Find Academic Supervisors

• Cyber Security
• IT Security
• Network Security
• +4 more

Christoph Lang-Muhr is Professor and Academic Director of the Information Security Master's program at the Department of Computer Science and Security, University of Applied Sciences St. Pölten. Since 2010, he has served as researcher at the Centre of Competence in IT Security, leading projects in cyber defense, mobile security, and digital forensics education. His educational background includes: Master of Information Security (2011-2013), University of Applied Sciences St. Pölten Bachelor of IT Security (2009-2011), University of Applied Sciences St. Pölten Master of Telecommunications and Media (2007-2009), University of Applied Sciences St. Pölten Bachelor of Telecommunications and Media (2004-2007), University of Applied Sciences St. Pölten HTL Hollabrunn in Control and Regulation Technology (1995-2000) Lang-Muhr's research centers on practical cybersecurity applications with emphasis on mobile device security, digital forensics, and business continuity. He has pioneered simulation-based educational methods for disaster recovery training and conducted extensive iOS forensic analysis. His work bridges technical implementation with organizational security policies in enterprise mobility contexts. Publications from 2014-2022 reveal consistent focus on mobile security challenges, particularly iOS vulnerabilities and biometric authentication, alongside critical infrastructure protection for smart grids. His educational simulation games represent innovative approaches to holistic cybersecurity training, addressing both technical and human factors in incident response. Lang-Muhr leads the Centre of Competence in IT Security and directs projects including AIR LAB, CDCNÖ (Cyber Defense Center NÖ), and DuS (Digitization and Security). His applied research has produced security solutions for school networks, smart metering systems (SmartMeterIDS), and mobile device management frameworks deployed in Austrian public infrastructure.

Yalda ShahriariView profile

Associate Professor

• Neural Engineering
• Biomedical Signal Processing
• Brain-Computer Interface for Assistive Technology
• +3 more

Yalda Shahriari is an Associate Professor in the Department of Electrical, Computer, and Biomedical Engineering at the University of Rhode Island . Her research focuses on neural engineering , biomedical signal processing , and brain-computer interfaces for assistive technology. Education Postdoctoral Research Associate, University of California, San Francisco (2016) Ph.D., Biomedical Engineering, Old Dominion University (2015) M.Sc., Biomedical Engineering, Iran University of Science and Technology (2011) B.Sc., Electrical Engineering, Ferdowsi University of Mashad (2008) Research Interests include graph-based neural dynamics modeling, multimodal neuroimaging (EEG/fNIRS), and machine learning for healthcare. She develops hybrid BCI systems to assist patients with amyotrophic lateral sclerosis (ALS) and parkinson’s disease , emphasizing personalized algorithms and nonlinear neural modeling . Publication Trends show a focus on graph theory , machine learning , and hybrid BCI systems for neurological disorders. Her work integrates fNIRS , EEG , and motion capture to study brain-body interactions. Grants include multiple National Science Foundation (NSF) projects as Principal Investigator , covering smart textile NICU monitoring , auditory processing dynamics , and personalized BCI algorithms for ALS patients.

Michael BainView profile

Senior Lecturer

• Artificial Intelligence
• Machine Learning
• Inductive Logic Programming
• +7 more

Dr. Michael Bain is a Senior Lecturer at the School of Computer Science and Engineering, University of New South Wales (UNSW). His research focuses on integrating machine learning with declarative programming to create explainable AI systems, particularly for complex domains like bioinformatics, social networks, and medical informatics. He has taught courses including Machine Learning and Data Mining and Computational Bioinformatics . Key Research Areas: Explainable AI through logic programming Bioinformatics applications in systems biology Medical claim fraud detection using graphical models Swarm robotics and epigenetic learning Recent Publications highlight his work on fairness-aware AI, knowledge acquisition for event extraction, and hybrid models combining temporal features with collaborative filtering. He actively mentors students, with 8 current advisees and over 30 graduates, and has contributed to projects in online dating recommendation systems and dynamic systems control. Education: PhD in Statistics and Modelling Science from University of Strathclyde; BSc (Hons) from University of Edinburgh. He is affiliated with the Smart Services Cooperative Research Centre for industry grants.

Rushikesh K. JoshiView profile

Professor

• Software Engineering
• Distributed Systems
• Program Analysis
• +5 more

Rushikesh K. Joshi is a Professor in the Department of Computer Science & Engineering at the Indian Institute of Technology Bombay. His research focuses on computational models for programs, their structures and dynamics, including processes, interactions, concurrency, and distributed systems. He has taught advanced courses like CS 770 (Process Engineering), CS 787 (Language Engineering), and CS 757 (Design and Re-engineering). Research Interests: He specializes in software engineering, distributed systems, program analysis, and code refactoring. His work includes modular process modeling, trace language mining, and consistency models for business processes. He also explores objectification of procedural code through projects like Ox and AccessViz. Notable Projects: Anonymous Remote Computing (ARC), Filter Objects, Constore Graph Database, and Ox (objectification of Linux kernel structures). Funding sources include IBM, Microsoft, Infosys, and the Ministry of Information Technology. Scientific Contributions: His research has led to publications in IEEE Transactions, LNCS, and other venues. He has advised over 100 theses, including Ph.D. students Karnika Shivhare, Omkarendra Tiwari, and Vrinda Yadav.

• Risk Analytics
• Financial Econometrics
• Statistics
• +4 more

Dr. Rui Ren serves as a Researcher at the Chair of Statistics and Data Science within the Faculty of Business and Economics at the University of Augsburg since January 2025, following prior research roles at IDA Institute Digital Assets (2024) and Humboldt University of Berlin where she held a Marie-Skłodowska-Curie Individual Fellowship (2021-2022). Her academic credentials include a Ph.D. in Management Science from the University of Chinese Academy of Sciences (2019) and a B.Sc. in Mathematics from Minzu University of China (2013), complemented by a Visiting Ph.D. stint in Statistics at Humboldt University of Berlin (2018-2019). Rui Ren's research centers on Risk Analytics and Financial Econometrics with specialized expertise in network-based risk modeling for cryptocurrency ecosystems, sentiment-driven market behavior analysis, and high-frequency portfolio risk assessment. Her methodological approach integrates advanced statistical techniques with machine learning to address complex financial system dynamics, particularly in quantifying tail risks and cross-asset correlations. Her publication trajectory reveals an evolution from traditional market prediction using news sentiment toward sophisticated network analyses of crypto-asset interdependencies, demonstrating consistent innovation in financial risk quantification frameworks like the Financial Risk Meter (FRM). Her scientific recognition includes the prestigious Marie-Skłodowska-Curie Individual Fellowship and a Best Paper Award at the 2017 IEEE Symposium on Analytics and Risk. She actively contributes to thesis supervision and research collaboration within Prof. Okhrin's Chair, participating in statistical consulting services and research seminars focused on advancing data science applications in financial systems.

Stefan AxelssonView profile

Professor

• Digital Forensics
• Cybersecurity
• Fraud Detection
• +3 more

Stefan Axelsson is a Professor at Stockholm University's Department of Computer and Systems Science, specializing in digital forensics and cybersecurity. His research examines the intersection of emerging technologies like IoT with crime investigation challenges, particularly focusing on forensic methodologies in increasingly digitalized societies. Axelsson's research spans: Digital Forensics : Timestamp validation, filesystem analysis, and cloud/IoT evidence acquisition Cybersecurity : Fraud detection systems, log analysis, and attack kill chains Applied Machine Learning : Ensemble classifiers for financial fraud detection Policy Frameworks : National cybersecurity strategies and disinformation research His recent publications demonstrate a consistent focus on improving forensic accuracy through technical validation (e.g., exFAT timestamp studies) and practical tools (e.g., USB power delivery exploitation). Over 80% of his 2021-2025 publications involve empirical analysis of digital evidence systems, with growing emphasis on machine learning applications. No scientific awards, grants, research labs, or supervised students are mentioned in available sources.

Felix SchiedlowskiView profile

Researcher

• Politics of Transformation
• Change and Continuity
• Post-fossil Society
• +5 more

Felix Schiedlowski is a Researcher at the Centre for Interdisciplinary Regional Studies and Centre for Just Transition and Sustainability at Martin Luther University Halle-Wittenberg. His work focuses on the politics of transformation, particularly in the context of coal phase-out and structural change in Eastern Germany's Central German Mining District and Northern Ireland. Research Interests: Politics of Transformation – Change, Continuity, Gaps Post-fossil Society – Phasing-out Coal in Eastern Germany Energy Transition: The Unwinding of Coal Urban Change: Peace in Belfast, Northern Ireland Schiedlowski's research examines the temporal dimensions of structural change, particularly how communities navigate the transition from coal-dependent economies to post-fossil societies. His anthropological approach emphasizes 'gaps' in transformation processes and challenges optimistic narratives about the future, advocating for realistic expectations about structural change. His work combines ethnographic methods with policy analysis to understand social and political implications of energy transitions. His publications demonstrate consistent focus on temporal aspects of structural change across both German and Northern Irish contexts. The research bridges anthropology, energy policy, and political science, with particular emphasis on how communities experience and negotiate transitions. His 2025 article 'Thinking with Gaps between Coal and Post-Coal' represents the culmination of this research trajectory. Scientific Awards: Promos studentship of German Academic Exchange Service (2017) Schiedlowski actively engages in public anthropology through media appearances, lectures, and citizen participation initiatives. He has participated in MDR television shows, given lectures at public science events, and been interviewed for radio programs and podcasts about structural change. His work emphasizes translating academic insights into practical considerations for community engagement in transition processes. He collaborates with the SLEG Lab at the Centre for Interdisciplinary Regional Studies and has worked with institutions including the Max Planck Institute for Ethnological Research and the European Humanities University Vilnius on projects related to post-carbon futures and structural change.

Karolin WinterView profile

Assistant Professor

• Business Process Compliance
• Process Mining
• Natural Language Processing (NLP)
• +2 more

Karolin Winter is an Assistant Professor at the Department of Industrial Engineering and Innovation Sciences at Eindhoven University of Technology (TU/e). Her research focuses on business process compliance, process mining, and natural language processing (NLP) techniques. PhD in Computer Science from University of Vienna (2021) Research Interests: She specializes in digitalizing compliance management by bridging regulatory documents and process execution logs. Key areas include constraint discovery, predictive compliance monitoring, and exception handling in business processes. Recent Article Trends: Her work integrates NLP and process mining for automated compliance verification, deviation detection between external/internal regulations, and predictive monitoring of process-aware systems. 2021: Runner-up Best Dissertation Award (BPM 2021) 2020: Best Student Paper Award (ER 2020) Labs/Teams: Collaborates with the Information Systems group (IE&IS) at TU/e and international conference teams for CAiSE, BPM, and CBI.

Witold HeniszView profile

Professor

• Political and Social Risk Management
• ESG (Environmental, Social, and Governance) Integration
• Stakeholder Engagement
• +3 more

Witold Henisz is the Vice Dean and Faculty Director of the Impact, Value, and Sustainable Business Initiative at The Wharton School , University of Pennsylvania. He holds the Deloitte & Touche Professor of Management and co-founded the Wharton Political Risk Lab . As an Academy of International Business Fellow , he bridges political economy analysis with corporate strategy. Education: PhD in Business and Public Policy from UC Berkeley Research Focus: Political/social risk management, ESG integration, stakeholder engagement His recent research examines: Populist ideology as recursive systems Indigenous land claims and investment conflicts ESG news impact on sovereign creditworthiness Municipal credit risk associated with ESG outcomes As Academic Director of Wharton’s ESG programs, he has developed executive education content on: Net-zero transformation strategies Corporate diplomacy frameworks Board governance essentials His consultancy work with PRIMA LLC serves clients like: Multinationals (Shell, Rio Tinto) Intergovernmental bodies (IFC) NGOs (World Wildlife Fund)

• Process Mining
• Business Process Management
• Internet of Things (IoT)
• +5 more

Dr. Zeeshan Tariq is a Lecturer in Computer Science at Ulster University's School of Computing, Belfast campus. His research bridges theoretical computer science with practical applications in telecommunications, healthcare, and smart infrastructure through the School's Computer Science department. His research focuses on: Advanced process mining techniques for business process optimization Machine learning applications in IoT and smart home systems Medical image analysis using deep learning for ultrasound interpretation Security and privacy frameworks for federated process analytics Recent publications demonstrate strong industry relevance, particularly in telecommunications (BT collaboration) and healthcare diagnostics. His work shows increasing integration of semi-Markov models with deep learning architectures, addressing challenges in sparse data environments and real-time process monitoring across domains. Scientific recognition includes: TM Forum Catalyst Award for industry contribution to Beyond Connectivity (2022) Dr. Tariq actively contributes to major research initiatives including BTIIC Phase 2 (2024-2028) and the PwC Advanced Engineering Centre (2021-2026), with consultancy engagements through Innovation Voucher and Innovation Boost projects. His work aligns with UN Sustainable Development Goal 9 (Industry, Innovation, and Infrastructure) through technology transfer and industry-academia collaboration. He maintains active research leadership within Ulster's Computer Science group, focusing on practical implementations of process mining in industrial settings and medical diagnostics.

Samir BhattView profile

Professor

• Statistics
• Machine Learning
• Public Health
• +5 more

Samir Bhatt is a Professor of Machine Learning and Public Health at the University of Copenhagen's Faculty of Health and Medical Sciences, Department of Public Health, Section for Health Data Science and AI. He also holds a position as Professor of Statistics and Public Health at Imperial College London since 2016. His work focuses on developing mathematical, statistical, and computer science tools to address critical questions in human health. His educational background includes a DPhil in Statistical Genetics from the University of Oxford (2010), an MPhil in Computational Biology from the University of Cambridge (2006), and a BEng in Chemical and Bioprocess Engineering from the University of Bath (2005). Professor Bhatt's research spans the intersection of statistics, machine learning, and public health with particular emphasis on infectious disease modeling. His primary research areas include Bayesian inference, genomic epidemiology, and kernel methods applied to health data. His work bridges theoretical statistical approaches with practical public health applications, particularly in disease surveillance and outbreak response. The integration of AI with traditional epidemiological methods represents a key innovation in his research program. Analysis of his recent publications reveals a strong focus on applying advanced computational methods to pressing public health challenges. His work demonstrates consistent innovation in developing AI-driven approaches for infectious disease modeling, genomic surveillance, and survival analysis. Notable themes include the application of graph neural networks to epidemiological data, development of interpretable AI tools for public health decision-making, and sophisticated modeling of disease transmission dynamics across multiple pathogens including malaria, cholera, and respiratory viruses. Professor Bhatt has published extensively with over 107 research outputs to date. His work has received significant attention, with multiple publications covered by news outlets, referenced on social media platforms, and read by researchers on academic platforms like Mendeley. His research on AI for infectious disease modeling published in Nature demonstrates the high impact of his work in both academic and policy spheres. His research group at the University of Copenhagen appears to focus on developing computational tools for health data science, with particular emphasis on creating analytical frameworks that can be rapidly deployed during disease outbreaks. The development of GRAPEVNE (Graphical Analytical Pipeline Development Environment for Infectious Diseases) represents one such effort to create accessible tools for public health practitioners.

• Cardiometabolic Research
• Epidemiology
• Machine Learning
• +5 more

Jonas L. Isaksen is a Postdoctoral Researcher at the Department of Biomedical Sciences within the Faculty of Health and Medical Sciences at the University of Copenhagen. His research focuses on the physiology of circulation, kidney and lung, with particular emphasis on cardiometabolic research using advanced data analysis techniques. Dr. Isaksen's research interests span several interconnected domains: Application of machine learning and deep learning models for electrocardiogram (ECG) analysis Development of explainable AI approaches in cardiology Epidemiological studies using large Danish registers and population data Identification of novel associations in the cardiometabolic domain Creation of synthetic ECG data for open science in medical AI His publication record demonstrates a strong focus on applying artificial intelligence to cardiac diagnostics, with recent work spanning ECG analysis for dementia prediction, atrial fibrillation management, and mortality risk assessment. His 2025 publications particularly highlight methodological advances in ECG reconstruction and validation frameworks for AI-enabled medical tests. Dr. Isaksen has received recognition for his work, including the EliteForsk Rejsestipendium in 2021. As an educator, Dr. Isaksen teaches at all academic levels (BSc, MSc, and PhD), welcoming students interested in his research areas. His work is situated within the research group focused on 'Physiology of circulation, kidney and lung' at the University of Copenhagen.

Amit V. DeokarView profile

Professor

• Business Analytics
• Business Process Management
• Machine Learning
• +5 more

Amit V. Deokar serves as Associate Dean of Undergraduate Programs & Accreditation and Professor in the Department of Operations and Information Systems at UMass Lowell's Manning School of Business. He holds a Ph.D. in Management Information Systems from the University of Arizona and brings expertise from industrial and mechanical engineering backgrounds. His educational credentials include: Ph.D. in Management Information Systems, University of Arizona (2006) M.S. in Industrial Engineering, University of Arizona (2002) B.S. in Mechanical Engineering, V.J. Technological Institute, University of Mumbai (2000) Dr. Deokar's research centers on business analytics, machine learning applications, and business process management, with significant contributions to data mining, text mining, and artificial intelligence. His work bridges theoretical frameworks with practical implementations across healthcare, e-commerce, and transportation sectors, emphasizing real-world impact through industry collaborations and consulting engagements. His publication portfolio reveals a strategic focus on extracting actionable insights from complex data sources, particularly through process mining and text analytics. Recent work demonstrates increasing integration of generative AI techniques while maintaining strong methodological rigor in empirical validation. Key recognitions include: AIS Distinguished (cum laude) Member (2020) Association for Information Systems Teaching Excellence Award (2020) UMass Lowell IBM Faculty Award (2014) IBM Merill D. Hunter Award for Excellence in Research (2010) Dakota State University recognition As Associate Vice President of Technology for the Association for Information Systems, Editor-in-Chief of e-Service Journal, and leader of major conferences like AMCIS 2024, Dr. Deokar shapes academic discourse while securing research funding such as the 2017 Internal Seed Grant for urban transportation analytics. His industry consulting and keynote presentations on generative AI adoption further demonstrate applied scholarship.

Norman GöbelerView profile

Researcher

• Ecology
• Evolutionary Biology
• Geosciences
• +5 more

Norman Göbeler is a postdoctoral researcher at the University of Helsinki's Faculty of Biological and Environmental Sciences, affiliated with the Biological Stations. His research focuses on marine and freshwater ecology, particularly the impacts of climate change on coastal ecosystems. He utilizes experimental approaches to study marine heatwaves, biogeochemical cycling, and salinization effects on biodiversity. Recent research highlights include: Investigating how marine heatwaves alter benthic community metabolism and nutrient cycling Developing the HOTFLOOR experimental chamber system for seafloor warming simulations Demonstrating salinization-induced biodiversity losses in freshwater mesocosms Supervising graduate research on coastal carbon dynamics Scientific achievements: Recipient of the Outstanding Article by Early Career Researchers award (2023) His work is supported by the Walter ja Andrée de Nottbeckin Säätiö foundation, with active projects running through 2028. Göbeler maintains an open-access research approach and collaborates with international teams studying climate impacts on aquatic systems.

Ricardo HenaoView profile

Associate Professor

• Machine Learning
• Biostatistics
• Precision Medicine
• +7 more

Ricardo Henao is an Associate Professor of Biostatistics & Bioinformatics, Associate Professor in Surgery, and Assistant Professor in the Department of Electrical and Computer Engineering at Duke University. He is also a core member of the Duke Clinical Research Institute, the Duke Center for Applied Genomics and Precision Medicine, and the Duke Center for Statistical Genetics and Genomics. Education: Ph.D., Technical University of Denmark, 2011 Research Interests: Henao’s research integrates advanced machine learning with high-impact clinical and biological questions. His work spans infectious-disease diagnostics (including rapid host-response assays for bacterial vs viral infection), cardiovascular genomics and risk prediction, precision-medicine toolkits for transplant recipients, ophthalmic AI for retinal and cardiac imaging, and fairness-aware AI to ensure equitable healthcare delivery across demographic groups. Publications Trend: Across >317 peer-reviewed publications (2013–2025), a clear trajectory emerges from foundational statistical methodology to large-scale translational implementations. Recent 2025 papers emphasize automated harmonization of electronic health records, fairness-constrained predictive models for stroke risk, computer-vision systems for point-of-care echocardiography, and deep-learning segmentation of pulmonary vasculature—demonstrating a fusion of NLP, computer vision, and survival modeling to solve real clinical problems. Scientific Awards & Honors: While the supplied text does not list specific named awards, Henao’s funding portfolio serves as a proxy for recognition: he is PI or multi-PI on >34 active grants totaling tens of millions of dollars from NIH, NHLBI, NIAID, NSF, DoD, and private foundations such as Brigham and Women’s Hospital and the Henry M. Jackson Foundation. Advising & Grants: Henao mentors trainees at the intersection of data science and medicine. Current grant titles illustrate the breadth of mentee opportunities: PREEMPT: Prospective Randomized Evaluation and Management of Premature Atherosclerosis (NIH, 2025–2032) Machine Learning Guided Precision Genetic Testing for Monogenic Cardiovascular Disorders (NHLBI, 2024–2028) Synthesizing immunoinformatics and genetic epidemiology for malaria immunity signatures (NIAID, 2023–2028) NSF CC* Integration-Large: Scaling scientific workloads on distributed commodity GPUs (NSF, 2025–2027) Improving quality of life in SLE via stratified personalized health planning (DoD, 2022–2026) Multidisciplinary study of biological disparities in NASH progression (DoD, 2020–2025) Rapid point-of-care host gene-expression test for pre-symptomatic viral infection (DoD, 2021–2024) Clinical and molecular epidemiology of high-risk coronary plaque (NIH, 2019–2024) Labs & Teams: Henao leads the Division of Translational Biomedical Informatics within the Department of Biostatistics & Bioinformatics and participates in Duke’s Bass Connections and Data+ programs, embedding graduate and undergraduate students in interdisciplinary teams that span genomics, cardiology, surgery, and global health.