Find Academic Supervisors

Jyoti SinhaView profile

Professor

• Vibration Analysis
• Condition Monitoring
• Rotating Machinery
• +7 more

Jyoti Sinha is a Professor of Condition Monitoring and Plant Maintenance in the Mechanical and Aerospace Engineering department. He has extensive experience in vibration analysis, structural dynamics, and plant reliability. His research focuses on experimental and analytical techniques for fault diagnosis, rotating machinery health monitoring, and maintenance optimization. He has authored over 300 technical papers and serves as Editor-in-Chief of Maintenance, Reliability and Condition Monitoring (MARC) . Education: Ph.D. (Rotor Dynamics), University of Wales Swansea (2002) M.Tech. (Aerospace Engineering), IIT Bombay (1998) B.Sc. (Mechanical Engineering), Ranchi University (1988) Research Interests: Vibration-based condition monitoring Rotors and bearings fault detection Finite element modeling and model updating Asset management and Industry 4.0 integration Structural health monitoring Awards: Boyscast Fellowship (1999) Excellence awards for supervision (2012, 2014) 3rd Best Paper Award (2024) Advising & Grants: Supervising 10 PhD and 2 DProf students Completed 17 PhD and 325+ PG projects Secured £3M+ in research grants Labs & Teams: Head of Dynamics Laboratory Chair of International Conference on Maintenance Engineering (IncoME)

• Soft Robotics
• Microfluidics
• Nanofabrication
• +3 more

Ramses Martinez is an Assistant Professor in the Department of Industrial Engineering and Biomedical Engineering at Purdue University . He holds a B.A. in Applied Physics from Universidad Autonoma de Madrid (2004) and a Ph.D. in Physics and Materials Science from the Spanish National Research Council (CSIC) in 2009. Prior to joining Purdue, he conducted postdoctoral research in the lab of Prof. George M. Whitesides at Harvard University, focusing on nanofabrication, microfluidics, and soft robotics. Education B.A. in Applied Physics, Universidad Autonoma de Madrid (2004) Ph.D. in Physics and Materials Science, Spanish National Research Council (CSIC) (2009) His research bridges soft robotics , flexible electronics , and nanofabrication , with a focus on creating self-powered e-textiles , omniphobic paper-based devices , and programmable mechanical metamaterials . His work has led to over 25 publications and 9 patents, emphasizing practical applications in health monitoring and industrial automation . Notable projects include waterproof electronic decals for biofluid monitoring, smart bandages for chronic wound detection, and laser nanoforming methods for scalable metallic structures. His research has been recognized through the Fulbright Fellowship and the Marie Curie IOF Grant .

• Business Focused Predictive Analytics
• Social Media for Business Transformation
• Management of Innovation
• +5 more

Indranil Bose is a Distinguished Professor and Head of the Area of Excellence in AI, Data Science, and Business at NEOMA Business School, with a Ph.D. in Management from Purdue University. He specializes in business analytics, big data, and digital transformation, leveraging expertise from prior roles at IIM Calcutta, University of Hong Kong, University of Florida, and UT Arlington. Education: Ph.D. in Management (Purdue University, 1997) M.S. (Purdue University) M.S. (University of Iowa) B.Tech (IIT Kharagpur) Research Focus: His work spans predictive analytics, social media impact on business, innovation management, cybersecurity, fintech, and AI ethics. Key themes include consumer behavior in digital ecosystems, AI-driven decision systems, and ethical implications of emerging technologies. Publication Trends: Recent articles emphasize AI ethics, digital misinformation, and predictive modeling in finance/healthcare, reflecting a focus on societal impacts of technology. Methodologies include deep learning, sentiment analysis, and causal inference. Awards & Recognition: Winner, EFMD 2017 Case Competition Ranked 29th globally for Information Systems research productivity (2005–2014) Editorial Leadership: Senior Editor for Decision Support Systems and Pacific Asia Journal of the AIS ; Associate Editor for Information & Management , Journal of the AIS , and Communications of the AIS .

Aris SyntetosView profile

Professor

• Decision Science
• Supply Chain Forecasting
• Inventory Optimization
• +13 more

Professor Aris Syntetos is a Distinguished Research Professor and DSV Chair of Logistics and Manufacturing at Cardiff Business School, Cardiff University. He is the founder and Director of the PARC Institute of Manufacturing, Logistics and Inventory, which includes the RemakerSpace, and leads the university’s strategic partnership with DSV. Previously, he held faculty positions at the University of Salford and Copenhagen Business School. His research focuses on the integration of forecasting and inventory optimization, particularly in the context of intermittent demand, spare parts, closed-loop supply chains, and additive manufacturing. He is renowned for the Syntetos-Boylan Approximation and the Syntetos-Boylan-Croston classification method. His work is driven by sustainability and social impact, aiming to reduce inventory obsolescence and support circular economies. The 15 most recent publications highlight a strong trend toward integrating forecasting with inventory and maintenance decisions, with increasing emphasis on sustainability, social good, and advanced analytics. His work spans healthcare, automotive, retail, and humanitarian logistics, often employing machine learning and empirical validation. 2024 Goodeve Medal (Operational Research Society) 2016 Cardiff University Outstanding Doctoral Supervisor Award 2016 & 2019 Cardiff University Innovation and Impact Awards He has secured over £5 million in research funding as Principal Investigator from EPSRC, Innovate UK, and the Welsh Government, leading projects on remanufacturing, 3D printing, and sustainable supply chains. He advises major firms like Ocado, BT, and DSV, and his methods are used in commercial software. He supervises PhD students and actively promotes knowledge transfer. He is Editor-in-Chief of the IMA Journal of Management Mathematics and serves as Vice-President of the International Society for Inventory Research (ISIR). He has taught in the UK, China, Colombia, Denmark, France, Greece, Italy, and Latvia, primarily in Operations Management and Applied Statistics.

Mateo Valero CortésView profile

Professor

• Computer Architecture
• High-Performance Computing
• Parallel Computing
• +3 more

Mateo Valero Cortés is a renowned Professor of Computer Architecture at the Polytechnic University of Catalonia and Director of the Barcelona Supercomputing Center (BSC). He has held academic and leadership roles since 1974, advancing high-performance computing (HPC) and computer architecture research. His work includes pioneering contributions to vector architectures, multithreading, and instruction-level parallelism. Education includes a Telecommunications Engineering degree from the Polytechnic University of Madrid (1974) and a PhD in Telecommunications Engineering from the Polytechnic University of Catalonia (1980). His research spans over 700 publications, focusing on HPC systems, parallel computing, and supercomputing infrastructure. Key research interests include vector processing, super-scalar processors, and task-based programming models. Recent work emphasizes scalable architectures for exascale computing and energy-efficient hardware-software co-design. Notable achievements include the Eckert-Mauchly Prize (highest in computer architecture), Seymour Cray Award, and Charles Babbage Prize. He has led initiatives like the Spanish Supercomputing Network (RES) and PRACE (European HPC partnership). Academic affiliations include the Royal Academy of Engineering of Spain, ACM Fellow, and IEEE Fellow. He has received 13 honorary doctorates and awards such as Mexico’s Order of the Aztec Eagle. Current projects include the Mont-Blanc HPC prototype and ERC-funded research on multi-core chip design. His BSC oversees over 300 researchers and manages MareNostrum supercomputers.

• Software Engineering
• Artificial Intelligence
• Machine Learning
• +7 more

Maxime CORDY is a Research Scientist at the Interdisciplinary Centre for Security, Reliability and Trust (SnT) , University of Luxembourg, within the Security, Design and Validation group (SerVal) . He holds a PhD from the University of Namur (Belgium, 2014) and specializes in software engineering, applied artificial intelligence, and cybersecurity. His work focuses on adversarial machine learning, deep learning robustness, and model checking for critical systems. Research interests include adversarial attacks on tabular data , energy system optimization , code understanding models , and software quality assurance . Recent projects address challenges in secure AI deployment, automated test generation, and fault detection in large language models. Publications emphasize empirical studies on adversarial defenses, data augmentation for code models, and energy consumption forecasting. He contributes to tools like Daedalux (variability-aware model checking) and benchmarks like Tabularbench for adversarial robustness evaluation. Current affiliations include leadership within the SerVal group and collaborations on interdisciplinary projects such as MALETSQUE (Machine Learning Techniques for Software Quality Evaluation). His work bridges theoretical computer science with practical applications in energy systems, medical imaging, and space program design.

• Model-Based Systems Engineering
• Software Development
• Aerospace Engineering
• +4 more

Konstantinos KANAVOURAS is a Doctoral Researcher at the Interdisciplinary Centre for Security, Reliability and Trust (SnT) within the University of Luxembourg, specifically in the Space Systems Engineering research group (SpaSys). He is advised by Prof. Andreas Hein and focuses on model-based systems engineering, software development, and aerospace engineering applications in satellite systems. His research integrates agile methodologies and machine learning for spacecraft design and anomaly detection. Kanavouras holds a Master’s degree from Aristotle University of Thessaloniki (2021) and contributed as an avionics engineer to ESA’s AcubeSAT nanosatellite project under the Fly Your Satellite! program. His work emphasizes lightweight data management tools, PocketQube missions like POQUITO, and fractionated satellite systems for planetary observation. His publications highlight trends in small satellite development, including agile systems engineering practices, thermal anomaly detection via machine learning, and adapting space projects to remote collaboration (e.g., during the pandemic). Key projects include the University of Luxembourg’s first PocketQube mission (POQUITO) and lean approaches to spacecraft subsystems. Current affiliations include the SpaSys group at SnT, with a focus on interdisciplinary collaboration between systems engineering and software development. No scientific awards are listed, but his work aligns with cutting-edge space technologies and mission design.

• Language Education
• Slavic Studies
• Educational Technology
• +3 more

Miloš Pupavac is affiliated with Singidunum University in Belgrade, Serbia, where he contributes to research and teaching in foreign language education. His work spans both traditional and digital methodologies, with a focus on Russian language instruction and the impact of technology on pedagogy. Education: Master's and Doctoral studies in Russian Language and Literature and Language, Literature, Culture at the University of Belgrade. Research interests include the motivational role of textbooks, hybrid teaching models, translation reliability in the AI era, and the effectiveness of digital tools like AI-generated texts, e-books, and presentations in language classrooms. His publications often intersect foreign language teaching , educational technology , and pedagogical innovation . Recent publications (2021–2025) analyze challenges in online/hybrid teaching, AI integration, communication disruptions, and presentation-based pedagogy. These works reflect trends in technology-enhanced language learning and digital literacy . Contact: mpupavac@singidunum.ac.rs .

Mayank GoelView profile

Assistant Professor

• Mobile Computing
• Sensing
• Signal Processing
• +7 more

Mayank Goel serves as an Assistant Professor in the Software and Societal Systems Department (S3D) at Carnegie Mellon University's School of Computer Science. His research bridges computer science and societal impact through practical sensing systems that leverage existing environmental devices for health monitoring and human-computer interaction without requiring hardware modifications. Dr. Goel specializes in mobile computing, signal processing, and machine learning to develop unobtrusive health technologies applicable to real-world scenarios. His core research areas include passive activity recognition for chronic disease management (particularly multiple sclerosis), privacy-preserving acoustic sensing, smartwatch-based clinical interventions for post-operative care, and equitable healthcare systems for global development contexts. He emphasizes end-to-end solutions through close collaboration with medical professionals and designers to ensure immediate deployability outside laboratory environments. Analysis of his 2024-2025 publications reveals a strong interdisciplinary focus spanning computer science, biomedical engineering, and clinical practice. Key trends include longitudinal digital phenotyping for neurological conditions, on-device privacy preservation in activity recognition, and multimodal procedural assistance systems. His work consistently addresses real-world challenges in sensor placement flexibility, user adoption barriers, and equitable access to medical technologies. No scientific awards were mentioned in the available documentation. Information regarding student advising, research grants, or laboratory affiliations was not specified in the provided materials, though his publication record indicates active collaboration with medical professionals and bio-engineers for clinical validation of health technologies.

Bradley SchmerlView profile

Researcher

• Software Engineering
• Software Architecture
• Self-Adaptive Systems
• +5 more

Bradley Schmerl serves as a Principal Systems Scientist in the Software and Societal Systems Department (S3D) within Carnegie Mellon University's School of Computer Science. His research advances software engineering practices for modern challenges in distributed heterogeneous systems, self-adaptation, and cyber-physical integration. He leads the ABLE research group and actively mentors students in the Masters in Software Engineering program while teaching core courses like Software Architecture and Software Engineering Practicum. Dr. Schmerl's work addresses critical challenges in composing continuously evolving software systems, including components from untrusted third parties and on-the-fly recomposition for environmental changes. His research develops reusable, analyzable tools for software composition with emphasis on model-based adaptation, uncertainty management, and cross-language integration. Key projects include Rainbow for runtime architecture reflection, Acme for formal architectural foundations, and Cyber-physical Systems research linking software models with physical dynamics. Analysis of his 2023-2025 publications reveals intensifying focus on robotics software architecture (particularly ROS-based systems), explainable AI for architectural tradeoff analysis, and configuration management in adaptive systems. Trends show growing integration of machine learning for auto-tuning, empirical studies of misconfigurations, and dimensionality reduction techniques for visualizing design spaces—consistently bridging theoretical rigor with practical tool development for real-world applications. Scientific Awards: No specific awards were documented in the source materials. Dr. Schmerl serves as Practice Area Lead and mentor in CMU's Masters in Software Engineering program, guiding client projects including Rainbow UI for self-adaptive framework interfaces, CoBot UI for telepresence robots, and Educational Telepresence Tasking Language development. His research receives support through ABLE group projects funded by grants targeting software architecture foundations, adaptation mechanisms, and cyber-physical system validation. As a core member of the ABLE research group, he directs investigations into architecture-based self-adaptation with active projects spanning Rainbow (runtime architecture models for dynamic adaptation), Acme (formal architectural styles and tools), and Cyber-physical Systems (software-physical model integration). The group also maintains legacy work in End-User Architecting, Architecture Evolution, and service-oriented platforms for intelligence analysis through SORASCS.

Rayid GhaniView profile

Professor

• Machine Learning
• Artificial Intelligence
• Data Science
• +5 more

Rayid Ghani is a Professor at Carnegie Mellon University (CMU), affiliated with both the Machine Learning Department (School of Computer Science) and the Heinz College of Information Systems and Public Policy. He co-leads CMU’s Responsible AI Initiative and leads the Data Science and Public Policy Group and the Data Science for Social Good Program. His work focuses on applying machine learning, AI, and data science to address social and policy challenges in health, criminal justice, education, public safety, workforce development, and sustainability, with an emphasis on fairness, equity, and transparency in AI systems. Education: PhD in Software Engineering PhD in Societal Computing Rayid’s research spans three pillars: (1) building AI systems for human collaboration to improve decision-making, (2) embedding fairness and equity in AI design, and (3) ensuring reliability and resilience of AI systems in dynamic environments. He emphasizes application-grounded experimental design and stakeholder engagement. His recent publications include frameworks for fair ML experimentation, analyses of fairness-accuracy trade-offs, and AI applications in child welfare, public health, and housing policy. He has also contributed to policy discussions, including congressional testimonies on responsible AI procurement. Advising & Grants: Rayid collaborates with governments, NGOs, and academic institutions on projects like eviction prevention, HIV care retention, and bias mitigation in public policy. He advises non-profits and startups on data science strategy and ethics. Labs & Teams: He leads the Data Science and Public Policy Group and the Data Science for Social Good Program at CMU, fostering interdisciplinary collaborations between computer scientists, social scientists, and policymakers.

• Power Electronics
• Renewable Energy Systems
• Motor Drives
• +3 more

Dr. Ramkrishan Maheshwari is an Associate Professor at the Institute of Mechanical and Electrical Engineering, University of Southern Denmark, specializing in power electronics and motor drive systems. His research focuses on advanced power converter topologies, wide bandgap semiconductors, and renewable energy integration. University: University of Southern Denmark Rank: Associate Professor Research: Power Converters, PWM Techniques, Wide Bandgap Devices Recent work involves small DC-link capacitors, machine learning-based component selection, and hydrogen production systems. His Google Scholar articles highlight innovations in converter design and control algorithms. Awards include the BHJ Foundation Teaching Prize (2023) and a Best Paper Award (ICPEE 2021). He supervises PhD students like M. A. Khan and R. K. Mahapatra and leads projects such as 'Efficient Cost Saving Grid Friendly PtX Converter' funded by Mads Clausens Fond.

Michael OberstView profile

Assistant Professor

• Causal inference
• Machine learning
• Healthcare decision-making
• +3 more

Michael Oberst is an Assistant Professor of Computer Science at Johns Hopkins University's Whiting School of Engineering, affiliated with the Malone Center for Engineering in Healthcare and the Data Science and AI Institute. His research focuses on developing reliable machine learning systems for healthcare decision-making, emphasizing causal inference and robust performance across diverse clinical settings. Key research themes include: Ensuring ML system reliability comparable to FDA-approved medical tools Causal reasoning in observational healthcare data Robustness to dataset shifts across hospitals Algorithmic fairness under unobserved confounding Medical adaptation of large language models Recent publications (2025-2024) demonstrate trends in prediction-powered inference, clinical validation frameworks, and robustness evaluation methods. His work appears in top ML venues (NeurIPS, ICML, UAI, EMNLP) and translational medicine journals. Michael holds a BS in Statistics from Harvard University and a PhD in Computer Science from MIT, with postdoctoral training at Carnegie Mellon University's Machine Learning Department. His group actively seeks PhD students and postdocs for developing trustworthy AI solutions in healthcare.

• Steel Structures
• Cold-formed Steel Structures
• Stainless-Steel Structures
• +6 more

Hannah Blum serves as the Alain H. Peyrot Associate Professor in Structural Engineering within the Department of Civil and Environmental Engineering at the University of Wisconsin-Madison. Her research program focuses on infrastructure resilience, next-generation structural design methodologies, and advanced visualization techniques including extended reality applications, supported by funding from federal agencies, industry associations, and private companies. Her academic credentials include a PhD in Civil Engineering from the University of Sydney (2017), complemented by MS (2012) and BS (2010) degrees in Civil Engineering from Johns Hopkins University. Dr. Blum's research program spans critical domains in structural engineering with particular emphasis on steel systems. Key focus areas include: Steel, cold-formed steel, and stainless-steel structural systems Steel deck and joist system behavior Structural stability and reliability analysis Virtual and augmented reality applications in structural steel fabrication Data-driven approaches to structural engineering problems Analysis of her 15 most recent publications (2023-2025) reveals a concentrated research trajectory centered on data-driven design methodologies, advanced material systems (particularly stainless steel and high-strength alloys), and immersive technology integration. Notable trends include machine learning applications for buckling prediction, experimental validation of novel structural systems, and mixed-reality solutions for fabrication processes. Her distinguished recognition includes: University of Wisconsin-Madison Chancellor’s Teaching Innovation Award (2024) College of Engineering Harvey Spangler Award for Innovative Teaching (2023) American Institute of Steel Construction Terry Peshia Early Career Faculty Award (2023) Structural Stability Research Council McGuire Award for Junior Researchers (2022) Structural Stability Research Council Yoon Duk Kim Young Researcher Award (2021) Dr. Blum actively mentors graduate students through CIV ENGR 790 (Master's Research) and 890 (Pre-Dissertator's Research) courses while securing diverse research funding streams. Her professional service includes active participation in steel design standards committees for structural, cold-formed, and stainless-steel systems through organizations like the Structural Stability Research Council. Her experimental and computational research requires specialized facilities for structural testing and digital visualization, though specific laboratory names are not documented in the provided materials. Current projects demonstrate strong industry collaboration, particularly with steel manufacturing and construction technology firms.

• Software Technology
• Artificial Intelligence
• Educational Software
• +5 more

Maria Virvou serves as Professor and Chair of the Department of Informatics at the University of Piraeus, where she also directs the Graduate Program in Informatics and leads the Research Laboratory 'Software Technology'. She holds significant institutional leadership roles including membership in the University Senate and has chaired the Department of Informatics for multiple terms. As Editor-in-Chief of Springer book series 'Learning and Analytics in Intelligent Systems' and 'Artificial Intelligence-Enhanced Software and Systems Engineering', she maintains substantial academic influence across international scholarly platforms. Dr. Virvou earned her PhD in Computer Science and Artificial Intelligence from the University of Sussex with a scholarship from the State Scholarships Foundation, a Master of Science in Computer Science from University College London, and her undergraduate degree from the Department of Mathematics at the National and Kapodistrian University of Athens. Her educational background in both mathematics and computer science has provided a strong foundation for her interdisciplinary research approach. Professor Virvou's research spans Software Technology, Artificial Intelligence, Educational Software and Games, User Modeling, and Human-Computer Interaction. She has pioneered work in personalized interactive software systems, applying fuzzy logic and machine learning techniques to create adaptive educational environments. Her recent work demonstrates a strategic expansion into AI applications for healthcare, with significant contributions to medical diagnostics using large language models and multimodal AI systems. She has also made notable advances in smart tourism applications through personalization techniques. With over 400 publications to her name, Professor Virvou's scholarly output shows a clear progression from foundational work in user modeling toward increasingly sophisticated AI applications across multiple domains. Her publication trends reveal a strategic focus on explainable AI, multimodal systems, and practical implementations that bridge theoretical advances with real-world applications, particularly in healthcare and education sectors. Ranked #1 worldwide in 'User Modelling' publications (147,450 total publications) according to Scopus Ranked #1 worldwide in 'Educational Software' publications according to both Scopus and Microsoft Academic Search Recognized among the top 2% of most influential Artificial Intelligence scientists worldwide by Stanford University General Co-Chair at the 14th IISA Conference 2023 Invited Keynote Speaker at the 35th IEEE International Conference on Software Engineering Education and Training (CSEE&T 2023) As Director of the Research Laboratory 'Software Technology', Professor Virvou has built a robust research team focused on AI applications across multiple domains. She co-founded and co-chairs the IEEE Intelligent Information Systems and Applications international conference series, creating a significant platform for scholarly exchange. Her leadership extends to editorial roles with major academic publishers and active participation in international research collaborations that have secured substantial funding for innovative projects in AI and software engineering.