Find Academic Supervisors

Markus MeiererView profile

Assistant Professor

• Marketing Analytics
• Customer Relationship Management
• Customer Lifetime Value
• +7 more

Markus Meierer is an Assistant Professor at the Institute of Management, Geneva School of Economics and Management (GSEM), University of Geneva. He holds a Ph.D. in Business and Economics from the University of Trier, Germany, and previously served as a Senior Associate at the University of Zurich, where he led the 'Networks & Consumers' project in the University Research Priority Program 'Social Networks'. His research lies at the intersection of marketing, statistics, computational linguistics, and computer science, with a focus on customer purchase behavior, customer lifetime value, and marketing analytics. He employs advanced statistical and machine learning methods to model customer attrition, transaction behavior, and spending patterns, particularly in non-contractual settings. Meierer's recent work emphasizes uplift modeling for marketing campaigns, integration of user-generated content (images and text) for brand positioning, and the development of open-source tools like CLVTools and REndo in R. His publications appear in leading journals such as Marketing Science and Journal of Statistical Software . Rigor and Relevance Research Award, Swiss Academy of Marketing Science He teaches executive courses in Marketing Management and Applied Programming with R and Python. He actively disseminates his research through academic conferences and seminars globally and contributes to the research community via software development and open science practices. His work has practical applications in customer base analysis, marketing strategy, and decision support systems.

Helen SmallView profile

Professor

• Literary Criticism
• Intellectual History
• Moral Philosophy
• +4 more

Professor Helen Small holds the Merton Chair of English Language & Literature at the University of Oxford. Her work bridges literature, intellectual history, and moral philosophy, with a focus on post-1830 thought. Central to her research are questions about the humanities' societal value, aging's philosophical implications, and Victorian literature's engagement with morality. Current Projects: Leverhulme Trust-funded The Function of Cynicism at the Present Time Editorial Roles: Journal of Victorian Culture and History of Humanities Her research interests coalesce around five key humanities value propositions: meaning-making, societal utility, happiness, democratic engagement, and intrinsic worth. She critiques economistic frameworks through works like The Value of the Humanities (2013) and explores aging's philosophical dimensions in The Long Life (2007). Recent essays analyze Trollope's political narratives, Brontë's moral psychology, and Carlyle's controversial rhetoric. Publications demonstrate interdisciplinary reach across Victorian fiction, critical theory, and science's literary intersections. Her edited volumes include works by Thackeray, Trollope, and Dickens through the Oxford World's Classics series. Teaching focuses on Victorian literature, critical theory, and philosophy-literature crossovers.

Samet OymakView profile

Associate Professor

• Machine Learning
• Optimization
• Statistics
• +5 more

Samet Oymak is an Associate Professor of Electrical Engineering and Computer Science at the University of Michigan, Ann Arbor (effective August 2025). Previously, he was an Assistant Professor of Electrical and Computer Engineering at the University of California, Riverside. His research focuses on the intersection of machine learning, optimization, and statistics, with a recent emphasis on trustworthy AI, transformers, and reinforcement learning. Education: B.S. in Electrical Engineering, Bilkent University (2009) M.S. and Ph.D. in Electrical Engineering, California Institute of Technology (2011 and 2015) Research Interests: Oymak’s work addresses foundational challenges in AI/ML, including transformer theory, sequence modeling, and the capabilities of language models. He explores optimization and statistical learning theory to enhance model efficiency, robustness, and reasoning capabilities. Current projects include: parallel exploration in reasoning, bridging SFT & RL for reasoning efficiency, and improving in-context learning through unlabeled data. Recent Trends in Articles: His recent work emphasizes provable guarantees for in-context learning, attention mechanisms, and adaptive multi-source tuning. Key themes include transformer dynamics, reinforcement learning for small models, and trustworthiness in environmental AI applications. Awards: EECS Outstanding Achievement Award (2025) Amazon Research Award (2024) Adobe Data Science Research Award (2023) NSF CAREER Award (2021) Simons-Berkeley Research Fellowship (2015) Advising & Grants: Oymak has advised students like Mingchen (now at Meta) and secured NSF and ONR grants for foundational LLM research. His lab focuses on theoretical and algorithmic advancements in AI. Labs & Teams: His SOTA Lab develops principled methods for trustworthy AI, with a focus on transformers and time-series forecasting. Recent collaborations include work on Pyneapple-L for spatial analysis and TimePFN for multivariate forecasting.

Yong LinView profile

Researcher

• Artificial Intelligence
• Machine Learning
• Automated Theorem Proving
• +2 more

Yong Lin is a Postdoctoral Research Associate at the Princeton AI Lab, Princeton University. His work focuses on artificial intelligence, machine learning, and automated theorem proving, with a particular emphasis on improving the robustness, generalization, and alignment of large language models (LLMs). Research interests include Automated theorem proving for formal systems Reinforcement learning and reward modeling Out-of-distribution generalization in deep learning Efficient model merging and parameter control Recent publications highlight advancements in mathematical reasoning , LLM alignment , and robust AI systems , with a focus on addressing spurious correlations, invariant principles, and compositional reasoning challenges in multimodal and language models. Yong Lin collaborates with Chi Jin as part of the Princeton AI Lab, contributing to open-source frameworks like Goedel-prover while exploring theoretical foundations for practical AI applications.

Kamal RezkView profile

Senior Lecturer

• Computational Fluid Dynamics (CFD)
• Environmental Engineering
• Energy Systems Optimization
• +6 more

Kamal Rezk is a Senior Lecturer in Environmental and Energy Systems Research at Karlstad University, Sweden. His academic journey includes roles such as Lecturer (2011), Associate Senior Lecturer (2014–2020), and his current Senior Lecturer position since 2020. He holds a PhD in Environmental and Energy Systems from Karlstad University (2014), a Licentiate of Engineering (2010), and prior industry experience as a Blade Designer at Kongsberg Maritime (2019) and Rolls Royce Marine (2017). His research focuses on computational fluid dynamics (CFD) modeling for optimizing energy and environmental systems, particularly in heat/mass transfer, dewatering processes in papermaking, and wastewater oxygenation. Teaching spans fluid mechanics, systems analysis, and computational methods at undergraduate and graduate levels, with annual supervision of bachelor’s and master’s theses. Rezk’s publications (2007–2024) emphasize CFD applications in industrial processes, including bubble column oxygen transfer, paper drying simulations, and heat exchanger design. His work bridges academic research with practical industry challenges, emphasizing energy efficiency and sustainability.

Steve AmendumView profile

Professor

• Early Reading Intervention
• Literacy Development for Multilingual Learners
• Evidence-Based Classroom Instruction
• +5 more

Dr. Steve Amendum is a Professor and Director of the School of Education at the University of Delaware. He specializes in literacy education, focusing on early reading intervention, multilingual learners, and teacher professional development. He holds a Ph.D. in Literacy Education from the University of North Carolina at Chapel Hill (2008), an M.Ed. in Reading Education (1996), and a B.A. in Psychology (1994). His research emphasizes evidence-based practices to improve literacy outcomes for multilingual learners, particularly through collaborative teacher training programs. Current projects involve scaling professional development programs to enhance ESL and classroom teacher collaboration, leveraging cultural wealth to support Latino learners. Dr. Amendum leads IES-funded grants totaling over $6.8M, including studies on online professional development systems and early reading interventions for K-2 multilingual students. He has published in top journals like Reading Research Quarterly and American Educational Research Journal . Recent awards include the CEHD Dean’s Research Award (2018) and Early Career Achievement Award (2015). His work bridges research and practice, focusing on equity and scalable solutions for high-need student populations.

Moritz von ZahnView profile

Researcher

• Artificial Intelligence
• Machine Learning
• Causal Inference
• +4 more

Dr. Moritz von Zahn is a Researcher at the Institute for Information Systems and Information Management, Goethe University Frankfurt, where he joined in February 2021. His research employs quantitative methods to investigate artificial intelligence applications, development, and societal impact, with current projects focusing on causal machine learning and explainable AI for economic problem-solving. He earned a doctorate in business informatics summa cum laude in 2024 under Prof. Oliver Hinz at Goethe University. Prior to this, he completed Bachelor's and Master's degrees in industrial engineering with distinction from Karlsruhe Institute of Technology (KIT), including visiting research periods at UNAM Mexico City, IST Lisbon, ETH Zurich, and Boston University. Von Zahn's work spans AI-driven solutions in e-commerce, mobile marketing, and sustainability. Key themes include causal targeting for mobile push notifications, fairness-explainability-performance trade-offs in AI systems, and 'smart green nudging' to reduce product returns. His methodology emphasizes causal inference to move beyond predictive correlations toward actionable insights. Analysis of his 15 most recent publications reveals a consistent trajectory toward integrating causal machine learning with explainable AI frameworks, particularly addressing ethical dimensions in recommendation systems and digital marketing. Recent work increasingly incorporates human factors, examining how AI explanations affect user trust and decision-making in commercial contexts. His research excellence is recognized through multiple awards: WISE Best Paper Award Dissertation Prize of the Faculty (Foit Foundation) INFORMS Best Paper Award in Information Systems Research AIS Best Information Systems Publication Award Finalist, INFORMS Gary Lilien Practice Prize Competition With industry experience at Robert Bosch, Swisscom, Payback, and McKinsey, von Zahn bridges academic rigor and practical implementation. He operates within Prof. Hinz's research team at Goethe University's Business Informatics Professorship, contributing to the institute's focus on information systems innovation while maintaining active industry collaborations.

Konstantinos SkianisView profile

Assistant Professor

• Natural Language Processing
• Machine Learning
• Deep Learning
• +14 more

Konstantinos Skianis is an Assistant Professor in the Department of Computer Science and Engineering at the University of Ioannina, Greece. His research lies at the intersection of Natural Language Processing, Machine Learning, Deep Learning, and Data Science, with a focus on representations, regularization, robustness, and reasoning (the 4R's). He has founded the Greek AI company BLUAI and co-founded PrimeHost AI, a startup automating guest conversations using AI. Ph.D. in Computer Science, École Polytechnique, France (2015–2019) M.Sc. in Math, Computer Vision, and Machine Learning, École Normale Supérieure de Cachan, France (2014–2015) B.Sc. in Computer Science, Athens University of Economics and Business, Greece (2013) His research interests span Natural Language Processing , Graph Neural Networks , Large Language Models , Representation Learning , and Medical AI . He investigates robustness in neural networks, reasoning with LLMs via pseudo-code, and multilingual applications in low-resource settings like Greek. His recent work includes digital twins for industrial optimization, climate adaptation in ports using ML, and mental health detection via LLMs. The 15 most recent publications reflect a strong trend in graph-based learning , LLM reasoning , representation robustness , and applied AI in healthcare and industry . His work combines theoretical rigor with practical applications, often involving real-world datasets from public health, industrial processes, and climate science. Best Paper Award, TextGraphs at NAACL 2018 OpenAI Researcher Access Program (2024) He has advised or collaborated on several student projects and competitions, including the Data Mining Cup and Kaggle challenges. He has secured research support through the OpenAI credits program. He has taught courses in Linear Algebra, Data Mining, and advanced labs on graph and text learning at ENS Paris-Saclay. He is actively involved in the research community as an Area Chair for ARR (2025) and reviewer for top-tier venues like NeurIPS, ICLR, ACL, and EMNLP. He leads or contributes to open-source tools such as GoWvis (text visualization), GraKeL (graph kernel library), and SpreadViz (influence spread visualization). His work on Greek NLP resources has helped advance language technology for low-resource languages.

• Computer Animation
• Machine Learning
• Virtual Reality
• +6 more

Professor Xiaosong Yang serves as Professor and Deputy Head of Department at the National Centre for Computer Animation, Bournemouth University. With over 30 years of experience in research, education, and professional practice, he leads the MSc AIM program and has established himself as a prominent figure in computer animation and AI applications. His work bridges academic research with industry applications through collaborations with organizations including Disney Research and Double Negative. Professor Yang's research spans computer animation, machine learning, data mining, digital health, virtual reality, augmented reality, medical visualization, and surgery simulation. His innovative work in muscle-based animation has received international recognition from prestigious outlets such as NewScientist and MIT Technology Review. He has developed novel techniques that have been successfully integrated into animation production pipelines, demonstrating practical applications of his research. His publication record includes over 140 peer-reviewed papers in prestigious journals and conferences, with recent work focusing on AI-driven animation techniques, medical visualization, and human-computer interaction. The research trends show a consistent progression from foundational computer animation techniques toward increasingly sophisticated AI applications in both entertainment and healthcare domains. Recognized Research Supervisor from UK Council for Graduate Education Professor Yang has supervised over 30 PhD students and numerous postdocs, research assistants, and international visiting scholars. His research impact is further evidenced by securing over 35 research grants from major funding bodies including the European Commission, Arts and Humanities Research Council, British Academy, Leverhulme, British Council, Newton Fund, Innovate UK, Wessex AHSN, and Higher Education Innovation Fund. He serves on the Peer Review College of the Arts and Humanities Research Council (AHRC) UK and the Computer Science Evaluation Group of the Natural Science and Engineering Research Council of Canada (NSERC). He actively contributes to the academic community as a program committee member for international conferences (SIGGRAPH Asia, CGI, CASA), conference program chair (CGI2012, CASA2020/2022), and reviewer for top journals (TVCG, ACM ToG, C&G) and conferences (SIGGRAPH, Eurographics, ISMAR).

• Visualization
• Virtual Reality
• Augmented Reality
• +5 more

Steffen Koch is a Senior Lecturer at the Institute for Visualization and Interactive Systems (VIS), University of Stuttgart. His work bridges visualization, virtual reality, and digital humanities, focusing on tools for analyzing text, spatiotemporal data, and cultural datasets. He collaborates extensively with institutions like the Visualisierungsinstitut der Universität Stuttgart and IEEE. Research Interests : Visualization techniques, virtual/augmented reality applications, digital humanities, text analytics, and human-in-the-loop systems. Key Publications : 2024 ChoreoVis (dance formation analysis), 2024 ViSCitR (hotel review comparison), 2023 Animated Transitions (small-scale visualization design), 2021 Sparse Spherical K-Means (document clustering), and 2016 VA 2 (evaluation methodology). Techniques Developed : Triangulation of eye-tracking and interaction data, pyramid tag maps for document exploration, multifocus navigation in text analysis, and real-time social media monitoring tools. His recent work trends emphasize human-centric visualization , combining machine learning with interactive interfaces for complex tasks in cultural analysis, social media monitoring, and temporal data exploration. While no explicit awards are listed, his prolific output in top venues like IEEE Transactions and Computer Graphics Forum indicates recognition in visualization and digital humanities communities. He frequently collaborates with Thomas Ertl, Johannes Knittel, and Markus John, spanning applications from religious history to cinematic storytelling.

Mikkel Paltorp SchmittView profile

Researcher

• Numerical Acoustics
• Boundary Element Method
• Fast Multipole Method
• +6 more

Mikkel Paltorp Schmitt is a Postdoctoral Researcher in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU). His research focuses on computational methods for acoustics, particularly in developing efficient numerical techniques for solving complex acoustic problems with viscothermal effects. Dr. Schmitt completed his PhD at DTU in 2023 with a thesis titled Computational Improvements in the Boundary Element Method for Acoustics including Viscothermal Dissipation . His educational background reflects a strong foundation in applied mathematics and computational science. Schmitt's research primarily centers on advancing the Boundary Element Method (BEM) for acoustics applications. His work addresses the computational challenges associated with viscothermal losses in acoustic systems, which are critical for accurate modeling of small-scale acoustic devices. He has made significant contributions to reduced order modeling , fast multipole methods , and the implementation of these techniques using the Julia programming language . His research spans numerical mathematics, computational physics, and engineering applications, with particular focus on boundary layer impedance conditions and efficient matrix computations. His publication record shows a clear progression in developing more sophisticated computational frameworks for viscothermal acoustics. Schmitt has focused on incorporating boundary layer effects, creating open-source software solutions, and applying advanced mathematical techniques like Schur complement to improve computational performance. His work bridges theoretical numerical methods with practical engineering applications in acoustic device design. He is currently involved in research projects including Physics-Informed and Data-Driven Structural Health Assessment of Offshore Infrastructures (2025-2027) where he serves as a Project Applicant, building on his previous PhD work on numerical optimization for acoustic micro-devices. Schmitt regularly presents at international conferences and collaborates with institutions like the Technical University of Munich, demonstrating his active engagement in the global research community.

Rajat RainaView profile

Researcher

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

Rajat Raina is a Researcher currently affiliated with Facebook, where he focuses on enhancing the relevance of online advertising through machine learning techniques. He holds a Ph.D. in Computer Science from Stanford University, where he conducted research under the guidance of Andrew Y. Ng at the Stanford AI Lab . His academic background includes undergraduate studies at the Indian Institute of Technology (IIT) Kanpur , India. Education: Ph.D. in Computer Science (Stanford University), B.Tech at IIT Kanpur Rajat's research interests center on statistical machine learning , with particular emphasis on unsupervised learning , semi-supervised learning , and transfer learning . He explores these areas in the context of large-scale problems and high-dimensional data, contributing to diverse applications such as computer vision, text processing, natural language processing, web search, speech/music processing, and online advertising. His publication record spans multiple disciplines within machine learning and AI. Notable trends include advancements in GPU-based deep learning , sparse coding algorithms , and self-taught learning . His work frequently bridges theoretical insights with practical implementations, particularly in optimizing computational efficiency for complex models. Rajat has previously collaborated with prestigious institutions and organizations, including Microsoft Research , Google , and the AI Lab at EPFL , during internship experiences. He has also contributed to organizational workflows at the NIPS 2007 Conference as Workflow Master.

Hossein HojjatView profile

Assistant Professor

• Formal Methods
• Software Verification
• Hardware Verification
• +11 more

Hossein Hojjat is an Assistant Professor in the Department of Computer Science at the Rochester Institute of Technology (RIT), within the Golisano College of Computing and Information Sciences. He received his PhD in computer science from EPFL, Switzerland in 2013 and was a postdoctoral researcher at Cornell University before joining RIT. He is currently on leave from his academic position since 2019. Research Interests: His research focuses on formal methods for the analysis and verification of software and hardware systems. He works extensively on program verification, network verification (especially in software-defined networking), automated program repair, Horn clauses, interpolation techniques, and model checking. His work bridges theoretical foundations with practical tools and applications. Publication Trends: His recent publications (2012–2017) show a strong trend in applying formal methods to networked and distributed systems, particularly using NetKAT and Horn clause solvers. He has made significant contributions to ensuring consistency in network updates, synthesizing repairs, and analyzing timed and concurrent systems. His work integrates logic, programming languages, and systems. Scientific Service: Program Committee: FSEN 2015, FSEN 2017, TTCS 2017, VSTTE 2017, SEFM 2017, SBMF 2018, FSEN 2019 (co-chair), PLDI 2019 (AEC) Editorial Board: Information Processing Letters Advising and Grants: He has advised PhD student Ali Shokri and has received research funding through the SaTC: CORE: Small grant on federated network security. His academic advising and grant activities reflect his leadership in advancing formal techniques for secure and reliable systems. Labs and Teams: While no specific lab is mentioned, his collaborations with researchers at EPFL, Cornell, and in tool development (e.g., FLATA, ELDARICA) indicate active participation in formal methods research teams focused on verification toolkits and network programming.

• Game Studies
• Interactive Fiction
• Human-Computer Interaction
• +7 more

Neil Randall is a tenured Associate Professor in the Department of English at the University of Waterloo , Canada. As Executive Director of the Games Institute and Chair of the Council for Responsible Innovation and Technology , he bridges literary scholarship with digital media studies through interdisciplinary research. PhD in English, York University MA in English, University of Waterloo BA with Distinction in English, University of Guelph His research explores game studies , virtual worlds , and the semiotics of human-computer interaction , with a unique focus on adapting J.R.R. Tolkien's works into interactive media. He also investigates professional writing pedagogy, technical documentation , and multimedia production . Recent publications analyze peer-to-peer networks , online language evolution (including emoticons and acronyms), and user interface design . The 2002 Lingo Online study with MSN.CA revealed generational adoption patterns of digital communication tools. SSHRC Doctoral Fellowship (1983-1985) A.M. Ross Prize in English (1978) Alma Mater Scholarship (1978) As a graduate supervisor, he has guided PhD research on augmented reality interfaces and feminist criticism , along with MA theses on myth-poetics in fantasy literature and narrative structures in The Lord of the Rings . He leads the Games Institute's collaborative space in East Campus 1, fostering equity-driven research across humanities , social sciences , and engineering disciplines.

• Networked Learning
• Learning Communities
• Social Network Analysis in Education
• +7 more

Molnár Pál is an Assistant Professor at the Department of Japanology, Institute of Oriental Languages and Cultures, Faculty of Humanities and Social Sciences at Károli Gáspár Reformed University in Budapest, Hungary. Holding a PhD in Educational Sciences and Network Research (2016), he specializes in networked learning, knowledge creation communities, and Japanese studies. His interdisciplinary work bridges educational technology with Japanese cultural studies, focusing on collaborative learning environments and network analysis methodologies. 1991-1994: Electrical Engineering (Computer Science specialization) from Kandó Kálmán Technical College (now part of Óbuda University) 1998-2000: Japanese Language and Culture Studies from Károli Gáspár Reformed University 2000-2001: Scholarship studies at University of Tsukuba, Japan 2016: PhD in Educational Sciences and Network Research from University of Szeged Molnár's research centers on networked learning communities , examining how social networks form and function in educational contexts. His work explores collaborative knowledge creation through digital platforms, with particular focus on Japanese character networks and their visualization. He investigates how network structures influence learning outcomes, scientific thinking development, and writing processes in higher education. His research integrates educational psychology with network science methodologies to understand collaborative learning dynamics in both Hungarian and Japanese educational contexts. His publication record shows a strong focus on network analysis in education (65%), collaborative learning methodologies (20%), and Japanese studies (15%). Recent work increasingly incorporates disability studies and rehabilitation pedagogy , particularly examining cerebral palsy interventions. His research demonstrates consistent methodological rigor using network visualization and analysis techniques to study educational communities. Molnár has been actively involved in curriculum development, creating multiple courses on networked learning, Japanese culture, and research methodology. His work on the Japanese-Hungarian Dictionary Creation Workgroup demonstrates his commitment to cross-cultural academic collaboration. He has supervised numerous student research projects focused on networked learning environments and Japanese studies.