Find Academic Supervisors

Saurabh Amin is a Professor in the Department of Civil and Environmental Engineering at the Massachusetts Institute of Technology (MIT), where he also serves as the Edmund K. Turner Professor and Undergraduate Officer. He is a Principal Investigator at the Laboratory of Information and Decision Systems and holds affiliations with the Operations Research Center and the Center for Computational Science and Engineering. His educational background includes: B.Tech. 2002, Indian Institute of Technology (IIT) Roorkee M.S. 2004, University of Texas (UT) Austin Ph.D. 2011, University of California (UC) Berkeley Saurabh Amin's research focuses on the design and control of infrastructure systems using game theory and optimization in networks. His work spans three main areas: resilient network control, information systems and incentive design, and optimal resource allocation in large-scale infrastructure systems. By concentrating on critical infrastructure domains including highway transportation, electric power distribution, and urban water networks, his research develops innovative theory and tools to enhance system performance against both stochastic and adversarial disruptions. His approach involves modeling cyber-physical interactions in infrastructures to assess vulnerabilities, developing detection and response tools for failures at various scales, and designing economic incentive schemes that improve aggregate public good while accounting for dependencies and private information among strategic entities. Amin's work bridges mathematical systems theory with practical civil engineering applications, creating a rigorous theoretical foundation for infrastructure resilience that addresses diverse failure mechanisms from natural disasters to deliberate malicious actions. His recent publications demonstrate a strong focus on decarbonization of energy systems, resilient infrastructure planning under climate uncertainty, optimization methods for complex networked systems, and game-theoretic approaches to sustainable infrastructure management. His work increasingly integrates artificial intelligence and machine learning techniques with traditional control theory to address contemporary challenges in infrastructure resilience and sustainability. The research shows a clear trajectory toward addressing climate change impacts on infrastructure systems while maintaining economic efficiency and operational reliability. Professor Amin has received numerous prestigious awards and honors: Common Ground Excellence in Teaching Award, 2025 HSCC Test-of-Time Award, 2024 MIT CEE, Distinguished Service and Leadership Award, 2023 Samuel M. Seegal Prize (SoE) – inspiring students in pursuing and achieving excellence, 2022 Earll M. Murman for Excellence in Undergraduate Advising, 2022 C3.ai Digital Transformation Institute Research Award, 2020 MIT, Ole Madsen Mentoring Award, 2020 MIT, Energy Initiative Research Award, 2020 National Academy of Engineering, China-America Frontiers of Engineering Symposium speaker, 2019 MIT, Robert N. Noyce Career Development Professor, 2015-2018 Google Faculty Research Award, 2015 National Science Foundation CAREER Award, 2015 Siebel Energy Institute Research Award, 2015 MIT, Solomon Buchsbaum AT&T Research Fund Award, 2012 Professor Amin has been actively involved in significant research projects including the C3.ai DTI project on Causal Reasoning for Real-Time Attack Identification in Cyber-Physical Systems and another on Learning in Routing Games for Sustainable Electromobility. He serves as the chief scientist on multi-institutional NSF grants, including the $9 million Foundations of Resilient Cyber-Physical Systems (CPS) project. His teaching portfolio includes courses such as 1.008 Engineering for a Sustainable World, 1.104 Sensing and Intelligent Systems, 1.020 Engineering Sustainability: Analysis and Design, and 1.208 Resilient Networks. As Undergraduate Officer, he plays a key role in shaping the educational experience for civil and environmental engineering students at MIT. Professor Amin leads the Resilient Infrastructure Networks Lab at MIT, where his team develops theoretical foundations and practical tools for infrastructure resilience. The lab focuses on the intersection of control theory, game theory, and optimization applied to cyber-physical infrastructure systems. Current research directions include pandemic-resilient urban mobility and hurricane-resilient smart grid operations, reflecting the lab's commitment to addressing pressing societal challenges through rigorous systems engineering approaches.

• Infrastructure Resilience
• Cyber-Physical Systems Security
• Game Theory Applications
• +7 more

Özer Özkahraman is a postdoctoral researcher at the Division of Robotics, Perception and Learning (RPL) at KTH Royal Institute of Technology. He works under Ivan Stenius and John Folkesson, focusing on underwater mission planning, simulation, and integration of autonomous systems. His email is ozero@kth.se . He completed his PhD at KTH under Petter Ögren, researching large-scale multi-agent coverage planning for autonomous underwater vehicles (AUVs). Current projects include the SMaRCSim multi-domain simulation platform and development of underwater vehicles like LoLo, SAM, and Evolo. Research interests span autonomous underwater systems, multi-agent coordination, control systems, and simulation infrastructure. He emphasizes modular, accessible frameworks for vehicle testing and real-world deployment. His work bridges theoretical methods (e.g., control barrier functions) with practical applications in marine robotics. Publications focus on AUV navigation, environmental sensing, and adaptive control. Projects like Real2Sim aim to align simulation with real-world vehicle dynamics using motion capture data. He collaborates internationally on topics like data-driven damage detection and model compression for resource-constrained robots. No academic awards are explicitly mentioned. He actively seeks collaborators for projects in sonar simulation, flow field modeling, and cyber-physical system integration.

• Autonomous Underwater Vehicles (AUVs)
• Multi-Agent Systems
• Robotics
• +6 more

Magnus Boman is a Professor of AI and Health at the Department of Medicine, Solna, Karolinska Institutet (KI), where he leads the AI@KI initiative to support researchers in AI integration. He is affiliated with the Chronic Inflammatory Disease Epidemiology research group under Johan Askling. His research focuses on AI applications in precision medicine, multimodal prediction, ethical norms in AI systems, energy-efficient computing, and quantum sensor data interpretation. Research Interests: Artificial Intelligence in healthcare and precision medicine Multimodal data analysis for disease prediction and treatment Machine learning for clinical decision support systems Ethical and societal implications of AI Grants: Swedish Research Council: Improving breast cancer histology image classification (2024-2026) Scalable Federated Learning (2022-2025) Ai in sustainable cities (VINNOVA, 2019) Advising & Students: Supervised over 50 PhD and Master's students across KI, KTH, and Stockholm University, focusing on AI applications in healthcare, machine learning, and computational epidemiology. Notable projects include predictive modeling for mental health outcomes and variant filtering in genetic data. Labs & Teams: Leads AI@KI, fostering AI adoption in medical research. Collaborates with the Johan Askling group on epidemiology and chronic disease studies.

• Artificial Intelligence in Healthcare
• Precision Medicine
• Machine Learning Applications in Healthcare
• +5 more

Marco L. Della Vedova is a Senior Lecturer in Applied Artificial Intelligence at Chalmers University of Technology, Sweden. He works in the Vehicle Engineering and Autonomous Systems division within the Department of Mechanics and Maritime Sciences, as part of Prof. Mattias Wahde's research group. Since 2025, he has served as Director of the Data Science and AI master's programme (MPDSC) at Chalmers, where he teaches courses including Introduction to Artificial Intelligence and Digitalization in Sports. Dr. Della Vedova earned his academic foundation at the University of Pavia, Italy, where he completed his BSc (2006), MSc (2009), and PhD (2013) in Computer Engineering. His doctoral research focused on "Real-Time Physical Systems and Electric Load Scheduling" under Prof. Tullio Facchinetti. During his PhD studies, he spent a year at U.C. Berkeley hosted by Prof. Francesco Borrelli at the Model Based Predictive and Distributed Control Lab. His research spans multiple AI domains with a strong emphasis on interpretability. Dr. Della Vedova develops interpretable methods for conversational AI, naturalness evaluation of forests using canopy height models, and geospatial applications. His work bridges theoretical AI with practical societal benefits, particularly in environmental monitoring, transportation systems, and orienteering. He has previously contributed to cloud computing, hate speech detection, and cyber-physical energy systems, demonstrating his interdisciplinary approach to AI research. Dr. Della Vedova's publication record reveals a consistent trajectory of impactful research across multiple domains of artificial intelligence. His recent work shows a strong focus on interpretability in AI systems, with significant contributions to natural language processing, geospatial analysis, and causal inference. The research demonstrates both theoretical depth and practical applications, particularly in environmental monitoring and social media analysis. His methodology often combines traditional machine learning approaches with novel interpretability techniques, creating bridges between complex AI systems and human understanding. Dr. Della Vedova has received several prestigious recognitions for his work: Best PhD thesis award from the Order of the Engineers of Bergamo (2013) Italian champion of Il Cervellone (2012) Top Italian performer in IEEEXtreme 6.0 programming competition (148th overall globally, 2012) Premio Arturo Schena award from Fondazione Credito Valtellinese (2010) With over 50 students supervised through bachelor's and master's theses, Dr. Della Vedova has established himself as a dedicated mentor in the AI community. His current PhD students include Minerva Suvanto working on interpretable NLP and Vivien Lacorre developing AI for railway infrastructure inspection. His supervision spans diverse topics from forest naturalness evaluation to hate speech detection and transportation optimization. Beyond formal supervision, he actively contributes to educational initiatives including serving as Director of Chalmers' Data Science and AI master's program and developing innovative teaching methods that connect theoretical concepts with real-world applications. Dr. Della Vedova is deeply embedded in both academic and professional communities. He leads the Applied Artificial Intelligence research group at Chalmers while maintaining strong connections with European research networks through projects like the ERASMUS+ EUrienteering initiative. His interdisciplinary approach is reflected in collaborations across computer science, environmental science, and social sciences. Notably, he applies his AI expertise to orienteering both as a researcher developing localization methods and as a licensed Event Advisor for the International Orienteering Federation, demonstrating how his professional and personal interests converge in innovative ways.

• Artificial Intelligence and Machine Learning
• Interpretable and Explainable AI
• Geospatial AI
• +7 more

Yvonne Hallesson is an Associate Professor and Senior Lecturer in Swedish at Uppsala University's Department of Nordic Languages, where she serves as Director of Studies for postgraduate education. Her research centers on subject literacy, focusing on students' reading and writing development through school texts across disciplines. Her academic qualifications include: Associate Professor of Swedish, Uppsala University (2021) Senior Lecturer in Swedish Language, Uppsala University (2019–present) Senior Lecturer in Didactics with focus on Swedish, Uppsala University (2016–2019) PhD in Language Didactics, Stockholm University (2015) Licentiate in Educational Science, Stockholm University (2011) Master of Education in Swedish and English, Stockholm University of Education (1999) Hallesson investigates the relationship between language and learning, particularly how students develop subject-specific literacy through text work in social sciences and other disciplines. Her research examines reading comprehension strategies, writing development, and the integration of national minority languages in educational contexts. She analyzes how text properties affect learning across different school subjects and student demographics. Her recent publications reveal consistent focus on subject literacy development, with particular attention to reading comprehension in social sciences and representation of national minority languages in textbooks. Her work demonstrates strong methodological emphasis on discourse analysis and classroom-based research, often involving collaborative teacher-researcher partnerships to bridge theory and practice. Hallesson supervises doctoral students including Jim Stefansson (main supervisor) and Linnea Wenell (assistant supervisor), and mentors numerous graduate students. She has secured significant research funding from the Swedish Research Council for projects including "Läsmetoder under lupp" (2022) and the current "Building subject knowledge in text" (2025-2027). She actively contributes to academic governance as board member of the Department of Nordic Languages and the research network SMDI, and coordinates Uppsala University's subject didactic network for Swedish and Swedish as a second language. She also represents the Swedish Teachers' Association in the Language Care Group.

• Subject Literacy
• Reading Development
• Writing Development
• +4 more

Dr. Muhammad Humayoun is a Senior Lecturer at Karlstad University, Sweden, specializing in informatics education and computational linguistics research. He holds a Ph.D. from University of Grenoble Alpes (2012) and M.Sc. from Chalmers University (2006). His academic career spans over 15 years across Pakistan and Sweden, including teaching roles at COMSATS University, University of Central Punjab, and Higher Colleges of Technology. Research Focus: His work centers on NLP applications for under-resourced languages like Urdu/Punjabi, including text summarization, abusive language detection, and formalization of mathematical texts. He has developed linguistic resources (corpora, lexicons) and contributed to plagiarism detection systems in programming courses. Teaching Contributions: Designed and taught over 15 courses across multiple universities, including advanced programming, AI, and cloud computing modules. Currently teaches graduate-level courses like 'Legal Tech, AI and Rules-as-Code' and undergraduate software engineering courses at Karlstad University. Awards: Recognized for Urdu threat detection system (3rd place, 2021) and top rankings in Urdu fake news competitions. His work on Urdu summarization corpora has been widely cited in computational linguistics research.

• Machine Learning
• Computational Linguistics
• Natural Language Processing
• +5 more

Paolo Monti is a Professor and Head of the Optical Networks Unit at Chalmers University of Technology's Department of Communications, Antennas and Optical Networks. With extensive expertise in optical communication infrastructures, he leads research focusing on energy efficiency, network resiliency, programmability, automation, and techno-economics of optical networks. His work spans multiple international collaborations with funding from major research bodies across EU, USA, and Asia. Professor Monti's research interests center around next-generation optical networking technologies. His work explores the integration of artificial intelligence and machine learning with optical networks, quantum-classical network convergence, 6G infrastructure development, and network automation. His research addresses critical challenges in network energy consumption, reliability under failure conditions, and cost-effective deployment strategies for emerging communication technologies. The research group under his leadership develops frameworks for optical network monitoring, security, and resource optimization using advanced computational techniques. Analysis of his recent publications reveals a strong trend toward AI/ML integration with optical networking, with significant focus on quality of transmission estimation, network automation, and 6G readiness. His work increasingly combines quantum technologies with classical optical networks while addressing practical implementation challenges in multi-band elastic optical networks. The publications demonstrate a progression from theoretical network design to practical implementations with real-world validation. Professor Monti has received recognition as a Senior Member of IEEE, highlighting his contributions to the field of communications and networking. As an academic leader, Professor Monti has been involved as Principal Investigator, co-PI, and main technical leader in numerous national and international projects. His educational contributions include teaching courses at undergraduate, Master's, and PhD levels, as well as developing ICT-focused education programs. His research has been supported by major funding bodies including the European Commission, VINNOVA, and Wallenberg Centre for Quantum Technology. The Optical Networks Unit under Professor Monti's leadership operates as a vibrant research environment focusing on both theoretical and experimental aspects of next-generation optical communications. The unit maintains strong collaborations with industry partners and academic institutions worldwide, participating in multiple EU-funded projects and national initiatives focused on quantum communications and 6G infrastructure.

• Optical Networks
• 6G Communications
• Quantum-Classical Networks
• +7 more

Helena Lindgren is a Professor at Karolinska Institutet and a visiting professor at the University of Gondar (Ethiopia) and the University of Nairobi (Kenya). Her research focuses on maternal and child health, particularly birth injuries prevention, midwifery-led care, and global health equity. She leads the MIDWIZE project to improve care in low-resource settings and the EMMA program addressing migrant women's healthcare needs. Academic roles: Professor in Reproductive Health, Department of Women's and Children's Health, Karolinska Institutet. International collaborations: Ethiopia, Kenya, Malawi, and Somalia through capacity-building programs. Research initiatives: MIMA model (reducing birth injuries), MIDWIZE (midwife-led care frameworks), and EMMA (migrant maternal care). She has published over 80 articles, authored textbooks on midwifery, and supervised 10+ doctoral students. Her work emphasizes evidence-based practices, cultural sensitivity, and midwife empowerment.

• Maternal Health
• Midwifery Practices
• Birth Injuries Prevention
• +4 more

Karl de Fine Licht is an Associate Professor in Ethics and Technology at Chalmers University of Technology, working within the Department of Science, Technology and Society under the School of Technology Management and Economics. He holds a PhD in Practical Philosophy and is actively involved in course development, skills development, and teaching in the field of "technology and ethics" at both basic and advanced levels. He is a member of the management group for CHAIR and participates in Tracks academic programs. Dr. de Fine Licht's research spans several interconnected domains focused on ethical considerations in technological development and implementation. His primary research interests include social sustainability in urban development, artificial intelligence governance in public decision-making, and ethical challenges in higher education. His work examines how design choices in urban environments affect social inclusion, how AI systems can be deployed justly in public sector applications, and how emerging technologies like generative AI impact academic integrity and pedagogy. His publication record demonstrates a consistent trajectory toward increasingly complex ethical frameworks for technology assessment. The most recent articles reveal a strong focus on procedural justice frameworks for resolving value conflicts in AI governance, critical examinations of generative AI in educational contexts, and refined conceptual approaches to sustainability. His work bridges theoretical philosophy with practical applications, particularly in public sector technology implementation and urban design ethics. Dr. de Fine Licht is currently leading two significant research projects: "Just Solar Energy – Photovoltaics in urban and rural contexts from a justice perspective" (2023-2025) funded by the Swedish Energy Agency, and "Rättvisa och skälighet: Om hur vi bör fördela kostnaderna för VA" (2022-2023) funded by The Swedish Water & Wastewater Association. These projects reflect his ongoing commitment to applying ethical frameworks to real-world technological implementation challenges, particularly those involving resource distribution and energy justice. His academic contributions extend to course development in technology and ethics, where he has integrated his research findings into educational practice. Through his work with CHAIR and Tracks programs, he helps shape interdisciplinary approaches to technology assessment that incorporate ethical considerations from the earliest stages of technological development.

• Ethics and Technology
• Social Sustainability
• Artificial Intelligence Governance
• +4 more

Thomas Vakili is a doctoral researcher at the Department of Computer and Systems Science , Stockholm University , affiliated with the Natural Language Processing Research Group . His work intersects language technology, artificial intelligence, and privacy protection , focusing on mitigating data leakage risks in large language models while maintaining utility. Education: MSc in Computer Science and Engineering (KTH Royal Institute of Technology) Research explores privacy-preserving NLP through techniques like pseudonymization and de-identification, with applications in clinical data and Swedish electronic health records . Recent projects include End-to-End Pseudonymization of BERT models and analysis of eponyms in clinical text . Publications in BMC Medical Informatics , LREC , and NoDaLiDa conferences address privacy risks , model utility , and cross-lingual NLP . He supervises bachelor's and master's theses and teaches courses in NLP , Internet Search Techniques , and Digital Business Strategies .

• Natural Language Processing
• Artificial Intelligence
• Data Privacy
• +4 more

Sonia-Florina Horchidan is a doctoral researcher at the KTH Royal Institute of Technology in Stockholm, Sweden. She is affiliated with the Division of Software and Computer Systems within the School of Electrical Engineering and Computer Science (EECS), working under the supervision of Associate Professor Paris Carbone in the Data Systems Lab . Her academic activities include teaching assistant roles for courses in distributed systems and data storage paradigms. Research Focus: Graph Databases Machine Learning on Graphs Stream Processing Federated Learning with Privacy Guarantees Approximate Query Processing via ML Serverless Streaming Graph Analytics Scientific Contributions: Her recent publications at venues like EDBT, VLDB, and AI4DB explore intersections between graph data management and machine learning. Key themes include ML inference optimization in streaming systems, privacy-preserving health data forecasting, and innovative graph query execution models. Honors & Service: Co-authored Best Paper at DEEM 2022 Best Paper Award at VLDB 2023 PhD Workshop Program Committee Member, aiDM 2024 Reproducibility Committee Member, ACM SIGMOD/PODS

• Graph Databases
• Machine Learning on Graphs
• Stream Processing
• +3 more

Ellinor Tengelin serves as a Senior Lecturer at Mid Sweden University's Department of Health Sciences in Östersund, holding a Doctor of Philosophy. She teaches across undergraduate and advanced levels in the Health and Rehabilitation in Working Life program, integrating critical perspectives into healthcare education. Her educational trajectory includes a Master's in Public Health and Licentiate in Medical Science from the University of Gothenburg, culminating in a 2019 doctoral thesis at Jönköping University on norm criticism in healthcare education. This foundation informs her research at the intersection of health science and critical theory. Dr. Tengelin's research synthesizes norm-critical frameworks with health equity analysis to examine power structures affecting marginalized populations. Her work spans mental health support systems , queer-inclusive healthcare education , and discourse analysis of institutional practices , with emphasis on workplace mental health resources and young people's health strategies. She actively develops norm-critical pedagogical tools for nursing curricula. Recent publications (2023-2025) reveal a concentrated focus on norm-critical implementation challenges in clinical education, managerial support systems for employees with mental disorders, and salutogenic approaches to youth mental health. Her collaborative Scandinavian research employs qualitative methodologies to expose systemic barriers while proposing actionable interventions for inclusive healthcare systems. No scientific awards were documented in the source material. Her ongoing research projects address labor market inclusion for social assistance recipients and organizational frameworks for cross-sectoral knowledge exchange. As co-editor of Jämlik vård: normmedvetna perspektiv (2022), she advances norm-critical discourse in Scandinavian healthcare, while participating in the international LGBTQ Care project examining queer perspectives in clinical education. Within Mid Sweden University's Department of Health Sciences, Tengelin collaborates with multidisciplinary teams across Scandinavia, contributing to discourse analysis frameworks and norm-critical educational reforms. Her work bridges academic research with practical healthcare system improvements through institutional partnerships.

• Norm-Critical Health Science
• Mental Health
• Work Ability
• +4 more

Jenny Kunz is a Research Fellow at Linköping University's Department of Computer and Information Science (IDA), within the Artificial Intelligence and Integrated Computer Systems (AIICS) division. She holds a PhD in Computer Science from LiU (2024) and previously completed a Master’s in Language Technology from Uppsala University and a Bachelor’s in Computer Science from Humboldt University of Berlin. Her research focuses on interpretable and explainable NLP, including parameter-efficient language adaptation, self-rationalizing models, and understanding linguistic information storage in neural networks. She has contributed to studies on LLM-generated explanations, cross-lingual transfer learning, and the challenges of under-resourced languages. Kunz has taught courses such as Language and Computers and has held roles including Chair of the IDA PhD Council and co-representative on the IDA Board. Her work spans over 15 peer-reviewed publications in venues like ACL and COLING, addressing topics from model interpretability to multilingual NLP challenges.

• Natural Language Processing (NLP)
• Interpretability of NLP Models
• Explainable AI
• +4 more

Örjan Dahlström is an Assistant Professor at Linköping University, affiliated with both the Department of Psychology (PSY) and the Department of Behavioral Sciences and Learning (IBL) . His research focuses on speech perception under adverse listening conditions, utilizing neuroimaging techniques like fMRI and MEG. Active in interdisciplinary research combining technical, medical, and behavioral science Involved with HOSS-lab (Human Open Science Services) Uses clinical registries as key research resources Research interests revolve around how the brain compensates for auditory limitations through cognitive processing, with special attention to: Speech understanding in noise and hearing loss Experimental manipulation of cognitive expectations Neuro-cognitive mechanisms in communication Statistical analysis of complex datasets Recent publications span multiple disciplines, including sports medicine, child language development, mental health interventions, and audiological assessment. His work demonstrates strong emphasis on clinical applications and behavioral data analysis. Teaches scientific methods and experimental design Collaborates across technical, medical, and behavioral science domains Active in large-scale database research

• Speech Perception
• Cognitive Neuroscience
• Hearing Loss
• +4 more

Andreas Jemstedt is a Senior Lecturer and Associate Professor (docent) in Education at Stockholm University , affiliated with the Department of Education and the Higher Education Learning Practices (HELP) research group. His work bridges cognitive science and educational practice. Research Interests: Metacognition and self-regulated learning Memory acquisition and forgetting prevention Technology-enhanced learning and AI in education Second language acquisition in migration contexts Impact of sleep deprivation on cognitive performance Key Contributions: Developed Pure Question-Based Learning frameworks Created Two-Page Study Manual intervention Investigated psychological/social factors in language proficiency Explored metacognitive monitoring in sleep-deprived states

• Metacognition
• Memory
• Self-Regulated Learning
• +4 more