His research focuses on advanced plant phenotyping in field and greenhouse environments, precision agriculture, and the development of automated sensing systems for agricultural applications. Education: Ph.D., Environmental Science and Technology, Niigata University, Japan (2014) M.E., Agricultural Soil and Water Engineering, China Agricultural University (2010) B.E., Hydraulic and Hydro-Power Engineering, China Agricultural University (2008) Research interests include integrating robotics and remote sensing technologies for crop monitoring, with a focus on soybean and wheat breeding. His recent work explores solar-induced chlorophyll fluorescence (SIF) for plant phenotyping and proximal sensing for crop management in the Great Plains. Funding highlights: ORED Grant (2019-2019): $20,000 for SIF-based phenotyping research IIC Grant (2020-2020): $94,659 for precision agriculture automation EPA Grant (2019-2022): $150,000 for saline wetland monitoring Collaborates extensively with interdisciplinary teams across Nebraska's agricultural research community, emphasizing field-based phenotyping and sustainable agronomic practices.

• Hardware-Software Co-design
• Deep Learning Security
• Intellectual Property Protection
• +5 more

Huili Chen is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of California San Diego's Jacobs School of Engineering. Her research spans the intersection of hardware security, deep learning, and human-robot interaction, with a particular focus on intellectual property protection for AI systems and socially interactive robotics for education and family settings. Her research interests focus on hardware-software co-design for secure and robust deep learning systems, with specific expertise in neural network watermarking, hardware Trojan detection, and privacy-preserving AI. She has made significant contributions to federated learning security, developing frameworks like GALU for logic unlocking and AdaTest for hardware Trojan detection. In human-robot interaction, she investigates long-term multi-person interactions, particularly in home environments with children and parents, exploring how robots can enhance engagement and learning through adaptive role-playing. Her publication trends reveal a dual research trajectory: one branch focused on deep learning security and hardware co-design (accounting for approximately 60% of her recent work), and another dedicated to socially assistive robotics and human-robot interaction (40%). The security research often involves innovative approaches combining reinforcement learning with hardware constraints, while her HRI work emphasizes longitudinal studies of robot-child-parent dynamics in naturalistic settings. Dr. Chen actively collaborates with leading researchers including Farinaz Koushanfar at UC San Diego and Cynthia Breazeal at MIT Media Lab. Her work has been published in top-tier venues including IEEE Transactions on Affective Computing, ACM Transactions on Embedded Computing Systems, International Conference on Computer Vision (ICCV), and the ACM/IEEE International Conference on Human-Robot Interaction (HRI). She is a key contributor to the DAMI-P2C project, which has developed datasets and models for analyzing parent-child multimodal interactions, and leads research on hardware security frameworks for deep neural networks. Her laboratory combines expertise in computer architecture, machine learning, and social robotics to develop systems that are both technically secure and socially effective.

Yu LiuView profile

Professor

• Sustainable Computing
• Energy-Efficient Software
• Programming Languages
• +3 more

Yu Liu is a Professor at the School of Computing, Binghamton University, where he has been a faculty member since 2008. His research focuses on sustainable computing, energy efficiency, and security in emerging software systems, with significant contributions to programming languages, robotics, and software-hardware interfaces. Education: MSE, Johns Hopkins University PhD, Johns Hopkins University Professor Liu's research centers on sustainable computing and energy-aware software systems. He investigates programming language designs for energy prediction, develops reliable robotics software through managed languages, and creates secure software-hardware interfaces. His work bridges theoretical foundations with practical applications in data centers, UAV systems, and multi-threaded environments, emphasizing verifiable sustainability metrics and energy-efficient resource management. His publication trends reveal a consistent focus on sustainable computing evolution, with recent work advancing verifiable sustainability frameworks in data centers (2023-2024), energy accounting for multi-threaded applications (2020), and secure cache management techniques (2022). The research spans programming language theory, robotics safety, and hardware security, demonstrating interdisciplinary integration across computer science subfields. Scientific Awards: NSF CAREER Award Google Faculty Research Award Fulbright Scholarship Professor Liu has secured significant research funding through his NSF CAREER Award and Google Faculty Research Award, supporting innovations in sustainable computing infrastructure. His Fulbright Scholarship enabled international collaboration in Slovenia, advancing global research in energy-efficient software systems. He regularly teaches core courses including Programming Languages (CS471/571) and Sustainable Computing (CS680G), mentoring the next generation of computer scientists. While specific lab affiliations aren't detailed in the source text, his research on UAV software (Jcopter) and cache security (Composable cachelets) suggests active involvement in robotics and systems security research groups at Binghamton University.

Iain CouzinView profile

Professor

• Collective Behavior
• Animal Behavior
• Neuroscience
• +3 more

Professor Iain Couzin is a leader in the study of collective behavior at the University of Konstanz and director of the Max Planck Institute of Animal Behavior. He holds roles in the Cluster of Excellence Centre for the Advanced Study of Collective Behaviour (CASCB) and the Department of Biology. His research integrates biology, robotics, and computational science to understand how groups of animals (e.g., locusts, fish, birds) coordinate decisions and movements. Key tools include virtual reality for animals, automated tracking systems like TRex, and the Centre for Visual Computing of Collectives (VCC) facility. Research focuses on emergent collective phenomena, such as swarming in locusts, schooling in fish, and cooperative hunting. He emphasizes interdisciplinary approaches, collaborating with neuroscientists and engineers to bridge scales from neural circuits to global migration patterns. Notably, his work on locust swarms has societal implications for predicting agricultural threats. Awards: Highly Cited Researcher (Clarivate Analytics, 2018-2022). Facilities: Imaging Hangar, VCC research building, and novel VR setups for animal studies. Future Goals: Exploring collective memory, cross-species communication, and neuron-to-society-level decision-making. His grants and collaborations support cutting-edge experiments, including multi-scale locust studies and the development of algorithms for collective sensing in robotics. Leadership in bridging theory, experiment, and application positions him at the forefront of this interdisciplinary field.