Find Academic Supervisors

Katerina PotikaView profile

Associate Professor

• Algorithmic design
• Social network analysis
• Machine learning
• +6 more

Dr. Katerina Potika is an Associate Professor in the Department of Computer Science at San José State University (SJSU), where she has held positions since Fall 2015. Her academic journey includes roles as an Adjunct Lecturer at Santa Clara University (2013-2015) and various teaching positions at institutions in Greece and the U.S. from 2004 onwards. Education: She earned a Ph.D. in Electrical and Computer Engineering (2004) and a Diploma in Electrical and Computer Engineering (1997) from the National Technical University of Athens, Greece. Her doctoral thesis focused on approximation algorithms in optical networks under Prof. Stathis Zachos. Research Interests: Her work spans algorithm design, social network analysis, machine learning, blockchain, computer networks, and graph visualization. Recent projects include analyzing misinformation in infodemics, smart city applications, and community detection in graphs. She leads the 2022-2023 SJSU RSCA grant on network analysis for understanding COVID-19 infodemics. Professional Activities: She serves as a Guest Editor for Big Data Research , a Demo Chair for IEEE BDS 2020, and has organized workshops like the 2013 IEEE International Symposium on Mobile Cloud Computing. She reviews for journals like Algorithms and Future Generation Computer Systems . Teaching: Teaches advanced courses such as Machine Learning on Graphs (CS 276), Social Network Analysis (CS 176), and Data Structures and Algorithms (CS 146). Developed the undergraduate CS 176 course on social network analysis. Grants & Collaborations: Awarded grants including the College of Science RSCA time (2019) and a STCCS-funded project on smart city platforms (2017). Active in mentoring graduate and undergraduate researchers, with over 7 students supervised in recent semesters. Languages: Fluent in Greek (native), English, and German.

Melissa NessView profile

Professor

• Milky Way formation
• Chemical evolution and nucleosynthesis
• Galaxy formation

Professor Melissa Ness is a Professor of Astronomy at the Research School of Astronomy & Astrophysics (RSAA), Australian National University (ANU). She leads research in Milky Way formation, chemical evolution, and galaxy formation. Her work combines stellar spectroscopy, data-driven models, and large-scale surveys like GALAH and Gaia. Education: PhD in Astronomy. Research focuses on Milky Way structure, chemical abundance patterns, and nucleosynthesis. She explores the interplay between stellar ages, element abundances, and orbital properties to understand galaxy formation processes. Key research projects include ACACIAS (Local Group census), chemical tagging in the Milky Way halo, and disk archaeology. She supervises PhD student Sarah Aquilina. Publications span over 112 articles, with recent work on dwarf galaxy abundance analysis, lithium-rich stars, and Sagittarius stream dynamics. Active in collaborations like the ANU Futures Scheme project (2024–2028), focusing on advancing astronomy research and instrumentation. Labs/Teams: Involved with the GALAH survey team, AITC (Astronomy Instrumentation & Technology Centre), and international collaborations like the Apache Point Observatory Galactic Evolution Experiment (APOGEE).

Richard SternView profile

Professor

• Robust Speech Recognition
• Auditory Perception
• Signal Processing
• +2 more

Professor Richard Stern holds joint appointments in Electrical and Computer Engineering, Computer Science, and Biomedical Engineering at Carnegie Mellon University. His research bridges auditory perception with speech technology, particularly in developing noise-robust speech recognition systems. His PNCC algorithm revolutionized feature extraction for speech recognition in noisy environments. Research emphases include: Auditory-inspired signal processing Robust automatic speech recognition Computational models of binaural hearing Music information retrieval Biomedical applications of audio analysis Recent work demonstrates growing interest in human-robot interaction and respiratory monitoring applications. Publications increasingly incorporate deep learning while maintaining foundations in auditory physiology. Honors include the IEEE Signal Processing Society 2019 Best Paper Award for PNCC research. Current projects investigate neural audio processing models and online learning for sound event detection.

• Epidemiology
• Health Economics
• Mathematical Modeling
• +3 more

Dr Joachim Worthington is an Honorary Research Fellow at the Sydney School of Public Health (University of Sydney), part of the Faculty of Medicine and Health. He specializes in mathematical epidemiology and health economics, focusing on liver and colorectal cancers. His work involves developing and validating complex models to inform policy decisions on cancer screening, prevention, and healthcare resource allocation. His research interests include improving the interpretability and reliability of mathematical models in epidemiology, particularly for cancer surveillance and cost-effectiveness analyses. He collaborates with institutions like the Daffodil Centre and government agencies to evaluate the impact of screening programs and policy changes. Key projects include assessing liver cancer prevention strategies, recalibrating colorectal cancer models, and analyzing pandemic disruptions to screening programs. He has contributed to clinical guidelines and policy documents, such as Australia’s hepatocellular carcinoma surveillance guidelines. His recent publications emphasize pandemic-related healthcare challenges, biomarker-based treatment planning, and rural health equity in screening access. He co-chairs the Cancer Council’s liver cancer portfolio and the Daffodil Centre’s Early and Mid-Career Researcher Network, fostering collaboration among researchers. He has received grants for studies on metabolic liver disease prevention, bowel cancer screening policy, and cost-effectiveness analyses. His work bridges epidemiology, mathematics, and public health to address real-world healthcare challenges, particularly in cancer prevention and equity.

• Machine Learning
• Finance
• Artificial Intelligence
• +7 more

Stefan Zohren is a Professor of Engineering Science at the University of Oxford, holding roles as an Associate Professor, Research Associate at the Oxford Internet Institute, and Stipendiary Lecturer at Worcester College. He leads research in applied machine learning across finance, economics, and natural sciences, with a focus on deep learning, reinforcement learning, network analysis, and quantum computing applications. His work bridges academia and industry through collaborations with NVIDIA, Graphcore, Nokia, and Lockheed Martin. Education: DPhil in Mathematical Physics from Imperial College London. He teaches machine learning modules in the MSc in Social Data Science program at the Oxford Internet Institute, including intensive courses on Applied Machine Learning. Research interests emphasize financial time series analysis, generative AI for trading strategies, and quantum computing. Recent publications explore stock market instability via graph auto-encoders, generative AI benchmarking in finance, and embedding compression techniques for noisy regression tasks. Scientific contributions include developing LOB-Bench for limit order book analysis and Position, a multimodal LLM for time series reasoning. His work addresses challenges in portfolio optimization, credit rating forecasting, and market shock causality. Collaborations with Man Group as Principal Quant and involvement in the Creative Destruction Lab’s FinTech stream highlight his industry engagement. He advises on projects ranging from SMEs to global firms, applying ML to finance and healthcare domains.

Linqing WenView profile

Professor

• Gravitational Wave Astrophysics
• Multi-messenger Astronomy
• High-performance Computing
• +4 more

**Linqing Wen** is a Professor in the School of Physics, Mathematics and Computing at The University of Western Australia (UWA). He leads the Gravitational Wave Astronomy Group and holds roles such as Chief Investigator in the ARC Centre of Excellence for Gravitational Wave Discovery (OzGrav). His work focuses on gravitational wave astrophysics, real-time detection, and multi-messenger astronomy, with contributions to the LIGO-Virgo Collaboration (LVC) and the SPIIR search pipeline. **Education**: PhD in Astrophysics from MIT (2001), MS in Physics and ME in Computer Science from PSU (1995), and BS in Condensed Matter Physics from USTC (China, 1992). Professional experience includes roles at Caltech, the Max-Planck Institute, and UWA since 2004. **Research Interests**: Gravitational Wave Astrophysics, High-performance Computing, Machine Learning for GW detection, and Early Warning Systems. Notable projects include SPIIR pipeline development, GW multi-messenger studies, and GPU acceleration for real-time analysis. **Awards**: 2016 Special Breakthrough Prize (shared), ARC Future Fellowship (2009), UPRF Fellowship (2007), and Rossi Fellowship (1995). **Grants & Projects**: Over $30M in funding, including ARC grants for GW detection, multi-messenger astronomy, and pulsar timing arrays. Active roles in OzGrav, UWA research clusters, and international collaborations like the LVC and CRAFT. **Teaching**: Courses include SCIE1121 (introductory astronomy) and specialized modules on Gravitational Wave Astronomy.

Nicholas GibbinsView profile

Associate Professor

• Semantic Web
• Hypertext and Hypermedia
• Computer Science
• +3 more

Nicholas Gibbins is an Associate Professor in the Web and Internet Science group at the University of Southampton's School of Electronics and Computer Science. His research focuses on Semantic Web infrastructure, hypertext systems, and knowledge-intensive applications. He supervises PhD students in Computer Science and actively leads undergraduate programmes in the field. Education: Not explicitly stated in text. Research interests include Semantic Web technologies, hypermedia design, and interdisciplinary projects at the intersection of computing and humanities. Notable projects include EU-funded TrendMiner and EPSRC-supported Semantic Media initiatives. His work spans academic publications on narrative systems, music informatics, and provenance-driven data management. Teaching responsibilities include programme leadership for undergraduate Computer Science and courses on databases, artificial intelligence, and hypertext systems. He collaborates on grants from agencies like EPSRC and ESRC, exploring big data policy, lab notebook digitization, and musicology data.

Daniel de LengView profile

Assistant Professor

• Applied Human-Centered AI
• Explainable AI
• Autonomous Systems
• +5 more

Daniel de Leng is an Assistant Professor at Linköping University (Sweden), affiliated with the Department of Computer and Information Science (IDA) and the Human-Centered Systems (HCS) division. He is part of the Semantic Web group and focuses on applied human-centered AI, particularly in autonomous systems and explainable AI. His work integrates symbolic and subsymbolic methods to address challenges in real-world AI deployment. Education: PhD (2019) and Licentiate (2017) in Computer Science from Linköping University, and MSc/BSc (2013/2011) from Utrecht University (Netherlands). Research Interests: Stream reasoning under uncertainty, autonomous system safety, and semantic integration in robotics. He developed the DyKnow-ROS framework for robot stream reasoning and contributed to the Stellar AIOps environment for AI research. His work emphasizes human-understandable explanations for AI decisions. Awards: Recipient of the SAIS Master's Thesis Award. Advising & Grants: Managed the AI Academy initiative, part of the AIOps ELLIIT Infrastructure and AI Academy management teams. Previously served as Saab Aeronautics' first Point of Contact for AI (until 2022). Labs & Teams: Active in the Reasoning and Learning Lab (ReaL), Semantic Web group, and Human-Centered Systems division. Collaborates on projects like Urdarbrunnen (AI for combat search and rescue) and multi-agent robotics systems.

Maya GomesView profile

Assistant Professor

• Stable Isotope Geochemistry
• Biogeochemical Cycling
• Carbonate Sedimentology
• +2 more

Maya Gomes is Assistant Professor in the Morton K. Blaustein Department of Earth & Planetary Sciences at Johns Hopkins University. Her research uses stable isotope geochemistry to reconstruct biogeochemical cycles across Earth's history, examining interactions between sulfur, carbon, and oxygen cycles during major evolutionary and climatic transitions. Research integrates modern environmental studies with geological archives, using lakes, microbial mats, and coastal systems as analogues to interpret ancient geochemical records. Current focus includes sulfur cycle dynamics in carbon storage ecosystems and pyrite formation processes. Publications demonstrate innovative applications of sulfur isotopes in paleoenvironmental reconstruction, with recent emphasis on blue carbon ecosystems and coastal sediment biogeochemistry. Research bridges geochemistry, microbiology, and sedimentology. Gomes directs field and laboratory investigations of modern sedimentary systems to develop improved interpretations of ancient biogeochemical records, particularly during ocean anoxic events.

Mitzy L SchaneyView profile

Assistant Professor

• Environmental Science
• Soil Science
• Geomorphology
• +3 more

Mitzy L Schaney, PhD is an Assistant Professor in the Social Science Division at the University of Pittsburgh at Johnstown, serving as Director of Environmental Studies. Her research focuses on soil science, geomorphology, and environmental science with specialization in landform development and paleoclimatic interpretations. She holds a BS in Anthropology/Archaeology from Mercyhurst University (1999), MS and PhD in Geology from West Virginia University (2005 and 2019). Her teaching portfolio includes courses such as Physical Geography, Environmental Planning, Water Resources, and Field Studies in Environmental Science. She actively integrates fieldwork and applied research into her academic programs. Schaney's work emphasizes interdisciplinary approaches, combining geologic records with climatic frameworks to study anthropogenic impacts on ecosystems. Her recent studies include UAV-based assessments of coastal marshlands and paleoclimatic analyses of Appalachian soil profiles. She has contributed to projects at Canaan Valley National Wildlife Refuge and Saxis Wildlife Management Area. Her professional experience spans public and private sector roles in environmental science and archaeology. Research highlights include radiocarbon dating methodologies for peatland chronology and investigations into mid-Holocene dry climate events. Schaney’s academic leadership includes directing undergraduate research initiatives and coordinating interdisciplinary environmental studies programs.

• Distributed Systems
• Network Function Virtualization
• Systems Correctness
• +4 more

Aurojit Panda is an Assistant Professor of Computer Science at New York University's Faculty of Arts and Science. His research focuses on lightweight mechanisms for system correctness, network function virtualization, and distributed systems. He holds a Ph.D. from UC Berkeley and a B.S. from Brown University. Education: Ph.D., Computer Science, UC Berkeley (2017) B.S., Math-Computer Science, Brown University (2008) Research Interests: Network Function Virtualization (NFV) Distributed Systems Correctness Machine Learning Tooling and Optimization Operating Systems and SmartNIC Offloading Recent work includes designing distributed shared logs for datacenters, analyzing straggler effects in large model training, and developing tools like NNSmith for deep learning compiler testing. Teaching includes courses on distributed systems, operating systems, and big data/machine learning. He is actively involved in research teams such as NetSys Lab and contributes to open-source projects like NetBricks and BESS.

Marina BednyView profile

Associate Professor

• Brain development and plasticity
• Cognitive neuroscience
• Conceptual development
• +5 more

Marina Bedny is an Associate Professor in the Department of Psychological & Brain Sciences at Johns Hopkins University’s Krieger School of Arts & Sciences. Her research focuses on neuroplasticity and cognitive development, particularly how sensory experience shapes brain organization and conceptual understanding. Her lab investigates cortical plasticity in blind individuals, studying how 'visual' cortex regions repurpose for language, mathematics, and executive functions. She also examines conceptual development, exploring how blind and sighted individuals form abstract concepts like sight-related verbs or light phenomena. Key research themes include: Cortical Plasticity: Functional reorganization of occipital cortex in blindness, including cross-modal processing and executive control functions. Conceptual Development: Role of sensory experience in forming knowledge about events (e.g., 'sparkling'), objects, and mental states. Developmental Mechanisms: Sensitive periods for neural specialization and the interplay between genes and environment. Her lab’s recent work highlights functional flexibility in developing cortex, with studies demonstrating that visual deprivation leads to recruitment of occipital areas for higher-order cognition. Collaborative projects explore Braille reading neurobiology and computational models of semantic processing. Bedny mentors graduate students and postdoctoral fellows in experimental psychology and cognitive neuroscience, with active programs for undergraduate research assistants and blind/visually impaired summer interns. Lab activities include: Summer research internships for blind/visually impaired students Undergraduate research assistantships (10+ hours/week commitment) Collaborations on neuroimaging (fMRI) and behavioral experiments Bedny’s research has been published in Cerebral Cortex , Cognition , and NeuroImage , with a focus on bridging philosophical questions about the human mind with empirical neuroscience methods.

• Climate Science
• Atmospheric Dynamics
• Climate Reanalysis
• +3 more

Dr Paul Berrisford is a researcher affiliated with the University of Reading and the European Centre for Medium-Range Weather Forecasts (ECMWF). His work focuses on atmospheric dynamics, climate reanalysis, and stratospheric-tropospheric interactions. He has contributed to major projects like the ERA-Interim reanalysis system, advancing data assimilation techniques and climate modeling. His research spans topics including temperature variability, energy transport, hydrological cycles, and polar climate processes. Key research interests include understanding climate dynamics through reanalysis datasets, investigating atmospheric circulation patterns (e.g., North Atlantic Oscillation), and assessing the impacts of stratospheric processes on tropospheric weather systems. His work bridges observational data analysis and modeling, with a focus on improving climate monitoring and prediction systems. Publications highlight contributions to global surface temperature trends, energy balance studies, and atmospheric conservation properties. Berrisford collaborates widely, with co-authors from institutions like ECMWF, the UK Met Office, and leading universities. His research aligns with broader efforts to enhance climate model accuracy and address challenges in climate variability analysis. Labs/Teams: Associated with the ECMWF Reanalysis Team and the University of Reading's Meteorology Department.

Xiaowei JiaView profile

Assistant Professor

• Artificial Intelligence
• Data Mining
• Machine Learning
• +3 more

Xiaowei Jia is an Assistant Professor in the Department of Computer Science at the University of Pittsburgh, affiliated with the School of Computing and Information. He holds a Ph.D. from the University of Minnesota (supervised by Prof. Vipin Kumar) and B.S./M.S. degrees from the University of Science and Technology of China and SUNY Buffalo. His research focuses on integrating scientific theory with machine learning to address societal challenges in environmental science, hydrology, and climate modeling. Key research interests include physics-guided machine learning, spatio-temporal data mining, and fairness-aware models. He has developed frameworks like Physics-Guided Neural Networks (PGNN) and Spatial-Net for heterogeneous data, addressing issues in stream temperature prediction, crop yield modeling, and climate change projections. His work has been recognized with awards including the Best Dissertation Award (Minnesota) and multiple Best Paper awards in top conferences. Dr. Jia teaches advanced machine learning courses, emphasizing knowledge integration into AI systems. His lab collaborates extensively with domain scientists to advance applications in agriculture, hydrology, and environmental monitoring. Ongoing projects include high-resolution methane emission datasets (X-MethaneWet) and fairness-aware learning for resource distribution.

• Climate Science
• Meteorology
• Atmospheric Science
• +3 more

Andy Turner is a Professor at the University of Reading, specializing in climate science and meteorology. His research focuses on monsoon dynamics, climate change impacts, and atmospheric processes, particularly in South and East Asia. He has contributed to over 130 publications, including studies on monsoon variability, aerosol effects, and extreme weather events. Turner's work explores mechanisms governing monsoon systems, climate model biases, and future projections under global warming scenarios. He collaborates extensively on initiatives like the INCOMPASS field campaign and contributes to IPCC assessments. Despite no explicit mention of awards or students in the provided texts, his research emphasizes interdisciplinary approaches to understanding climate systems and their societal implications.