Find Academic Supervisors

Massimo CandianiView profile

Professor

• Gynecology
• Obstetrics
• Reproductive Medicine
• +3 more

Massimo Candiani is a Full Professor at Vita-Salute San Raffaele University's Faculty of Medicine and Head of the Complex Operative Unit of Gynaecology-Obstetrics and Reproductive Medicine at IRCCS San Raffaele Hospital in Milan since 2009. He also serves as Director of the School of Specialty in Obstetrics and Gynaecology and holds leadership roles in multiple scientific societies including SEGI and ESGE. Education: MD from University of Milan (1989), specialization in Gynecology (University of Pavia, 1993), European Certificate in Gynecologic Endoscopy (University of Auvergne, 1993) Career: Senior roles at San Raffaele Hospital, San Paolo Hospital, and Mangiagalli Clinic spanning 30 years Research: Surgical innovation in gynecology, fetal surgery, and reproductive medicine His research focuses on endoscopic surgery , fetal interventions , and genital tract malformations , with recent publications spanning reproductive biology , neonatal imaging , and urogynecology . With over 300 publications, he has pioneered laparoscopic techniques in both benign and oncological gynecological surgery. Key affiliations include: President of Società di Endoscopia Ginecologica Italiana (SEGI) Executive Board member of European Society for Gynaecologic Endoscopy (ESGE) Scientific advisory roles in foundations like "Fondazione Donna e Feto"

Joseph MakinView profile

Assistant Professor

• Mathematical Neuroscience
• Neural Computation
• Brain-Machine Interfaces
• +7 more

Joseph Makin is an Assistant Professor of Electrical and Computer Engineering at Purdue University, affiliated with the Elmore Family School of Electrical and Computer Engineering. His research focuses on mathematical neuroscience, neural computation, and brain-machine interfaces (BMIs), with a strong emphasis on machine learning applications in neuroscience. He leads the Makin Lab, which develops algorithms for BMI/BCI systems, investigates theoretical aspects of neural computation, and explores pure machine learning principles inspired by biological systems. Education: Not explicitly listed in provided texts. His work bridges computational neuroscience and engineering, particularly in decoding speech and movement from neural activity using ECoG arrays. Notable projects include real-time speech decoding in paralyzed individuals and developing encoder-decoder frameworks for cortical activity translation. The lab also focuses on advancing recurrent neural network architectures and probabilistic graphical models, as evidenced by their textbook An Introduction to Modern Statistical Learning . Recent research trends emphasize self-supervised learning for speech decoding, hybrid system modeling of biological processes, and translational applications in neuroprosthetics. His team collaborates with neurosurgeons and biomedical engineers to translate theoretical insights into clinical tools. Scientific Awards: No explicit awards listed, though his BCI project reached finalist status in a major competition. Makin advises graduate students in machine learning and BMI projects, prioritizing strong backgrounds in neural networks and statistical methods. The Makin Lab is part of Purdue's broader efforts in computational neuroscience and biomedical engineering.

• Artificial Intelligence
• Reinforcement Learning
• Bioinformatics
• +4 more

Leander Schietgat is a postdoctoral researcher at Vrije Universiteit Brussel (VUB) working within the Informatics and Applied Informatics department. He is affiliated with the Federated Labs AI and Robotics research group and holds an ORCID identifier (0000-0002-2097-0974). His research spans multiple interdisciplinary domains where artificial intelligence intersects with practical applications. Dr. Schietgat's research interests focus on applying machine learning techniques to diverse domains. His work in reinforcement learning addresses real-world manufacturing challenges through job shop scheduling algorithms. In bioinformatics, he investigates metagenomics and haplotype analysis for microbial communities. His research also extends to computational toxicology, developing methods for toxicophore detection and mutagenicity prediction. This interdisciplinary approach demonstrates his ability to adapt AI methodologies across different scientific domains. His publication record shows consistent output with four research articles between 2021-2023, demonstrating expertise in both theoretical machine learning frameworks and practical implementations. The research trends indicate a strong focus on applying explainable AI to trustworthy control systems, with particular emphasis on reinforcement learning applications in manufacturing and biological systems. Dr. Schietgat is actively involved in multiple research projects including: CTRLxAI=T(H)RUST (2022-2026): CTRL schemes merged with eXplainable AI for t(h)rustworthy control of physical dynamic systems IOF GEAR: Artificial Intelligence Lab (2022-2026) TAILOR: Foundations of Trustworthy AI (2020-2024) ESF project: A people-oriented AI training through serious game experiences (2019-2021) RAINboW: Responsible AI for a smart connected world (2019-2022) He is scheduled to participate in the event 'Europe's Vision on Responsible AI: Research, Practice, Policy' as an organizer in April 2025, indicating his active engagement with the broader AI policy community.

John R. KenderView profile

Professor

• Computer Vision
• Artificial Intelligence
• Video Understanding
• +7 more

John R. Kender is a Professor in the Department of Computer Science at Columbia University, affiliated with the Fu Foundation School of Engineering and Applied Science. He previously served as former Vice Dean of the same school and has been recognized as a Great Teacher by the Society of Columbia Graduates and a Distinguished Faculty Teacher by the Columbia Engineering School Alumni Association. His research focuses on computer vision, video understanding, gesture recognition, and artificial intelligence, supported by grants from DARPA, NSF, and industry partners like IBM and Microsoft. He has advised over 25 doctoral students and co-chaired the 2016 ACM International Conference on Multimedia Retrieval (ICMR). His teaching spans courses in object-oriented design, visual interfaces, and visual databases, with a strong emphasis on project-based learning. Kender's work includes pioneering contributions to semantic video indexing, multimodal analysis, and transfer learning techniques. Education: Advanced degrees in Computer Science (details not explicitly stated in texts). His research interests bridge computational methods and human-centric applications, including gesture-audience engagement correlations using EEG, cultural differences in news video production, and visual meme tracking in social media. His projects, such as the NSF-funded 'Tagging and Browsing Videos According to Affinity Groups,' highlight user-centered multimedia systems. Kender's work has been published in over 100 peer-reviewed articles and patents, addressing challenges in video segmentation, motion anticipation, and neuroimage alignment. Recent articles explore geometric approaches to transfer learning, 3D motion prediction, and cultural analysis through computational aesthetics. These reflect a trend toward cross-disciplinary methods combining AI, computer vision, and social sciences. Awards: Multiple teaching recognitions, including Columbia's top faculty teaching honors. Advising highlights include mentoring students across decades, with notable alumni contributing to academia and industry. His grants total over $10M from federal and industrial sources. Kender's lab focuses on real-world applications like educational video analysis, medical imaging, and event-driven multimedia systems. Current projects include developing explainable AI for video clustering and probabilistic GAN-based human motion prediction.

Longbing CaoView profile

Professor

• Artificial Intelligence
• Data Science
• Machine Learning
• +7 more

Professor Longbing Cao is the Distinguished Chair in Artificial Intelligence and Director of the Frontier AI Research Centre at Macquarie University. He holds dual PhDs in Artificial Intelligence and Computing Science, an MSc in Communication, and a Bachelor's degree in Electrical Engineering. His research spans AI, data science, behavior informatics, and enterprise innovation, with over 400 publications and significant industry collaborations. Education: PhD in Pattern Recognition and Intelligent Systems, Graduate University of Chinese Academy of Sciences PhD in Computing Science, University of Technology Sydney MSc in Communication Bachelor's in Electrical Engineering Research Interests: Focuses on AI systems, federated learning, time series forecasting, and humanoid robotics. His work integrates theoretical advancements with real-world applications in finance, healthcare, and smart cities. Recent Articles: Explores cutting-edge topics like generative AI (ProgDiffusion), federated learning (FedSI), and pandemic modeling (waning effect of interventions). These contributions highlight advancements in both foundational AI and applied data science. Awards: Eureka Prize (2019), ACM Distinguished Scientist (2019) Grants & Projects: Leads major initiatives such as the Federated Omniverse Facilities for Smart Digital Futures (2024-2025) and Data Complexity and Uncertainty-Resilient Deep Variational Learning (2024-2027). Prior roles include Founding Director of UTS Advanced Analytics Institute (2011-2014). Labs & Teams: Directs the Frontier AI Research Centre and collaborates with global institutions like Microsoft, Commonwealth Bank, and Shanghai Stock Exchange.

Sharon JohnsonView profile

Professor

• Healthcare Delivery Systems
• Lean Methods
• Health Informatics
• +4 more

Sharon Johnson is a Professor at Worcester Polytechnic Institute (WPI) within The Business School and serves as Director of the Industrial Engineering Program. She holds a PhD from Cornell University (1989), an MS from Cornell (1986), and a BS from the University of Michigan (1983). Her research focuses on healthcare delivery systems, leveraging lean methodologies and information technologies to improve care processes. She is a founding member of WPI's Healthcare Delivery Institute (HDI) and has secured grants from the National Science Foundation and the New England Veterans Engineering Resource Center. Research interests include applying lean methods in healthcare, electronic health record (EHR) system impacts on care delivery, and process analysis modeling. Notable projects include evaluating lean strategies for suicide safety interventions, optimizing mobile integrated health systems, and analyzing EHR effects on primary care. Her work aligns with UN SDGs 3 (Health & Well-Being), 4 (Education), 8 (Economic Growth), and 9 (Industry & Innovation). Johnson has received prestigious awards such as the Chairman’s Exemplary Faculty Prize (2019) and leadership roles in professional societies like the Institute of Industrial Engineers. She advises numerous student projects, including robotic surgical instrument design and emergency department workflow improvements. Her research emphasizes operational excellence and continuous improvement in healthcare and business systems.

• Robotics
• Legged Locomotion
• Hybrid Dynamical Systems
• +5 more

Nelson Rosa is an Assistant Professor in the Mechanical, Materials, and Aerospace Engineering Department at Illinois Institute of Technology, affiliated with the Armour College of Engineering. His research focuses on hybrid dynamical systems, robotics, legged locomotion, and algorithmic design. He holds a B.E. and B.A. in Engineering Sciences from Dartmouth College (2005/2006), a Ph.D. in Mechanical Engineering from Northwestern University (2018), and a postdoctoral fellowship at the University of Stuttgart (2020). Education: Postdoctoral Fellow, University of Stuttgart (2020) Ph.D. Mechanical Engineering, Northwestern University (2018) B.E./B.A. Engineering Sciences, Dartmouth College (2005–2006) His research interests emphasize gait generation for bipedal robots, energy-efficient locomotion, and dynamic system analysis. Key contributions include topological approaches to gait optimization and the application of virtual holonomic constraints. He received the Humboldt Postdoctoral Research Fellowship (2021–2023) from The Alexander von Humboldt Foundation. Publications span topics like energetically optimal gaits and passive dynamic walking, with notable work in IEEE Transactions on Robotics and the IEEE International Conference on Robotics and Automation (ICRA). His work bridges theoretical dynamical systems with practical robotic implementations.

Yanran DingView profile

Assistant Professor

• Optimization-based control
• Actuator design
• Trajectory planning
• +4 more

Yanran Ding is an Assistant Professor in the Department of Robotics at the University of Michigan, directing the Agile Robot Control and Design (ARCaD) Lab. His research focuses on optimization-based control, actuator design, and trajectory planning for legged robots, aiming to bridge theoretical foundations with practical hardware implementations. He holds a Ph.D. from the University of Illinois at Urbana-Champaign and a B.S. from Shanghai Jiao Tong University. Prior to U-M, he was a Postdoctoral Associate at MIT’s Biomimetic Robotics Lab. Education: Ph.D., Mechanical Engineering, University of Illinois Urbana-Champaign (2017–2021) M.S., Mechanical Engineering, University of Illinois Urbana-Champaign (2015–2017) B.S. (with Honors), Joint Institute, Shanghai Jiao Tong University (2011–2015) Research interests emphasize optimization-based control algorithms , actuator design , and reinforcement learning applied to legged robots. His work enables agile motions through mechatronics integration and real-time model predictive control (MPC). Recent projects include energy-efficient legged locomotion and humanoid robot actuator design funded by a $1.2M grant. Publications span topics like MPC for dynamic walking, self-collision avoidance, and open-source robotics kits. Notable trends include advancing MPC techniques for real-world robot agility and integrating hardware-software co-design principles. Awards include the 2021 Best Paper Finalist Award (Model-Based Optimization TC), 2019 Coordinated Science Lab Best Robotics Demo Award, and multiple scholarships for academic excellence. ARCaD Lab is actively hiring post-doctoral researchers. Current research includes robotic jumping, terrain-adaptive locomotion, and collaborative robotics systems. Lab activities include public demonstrations (e.g., robot dog Go2 at Ann Arbor Museum) and international conference presentations (ICRA, Humanoids).

Carey JewittView profile

Professor

• Multimodal Analysis
• Digital Communication
• Educational Technology
• +6 more

Professor Carey Jewitt is a leading academic in digital communication and multimodal interaction, holding a position at the UCL Knowledge Lab within the IOE - Faculty of Education and Society. Her work explores how digital technologies reshape touch, interaction, and learning. She leads the ERC-funded IN-TOUCH project investigating digital touch communication and previously directed the MODE project advancing multimodal research methods. Jewitt’s research spans education, healthcare, and industry, emphasizing interdisciplinary collaboration across computer science, sociology, and the arts. Key roles include Chair of UCL’s Collaborative Social Science Domain and Director of the UCL Knowledge Lab. She has authored foundational texts like Introducing Multimodality (2016) and secured prestigious grants from the ERC and ESRC. Her work bridges theory and practice, addressing societal impacts of digital touch in healthcare, education, and everyday life. Research interests focus on multimodal communication, digital touch, sensory ethnography, and educational technology. Notable outputs include studies on tactile emoticons, industrial robotics, and pandemic-era remote touch practices. She collaborates with engineers, designers, and artists to develop innovative methodologies, such as combining ethnography with haptic prototyping. Awards include the ERC Consolidator Grant and UK Research Council Academic Fellowship. Her lab’s work addresses ethical and design challenges in emerging touch technologies while advocating for inclusive, multimodal approaches to digital interaction.

Tim WelscheholdView profile

Researcher

• Reinforcement Learning
• Imitation Learning
• Mobile Manipulation
• +7 more

Dr. Tim Welschehold is a Junior Research Group Leader and former Substitute Professor at the Department of Computer Science, University of Freiburg, Germany. He is affiliated with the Faculty of Engineering and conducts research in the Robot Learning Lab and Autonomous Intelligent Systems group. He completed his PhD in Computer Science under Prof. Wolfram Burgard and has held senior research and leadership roles since 2020. Research Interests: His work centers on reinforcement learning, imitation learning, mobile manipulation, and dynamical systems. He explores how robots can learn complex manipulation tasks from human demonstrations and improve autonomy through deep learning and adaptive policies. His research integrates perception, reasoning, and action for real-world robotic applications. The recent publications highlight a strong trend in mobile manipulation, with a focus on learning from demonstrations, uncertainty-aware perception, and task-driven co-design of robotic systems. His work frequently appears in top-tier robotics conferences such as ICRA, IROS, CoRL, and RA-L, often in collaboration with Prof. Abhinav Valada and other members of the Freiburg robotics community. Scientific Awards: Best Paper Award, IROS 2022 Workshop on Mobile Manipulation and Embodied Intelligence Advising and Grants: Dr. Welschehold has co-supervised several Master’s students, including Abdelrahman Younes, Erick Rosete-Beas, and Iman Nematollahi. His research has been supported by major funding bodies such as the German Research Foundation (DFG), NVIDIA, Toyota Motor Europe, and the Carl Zeiss Foundation through projects like ReScale and BrainLinks-BrainTools. Labs and Teams: He is a key member of the Robot Learning Lab and the Autonomous Intelligent Systems group at the University of Freiburg, contributing to projects such as OpenDR, OML, and ReScale, which aim to advance scalable and responsible learning for assistive robotics.

Günter RudolphView profile

Professor

• Computational Intelligence
• Evolutionary Algorithms
• Optimization
• +5 more

Prof. Günter Rudolph is a Professor of Algorithmic Foundations and Education in Computer Science at the Technical University of Dortmund's Department of Computer Science. He leads Chair 11: Algorithm Engineering, focusing on Computational Intelligence (CI), Evolutionary Algorithms, and their applications in optimization and gaming. His research emphasizes theoretical analysis of CI methods, practical guidelines for operationalization, and applications in engineering, energy systems, and entertainment. Key research areas include multi-objective optimization, evolutionary robotics, and computational intelligence in games such as StarCraft and car racing simulations. He has advised numerous students on topics ranging from autonomous driving systems to procedurally generated game content. Rudolph collaborates internationally, including with CINVESTAV-IPN (Mexico) on multiobjective control methods and BMWi-funded projects on energy systems and forming process predictions. His work spans academic publications in journals like Genetic Programming and Evolvable Machines and conferences such as IEEE CIG. Notable projects include developing adaptive car racing controllers, optimizing energy supply systems in industrial parks, and advancing AI strategies in real-time strategy games. Rudolph's group actively contributes to game AI research through competitions like the StarCraft AI Competition and the Simulated Car Racing Championship, emphasizing the intersection of computational intelligence and entertainment.

• Proof Assistants
• Formal Verification
• Inductive and Coinductive Datatypes
• +12 more

Andrei Popescu is a Senior Lecturer (Associate Professor level) in the Department of Computer Science at the University of Sheffield, where he conducts research in formal methods, proof assistants, and information flow security. He previously held academic positions at Middlesex University and TU Munich. University: University of Sheffield Department: Department of Computer Science Previous Affiliations: Middlesex University, TU Munich His research focuses on the logical foundations and practical applications of proof assistants, particularly Isabelle/HOL. He has made foundational contributions to inductive and coinductive datatypes, syntax with bindings, higher-order logic, and the formal verification of secure systems. His work bridges theoretical logic with real-world systems such as conference management (CoCon) and social media platforms (CoSMeDis). The recent publications highlight a strong trend in formalizing deep logical results (e.g., Gödel’s incompleteness theorems), advancing datatype theory, verifying complex security properties, and applying formal methods to practical systems. His work consistently appears in top-tier venues such as POPL, CAV, ITP, and CSF. Distinguished Paper Award at POPL 2025 Distinguished Paper Award at POPL 2024 Distinguished Paper Award at POPL 2023 RS 3 Best Paper Award for 2012–2013 He has advised PhD students including Lorenzo Gheri and has been actively involved in organizing major academic events such as the Midlands Graduate School, CPP, ITP, and TABLEAUX conferences. He has served on numerous program committees including POPL, ITP, CSF, and CAV, and has led research projects funded by VeTSS and industrial partners. He is a key contributor to the Isabelle proof assistant ecosystem, particularly in the development of the (co)datatype package and foundational consistency results. His work combines deep theoretical insight with practical implementation, making significant impacts in both academia and applied security.

Jeff OffuttView profile

Professor

• Software Testing
• Mutation Testing
• Model-Based Testing
• +8 more

Jeff Offutt is a Professor and Chair of the Department of Computer Science at the University at Albany, part of the College of Nanotechnology, Software, & Engineering. Previously, he was a Full Professor with Tenure in Software Engineering at George Mason University, where he led the MS in Software Engineering program and developed numerous courses in software testing, web engineering, and software analysis. His research has significantly influenced both academia and industry in software testing and engineering education. His research interests span software testing, mutation testing, model-based testing, automatic test data generation, web application testing, and software engineering education. He has pioneered techniques such as bypass testing and model-based testing, with applications embedded in tools like Selenium. His work emphasizes practical, impactful research—what he calls "science fiction"—with a clear path to real-world use by software engineers. His recent publications reflect a strong trend in cost-effective mutation testing, model-based testing strategies, educational innovations like self-paced learning models (e.g., SPARC), and security aspects of web applications. The keywords across his work include software testing, empirical studies, automation, and education, with subfields such as fault localization, test oracles, input partitioning, and scalable pedagogy. John Toups Presidential Medal for Excellence in Teaching (2020) George Mason University Faculty Member of the Year (2020) Virginia Outstanding Faculty Award (2019) George Mason Teaching Excellence Award (2013) 10-year Most Influential Paper Award, MODELS 2020 Best Paper Award, ICST 2021 Most Influential Paper, ISSRE (2019) Jeff Offutt has advised numerous students and collaborated internationally, including at Swedish universities. He has led major educational initiatives such as the NSF-funded SPARC project, aimed at improving scalability and diversity in introductory CS courses. He has also been actively involved in curriculum development, including co-authoring IEEE/ACM guidelines for software engineering programs. His leadership extends to founding and chairing major conferences like IEEE ICST and serving as Editor-in-Chief of Software Testing, Verification and Reliability . He leads research projects on smart test automation, minimal mutation, and K–5 computer science integration. His work has resulted in widely used tools including muJava, Mothra, and SpecTest. He frequently gives keynote talks on testing, education, and research methodology, and is known for his popular talk "How to Get Your Paper Rejected."

Phil HusbandsView profile

Research Professor

• Artificial Intelligence
• Evolutionary Robotics
• Artificial Life
• +12 more

Phil Husbands is a Research Professor of Artificial Intelligence in the Department of Informatics at the University of Sussex, School of Engineering and Informatics. He is a co-founder of the Centre for Computational Neuroscience and Robotics (CCNR) and a founding member of the Evolutionary and Adaptive Systems group (now the AI Group), reflecting his deep interdisciplinary engagement across engineering, neuroscience, and computer science. His research interests span artificial life, evolutionary robotics, biologically inspired neural networks, computational neuroscience, machine learning, and the history and philosophy of AI. He has pioneered work on GasNets (neural networks with diffusible modulators), insect-inspired navigation, and bio-inspired optimization algorithms. His work often bridges theory and application, integrating insights from biology into adaptive robotic systems. The recent publications highlight a strong trajectory in bio-inspired computation, including evolutionary swarm algorithms, neuromodulation models, cognitive architectures, and tactile robotics. His work increasingly explores foundational questions in agency, time perception, and autonomy, suggesting a move toward more comprehensive models of artificial cognition. Scientific Awards: No specific awards listed in the provided text. Phil Husbands has been involved in numerous research grants from major funders including the European Union, EPSRC, BBSRC, and Wellcome Trust. He has supervised multiple doctoral students and collaborated widely, notably with Andy Philippides and Michael O’Shea. He is actively engaged in postgraduate research supervision and public dissemination of science through books like Robots: What Everyone Needs to Know . He leads and contributes to interdisciplinary research teams such as the Evolutionary and Adaptive Systems group and the Centre for Computational Neuroscience and Robotics, fostering collaboration between computer scientists, neuroscientists, and roboticists.

• Gene Therapy
• Molecular Physiology of Hearing
• Auditory Synapses
• +7 more

Saaid Safieddine is a Permanent Researcher (DR2 CNRS) at the Institut Pasteur in Paris, where he co-leads the Auditory Cognition and Communication team. His research focuses on developing translational gene therapies for hereditary and acquired hearing loss, particularly targeting genetic deafness such as DFNB9 caused by otoferlin mutations. He works within a multidisciplinary team that includes engineers, researchers, and ENT surgeons, aiming to bridge molecular discoveries with clinical applications through robotic delivery systems. His primary research interests include: Molecular physiology of hearing and auditory synapses Gene therapy for inner ear abnormalities Adeno-associated virus (AAV) vector design and tropism Otoferlin gene expression and regulation Translational neuroscience and preclinical modeling Cell-specific targeting in the inner ear His recent publications (2019–2025) demonstrate a consistent and impactful research trajectory in gene therapy for deafness. The work shows a progression from foundational studies on synaptic mechanisms to advanced preclinical models and clinical translation. Key themes include the development of dual-AAV systems for otoferlin delivery, in vivo tropism studies across developmental stages, restoration of auditory processing, and expanding therapeutic time windows in Usher syndrome models. His research increasingly integrates developmental biology, viral engineering, and behavioral neuroscience to address the full spectrum of hearing restoration. Saaid Safieddine has not been noted to have received specific scientific awards in the provided texts. However, his leadership in high-impact projects such as the ANR "Robocop" initiative and collaborations with clinical centers (Pitié-Salpêtrière and Necker Hospitals) indicate significant research funding and institutional support. He supervises PhD students and postdoctoral researchers, contributing to the training of next-generation scientists in auditory neuroscience and gene therapy. He is involved in multidisciplinary research teams and collaborative projects that integrate: The Auditory Cognition and Communication team at Institut Pasteur The RobOtol robotic surgery project for otological interventions Clinical ENT departments at Pitié-Salpêtrière and Necker University Hospitals The Hearing Institute for translational development ANR-funded projects like "Robocop" for active cochlear implants