Find Academic Supervisors

• Machine Learning
• Artificial Intelligence
• Robotics
• +3 more

Charles L. Isbell Jr. is a Professor in the College of Computing at Georgia Institute of Technology. His work spans foundational research in artificial intelligence, machine learning, and robotics, with a focus on human-robot interaction, reinforcement learning, and educational technology. He leads efforts in scalable online education programs and contributes to policy discussions on AI's societal impact. Research Interests: Isbell's research emphasizes practical and theoretical advancements in AI systems, including Bayesian methods, multi-agent systems, and the ethical implications of AI. He explores how machines can learn from human interaction and adapt to complex environments. His recent work addresses challenges in robotics collaboration, scalable educational platforms, and the development of robust reinforcement learning algorithms. Publications Trends: His articles reflect a commitment to advancing AI through interdisciplinary approaches, with contributions to robotics collaboration (e.g., Nash equilibrium frameworks), scalable education systems, and foundational work in reinforcement learning and imitation learning. Recent collaborations include studies on machine learning systems engineering and long-term AI policy. Awards: No awards explicitly listed in the provided text. Advising & Grants: While specific grants are not detailed here, his extensive publication record indicates sustained research funding and mentorship of students in AI and robotics. His work on online education at scale highlights contributions to pedagogical innovation.

• Predictive Maintenance
• Machine Learning
• Fault Detection
• +5 more

Dr. Lilach Goren Huber is a Senior Lecturer and R&D Projects Leader at the ZHAW School of Engineering, specializing in predictive maintenance and data-driven solutions for industrial systems. She leads multiple projects focusing on AI applications in anomaly detection (e.g., wind turbines, solar power plants), physics-informed machine learning for sensor error correction, and fault prognostics under data scarcity. Her work bridges academic research with industrial implementation, emphasizing scalable deep learning frameworks for commercial fleets and energy infrastructure. Research interests include predictive maintenance strategies, machine learning for industrial IoT, and hybrid prognostics combining domain knowledge with AI. She has published extensively in journals like International Journal of Prognostics and Health Management and Journal of Big Data , with a focus on renewable energy systems, SCADA data analysis, and cross-domain transfer learning. Current Projects: Physics-informed ML for elastomer sensors, end-to-end fault prognostics for power grids, and hybrid prognostics research. Past Contributions: Developed decision support systems for laser cutting machines, optimized hydroelectric maintenance schedules, and created risk-based frameworks for Swiss national road safety equipment. She actively contributes to the Expert Group Smart Maintenance and serves as a project leader in the ZHAW-PARC initiative. Her work addresses technical challenges like data contamination, sensor calibration errors, and real-world implementation barriers in industrial AI adoption.

Youngki KimView profile

Associate Professor

• Automotive Engineering
• Integrated Design and Manufacturing
• Machine Learning
• +5 more

Youngki Kim is an Associate Professor in the Department of Mechanical Engineering at the University of Michigan-Dearborn , within the College of Engineering and Computer Science . His research focuses on Automotive Engineering , Integrated Design and Manufacturing , Machine Learning , Optimization , and Intelligent Systems , with specific interests in lithium-ion batteries, vehicle modeling/simulation, energy management of advanced powertrains, and connected/autonomous vehicles. He holds a Ph.D. in Mechanical Engineering from the University of Michigan-Ann Arbor (2014) and advanced degrees from Seoul National University (M.S. 2003, B.S. 2001). Research & Grants Recipient of multiple industry grants (e.g., Hyundai, Ford, NSF) for projects like battery thermal management, predictive control algorithms, and one-pedal driving systems. Developed frameworks for eco-climate control, energy-efficient HVAC systems, and battery state-of-charge/health estimation. Active in cross-disciplinary research combining machine learning with traditional engineering methodologies. Teaching Instructs advanced courses such as Control Systems Analysis , Linear Systems Control , and Predictive Control of Dynamic Systems . Specializes in battery systems modeling, energy management, and mechatronics. Lab affiliations include the DAIR (Distributed Automotive Innovation Research) initiative and collaborates extensively with automotive industry partners. His work emphasizes practical applications of control theory and data-driven methods to improve vehicle efficiency and safety.

R. Anthony ReeseView profile

Professor

• Copyright Law
• Trademark Law
• Intellectual Property Law
• +3 more

R. Anthony Reese is a Chancellor's Professor of Law at the University of California, Irvine School of Law, specializing in copyright, trademark, and internet aspects of intellectual property law. He has taught at Stanford Law School, NYU School of Law, and international programs. Education: J.D., Stanford Law School (with Distinction) B.A., Yale University (Russian Language and Literature, summa cum laude) Research Interests: His work focuses on copyright doctrine complexity, termination of transfer provisions, and work preservation. He advocates for nuanced interpretations of fair use and transformativeness, emphasizing purpose over content changes in legal rulings. His scholarship addresses challenges in adapting copyright law to the digital era, including ISP liability and digital rights management. Scientific Awards: 2019 IP Vanguard Award by the California Lawyers Association's IP Law Section Public Service: Reese has served on boards of the Orange County Lavender Bar Association, Yale-China Association, and advised the Texas Judicial Council’s Committee on Public Access to Court Records. He co-founded the Copyright Principles Project, proposing U.S. copyright reforms.

Karel A. KroezeView profile

Researcher

• Statistics
• Computational Statistics
• Data Science
• +11 more

Karel A. Kroeze is a Researcher at the Behavioural Data Science Institute (BDSI) at the University of Twente, specializing in Instructional Technology. With an h-index of 52, he has established himself as a significant contributor in the fields of adaptive learning systems, educational data mining, and psychometrics. His work bridges computer science, statistics, and educational theory to develop innovative assessment and feedback mechanisms. His educational background includes a Master's degree in Methodology and Statistics for the Behavioural, Biomedical and Social Sciences from Utrecht University (2015) and a Bachelor's degree in European Studies from the University of Twente (2013). This interdisciplinary foundation supports his current research in complex data analysis for educational applications. Kroeze's research focuses on developing adaptive systems for learning environments, particularly in inquiry-based education. His work on automated hypothesis assessment, concept mapping, and computerized adaptive testing demonstrates his commitment to improving educational outcomes through data-driven approaches. He explores how adaptive scaffolds, learner models, and automated feedback can enhance the quality of student inquiry and hypothesis formation in science education. His publication record shows consistent output from 2014 through 2024, with recent work expanding into health economic modeling validation and electoral system analysis. This demonstrates both depth in his core educational technology domain and breadth across applied statistical methods. As evidenced by his numerous datasets on GitHub related to adaptive hypothesis grammars across multiple domains (Electrical Circuits, Supply and Demand, Buoyancy, Photosynthesis, and Heat transfer), Kroeze develops practical tools that parse and assess student hypotheses in various scientific contexts. His research contributes to several UN Sustainable Development Goals, particularly in the area of quality education.

• Civil Engineering
• Computational Fluid Dynamics
• Porous Media Flow
• +3 more

Dr. Luka Malenica is a dedicated researcher at ETH Zurich's Professorship for Durability of Engineering Materials, focusing on computational modeling of material degradation processes in civil infrastructure. His work bridges theoretical numerical methods with practical engineering applications through direct involvement in the department's research operations. His research program centers on multiphase flow in porous media , corrosion mechanisms in reinforced concrete , and advanced numerical techniques . He employs pore-scale direct numerical simulations to investigate steel-concrete interface phenomena, developing predictive models for infrastructure durability. His methodological innovations include control volume isogeometric analysis, adaptive multiresolution modeling, and deep learning applications for adaptive meshing—addressing challenges in heterogeneous media flow and transport phenomena. Analysis of his 15 most recent publications reveals a consistent research trajectory since 2015, with accelerating output in corrosion science (5 papers in 2024-2025) and computational methods (7 papers 2019-2022). Key interdisciplinary connections span civil engineering (corrosion in reinforced concrete), chemical engineering (bubble column reactors), and hydrology (karst aquifer modeling), unified by his expertise in numerical simulation of transport phenomena. Recent work demonstrates increasing sophistication in modeling macrovoid formation at steel-concrete interfaces and capillary-driven multiphase systems.

Albert Qiaochu JiangView profile

Research Fellow

• Machine Learning
• Theorem Proving
• Artificial Intelligence
• +3 more

Albert Qiaochu Jiang serves as a Visiting Research Fellow at the Department of Computer Science and Technology, University of Cambridge. His research integrates machine learning with formal theorem proving, focusing on neural theorem provers and mathematical reasoning systems. He leads the reasoning team at Mistral AI while maintaining academic supervision at Cambridge. His research interests center on machine learning for theorem proving , with specific expertise in neural-symbolic integration, autoformalization, and large language models for mathematical reasoning. His work bridges artificial intelligence with formal verification, developing systems that enhance automated reasoning capabilities through neural networks. Current projects involve improving premise selection for theorem provers, multilingual mathematical formalization, and creating efficient architectures for mathematical language models. Analysis of his recent publications reveals a strong focus on advancing neural theorem proving through innovative architectures like Target-Based Automated Conjecturing and Magistral. His research trajectory shows increasing sophistication in integrating language models with formal verification systems, with significant contributions to datasets like Numinamath and frameworks like Llemma. Key trends include optimizing compute efficiency in proof generation, enhancing multilingual mathematical reasoning, and developing interactive human-AI collaboration systems for formal mathematics. While no scientific awards are currently documented in available sources, his research output demonstrates significant impact in the intersection of AI and formal methods. As leader of Mistral AI's reasoning team, Jiang directs research on neural theorem proving systems while contributing to academic supervision at Cambridge. His work involves substantial industrial-academic collaboration, leveraging resources from both institutional contexts to advance mathematical AI. Current projects focus on creating practical systems for mathematical automation with real-world verification applications. His research operates at the intersection of academia and industry through Mistral AI's reasoning team, where he develops neural theorem proving systems with practical applications in formal verification. This dual affiliation enables rapid translation of theoretical advances into deployable tools for mathematical automation.

Nikola SimidjievskiView profile

Research Fellow

• Machine Learning
• Biomedical Data Integration
• Neuroscience
• +4 more

Dr. Nikola Simidjievski is a Research Fellow at the University of Cambridge's Department of Computer Science and Technology. His research focuses on machine learning applications in natural sciences, including oncology, neuroscience, and biomedical informatics. He specializes in multimodal data integration, dynamic systems modeling, and explainable AI techniques. Simidjievski contributes to interdisciplinary projects such as AI-driven space research and Earth observation systems (e.g., AIAtlas). His work emphasizes computational methods for small-sample biomedical data and interpretable neural networks. Education background not explicitly stated, but his research spans computational biology, aerospace telemetry analysis (e.g., Mars Express spacecraft), and environmental modeling. He collaborates on projects like GalaxAI for spacecraft data analysis and PATHS for medical imaging. Key tools developed include Healnet for biomedical data fusion and RO-FIGS for tabular ensemble methods. No scientific awards listed. Advising no listed students, but contributes to academic initiatives like the Human Brain Project's neuroscience training programs. Engages in open-source projects (e.g., AITLAS toolbox for Earth observation). Current research trends include tabular data augmentation methods (TabEBM, TabMDA) and efficient transformer-based models for medical imaging (PATHS).

• Autonomous Vehicles
• Robotic Perception
• Sensor Fusion
• +2 more

Eduardo Nebot is Emeritus Professor and former Patrick Chair in Automation and Logistics at the University of Sydney, where he founded the Australian Centre for Robotics. His research develops perception and navigation systems for autonomous vehicles, focusing on robust operation in complex environments. Nebot's work enables autonomous systems for mining, transportation, and field robotics applications. Research interests include sensor fusion, cooperative perception, probabilistic tracking, and validation methods for autonomous systems. Current projects investigate V2X-enabled cooperative driving, pedestrian trajectory prediction, and robust localization under environmental changes. Publication trends highlight multi-sensor perception systems, with recent work emphasizing domain adaptation for 3D detection, safety validation frameworks, and human-robot interaction in autonomous driving. Articles consistently address real-world deployment challenges in industrial and urban settings. Fellow of the Australian Academy of Technology and Engineering (2016) Fellow of the IEEE (2016) The Australian Centre for Robotics collaborates with industry partners on autonomous haulage systems and intelligent transportation. Nebot has supervised numerous PhD candidates in robotics and maintains research partnerships with mining and automotive sectors.

Maggie MakarView profile

Assistant Professor

• Machine Learning
• Causal Inference
• Healthcare Applications
• +3 more

Maggie Makar is an Assistant Professor in the Department of Computer Science and Engineering (CSE) at the University of Michigan. Her research focuses on the intersection of machine learning and causal inference, particularly in developing robust and data-efficient methods for healthcare and resource-constrained settings. She received her PhD from MIT's CSAIL and a bachelor's degree in math and economics from UMass Amherst. Her work emphasizes causal reasoning to improve predictive models' reliability, with applications in healthcare decision-making and policy evaluation. Key areas include offline reinforcement learning for medical interventions, confounding variable handling, and mitigating biases in AI systems. Dr. Makar teaches courses such as CSE 598 (Causality and Machine Learning) and mentors PhD students in causal ML and healthcare AI. She actively seeks to expand her research group and collaborate on projects blending causal theory with real-world applications. Education: PhD in Computer Science, MIT (CSAIL), 2018 Bachelor's in Mathematics and Economics, University of Massachusetts Amherst, 2013 Awards: CAREER Award (NSF, 2024) CRII: SCH Grant (NSF, 2022) Research Themes: Causal inference in machine learning Data-efficient methods Healthcare applications Algorithmic fairness

Jing ZhangView profile

Lecturer

• Computer Vision
• Machine Learning
• Generative AI
• +5 more

Dr. Jing Zhang is a Lecturer in the School of Computing at the Australian National University (ANU) , within the ANU College of Systems & Society . Previously, he served as a Research Fellow at ANU (2021–2022) and earned his PhD in 2021 under the supervision of Nick Barnes. His academic journey includes a Master’s (2010) and Bachelor’s (2007) from Northwestern Polytechnical University . Education: PhD, Australian National University, 2021 (Supervisor: Nick Barnes) Master’s Degree, Northwestern Polytechnical University, 2010 Bachelor’s Degree, Northwestern Polytechnical University, 2007 Research Interests: Jing Zhang’s research focuses on computer vision and machine learning , with a particular emphasis on generative AI . His work addresses challenges in image, video, and audio generation/editing; explainable model adaptation; out-of-distribution detection; and adversarial attacks/defenses. He explores techniques to enhance model robustness and generalization across diverse domains. Awards & Recognition: CVPR 2020 Paper Award Nominee for “UC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional Variational Autoencoders” Teaching & Supervision: Jing Zhang teaches COMP8536: Advanced Topics in Computer Vision (Semester 2, 2024) and ENGN4528/6528: Computer Vision (Semester 1, 2025). He currently supervises 4 PhD students as their primary advisor, focusing on cutting-edge research in computer vision and AI. Professional Contributions: Co-organizer of tutorials on salient object detection (ACCV 2019) and uncertainty estimation (ICCV 2021) Regular reviewer for top venues: CVPR, ICCV, ECCV, TPAMI, IJCV, and others Research Groups: While not explicitly stated, his work is likely affiliated with ANU’s computer vision research groups, focusing on generative AI and robust machine learning systems.

Egidio FaloticoView profile

Assistant Professor

• Robotics
• Biorobotics
• Computational Neuroscience
• +4 more

Egidio Falotico is a Tenure Track Assistant Professor (RTD-B) at the BioRobotics Institute, Scuola Superiore Sant’Anna, Pisa, Italy. He leads the BRAin Inspired Robotics (BRAIR) Lab and holds roles such as Principal Investigator (PI) for the PROBOSCIS project and co-leader of Sub-Project 10 (Neurorobotics) in the Human Brain Project (HBP). His research focuses on brain-inspired motor control, soft robotics, and neurorobotics, integrating computational neuroscience with robotic systems. Education: Dual PhD in Biorobotics (Scuola Superiore Sant’Anna) and Cognitive Science (University Pierre et Marie Curie). Master’s in Computer Science (University of Pisa). Research interests include control mechanisms for soft robots using AI, bio-inspired motor control, and neuroscientific principles applied to robotics. He has contributed to projects like HBP, where he developed the Neurorobotics Platform, and PROBOSCIS, aiming to create elephant-trunk-inspired robots. Teaching includes leading PhD courses on Brain-Inspired Motor Control and Robotics at the University of Pisa. He has supervised EU-funded students and managed interdisciplinary teams in neurorobotics and soft robotics. Key projects involve HBP’s Neurorobotics Platform, soft robot control (e.g., PROBOSCIS), and collaborations in sensorized systems and adaptive learning algorithms. His work bridges neuroscience, AI, and robotics to advance soft robotic systems and bio-inspired models.

• Statistical Methodology
• Latent Variable Models
• Tensor Decompositions
• +3 more

Zhongyuan Lyu is a Research Fellow (Postdoctoral Research Scientist) at Columbia University's Data Science Institute, mentored by Professors Yuqi Gu and Kaizheng Wang. His research focuses on statistical methodology for latent structures in mixture models, graphical models, and tensor decompositions, with applications to heterogeneous data analysis. Prior to Columbia, he earned his PhD in Mathematics from the Hong Kong University of Science and Technology under Professor Dong Xia's supervision. His academic background includes advanced work in high-dimensional data analysis, latent variable modeling, and computational statistics. Research interests emphasize developing theoretically grounded algorithms for complex data types, particularly in network science and multilayer data frameworks. Recent publications highlight contributions to spectral clustering optimization, adaptive transfer learning frameworks, and tensor-based methodologies for higher-order networks. His work bridges statistical theory and practical applications, addressing computational limits and optimal estimation challenges in modern data science problems. No scientific awards or grants are explicitly mentioned in the provided data. His current position is full-time within the Data Science Institute's research team.

Yangming LiView profile

Research Fellow

• Image Analysis
• Machine Learning
• Mathematical Modeling
• +7 more

Yangming Li is a Research Fellow at the Department of Applied Mathematics and Theoretical Physics (DAMTP) at the University of Cambridge, affiliated with the Cambridge Image Analysis research group. His work bridges applied mathematics, theoretical physics, and machine learning, with a focus on developing innovative models for image analysis, generative processes, and natural language understanding. Research interests include Fourier Neural Operators, diffusion models, generative adversarial networks (GANs), and their applications in solving complex problems across scientific computing and data-driven domains. His recent contributions explore operator learning for PDEs, robust diffusion models under noisy conditions, and adversarial attacks in text watermarking systems. Publications highlight advancements in operator-based neural networks, risk-sensitive generative modeling, and domain-aware NLP frameworks. His methodologies emphasize mathematical rigor while addressing practical challenges like missing data and model expressivity limitations. Active collaborations span interdisciplinary teams at DAMTP and the broader University of Cambridge research community.

Umar RazaView profile

Senior Lecturer

• Internet of Things
• Machine Learning
• Wireless Sensor Networks
• +9 more

Dr. Umar Raza serves as a Senior Lecturer in Networking, IoT and Smart Systems at Manchester Metropolitan University's Department of Engineering, where he concurrently manages the Cisco Network Academy. His academic career includes prior lecturing roles in Robotics and Computing at Staffordshire University, with research spanning industrial applications of emerging technologies. His educational credentials include a PhD in Wireless Sensor Networks from the University of Bradford (2014), Postgraduate Certificates in Research Methods and Professional Higher Education from Staffordshire University (2005, 2007), an MSc in Electronics and Computer Systems from Huddersfield University (1995), and a BSc in Engineering Electronics from DeMontfort University Leicester. Dr. Raza's research centers on Internet of Things applications integrated with Machine Learning across industrial, agricultural, and healthcare domains. Current projects address IoT/ML solutions for domicile care monitoring, cognitive impairment assistance, natural disaster early warning systems, and smart farming in Pakistan, with emphasis on practical implementations in real-world environments. His recent publications demonstrate a pronounced interdisciplinary trajectory, particularly at the IoT-Machine Learning nexus for healthcare innovation (cardiac diagnostics, autism support wearables) and agricultural technology. Significant contributions also appear in blockchain scalability, vehicular network security, and 5G propagation modeling, reflecting both theoretical rigor and industry-relevant problem solving. Professional recognition includes: Fellow of the Higher Education Academy (FHEA) Dr. Raza actively supervises two MSc and two PhD candidates while leading multiple funded projects: Principal Investigator for KTP projects with Kindus Solutions (completed) and Trumeter Ltd Project Advisor for KTP with RAIT Ltd Lead researcher on NATO Science for Peace Security grant for natural disaster warning systems Principal Investigator for Ignite National Technology Fund smart farming initiative in Pakistan As Cisco Network Academy Manager, he drives industry-academia collaboration in networking education, developing curricula that integrate cutting-edge IoT and cybersecurity concepts while maintaining strong professional engagement through IEEE, IET, and BCS memberships.