Find Academic Supervisors

• High Performance Computing
• Agent Based Modeling
• Analysis of Large Datasets
• +4 more

Parantapa Bhattacharya is a Research Scientist at the Network Systems Science and Advanced Computing Division of the Biocomplexity Institute and Initiative at the University of Virginia. His work focuses on developing computational tools and systems for extracting meaningful insights from complex datasets, with applications spanning public health, social networks, and artificial intelligence. Dr. Bhattacharya's research interests center around High Performance Computing, Agent Based Modeling, Analysis of Large Datasets, and Explainable AI with special emphasis on Natural Language Processing models. His work bridges the gap between theoretical computer science and practical applications in epidemiology and social systems, creating scalable frameworks that can handle real-world complexity and data volume. His publication record shows a clear trajectory from social media analysis (2013-2016) toward pandemic modeling and response (2019-2022), reflecting the growing importance of computational epidemiology. His research spans multiple disciplines including computer science, epidemiology, social network analysis, and public health, with a consistent focus on developing scalable computational frameworks. The most recent publications demonstrate significant contributions to pandemic response infrastructure, with several papers related to the PanSim framework for agent-based pandemic modeling. Notable recognitions include being a Finalist for the 2021 ACM Gordon Bell Special Prize for High Performance Computing-Based COVID-19 Research and having work published in Nature Computational Science, highlighting the impact and relevance of his research during the global pandemic. Dr. Bhattacharya has developed significant computational infrastructure, most notably PanSim - a distributed pandemic simulator that enables large-scale agent-based modeling of disease transmission. His technical expertise spans distributed systems, high-performance computing, and machine learning, allowing him to tackle complex problems at the intersection of computer science and public health.

Rupsa BhowmickView profile

Assistant Professor

• Climatology
• Meteorology
• Natural Hazards
• +6 more

Rupsa Bhowmick is an Assistant Professor in the Department of Geography and Environmental Science at the University of Wisconsin-La Crosse. Her research focuses on climatology, meteorology, and natural hazards, particularly tropical cyclones and extreme weather events. She integrates machine learning, GIScience, and statistical methods to assess risks associated with severe weather. Education: Ph.D., Geography, Louisiana State University (2015-2020) M.Sc., Geography, University of Calcutta (2010-2012) B.Sc., Geography, University of Calcutta (2007-2010) Research Interests: She investigates the dynamics of extreme weather events using interdisciplinary approaches, including AI-driven forecasting and risk assessment. Her recent work explores tropical cyclone intensification, ENSO phase impacts on cyclone risk, and machine learning applications in climatology. Grants & Advising: Mentors undergraduates in field and data-based research projects. Active collaborator on projects like 'Variability in extreme typhoon wind risk in different ENSO phases.' Labs/Teams: Leads studies on AI-based weather forecasting and severe weather impact analysis through collaborations with institutions like Louisiana State University and NASA.

Philip ChanView profile

Associate Professor

• Machine Learning
• Data Mining
• Anomaly Detection
• +5 more

Philip Chan is an Associate Professor at Florida Institute of Technology's Department of Electrical Engineering and Computer Science, part of the College of Engineering and Science. His research focuses on machine learning, data mining, anomaly detection, and imbalanced learning with applications in cybersecurity, healthcare, and astrophysics. He leads the Laboratory for Learning Research (LLR), exploring adaptive intelligent systems and open-set recognition challenges. Chan teaches courses like Machine Learning and advises students on projects such as SEP event prediction and malware classification. His work emphasizes practical applications of AI, including fraud detection and device monitoring. Collaborations include interdisciplinary projects with physics and biomedical engineering. Research interests include representation learning, cost-sensitive learning, and meta-learning. He actively publishes in top venues, with recent studies on solar energetic particle prediction and novel category discovery. Advises several students, including Daniel Griessler (SEP forecasting) and Josias Moukpe (imbalanced learning).

Ehsan Shareghi NojehdehView profile

Assistant Professor

• Natural Language Processing
• Large Language Models
• Deep Generative Models
• +5 more

Ehsan Shareghi Nojehdeh is an Assistant Professor in the Department of Data Science and Artificial Intelligence at Monash University and an affiliated lecturer at the University of Cambridge. He leads a research team focused on predictive models for language (text and speech), with a background in postdoctoral work at Cambridge and prior roles at UCL. His research interests include probing and augmenting Large Language Models (LLMs), reasoning in legal contexts, safety of multimodal models, speech/text translation, and self-supervised learning. Education: PhD in Computer Science (Scalable Non-Markovian Sequential Modelling) from Monash University (2017). Professional roles include Deputy Course Director for the Master of AI program at Monash, service on academic committees (ECA Committee, 2022–2023), and senior reviewing roles at major conferences (ACL2025, EMNLP2023). He teaches units like FIT5217 Natural Language Processing and FIT5212 Data Analysis for Semi-structured Data. Key projects include a Paul Ramsay Foundation-funded study on media narratives around disadvantage in Australia (2022–2023). His work addresses UN SDGs through advancing education and AI ethics. Research collaborations span global institutions, with a focus on knowledge-intensive tasks like biomedical domain applications, graph-to-text generation, and generative model disentanglement. Publications emphasize safety, multimodal capabilities, and logical reasoning with LLMs, with recent work appearing in ACL, EMNLP, and NAACL venues. His team investigates LLM shortcomings, including alignment with human judgment, speech-specific vulnerabilities, and tool-based reasoning strategies.

Monika FeldmannView profile

Researcher

• Severe convective storms
• Climate prediction
• Subseasonal forecasting
• +3 more

Dr. Monika Feldmann is a PostDoc researcher at the Institute of Geography at the University of Bern, specializing in climate impacts . She is affiliated with the Climate Impacts research group and focuses on severe convective storms, large-scale meteorological connections, and AI-based weather forecasting. Her work aims to improve predictability of extreme weather events and evaluate novel computational models. Research Projects: Severe convective outbreaks and large-scale atmospheric drivers Subseasonal predictability of convective events in Central Europe Performance of AI models in forecasting convective environments Key Research Interests: She examines how large-scale atmospheric patterns influence severe thunderstorms, particularly in complex topographies like the Swiss Alps. Her work bridges traditional meteorology with advanced AI techniques to enhance forecast accuracy. Publications Overview: Recent work includes studies on AI-driven convective outlooks, subseasonal forecasting challenges, and the role of mountain environments in storm intensification. Her research emphasizes both theoretical advancements and practical applications for societal resilience against extreme weather. Labs/Teams: Active member of the Climate Impacts team at the Institute of Geography, collaborating on interdisciplinary projects involving climatology, atmospheric science, and computational modeling.

• Crime Prevention
• Criminal Justice
• Victimology
• +3 more

Prof. Christian Schwarzenegger is a Chair at the University of Zurich and serves as Vice Rectorate for Professorships and Scientific Information. His academic role focuses on criminology and criminal justice, with a strong emphasis on crime prevention and policy development. He collaborates closely with police departments, including the Zurich Cantonal Police and Zurich City Police, on projects addressing domestic violence, victim satisfaction, and threat management. His research spans diverse areas such as victimology, cybercrime prevention, and juvenile delinquency. Notable projects include evaluating police responses to domestic violence, analyzing satisfaction of crime victims, and developing evidence-based prevention strategies. He leads the Criminological Institute, which supports public safety initiatives through interdisciplinary collaboration and policy implementation. Key collaborations include work with the Prevention Department of the Zurich Police and the Attorney General’s Office. His work bridges academia and practice, aiming to reduce crime rates and improve community safety through rigorous evaluation and innovative strategies.

Karen McKinnonView profile

Assistant Professor

• Climate Change
• Extreme Weather Events
• Atmospheric Science
• +3 more

Karen McKinnon is an Assistant Professor at the University of California, Los Angeles (UCLA) , specializing in climate science with a focus on heat extremes and their impacts. She leads research on quantifying temperature trends in a warming world, particularly examining how the hottest days compare to average temperatures. Her work has been recognized with prestigious awards including the NSF CAREER Award ($944,511 over five years) and the Pritzker Emerging Environmental Genius Award . Her research interests span climate change attribution , extreme weather events , and atmospheric dynamics . Key projects include analyzing heatwave mechanisms, soil moisture effects on temperature extremes, and discrepancies between climate models and observational data. She employs advanced statistical methods and large ensemble simulations to disentangle natural variability from human-driven climate signals. Publications highlight work on humidity trends in arid regions, wildfire impacts on water resources, and the role of atmospheric circulation in heatwaves. Her NSF-funded CAREER project specifically targets summertime temperature extremes, aiming to improve predictive models for climate adaptation strategies. Awards: NSF CAREER Award (2024), Pritzker Emerging Environmental Genius Award Grants: $944,511 NSF grant for temperature extremes research Labs/Teams: Leads climate modeling and extreme event analysis teams at UCLA

Katrin WudyView profile

Professor

• Additive Manufacturing
• Laser Processing
• Materials Engineering
• +2 more

Prof. Dr.-Ing. Katrin Wudy is a Professor at the Technical University of Munich's School of Engineering and Design, specializing in Laser-based Additive Manufacturing. Her research encompasses the entire additive manufacturing process chain from material development and new process strategies to automation solutions and quality management. She completed her PhD at Friedrich-Alexander-Universität Erlangen-Nürnberg in powder and beam-based additive manufacturing. Her research interests focus on laser-based processing of plastics and metals, with particular emphasis on thermal analysis, beam shaping techniques, and process optimization. Recent work investigates melt pool behavior, multispectral imaging for temperature measurement, and productivity enhancement in powder bed fusion systems. Analyses of recent publications show strong focus on advanced beam shaping technologies, thermal process monitoring, and microstructure control in metal additive manufacturing, with emerging interests in AI applications and multi-material processing. Wudy has led the Additive Manufacturing working group at the Institute of Polymer Technology and served as Managing Director of the CRC 814 Additive Manufacturing consortium.

Lukas Hughes-NoehrerView profile

Researcher

• Healthcare Technologies
• Artificial Intelligence
• Human-Computer Interaction (HCI)
• +3 more

Lukas Hughes-Noehrer is a Researcher in Human Computer Systems at the University of Manchester’s School of Engineering, concurrently serving as Open Research Lead and NHS Lead for Computational Medicine in Anaesthesia, Critical Care, and Perioperative Medicine. He co-leads the Manchester Wearables Research Group and is an IG Organiser at the Alan Turing Institute. His research focuses on healthcare technologies, AI applications in clinical settings, and open research practices. Education: PhD in Artificial Intelligence and Museum Environments (University of Manchester, 2023). Key roles include teaching programming, machine learning, and AI ethics courses across Computer Science programs. Research interests span data-driven healthcare, ethical AI integration, wearable technologies, and open science advocacy. Notable work includes studies on clinician attitudes toward AI in diagnostics and pandemic-era barriers to data sharing. Publications cover clinical AI adoption, wearable sensor accuracy, and digital museum practices Two recent awards: Dorothy Bishop Prize shortlist (2024) and Exceptional Performance Prize Lead supervisor for Rawan Alotaibi’s PhD in Cardiovascular Sciences Active in professional bodies: ACM, SCATA, and Royal Society of Arts Current projects include open-source software investigations and cross-disciplinary AI studies in cultural institutions.

• Software Engineering
• Requirements Engineering
• Natural Language Processing
• +5 more

Liping Zhao is a Senior Lecturer in Information Management. His expertise spans Software Engineering (SE), Requirements Engineering (RE), Natural Language Processing (NLP), Machine Learning (ML), and Deep Learning (DL). He holds a PhD and MPhil from the University of Leeds. He is a founding member of the Asian Pacific Conference on Pattern Languages of Programs and led the UK’s first multi-disciplinary service science network (2007–2010). He has received IBM Faculty Awards (2004, 2005, 2008) and a Best Tool Demo Award for TRAM (Transforming Textual Requirements into Analysis Models). He serves as an Associate Editor for Springer’s Requirements Engineering and Wiley’s Expert Systems , and guest-edited special issues on topics like Knowledge Intensive Service Systems and Problem Orientation. His research focuses on applying NLP/ML to software engineering tasks, blockchain systems, and service-oriented computing. Key contributions include a theory of pattern-based software design and datasets like FN-REQ (frame-annotated requirements) and TRAM. His work addresses challenges in requirements classification, blockchain architectures, and business problem validation through ML. Recent projects include process mining for blockchain applications, hierarchical classification of software requirements, and developing taxonomies for blockchain systems. His datasets and tools are publicly available via Zenodo and Mendeley, supporting reproducible research and industry collaboration.

• Cloud Computing
• Distributed Systems
• Cybersecurity
• +3 more

Ramin Yahyapour is a researcher affiliated with the University of Göttingen and GWDG (Gesellschaft für wissenschaftliche Datenverarbeitung mbH Göttingen). His work focuses on cloud computing, distributed systems, cybersecurity, and high-performance computing. Key research areas include virtual machine migration optimization, data center energy efficiency, federated identity management, and parallel computing frameworks. He has collaborated extensively with institutions like the University of Göttingen and researchers such as Philipp Wieder, Faraz Fatemi Moghaddam, and Andrei Tchernykh. Research Interests: Cloud resource optimization Cybersecurity for distributed systems Workflow management in data-intensive environments Network function virtualization Machine learning for system scheduling Identity and access management Publications span topics like VM placement algorithms, data center power reduction, and federated identity systems, demonstrating expertise in both theoretical and applied computer science. His work often bridges academia and industry, addressing real-world challenges in cloud infrastructure and digital systems.

• Data Science
• Machine Learning
• Distributed Computing
• +4 more

Chris Mattmann is an academic affiliated with the University of Southern California, specializing in data science, machine learning, and high-performance computing. His work spans interdisciplinary applications in Earth sciences, climate modeling, and distributed systems. He has contributed to projects like the Earth System Grid Federation and Apache open-source software. His research emphasizes scalable data management, secure parser development, and automated machine learning systems. Education & Background: While specific educational details aren't listed, his extensive publication record in top-tier journals and conferences indicates advanced training in computer science and computational science. Research Interests: Focus areas include distributed computing infrastructure for scientific applications, machine learning for geosciences, and data-intensive systems. He explores challenges in data movement, scalable supercomputing, and open-source tools for scientific workflows. Publications: Over 150 peer-reviewed articles since 2003, with recent work on super-resolution satellite data, deep learning in geosciences, and secure parser systems. His contributions highlight advancements in both foundational computing and applied environmental science.

• Embedded Systems
• Internet of Things (IoT)
• Deep Learning
• +7 more

Hemant Ghayvat is an Associate Professor at Linnaeus University's Department of Computer Science and Media Technology, affiliated with the Faculty of Technology. He specializes in Embedded Systems, IoT, and Health Informatics, focusing on Ambient Assisted Living and AI-driven healthcare solutions. His research spans smart home technologies, wearable sensors, and predictive analytics for chronic disease management. Education: Bachelor's in Electronics and Communications (BIST, Bhopal, 2009) Master's in Microelectronics and VLSI (2011) PhD in Smart Home for Ambient Assisted Living (completed in New Zealand) Research Interests: Preventive Healthcare Monitoring Ambient Assisted Living for Elderly AI-Based Decision Making in Medicine Behavioral Pattern Analysis IoT Security and Privacy His work emphasizes fusion of sensor data and AI for real-time health interventions, such as gait rehabilitation for Parkinson's patients and anomaly detection in daily activities. Notable Projects: VCardiac: Framework for contactless heart disease detection via acoustic signals. Mitigating Health Inequalities: System thinking approach for regional healthcare improvement. Teaching: Courses in IoT, Embedded Systems, and Network Security at undergraduate and graduate levels. Labs/Teams: Active in the E-health – Improved Data to and from Patients group at Linnaeus University Centre for Data Intensive Sciences and Applications (DISA).

• Software-intensive systems
• Digital transformation
• Artificial intelligence
• +16 more

Anniken Susanne Thoresen Karlsen is an Associate Professor in the Department of ICT and Science at the Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology (NTNU). She has extensive experience in research and teaching, with a focus on digital transformation, software-intensive systems, and emerging technologies. Karlsen holds leadership roles including deputy chair of the interdisciplinary NTNU research group SHAPE and active participation in multiple research centers. Her educational background includes: PhD in Information Science from the University of Bergen (UiB) Master's degree in Information Technology from Aalborg University (AAU) in Denmark Master of Science in Economics from the Norwegian School of Economics (NHH) Computer Engineering degree from Møre og Romsdal School of Engineering (MRIH) One-year practical-pedagogical education from NHH and UiB NTNU's UniPed program for employees Professor Karlsen's research spans a wide spectrum of digital transformation topics. Her work focuses on the integration of concepts from multiple domains to design and develop software-intensive systems. She explores modern software development methodologies, socio-technical systems thinking, and systems engineering approaches. A significant portion of her research addresses emerging technologies such as digital twins, virtual reality, and service robots as building blocks for innovative solutions. She also investigates the digital economy and sustainable development through digital transformation, with particular emphasis on business modeling and change management. Her recent publications demonstrate a strong focus on applying digital technologies to solve real-world problems across multiple sectors. There's a clear trend toward using digital twin technology in various contexts including urban planning, offshore wind farms, and healthcare. Her work in search and rescue operations shows consistent application of AI and knowledge management techniques. Karlsen's research bridges theoretical concepts with practical applications, particularly in sustainability-focused domains like renewable energy and healthcare. Professor Karlsen actively supervises students at all academic levels and has been involved in numerous research projects. She has held various academic leadership positions including vice-dean, head of department, and research group leader. Her collaborative work spans multiple sectors including healthcare, search and rescue, offshore, banking, food, and construction industries. She is affiliated with several research groups and centers: SHAPE - SamHandlingsArena for Prosjektledelse og Endring (2023-present), Deputy Chair The Norwegian Open AI Lab (NAIL) (2023-present) Green2050 - The Centre for Green Shift in the Built Environment (2022-present) Forskningsarena for bærekraftsanalyse (2020-present), Deputy Leader Forskningsgruppen for bærekraftig digital transformasjon, SDT (2017-present), Founder and former Leader

Tan LekwijitView profile

Assistant Professor

• Healthcare Operations Management
• Health Information Technology
• Empirical Operations Management
• +3 more

Tan Lekwijit is an Assistant Professor in the Department of Information Systems at Arizona State University's W. P. Carey School of Business. He holds a Ph.D. from The Wharton School and an M.S. from Columbia University. His research focuses on leveraging health information technology and operational strategies to improve healthcare outcomes through data-driven insights. Education: Ph.D. in Operations, Information, and Decisions, The Wharton School, University of Pennsylvania M.S. in Management Science and Engineering, Columbia University Research Interests: His work examines connected healthcare/telehealth, health IT adoption, and behavioral economics in healthcare. He uses econometrics and large-scale data analysis from EHR systems and field experiments to address operational challenges in inpatient/outpatient settings. Recent Trends in Articles: Recent work explores multi-channel healthcare delivery models, telehealth impact on in-person care utilization, and step-down unit effectiveness post-ICU discharge. These studies highlight optimization of clinical workflows, patient engagement strategies, and technology adoption in healthcare systems. Awards: Mack Institute for Innovation Management Research Fellowship Professional Background: Prior academic roles include consulting work with the Bill & Melinda Gates Foundation and BTQ Financial. Industry experience includes process engineering at Procter & Gamble and operations strategy consulting at Accenture. Labs/Teams: Collaborates with Department of Veterans Affairs and NSF-funded Fireline Science on human-AI collaboration in healthcare contexts.