Find Academic Supervisors

Ethan WilliamsView profile

Assistant Professor

• Seismology
• Oceanography
• Acoustic Monitoring
• +3 more

Ethan Williams is an Assistant Professor at the University of California, Santa Cruz, Department of Earth & Planetary Sciences. His research focuses on distributed acoustic sensing (DAS) applications in seismology, oceanography, and environmental monitoring, particularly studying wave dynamics, subduction geohazards, and ocean-solid Earth interactions. Ph.D. in Geophysics, California Institute of Technology (2023) M.S. in Geophysics, California Institute of Technology (2019) B.S. in Geophysics and B.A. in Music, Stanford University (2017) Research Interests: Williams specializes in DAS technology, subduction zone geohazards, surface wave dynamics, and ocean-solid Earth coupling. His work bridges seismic monitoring, marine geophysics, and environmental sensing. Publication Trends: His recent articles emphasize DAS innovation for offshore seismic monitoring, wave propagation analysis, and integrating fiber-optic data with conventional measurements. Themes include earthquake detection, climate change impacts, and multi-scale ocean dynamics. Scientific Awards: 2022 SSA Student Presentation Award for 'Continuous seismic monitoring of a building over 20 years' Advising & Collaboration: Williams collaborates with institutions like the University of Washington and Caltech, working on NSF-funded projects such as the Ocean Observatories Initiative. He invites prospective students/postdocs to contact him via email.

Hua XuView profile

Professor

• Clinical Natural Language Processing
• Biomedical Informatics
• Large Language Models for Biomedicine
• +3 more

Dr. Hua Xu is the Robert T. McCluskey Professor of Biomedical Informatics and Data Science at Yale School of Medicine. He serves as Vice Chair for Research and Development in the Department of Biomedical Informatics and Data Science and as Assistant Dean for Biomedical Informatics at Yale School of Medicine. Dr. Xu leads the Clinical NLP Lab and is Chair of the NLP working group at the Observational Health Data Sciences and Informatics (OHDSI) program. Dr. Xu received his PhD in Biomedical Informatics from Columbia University, an MS in Computer Science from New Jersey Institute of Technology, and a BS in Biochemistry from Nanjing University. Dr. Xu is a renowned researcher in clinical natural language processing (NLP), having developed novel algorithms for important clinical NLP tasks such as entity recognition and relation extraction. His work has been top-ranked in over a dozen international biomedical NLP challenges. He has developed CLAMP, a comprehensive clinical NLP toolkit that has been successfully commercialized and adopted by hundreds of healthcare organizations worldwide. His research focuses on applying NLP technologies to diverse clinical and translational studies to accelerate clinical evidence generation using electronic health records data. Recently, he has been utilizing NLP to harmonize metadata of biomedical digital objects to promote FAIR principles in biomedicine, and his lab is actively working on developing large language models (LLMs) for diverse biomedical applications. Dr. Xu's recent publications demonstrate a clear trend toward leveraging large language models for biomedical applications. His work spans from benchmarking LLMs for clinical NLP tasks to developing specialized architectures like BiomedRAG (retrieval augmented LLMs for biomedicine). His research addresses critical healthcare challenges including adverse event extraction, oncology clinical trial analysis, and EHR-based association studies, showing how NLP can bridge the gap between unstructured clinical text and actionable medical insights. Dr. Xu's lab has achieved top rankings in numerous NLP challenges, including multiple #1 positions in i2b2 Temporal information extraction, SemEval Disease-modifier extraction, BioCREATIVE Chemical-induced disease extraction, and other prestigious competitions. His contributions to clinical NLP have significantly advanced the field's ability to extract meaningful information from complex medical texts. As the leader of the Clinical NLP Lab at Yale, Dr. Xu oversees research that forms a complete ecosystem: developing novel NLP methods, building robust software tools, and applying these technologies to clinical and translational research. His lab's work closes the loop between methodological innovation and practical healthcare applications, ensuring that advances in NLP directly benefit patient care and medical research.

Aaron YoungView profile

Associate Professor

• Human Augmentation
• Wearable Robotics
• Assistive Devices
• +7 more

Aaron Young is an Associate Professor in the Woodruff School of Mechanical Engineering at Georgia Institute of Technology and a program faculty member in the Biomedical Engineering School. He serves as Director of the Exoskeleton and Prosthetic Intelligent Controls (EPIC) Lab, focusing on robotic human augmentation through advanced control systems for prosthetics and exoskeletons. Education: Postdoctoral Fellow, University of Michigan (2014-2016) Ph.D., Northwestern University (2014) M.S., Northwestern University (2011) B.S., Purdue University (2009) Dr. Young's research addresses clinically viable control systems for wearable robotic devices, emphasizing intent recognition , EMG signal processing , and machine learning integration. His work targets mobility impairments from stroke, amputation, cerebral palsy, and neurological injuries, aiming to reduce metabolic costs, restore natural biomechanics, and enhance community ambulation. Key innovations include data-driven control frameworks , biomechanical terrain adaptation , and anthropometry-based personalization . His recent publications highlight advancements in deep learning for real-time biomechanics , EMG-informed joint estimation , and adaptive assistance systems . The EPIC Lab's facilities feature a terrain park with force plates , motion capture systems , and HumoTech simulation platforms for device testing. Scientific Awards: New Faces of Engineering (IEEE USA, 2017) Military Health System Team Award (2015) NSF Graduate Fellowship (2010) NDSEG Fellowship (2010) IEEE EMBC 3rd Place (2013) Projects include NSF-funded hip exoskeletons for stroke survivors, DoD-powered prostheses for amputees, and Pediatric knee exoskeletons for cerebral palsy. The lab cultivates interdisciplinary expertise in robotics , biomedical engineering , and human-machine interaction .

Jon GibbinsView profile

Professor

• Carbon Capture and Storage (CCS)
• Power Plant Engineering
• Coal and Biomass Combustion
• +7 more

Professor Jon Gibbins is a faculty member at the University of Sheffield within the School of Mechanical, Aerospace and Civil Engineering . He serves as the Centre Director of the UK Carbon Capture and Storage Research Centre and is a Research Area Champion for Solvent Post-Combustion . With over 30 years of experience in coal/biomass gasification and CCS since 2002, his work spans academic, industrial, and policy domains. He contributes to global CCS initiatives, including the SaskPower CCS Global Consortium Advisory Committee , and advises on energy system balancing, economics, and regulation. Research Interests: Carbon Capture and Storage (CCS) Power Plant Engineering Coal and Biomass Combustion Hydrogen Production with CCS Techno-economic Analysis Flexible Power Plant Operation Recent Article Trends focus on CCS integration, solvent optimization, hydrogen decarbonization, and combustion dynamics. His work emphasizes partnerships with industry and governments, particularly in China and the UK, addressing climate policy, energy security, and technology deployment.

Fedor DokshinView profile

Assistant Professor

• Computational and Quantitative Methods
• Environmental Sociology
• Political Sociology
• +4 more

Fedor Dokshin is an Assistant Professor in the Department of Sociology at the University of Toronto, Downtown Toronto (St. George) campus. His research bridges computational social science with environmental and political sociology, focusing on energy transitions, partisan dynamics, and social network structures. Key research areas include racial and income disparities in solar photovoltaic adoption, policy feedback mechanisms in renewable energy programs, and partisan influences on environmental decision-making. Fields of Study: Computational and Quantitative Methods, Environmental Sociology, Political Sociology, Social Networks Areas of Interest: Computational social science, Energy and the environment, Political polarization Research Trends: Dokshin's publications reveal a focus on energy justice, behavioral diffusion models, and political polarization. His work combines computational methods with environmental policy analysis, examining how socioeconomic factors and partisan identities shape renewable energy adoption. Articles demonstrate geographic heterogeneity in opposition to extraction projects, digital discourse analysis techniques, and institutional dynamics affecting scholarly knowledge production. Methodological Emphasis: Utilizes large-scale data analysis, spatial modeling, and automated textual analysis to explore energy-environment-society intersections. Research highlights the tension between technical solutions and social equity in energy transitions, with recurring themes of policy design, public engagement, and networked political behavior.

• Machine Learning
• Optimization
• Federated Learning
• +8 more

Peter Richtarik is a Professor at the King Abdullah University of Science and Technology (KAUST), specializing in Machine Learning, Optimization, and Federated Learning. He actively teaches courses such as Stochastic Gradient Descent Methods and mentors PhD and MS students like Konstantin Burlachenko, Kai Yi, and Lukang Sun. His research spans distributed optimization, parameter-efficient fine-tuning, and theoretical frameworks for non-convex and non-smooth problems. Recent work includes 2025 contributions to Bernoulli-LoRA (theoretical PEFT) and Gluon (LMO-based optimizers). He co-developed Thanos (block-wise pruning) and BurTorch (CPU-optimized training framework). His publications focus on communication efficiency ( ATA , LoCoDL ), differential privacy ( DP-RBCD ), and stochastic proximal methods. Richtarik received the Charles Broyden Prize for his work on quasi-Newton methods. He frequently presents at workshops like MLSS in Senegal and FLOW seminars.

• Embedded Machine Learning
• Neural Network Hardware Acceleration
• Quantized Neural Networks
• +3 more

Lukas Einhaus is a Researcher and PhD student in the Embedded Systems department at the University of Duisburg-Essen since April 2020, affiliated with the Intelligent Embedded Systems (IES) research group and contributing to initiatives including Elastic AI and the IoT Garage. His academic background includes: Bachelor of Science from University of Duisburg-Essen, thesis focused on programming abstractions for concurrent embedded systems Master of Science from University of Duisburg-Essen, specializing in distributed and reliable systems with thesis research on quantizing neural networks Einhaus's research centers on designing neural networks for efficient hardware implementation on FPGAs, with primary expertise in quantized or low-precision neural networks that reduce bit depth (typically 1-3 bits) for computations and information flow. This work enables energy-efficient AI solutions for embedded and IoT devices where resource constraints are critical. His publication record from 2021-2025 reveals consistent innovation in FPGA-based neural network optimization, with applications spanning fluid flow estimation, time-series analysis, and real-time stream processing. Core themes include Elastic AI for adaptive systems, precomputation techniques for convolutional layers, and hardware-aware neural architecture design. He previously contributed to the BMBF-funded project "KI-Sprung: LUTNet" (until March 2022), developing energy-efficient AI networks using elementary lookup tables for FPGA deployment. Einhaus actively mentors students through the IoT Garage initiative, supervising practical projects including drink-mixing machines, exoskeletons, and ball-challenge systems.

Matt J. RutherfordView profile

Associate Professor

• Autonomous Systems
• Embedded Systems
• Software Engineering
• +3 more

Matt J. Rutherford is an Associate Professor in the Department of Computer Science at the University of Denver, with a joint appointment in the Department of Electrical and Computer Engineering. He is Deputy Director of the Unmanned Systems Research Institute and a faculty fellow of Project X-ITE. His research focuses on autonomous systems, embedded systems, and software engineering, with extensive contributions to UAV navigation, control systems, and robotics. Rutherford holds a Ph.D. in Computer Science from the University of Colorado Boulder (2006), an MS (2001), and a BS in Civil Engineering from Princeton University (1996). His work emphasizes practical applications of software engineering principles in distributed and embedded systems. Notable projects include radar-based collision avoidance for UAVs, self-leveling landing platforms, and studies on electric vehicle charging impacts on power grids. Rutherford's research bridges theoretical computer science with real-world engineering challenges, particularly in unmanned systems and robotic autonomy. Key publications explore UAV flight control using neural networks, ground/ceiling effects in rotorcraft, and GPU-based real-time pose estimation. His contributions to model-driven systems and distributed testbed automation highlight long-term engagement with software reliability and scalable experimentation frameworks. Rutherford collaborates widely, including with institutions like the University of South Carolina and Politecnico di Torino. His interdisciplinary approach integrates robotics, aerospace engineering, and software engineering to advance autonomous system capabilities.

Zahraa AbdallahView profile

Senior Lecturer

• Machine Learning
• Data Science
• Time Series Analysis
• +6 more

Zahraa Abdallah is a Senior Lecturer at the School of Engineering Mathematics and Technology, University of Bristol. She holds a PhD and BSc in relevant fields. Her research focuses on Machine Learning, Data Science, Time Series Analysis, and their applications in Health Informatics, Neuroscience, and Bioinformatics. She leads projects on wearable technology integration for diabetes management and EEG-based disease classification. Her work emphasizes explainable AI and multimodal approaches. Zahraa is affiliated with the Bristol Doctoral College Initiative (BDFI) as an Academic Co-Director and collaborates with experts like Prof. Raul Santos-Rodriguez. Contact: zahraa.abdallah@bristol.ac.uk | Website: zahraa-abdallah.com Research Interests: Time Series Clustering & Forecasting EEG-based Disease Detection (Parkinson’s, Alzheimer’s) Smartwatch-Driven Healthcare Systems Explainable AI in Biomedical Applications Key Projects: Development of the CSTS benchmark for time series clustering Investigating insulin needs using automated delivery data Gene essentiality classification via graph neural networks Collaborations: Professor Raul Santos-Rodriguez (BDFI) Lucia Marucci (Systems & Engineering Biology)

Ellen RothenbergView profile

Professor

• Cellular and Developmental Biology
• Molecular Biology
• Immunology
• +3 more

Ellen Rothenberg is the Edward B. Lewis Professor of Biology at the California Institute of Technology (Caltech). She earned her A.B. in Biochemical Sciences from Harvard University (1972) and a Ph.D. in Molecular Biology from MIT (1977). Her academic career at Caltech spans roles from Assistant Professor (1982–88) to her current position. She holds the Ruddock Professorship (2007–21), Distinguished Professor (2021–), and Lewis Professor (2021–). Her research focuses on transcriptional networks governing T-cell development and signaling, particularly how hematopoietic stem cells commit to T-lymphocyte lineages. Using in vitro systems, molecular genetics, and genomic approaches, her lab explores transcription factor interactions, chromatin dynamics, and signaling pathways that drive T-cell identity. Key interests include understanding stem-cell multipotentiality loss, irreversible commitment mechanisms, and links to autoimmune predisposition. Rothenberg is an Andrew D. White Professor-at-Large at Cornell University (2021–2027), collaborating with immunology and molecular biology departments. She has organized international conferences, served on NIH review panels, and advises biotech firms like Century Therapeutics. Her honors include membership in the National Academy of Sciences, AAAS and American Academy of Arts and Sciences fellowships, and the Feynman Teaching Prize (2016). Education: A.B., Harvard University, Biochemical Sciences, 1972 Ph.D., Massachusetts Institute of Technology, Molecular Biology, 1977 Awards: AAAS Fellow, American Academy of Arts and Sciences Fellow Member, National Academy of Sciences American Association of Immunologists Distinguished Fellow Richard P. Feynman Prize for Excellence in Teaching (2016) External Roles: Cornell’s ADW-PAL (2021–2027), collaborating with faculty hosts Brian Rudd and Andrew Grimson Scientific Advisor, Century Therapeutics, A2 Biotherapeutics, Kite Pharma Her work bridges basic research and clinical applications, including immunogenomics symposia and public lectures on T-cell programming. Ongoing projects explore evolutionary origins of lymphocyte development through comparative analysis of basal vertebrates.

Eric WustrowView profile

Associate Professor

• Computer Security
• Network Security
• Anticensorship
• +4 more

Eric Wustrow is an Associate Professor in the Department of Electrical, Computer, and Energy Engineering (ECEE) at the University of Colorado Boulder. His research focuses on computer security, network security, anticensorship, and protocol security. He has advised multiple PhD students including Abdulrahman Alaraj, Jackson Sippe, and Gaukas Wang. Research Interests: Network security and protocol analysis Anticensorship techniques and measurement Internet measurement and infrastructure analysis Cryptography and secure system design Notable Contributions: Developed ZMap, a fast internet-wide scanning tool Pioneered anticensorship research through projects like Telex and TapDance Conducted impactful studies on TLS vulnerabilities and cryptographic key management Awards: Best Paper at USENIX Security 2012 IRTF Applied Networking Research Prize (2024) Third Prize in 2021 Internet Defense Prize Labs/Teams: Active contributor to Refraction Networking projects and cybersecurity tool development initiatives.

Thu NgoView profile

Researcher

• Systemic Functional Linguistics & Social Semiotics
• Multimodal digital literacy education
• Paralanguage
• +3 more

Dr Thu Ngo is a Researcher at UNSW Sydney, affiliated with the School of Arts, Design & Architecture. She holds a Ph.D. in Education Linguistics from the University of New England, Australia, focusing on evaluative language deployment. Her expertise spans Systemic Functional Linguistics, Multimodal Digital Literacy Education, Paralanguage analysis (body language, gesture, facial expressions), and Children’s Literature. Dr Ngo has received grants including the ACU Teaching Development Grant (2019-2020) for visualizing multimodal literacy pedagogy and the Explorance Faculty Research Grant (2021) for student-feedback-based course redesign. Her research integrates linguistic theory with practical educational applications. Recent work explores emotional expression in music education through semiotics, gesture in science communication, and the role of paralanguage in fostering equity for international students. She actively contributes to interdisciplinary projects, such as analyzing characterisation in children’s literature adaptations and refining appraisal frameworks in ESL contexts. Dr Ngo’s publications include a 2021 book on paralanguage modeling and peer-reviewed articles in journals like Research Studies in Music Education and Research in Science Education . She is also active on ResearchGate and maintains an ORCID profile.

Youssef TLICHEView profile

Assistant Professor

• Supply Chain Management
• Logistics
• Quantitative Methods
• +3 more

Youssef TLICHE is an Assistant Professor in Supply Chain Management and Logistics at EM Normandie, where he has been since 2021. He previously held an assistant professorship at Université Le Havre Normandie (2019–2020). He serves as Academic Director of the Graduate programs in Supply Chain Logistics Innovations. He holds a PhD in Management Sciences from Université Le Havre Normandie (2020) and an Engineering degree in Statistics and Data Analysis from ESSAI Tunis (2015). His research focuses on quantitative methods in supply chain management, including collaborative forecasting, decentralized supply chain optimization, and blockchain applications. He also explores decarbonization strategies in maritime logistics and the impact of AI on e-commerce. Key contributions include work on demand inference, inventory optimization, and blockchain in finance. His recent articles address topics such as AI-driven recommendation systems, wind-powered vessel financing, and Industry 4.0’s impact on supply chain vulnerability. No scientific awards are listed. His advising and grant activities remain unspecified in the provided text.

Jack MostowView profile

Research Professor

• Educational Technology
• Artificial Intelligence
• Natural Language Processing
• +3 more

Jack Mostow is a Research Professor affiliated with the Human-Computer Interaction Institute at Carnegie Mellon University. His work focuses on educational technology, artificial intelligence, and natural language processing. He is renowned for developing Project LISTEN's automated reading tutor that listens to children's oral reading, integrating speech recognition and machine learning to improve literacy. His research addresses challenges in child literacy, automated assessment, and the application of AI in education. Key contributions include advancing speech recognition accuracy for children’s voices, designing adaptive tutoring systems, and exploring EEG and visual features to gauge learning states. He has contributed to global education initiatives, such as the XPRIZE field study, and developed tools for iterative improvement of intelligent tutoring systems. His work emphasizes scalable educational technologies for under-resourced regions and data-driven approaches to enhance learning outcomes. Mostow’s publications span over two decades, addressing topics like reinforcement learning for instructional policies, semi-supervised affect perception, and difficulty-controllable question generation. His research bridges AI, education, and human-computer interaction, aiming to create systems that adapt to learners’ needs while advancing computational methods for educational analytics.

Peter SchossigView profile

Lecturer

• Thermal Energy Storage
• Solar Energy
• Energy Efficiency in Buildings
• +3 more

Prof. Dr. Peter Schossig is Director of the Division 'Heat and Buildings' at Fraunhofer Institute for Solar Energy Systems ISE in Freiburg. Since 2016, he has been a Lecturer at the University of Freiburg's Department of Sustainable Systems Engineering. He chairs the Scientific Committee of the International Renewable Energy Storage Conference (IRES), organized by EUROSOLAR. His research focuses on thermal energy storage, solar energy integration, and energy-efficient building systems. Key roles include managing over 250 employees in thermal systems and buildings divisions, leading major projects like Reallabor (heat pump living labs) and LC150 (low-charge heat pumps). Education: PhD in Architecture (2015), Karlsruhe Institute of Technology (KIT), thesis on micro-encapsulated phase change materials. Physics diploma (1998), University of Freiburg. Research Interests: Thermal energy storage systems and materials (PCMs, adsorption). Heat pumps, solar cooling, and renewable energy integration in buildings. Low-exergy heating concepts and sustainable building design. Notable Projects: Leading 'HArVest' for adsorption module development. Co-authoring over 150 publications on thermal systems and sustainability. Serving as external examiner for doctoral programs at Universities of Lleida, Warwick, ETH Zurich, etc. Labs/Teams: Fraunhofer ISE divisions under his leadership include 'Energy Efficient Buildings', 'Heating and Cooling Technologies', and 'High Temperature Solar Thermal' groups, with accredited testing facilities for thermal systems.