Find Academic Supervisors

• Financial Risk Management
• Asset Allocation
• Pricing
• +12 more

Majeed Simaan is an Assistant Professor of Finance and Financial Engineering at the School of Business, Stevens Institute of Technology. He holds a Ph.D. in Finance from Rensselaer Polytechnic Institute (RPI), completed in 2018, and joined Stevens as a tenure-track faculty member thereafter. His academic work focuses on risk management, asset allocation, and pricing, with applications in quantitative and computational finance. Research Interests: His primary research revolves around Financial Risk Management (FRM), with emphasis on asset allocation, portfolio optimization, and financial networks. He integrates machine learning, textual analysis, and network modeling to enhance traditional financial models. His work addresses estimation risk, model interpretability, and the application of reinforcement learning in portfolio construction. He is also a strong advocate for reproducible research and open-source software, particularly using R for empirical finance. The recent publications highlight a strong trend toward integrating machine learning and data science into financial decision-making. Topics include volatility modeling, portfolio efficiency, index tracking, and behavioral aspects of investing such as the 'buy the dip' strategy. The research spans theoretical modeling, empirical validation, and practical implementation, often leveraging public datasets and open tools. Scientific Awards and Recognition: Certified Financial Risk Manager (FRM) from GARP (August 2023) Research funded by the National Science Foundation (NSF) via CRAFT Featured in Bloomberg Markets and other media outlets for research on investor behavior Advising and Grants: Dr. Simaan mentors Ph.D. students through research courses such as FE 960 and MGT 960. His research has been supported by competitive grants, including funding from the NSF. He has served on key institutional committees including the Finance PhD Committee, Research Committee, and Teaching Effectiveness Evaluations Committee, reflecting his active role in academic governance. Labs and Research Teams: While no formal lab is named, his work is associated with computational and data-driven finance research, often involving collaboration on machine learning applications in finance. He maintains an active presence on RPubs and GitHub, sharing reproducible research vignettes on topics like financial networks, volatility modeling, and tactical asset allocation.

Gyunam ParkView profile

Researcher

• Process Mining
• Action-Oriented Process Mining
• Object-Centric Process Analysis
• +12 more

Gyunam Park is a Research Group Lead and Process and Data Scientist at Fraunhofer FIT and a Scientific Assistant at the Chair of Process and Data Science at RWTH Aachen University, a leading institution in computer science and engineering. He is actively involved in both research and teaching, contributing to the advancement of process mining, data science, and artificial intelligence. His work bridges academic research and industrial applications, particularly in SAP ERP systems and digital twins of organizations. Research Interests: Gyunam Park’s research focuses on Action-Oriented Process Mining (AOPM) , Object-Centric Process Analysis , and Responsible Machine Learning . He aims to transform process mining insights into actionable management decisions, ensuring transparency, fairness, and compliance. His work enables organizations to monitor operational constraints, generate corrective actions, and assess their impact using data-driven methods. Publication Trends: His recent publications emphasize object-centric approaches to process mining, predictive monitoring, constraint checking, and integration with AI planning. There is a strong trend toward preserving structural information in event logs, improving machine learning performance, and applying these techniques to real-world systems like SAP ERP and after-sales service processes. Scientific Awards: No awards are explicitly mentioned in the provided text. Advising and Grants: While no formal students are listed, Gyunam Park leads research projects and collaborates with industry partners such as Samsung Electronics and SAP. His projects involve root cause analysis, resource optimization, and educational data mining. He has developed open-source tools like ProAct and OCPA , indicating active grant or institutional support for software development and research dissemination. Labs and Teams: He is a core member of the Process and Data Science (PADS) group led by Prof. Wil van der Aalst at RWTH Aachen University and leads a research group at Fraunhofer FIT. These teams focus on cutting-edge research in process mining, data science, and AI, with strong industry collaborations and regular contributions to top conferences and journals.

Anna SidorovaView profile

Professor

• Artificial Intelligence in Business
• AI Ethics and Governance
• Digital Transformation
• +7 more

Anna Sidorova is a Professor and Chair of the Department of Information Technology and Decision Sciences at the G. Brint Ryan College of Business, University of North Texas. She holds a Ph.D. in Information Systems from Washington State University and has extensive experience in both academia and industry. Ph.D., Information Systems, Washington State University MBA, Washington State University B.A., Washington State University Her research focuses on Artificial Intelligence in Business , AI Ethics and Governance , Business Intelligence and Analytics , Text Mining , and Digital Transformation . She explores how organizations adopt and benefit from advanced technologies, particularly in decision-making and process improvement. The analysis of her publications reveals a strong trajectory in AI and machine learning applications in business, with a growing emphasis on ethical and governance aspects. Her work spans technical implementation and strategic implications, often published in premier journals like MIS Quarterly and Decision Support Systems . Dr. Sidorova has held significant leadership roles, including Academic Associate Dean for Undergraduate Programs at RCOB. She previously served as an Assistant Professor at the University at Albany, SUNY, and worked as a business consultant at PricewaterhouseCoopers, advising on business strategy and IT systems. She teaches graduate courses in Artificial Intelligence in Business, Information System Development, and Information Systems Theory and Research, contributing to the development of future business and technology leaders.

• Applied Linguistics
• Language Assessment
• Computational Linguistics
• +3 more

Zhi Li is a faculty member at the University of Saskatchewan in the Department of Linguistics . With expertise in applied linguistics and technology-assisted language learning , their work bridges language assessment and computational linguistics . Primary research: language testing , CALL , corpus-based analysis Key methodologies: NLP , text mining , multimodal analysis Research trends include: Automated Writing Evaluation (AWE): Systematic impact analysis on grammatical accuracy and ESL pedagogy . Assessment Validity: Argument-based approaches to placement tests and response time metrics . Technological Integration: Mobile-assisted learning, multimodal lecture analysis , and digital discourse patterns .

Marianne SchmolkeView profile

Professor

• Business Informatics
• Software Architecture
• Process Management
• +3 more

Marianne Schmolke is a Professor in Business Informatics at Wismar University of Applied Sciences, specializing in Software Architecture, Process Management, and Human Resource (HR) Information Systems. She serves as Vice Dean of the Faculty of Business Administration and Economics and Deputy Head of the Master's Program in Business Information Systems. Her academic career includes a bachelor's degree in business administration from Kiel University of Applied Sciences and a doctorate from Kiel University in 2016, focusing on process mining of e-learning processes in geoinformatics. Research interests: e-learning, process frameworks, media usage typologies, digital transformation of business processes Teaching: Summer semester: Human Resources Information Systems (BA BW), Systems Analysis and Software Architecture (B.Sc. WI), Human Resource Intelligence Analytics (MA HR) Winter semester: Human Resources Information Systems (B.Sc. WI), Enterprise Architecture Management (M.Sc. WI), International e-Government (M.Sc. WI) Leadership roles: Vice Dean of Business Department Deputy Head of Master's Program in Business Information Systems Collaborations: Supervised student projects with Foxy Power GmbH Active partnership with InnovationPort for digitalization initiatives

• Information Retrieval
• Digital Libraries
• Informetrics
• +5 more

Philipp Mayr-Schlegel is a Professor at the University of Göttingen’s Institute of Computer Science, leading the team 'Information & Data Retrieval' at GESIS - Leibniz-Institute for the Social Sciences. His research focuses on interactive information retrieval systems, scholarly recommendation mechanisms, and the integration of bibliometric methods with digital library technologies. University of Göttingen - Institute of Computer Science GESIS - Leibniz-Institute for the Social Sciences His work spans information retrieval , digital libraries , and applied informetrics , with particular emphasis on: Interactive search systems Non-textual ranking algorithms Scholarly document processing Knowledge representation Semantic search technologies User behavior analysis Recent research outputs (2025) include studies on LLMs for scholarly search, bibliometric reproducibility tools, scientific uncertainty annotation, and preprint adoption disparities. He has published extensively in Scientometrics , International Journal on Digital Libraries , and top conference proceedings like ACL and ECIR . As organizer of the International Workshop on Bibliometric-enhanced Information Retrieval (BIR) and Scholarly Document Processing (SDP) series, he has shaped academic discourse through editorial roles at ISSI , JCDL , and SocInfo conferences. His projects have attracted significant national and European funding.

• Computer Science
• Artificial Intelligence
• Creativity Support Tools
• +2 more

Konstantinos Zachos is a Research Fellow and Chief Technology Officer at the Centre for Creativity enabled by AI (Bayes Business School, City, University of London). His work focuses on integrating Artificial Intelligence with Human-Computer Interaction to develop Digital Creativity Tools that enhance innovation across sectors like journalism, manufacturing, social care, and elite sports training. He co-leads the JECT.AI spin-out and the QUEST research project. Expertise : AI for Creativity, Software Engineering, Requirements Engineering, Service Discovery Collaborations : Co-developed tools with Neil Maiden, James Lockerbie, and teams at City, University of London and Miele GmbH His recent publications highlight Generative AI applications in opportunity discovery, with empirical evaluations in newsrooms and manufacturing environments. Key themes include: Creative process modeling Cross-domain AI integration Interactive systems for collaborative innovation Requirements surfacing in evolving systems Konstantinos maintains IEEE membership and actively bridges technical innovation with Human-Centered Design principles.

Sonia CorianiView profile

Professor

• Quantum Chemistry
• Computational Spectroscopy
• Molecular Properties
• +7 more

Sonia Coriani is a Professor in Physical Chemistry at DTU Chemistry, Technical University of Denmark, since 2017. She leads a research group focused on theoretical chemistry and computational spectroscopy. Her academic journey includes a PhD from Aarhus University (2000), a permanent research scientist position at the University of Trieste (1999-2014), and associate professorship there since 2014. She held an adjunct associate professor position at the Centre for Theoretical and Computational Chemistry in Oslo (2007-2011) and was a Marie Curie IEF fellow (2010-2012) and AIAS-COFUND senior fellow (2015-2016). Education: Chemistry, University of Modena (1993) PhD in Theoretical Chemistry, Aarhus University (2000) Her research centers on developing quantum-chemical methodologies for static and dynamic molecular properties, particularly for systems with high dimensionality, complex environments, or novel spectroscopic phenomena. Key areas include non-linear optical experiments, magnetic circular dichroism, X-ray spectroscopies, and quantum computing applications in chemistry. Her recent publications highlight advancements in quantum linear response theory, polarizable embedding environments, X-ray absorption in water, and quantum algorithms for molecular properties. Collaborative work spans interdisciplinary projects with experimentalists, covering gas-phase molecules to biomolecular systems. Scientific Awards: Marie Curie IEF fellowship AIAS-COFUND senior fellowship Her work addresses challenges in ultrafast dynamics, photoionization, and the intersection of chemistry with physics and computational science, emphasizing accuracy and novel experimental guidance.

Bruce McLarenView profile

Researcher

• Artificial Intelligence
• Educational Technology
• Digital Learning Games
• +3 more

Bruce McLaren is a Researcher at Carnegie Mellon University ’s Human-Computer Interaction Institute and head of the McLearn Lab . His work bridges Artificial Intelligence and Educational Technology , focusing on Digital Learning Games , Intelligent Tutoring Systems , and Collaborative Learning . He co-founded the free math tutoring platform Mathtutor and has authored over 200 academic articles, holding five patents. Education : B.S. in Computer Science (1981), M.S. in Computer Science (1984), M.S. in Intelligent Systems (1994), Ph.D. in Intelligent Systems (1999) from the University of Pittsburgh. McLaren's research explores how digital learning games like Decimal Point enhance math education, emphasizing student agency , gender effects , and feedback mechanisms using AI. His studies on erroneous examples and collaborative argumentation tools (e.g., ARGUNAUT, LASAD) have transformed pedagogical strategies. Recent work integrates Large Language Models (ChatGPT) into educational games for automated feedback. His publications span AI in Education , STEM Learning , and Machine Ethics , with a focus on learning analytics , knowledge tracing , and adaptive tutoring . McLaren’s studies often involve partnerships with institutions globally, including the German Research Center for Artificial Intelligence and Saarland University. Scientific Awards : Best Paper Award at ECTEL 2023; top 0.5% scholar in AI (ScholarGPS). McLaren actively pursues grants from the NSF , U.S. Department of Education , and Accenture , totaling over $2 million in recent projects. His lab, McLearn, collaborates with educators and technologists to refine teacher orchestration tools and student modeling frameworks.

Panagiotis HadjidoukasView profile

Associate Professor

• Parallel Computing
• Distributed Systems
• High Performance Computing
• +3 more

Panagiotis Hadjidoukas is an Associate Professor and Head of the Laboratory for Computing at the Computer Engineering and Informatics Department, University of Patras, within the School of Engineering. His work focuses on high-performance computing systems and parallel programming models. His research spans parallel and distributed computing , runtime support for parallel programming models , and automation of AI/ML workloads . Key contributions include developing the torc runtime system for task parallelism and pioneering work in extreme-scale scientific simulations. His interests bridge theoretical computer science with practical applications in scientific computing and AI acceleration. Notable achievements include the ACM Gordon Bell Prize Winner (2013) for 11 PFLOP/s cloud cavitation simulations and Finalist (2015) for in-silico lab-on-a-chip microfluidics. His software tools ( torc_lite , torcpy ) enable efficient parallelism across diverse architectures. Doctor of Philosophy (2003), University of Patras Master of Science (2001), University of Patras Diploma in Computer Engineering (1998), University of Patras As Head of the Laboratory for Computing, he leads infrastructure development while maintaining active research collaborations with IBM Research and ETH Zurich. His teaching portfolio includes graduate courses on high-performance computing for data sciences and parallel processing principles.

• Artificial Intelligence
• Natural Language Processing
• Legal Informatics
• +5 more

Tomasz Pełech-Pilichowski serves as a Lecturer at the Institute of Computer Science within the Faculty of Computer Science at AGH University of Science and Technology in Kraków. He concurrently holds dual administrative leadership positions as Director of the AGH Recruitment Center and Rector's Representative for Recruitment, demonstrating significant institutional impact beyond his academic role. His research expertise spans artificial intelligence, natural language processing, and legal informatics with methodological foundations in deep learning and time series analysis. Recent work focuses on AI-driven solutions for legal text processing (text segmentation, hypertext law), educational technology (gamification, recruitment systems), and security applications (facial-age detection, anomaly identification). His interdisciplinary approach consistently bridges computer science with law, education, and environmental science through practical implementations. Analysis of his 2023-2025 publications reveals three dominant research trajectories: legal tech innovation (68% of output featuring text segmentation and regulatory automation), educational transformation (22% including gamified AI skill development), and security/environmental systems (10% covering IoT integration and pollution prediction). This progression shows increasing specialization in AI-NLP fusion techniques applied to domain-specific challenges, particularly within legal informatics where he has developed novel text recovery and visualization frameworks.

• Verification of Secure Systems
• Software Security
• Web Security
• +5 more

Pedro Adão is an Assistant Professor at the Department of Computer Science and Engineering (DEI) of the Instituto Superior Técnico (IST) , University of Lisbon . He is affiliated with the SQIG - Security and Quantum Information Group at the Institute of Telecommunications , formerly part of CLC - Logic and Computation Centre . His work bridges formal methods and computational cryptography , focusing on security protocols , software security , and quantum information security . PhD in Mathematics (2006, IST) with thesis "Formal Methods for the Analysis of Security Protocols" Graduated in Applied Mathematics and Computation (2002, IST) with thesis "Electronic Money" His research interests include verification of secure systems, composition of secure systems, mathematical foundations of cryptography, and physical/day-life security. His publications span topics like firewall configuration tools , smart contract verification , quantitative protocol analysis , and computationally sound symbolic cryptography . He has collaborated with institutions such as the University of Pennsylvania, Microsoft Research Cambridge, and INRIA-Microsoft Research Paris. Key teaching contributions include courses in Introduction to Algorithms and Data Structures , Computational Models in Security , and Software Security . He is a member of the SQIG research group and has organized the SQIG Information Security Seminar . Past teaching roles include lecturing at the Catholic University of Portugal and the Air Force Academy.

Diogo Ribeiro FerreiraView profile

Associate Professor

• Data Science
• Applied Machine Learning
• Deep Learning
• +6 more

Diogo Ribeiro Ferreira is an Associate Professor of Information Systems at Instituto Superior Técnico (IST) , part of the University of Lisbon . He is also a researcher at the Institute of Plasmas and Nuclear Fusion (IPFN) . His career spans teaching, research, and collaboration with institutions like JET (Joint European Torus) in nuclear fusion. His research focuses on Data Science , Applied Machine Learning , Deep Learning , and Process Mining , with applications in Nuclear Fusion (plasma diagnostics, disruption prediction) and Information Systems (workflow management, healthcare process analysis). He has taught courses like Data Administration in Information Systems and Data Analysis and Integration since 2003. His work bridges Computer Science and Physics , particularly in using Graph Neural Networks for plasma modeling and GPU Acceleration for high-performance computing. He has supervised over 20 PhD and MSc students, including André C. Santos and Fernando Szimanski, and co-authored 15+ recent publications in journals like Journal of Plasma Physics and Fusion Engineering and Design . Scientific Awards: 2012 ICGA Journal Award Best Paper Award at 9th IEEE International EDOC Conference 2005 Collaborations: Affiliated with JET Contributors and IPFN, focusing on real-time tomography and fusion reactor optimization.

• Computer Science
• Software Engineering
• Cybersecurity
• +3 more

Zühal Aslan Akyol is a Lecturer at the Department of Computer Technologies within Cide Rifat Ilgaz Vocational School at Kastamonu University, Turkey . She has been serving full-time since 2013 and was promoted to Department Head in 2025. Her academic journey includes a PhD in Information Systems (2018-2023) and a Master's in Electronics and Computer Education (2010-2013) from Gazi University , and a BSc in Computer Engineering (2017-2021) from Kastamonu University . PhD: Gazi University, Informatics Institute, Information Systems (2018-2023) Master's: Gazi University, Informatics Institute, Electronics and Computer Education (2010-2013) BSc: Kastamonu University, Faculty of Engineering and Architecture, Department of Computer Engineering (2017-2021) Her research focuses on Computer Science and Optical Engineering , particularly in fiber optic systems , database management , and programming education . Her publications emphasize data mining applications in optical amplifiers, remote laboratory design , and software tools for engineering education . She has contributed to projects like the Real-Time Remote Accessible Fiber Optic Systems Laboratory and the Bilgisayar Donanımı Mobil Destekli Eğitim Simülatörü (BDMES) . While she has co-authored articles on topics like noise figure enhancement in EDFA and temperature-dependent gain analysis , no explicit scientific awards are listed in the provided data.

R. Iris BaharView profile

Professor

• Computer Architecture
• Low Power Design
• Machine Learning
• +5 more

R. Iris Bahar is a Professor of Computer Science and Engineering at Colorado School of Mines, where she serves as Department Head of Computer Science since 2022. Previously, she held dual appointments as Professor of Engineering and Computer Science at Brown University for 26 years. Her research focuses on energy-efficient and reliable computing across system-level to device-level domains, with applications in near-data processing, robust machine learning for robotics, and noise-immune nanoscale circuit design. Research Interests include computer architecture, low-power design, and robotic system optimization. She pioneers hybrid generative-discriminative inference techniques for robot scene perception, achieving 40% accuracy improvements in occluded environments through FPGA-based Monte-Carlo sampling implementations. Her work on subthreshold CMOS noise modeling provides 1000x simulation speedups over SPICE-based methods. Near-Data Processing Architectures Approximate Computing Hardware/Software Co-design Nanoscale Noise Modeling Robust AI for Robotics Scientific Awards include the 2024 IEEE Undergraduate Teaching Award, 2019 Marie R. Pistilli Women in Engineering Achievement Award, and NSF CAREER award. She is an IEEE Fellow and ACM Distinguished Scientist. Funded Research spans NSF grants on thermal noise effects ($360k), durable data structures ($500k), and Brown SEED grants for autonomous robotics. Her lab trains 12+ students in hardware acceleration and probabilistic computing.