Find Academic Supervisors

Eli Ben-MichaelView profile

Assistant Professor

• Causal Inference
• Machine Learning
• Public Policy
• +8 more

Eli Ben-Michael is an Assistant Professor jointly appointed in the Department of Statistics & Data Science and the Heinz College of Information Systems and Public Policy at Carnegie Mellon University. He is affiliated with the CMU-NIST AI Measurement Science & Engineering Cooperative Research Center (AIMSEC). Previously, he was a postdoctoral fellow at Harvard University and earned his PhD in Statistics from UC Berkeley. Education: PhD in Statistics, UC Berkeley (2016–2020) Bachelor’s in Computer Science & Statistics, Columbia University Research Interests: Dr. Ben-Michael focuses on developing statistical and computational methods for causal inference and data-driven policy evaluation. His work bridges statistics, machine learning, and optimization to address challenges in public policy, healthcare equity, and social science. Key areas include synthetic control methods, balancing weights, safe policy learning, and causal effects in natural language processing. His research has been applied to topics such as gun control policies, healthcare disparities, and pre-trial risk assessment. Key Contributions: His methodological innovations include the Augmented Synthetic Control method and tools for policy learning under uncertainty. He has developed open-source R packages like augsynth and balancer to implement his methods. Labs/Teams: He is part of the AIMSEC collaboration, advancing AI measurement science, and contributes to interdisciplinary teams studying policy impacts in healthcare, education, and criminal justice.

Benjamin BrossView profile

Part-Time Lecturer

• Video Coding
• Signal Processing
• Machine Learning
• +3 more

Benjamin Bross is a part-time lecturer at HTW University of Applied Sciences Berlin and heads the Video Coding Systems group at Fraunhofer Heinrich Hertz Institute. He specializes in video coding standards, including HEVC (H.265) and VVC (H.266), contributing to their development and standardization. His work emphasizes open-source implementations like VVenC and VVdeC, deployed in broadcast and streaming systems. Education: Dipl.-Ing. in Electrical Engineering (RWTH Aachen University, 2008). Active in ITU-T VCEG and ISO/IEC MPEG since 2010, leading core experiments and editing key standards. Recognized with IEEE Best Paper (2013), SMPTE Merit (2014), and an Emmy (2017) for HEVC contributions. Research focuses on advanced compression techniques, machine learning integration, and real-time encoding. His team develops VVC tools for 4K/UHD, low-latency streaming, and adaptive bitrate systems. Recent work includes optimizing partitioning strategies and reducing encoding complexity in VVC implementations. Awards highlight his impact on video technology: IEEE Consumer Electronics Best Paper (2013), SMPTE Journal Certificate (2014), and an Emmy for HEVC (2017). Teaching emphasizes practical coding standards and their applications in multimedia systems.

Greg JansenView profile

Researcher

• Archival Science
• Data Science
• Machine Learning
• +3 more

Greg Jansen is a Senior Research Software Architect at the University of Maryland's College of Information Studies. He is affiliated with the Applied Research Lab for Intelligence and Security (ARLIS) and the Advanced Information Collaboratory (AIC). His research focuses on applying computer vision, machine learning, and data science to challenges in archival science, digital humanities, and information systems. Notable projects include developing the Brown Dog data transformation ecosystem, enhancing access to historical records, and creating frameworks for collaborative computational archival education. Key research interests include digital curation at scale, autocuration infrastructure, and the ethical use of cultural big data. His work spans government records analysis, census data sifting, and preservation of legacy systems like punchcards. Recent projects emphasize linking disparate datasets (e.g., WWII incarceration biographies) and building scalable cyberinfrastructure for billion-record archives. Publications highlight trends in applying AI-driven methods to historical data challenges, designing open-source tools for repositories, and ensuring equitable access to archival materials. Collaborations include work with the World Bank on social media analytics and international institutions to create transparency portals for sensitive datasets. Jansen leads technical development for initiatives like the 'Window Into Neuroregulation' (WIN) project and has pioneered frameworks for distributed historical data distribution (DRAS-TIC Linked Data). His contributions bridge software architecture with archival practice, advancing both technical and scholarly outcomes in information science.

Ali OzelView profile

Assistant Professor

• Fluid Mechanics
• Multi-scale Modeling
• Gas-Particle Flows
• +5 more

Dr. Ali Ozel is an Assistant Professor at Heriot-Watt University's School of Engineering & Physical Sciences, specializing in fluid-particle flows and multi-scale modeling. His work bridges academic institutions and industry collaborations, including INEOS, Princeton University, and Toulouse's Fluid Mechanics Institute. PhD in Fluid Mechanics (Toulouse, 2011) B.Sc. & M.Sc. in Aerospace Engineering (Istanbul Technical University) Research Master in Environmental & Applied Fluid Dynamics (von Karman Institute) His research focuses on: Multi-scale modeling of fluid-particle flows (micro/meso/macro) Solvent-based carbon capture technologies (Volume of Fluid simulations) Rheology of dense granular suspensions (shear thickening/thinning) Development of xFlow - Complex Flow Technologies for decarbonization Hybrid digital twins combining physics and machine learning Using data-driven approaches and advanced computational tools, Dr. Ozel formulates constitutive models for industrial applications in energy, pharmaceuticals, and manufacturing. His recent publications emphasize neural networks for fluid dynamics, particle stress modeling, and decarbonization strategies. Scientific Awards: Marie Curie Fellows Association Grant Dr. Ozel actively supervises PhD students and collaborates with international institutions on computational modeling projects. His xFlow initiative investigates clean energy integration and device optimization through rigorous multi-scale methodologies.

• Informatics
• Digital Technologies
• Sustainable Digitalization
• +6 more

Arianit Kurti is a full professor of informatics at Linnaeus University, where he serves as Head of both the Informatics Department and the Computer Science and Media Technology Department. With over 20 years of international academic experience across Sweden, Kosovo, and North Macedonia, Prof. Kurti brings extensive expertise in digital technologies and sustainable innovation. He previously held research leadership positions at RISE Research Institutes of Sweden. Prof. Kurti's research focuses on data-driven approaches for business innovation to achieve sustainable digitalization. His work spans multiple domains including forestry (Forest 4.0), furniture industry digital transformation (APPEND, STELLA), education technology (Edutain, IGNITE), and social inclusion (STEFORA). He leads the Data-driven Business Innovation research group and is active in the Interaction Design Research Group, DISA research center, and LNU Systems Community. His recent publications reveal a strong emphasis on practical applications of AI, IoT, and data analytics across diverse sectors. The research trajectory shows progression from mobile learning and context-aware systems in early career to current focus on sustainable digitalization, business model innovation, and domain-specific applications in forestry and manufacturing. Key themes include human-centered technology design, data ecosystems, and bridging academic research with real-world industry challenges. Prof. Kurti has secured approximately €8.5 million in research funding from prestigious sources including EU EACEA, EU H2020, Swedish Knowledge Foundation, Vinnova, Wallenberg Foundation, and FORMAS. He currently leads two major EU Horizon projects focused on AI and IoT technologies for forestry management. He has published over 70 peer-reviewed works spanning journals, conferences, and books, with recent output showing strong international collaboration patterns. His research groups maintain active partnerships with industry and academic institutions across Europe, with particular strength in Scandinavian countries and the Balkans.

Niklas ForsView profile

Assistant Professor

• Software Engineering
• Programming Languages
• Compiler Design
• +3 more

Niklas Fors is an Associate senior lecturer at the Department of Computer Science, Faculty of Engineering, Lund University, and participates in ELLIIT (the Linköping-Lund initiative on IT and mobile communication) as well as the LTH Profile Area on AI and Digitalization. His research pioneers code reuse in data-flow automation languages through the Bloqqi language prototype from his PhD thesis, alongside innovations in reference attribute grammars and software language tooling. Key domains include static program analysis, compiler design, and feature-based programming, with applications spanning cyber-physical systems and educational technology. Recent publications reveal a strong trend toward demand-driven static analysis techniques, efficient fixed-point attribute evaluation, and interactive program analysis exploration via property probes—showcasing practical implementations for Java code analysis and educational settings. His accolades include: Distinguished Artifact Award (2025) Distinguished Artifact Award (2024) Distinguished Paper Award (2024) As co-supervisor for PhD candidates Alfred Åkesson and Idriss Riouak, he leads projects like "Cloud Based Language Tooling," "Cloud Tooling for Large-Scale Cyber-Physical System Model-Based Development," and "Explainable Declarative Programming Analysis," securing funding from ELLIIT and Vinnova. His work integrates with Lund's AI and Digitalization profile area, focusing on scalable language tools for next-generation cyber-physical systems through the ELLIIT initiative.

GNEKPE ChristianView profile

Associate Professor

• Innovation Strategy
• Technology Management
• Circular Economy
• +4 more

Dr. GNEKPE Christian is an Associate Professor in the Department of Strategy, Entrepreneurship and Innovation at Toulouse Business School. His work bridges strategic management, technology innovation, and sustainability, with a focus on intellectual property strategies, circular economy adoption, and AI applications in sustainable infrastructure. He holds a PhD in Management and has extensive international research collaborations. Research Interests: His core areas include innovation strategy, technology management in cross-border contexts, and leveraging AI for energy efficiency. He explores how firms balance internal/external knowledge sourcing in biopharmaceutical and circular economy sectors, and the role of patent portfolios in shaping M&A equity stakes. Publications: Recent work (2023-2024) highlights AI-driven building retrofit predictions, regulatory influences on circular innovations, and SME leadership adaptations. Earlier studies (2016-2018) analyzed patent strategies' impact on global acquisitions and the dark side of inter-organizational relationships. Awards: Not listed in available materials. Advising & Grants: No specific advisees or grant details provided in current data. His research is typically funded through institutional and collaborative grants.

Leonardo BonatiView profile

Researcher

• Wireless Communications
• Telecommunications
• Network Security
• +4 more

Leonardo Bonati is an Associate Research Scientist in the Department of Electrical and Computer Engineering at Northeastern University. He specializes in cutting-edge wireless communication systems, particularly focusing on Open RAN, 5G/6G networks, and AI-driven network intelligence. His work emphasizes automated testing, network slicing, and security in software-defined cellular systems. He leads and contributes to high-profile projects such as AutoRAN and DigiRAN, funded under the CHIPS and Science Act, which aim to enhance open and disaggregated cellular network testing and digital twin frameworks. His research also involves experimental platforms like Colosseum and OpenAirInterface for large-scale wireless emulation and real-world testing. Key research interests include: AI-based network control (via dApps/xApps), digital twins for network validation, zero-touch deployment, and security in O-RAN interfaces. He holds multiple patents on topics ranging from network slicing to private 5G connectivity through steganography. Notably, Bonati was recognized among the top 2% most-cited scientists globally in 2024 by Stanford University. He collaborates actively with industry and academia on next-generation cellular technologies, contributing to both theoretical advancements and practical implementations. His advising and grant activities include co-PI roles on major initiatives like AutoRAN (testing automation) and DigiRAN (high-fidelity digital twins), totaling over $4M in funding. Bonati’s work bridges academic research and industry-ready solutions through platforms like Colosseum and the Open RAN Gym.

• Artificial Intelligence in Medicine
• Cardiovascular Disease
• Machine Learning Applications
• +3 more

Sumukh Vasisht Shankar is a Researcher at Yale University's Yale School of Medicine, Department of Internal Medicine. His work focuses on advancing artificial intelligence applications in cardiovascular diagnostics and healthcare data analysis, particularly leveraging deep learning and large language models (LLMs) to improve clinical decision-making. He specializes in developing algorithms for ECG analysis, structured data transformation from unstructured clinical reports, and scalable phenotyping across non-interoperable health systems. Key research areas include AI-driven detection of structural heart diseases, myocardial infarction, and atrial fibrillation risk assessment. His projects often involve multinational validation and integration of AI tools into clinical workflows, such as the CarDS-Plus ECG platform and digital twin strategies for clinical trial adaptation. Shankar's contributions bridge machine learning innovation with real-world medical challenges, aiming to enhance diagnostic accuracy, streamline healthcare data management, and personalize patient care. Recent work includes developing ensemble learning models for heart disease screening, retrieval-augmented generation (RAG) systems for clinical risk scoring, and AI-based surrogate imaging techniques for aortic stenosis. His methodologies emphasize scalability, adaptability to portable devices, and ethical compliance with data localization regulations.

• Biochemistry and cell biology
• Clinical sciences
• Genetics
• +3 more

Ismail Moghul is a Research Fellow at University College London (UCL), affiliated with the Institute of Ophthalmology and the Research Department of Cancer Bio. He holds a PhD in Data Science (UCL, 2016–2021), an MSc in Bioinformatics (Queen Mary University of London, 2015–2016), and a BSc in Biomedical Sciences (Queen Mary University of London, 2011–2015). His research focuses on biochemistry, clinical sciences, genetics, ophthalmology, immunology, and computational biology, with a particular emphasis on AI-driven diagnostics, genetic disease mechanisms, and epigenetic analysis. He collaborates internationally on projects such as the Eye2Gene initiative, leveraging deep learning for inherited retinal disease diagnosis. His work spans AI applications in ophthalmic imaging, including fundus autofluorescence analysis and retinal biomarker quantification. He has contributed to tools like Retinograd-AI and aIRDetect, which automate image grading for inherited retinal dystrophies. His research also explores genetic determinants of conditions like Fuchs endothelial corneal dystrophy and age-related macular degeneration, integrating multi-omics data and clinical cohorts. Moghul’s publications highlight advancements in epigenetic profiling, long-read sequencing pipelines, and the development of open-source bioinformatics tools such as GIMMEcpg and ReLayer. His interdisciplinary approach bridges computational methods with clinical challenges, addressing unmet needs in precision medicine and patient-centric care. He advocates for patient co-design in healthcare tools, as seen in his commentary on improving hospital letter comprehension for ophthalmic patients. His contributions span over 30 peer-reviewed articles since 2016, reflecting his expertise in AI, genomics, and translational ophthalmology.

• Artificial Intelligence
• Machine Learning
• Programming Education
• +3 more

Nicola Dainese is a Doctoral Researcher in the Department of Computer Science at the School of Science. His work focuses on the intersection of artificial intelligence and programming education, leveraging large language models (LLMs) to improve code generation, feedback mechanisms, and symbolic regression. Current projects include STN LITERACY (2023-2026) and FCAI Flagship (2020-2022, extended to 2026). Collaborations span institutions like the Finnish Center for Artificial Intelligence (FCAI) and international research teams. Research interests include: Large Language Models (LLMs) for code generation and feedback Model-based reinforcement learning in programming education Symbolic regression and function discovery via AI Educational technology for student assessment Open-source language models and ethical AI applications Recent publications highlight trends in leveraging LLMs for programming feedback, code repair, and symbolic regression. Key subfields include Monte Carlo Tree Search, prompt engineering, and vision-language models for AI planning. Active project member in DATALIT , addressing data literacy for responsible decision-making. Contributed to benchmarking studies on educational program repair and code world modeling.

V. Ashley VillarView profile

Assistant Professor

• Stellar Astrophysics
• Machine Learning in Astronomy
• Supernova Studies
• +3 more

Dr. V. Ashley Villar is an Assistant Professor of Astronomy at Harvard University, specializing in using machine learning and data-driven methods to study stellar explosions, mergers, and transient phenomena. She holds a PhD in Astronomy & Astrophysics from Harvard (2020) and a BS in Physics from MIT (2014). Her research focuses on supernova light curves, circumstellar interactions, and binary progenitor systems. She previously held positions at Columbia University as a Simons Junior Fellow and at Penn State as the inaugural Mercedes Richards Career Development Professor. Her work leverages large-scale surveys like LSST and JWST to analyze transients, develop classification tools (e.g., SPLASH, Superphot+), and model explosion mechanisms. Awards include NSF and Ford Foundation fellowships. She collaborates on projects such as the Young Supernova Experiment (YSE) and contributes to multimessenger astronomy efforts linking gravitational waves to electromagnetic counterparts. Key Projects: YSE Data Release 1 (1,975 supernova classifications), extrabol bolometric light curve estimator, SBI++ inference framework Grants: NSF, Simons Foundation, LIGO collaborations Labs/Teams: Harvard CfA Transient Research Group, LIGO HET Response Collaboration Her recent work includes analyzing GRB 221009A's supernova emission, kilonova light-curve interpolation, and binary merger signatures in double-peaked supernovae. She actively develops machine learning tools for transient classification and anomaly detection in big astronomical datasets.

Jeremy SingerView profile

Associate Professor

• Systems Software
• Garbage Collection
• Compilers
• +4 more

Jeremy Singer is a Reader in Programming Language Implementation at the School of Computing Science, University of Glasgow. He specializes in systems software, compilers, garbage collection, and secure runtime environments. His research focuses on advancing memory management techniques, many-core parallelism, and edge computing. Singer holds a PhD from the University of Cambridge (2006) in Static Program Analysis based on Virtual Register Renaming. He is a Senior Member of the ACM and a Fellow of the BCS. His academic roles include supervising PhD students in areas such as quantum memory management, federated graph neural networks, and secure memory systems. He has led multiple EPSRC-funded projects, including M4Secure (2023-2026), Capable VMs (2020-2024), and FRuIT (2017-2019). He teaches courses like COMPSCI1016 (Computational Thinking) and COMPSCI4021 (Functional Programming in Haskell). Singer’s research spans compiler design, runtime systems, and security. Notable contributions include work on SSA-based compiler techniques, Raspberry Pi cluster systems, and secure microPython implementations. He has authored over 80 publications and co-developed MOOCs on functional programming and data science. His awards include Fellow of the BCS and Senior ACM Membership. Current research interests include secure memory management, compiler optimizations for heterogeneous architectures, and edge computing security.

Beatriz SoretView profile

Associate Professor

• Satellite Communications
• Internet of Things (IoT)
• Wireless Networks
• +3 more

Beatriz Soret is an Associate Professor at Aalborg University's Department of Electronic Systems, part of The Technical Faculty of IT and Design. Her research focuses on satellite communications, IoT, wireless networks, and AI-driven network systems. She leads and collaborates on projects like STELLAR (2019-2021) and SATNEX V WI Y4.6 (2024-2025), addressing latency, reliability, and real-time data challenges in 6G and satellite networks. Her work emphasizes distributed computing, edge computing for Earth observation, and semantic communication frameworks. Key publications include advancements in RAN slicing for VR traffic, coded distributed computing, and AI-integrated network layers. She actively contributes to special issues on distributed intelligence and 6G technologies. Her research spans theoretical and experimental analyses of delay, age of information, and network performance in scenarios like LEO satellite constellations and industrial IoT. She collaborates with institutions globally, advancing non-terrestrial networks and smart connectivity solutions.

• Cell and molecular biophysics
• High resolution microscopy
• Reproductive Biology
• +7 more

Professor Lewis Francis is an Associate Professor in Reproductive Biology and a core member of the Gynaecological Oncology Research Group (RBGO) at Swansea University Medical School. He leads the Biophysics and Epigenetics research programs, focusing on advanced drug delivery systems and cellular heterogeneity in complex microenvironments. His work integrates cell and molecular biophysics with regenerative medicine, aiming to identify therapeutic targets through biomechanical and epigenetic analyses. Dr. Francis serves as Programme Director for the Nanomedicine MSc and holds roles as Director of ACNM and Deputy Director of the CALIN project. His teaching spans undergraduate courses in Genetic Analysis, Medical Genetics, and Carbohydrate Metabolism, alongside supervising Genetic and Biochemistry MSci students. He collaborates extensively with institutions like Houston Methodist Hospital Research Institute and the Morgan Advanced Studies Institute (MASI). Research interests include biomarker expression, gene regulation networks, and cell-matrix interactions. His lab develops high-resolution microscopy and bioinformatics tools to study epigenetic responses in cancer cells. Notable projects involve 3D biomimetic models for fibrosis studies and selenium nanoparticle impacts on cancer biomechanics. Dr. Francis' work bridges nanomedicine and clinical applications, with a focus on ovarian and endometrial cancers. He advocates for interdisciplinary approaches combining biophysics, AI, and regenerative medicine to address unmet clinical needs in reproductive and oncological fields.