İBRAHİM ERDEM SEÇİLMİŞ is a Professor of Economics at Hacettepe University's Faculty of Economics and Administrative Sciences. His research focuses on experimental economics, public goods theory, behavioral economics, and tax policy with notable contributions to understanding risk perception, social discount rates, and creative industries in Mediterranean economies. He has authored/co-authored over 40 peer-reviewed publications and edited several academic volumes. Key research areas include: Experimental analysis of public goods provision Behavioral responses to environmental policies Comparative analysis of social discount rates in transition economies Evaluation of tax reforms through behavioral lenses Impact of cultural industries on regional development Notable publications include seminal works on Turkish presidential election policies (2025), risk aversion in public goods (2019), and Mediterranean creative industries (2016). His work frequently bridges theoretical economics with policy applications, particularly focusing on sustainability and social equity. He has presented at international conferences such as the Regional Studies Association European Conference (2014) and contributed to edited volumes on game theory applications and economic philosophy. Current research trends emphasize behavioral tax policy design and the socio-economic consequences of rising inequality.
Professor Martin Doevenspeck is a distinguished scholar of Political Geography at the University of Bayreuth, where he holds a professorship in the Department of Political Geography within the Faculty of Biology, Chemistry & Earth Sciences. Since 2019, he has served as a Principal Investigator in the Cluster of Excellence "Africa Multiple" and as Spokesperson of the Research Section Mobilities. Previously, he was Managing Director of the Institute of Geography at the University of Bayreuth (2018-2022) and a member of the Executive Board of the Institute of African Studies Bayreuth. Professor Doevenspeck earned his "Diplom" in Geography from the University of Bonn and completed his PhD thesis entitled "Migration in rural Benin – Studies in social geography at an African border" at the University of Bayreuth in 2004. His academic journey includes positions as a Teaching and Research Assistant at the University of Bayreuth (2005-2011), Post-doc at the University of Bonn (2004), and various research and teaching roles focused on African geography and development. At the intersection of migration studies and political geography, Professor Doevenspeck's research focuses on (forced) im/mobilities, conflict, and border studies, with particular emphasis on African contexts. His current work centers on the project "Oil movements: the production and government of petro-(im)mobilities in East Africa" within the Bayreuth Cluster of Excellence "Africa Multiple," where he serves as Spokesperson of the Research Section Mobilities. His research spans West Africa (particularly Benin), Central Africa (Democratic Republic of Congo, Rwanda), and East Africa, examining how borders, conflicts, and resource extraction shape human mobility and territorial control. Analysis of Professor Doevenspeck's recent publications reveals a consistent focus on migration, borders, and conflict in African contexts, with increasing attention to environmental factors and resource extraction. His work demonstrates interdisciplinary approaches combining political geography, migration studies, and African studies. Recent publications particularly emphasize petro-mobilities, terrorism impacts on education, and migration control in West African borderlands, reflecting evolving research interests in the interplay between infrastructure development, security concerns, and human mobility. Professor Doevenspeck has supervised numerous doctoral and master's students, including Jaana M. Janßen, Fabian Liedl, Clemens Romankiewicz, Morgane Anziani-Vente, Muhire Blaise Mwanga, and Daniela Boß. His academic leadership extends to serving as Vice Dean of the Bayreuth International Graduate School of African Studies (BIGSAS) from 2012-2021, where he played a significant role in shaping graduate education in African studies. As a Principal Investigator in the Cluster of Excellence "Africa Multiple," Professor Doevenspeck leads research initiatives examining diverse manifestations of "Africa" across the continent and diaspora. His work involves collaboration with researchers from multiple disciplines and institutions, focusing particularly on mobilities as a key research section within the cluster. This collaborative research environment has produced significant insights into migration patterns, border dynamics, and conflict resolution in African contexts.
Dr. Mo Adda is a Principal Lecturer at the University of Portsmouth's School of Computing, part of the Faculty of Technology. He holds a PhD in Distributed Systems and Parallel Processing from the University of Surrey. His research focuses on network security, distributed systems, wireless networks, and cybercrime prevention. He leads projects in the Centre for Cybercrime and Economic Crime, exploring fault management in networks, blockchain applications, and IoT forensics. With 16 supervised theses, he advises on topics like energy-efficient cloud systems and machine learning for environmental modeling. His work bridges academia and industry, addressing challenges in software-defined networks, traffic control, and secure data sharing in social networks. Education: PhD in Distributed Systems (University of Surrey) Affiliations: Centre of Excellence in Defence, Risk & Resilience; Portsmouth Centre for Advanced Materials and Manufacturing Research Interests Dr. Adda's research spans: Network security and fault detection mechanisms Blockchain applications in IoT and forensics Energy-efficient cloud data center optimization Machine learning for climate modeling Self-organizing network protocols Grants & Collaborations His projects include collaborations with industry partners on secure data leakage detection in cloud systems and resilient wireless protocols for harsh environments. He has pioneered fault classification systems using clustering algorithms and fuzzy logic. Labs & Teams He contributes to the Centre for Cybercrime and Economic Crime, focusing on digital forensics and network intrusion analysis. His team develops frameworks for privacy management in social networks and proactive routing in software-defined networks.
Dr. Shan Lu is a Lecturer in Finance at the Department of Accounting and Finance, Kent Business School, University of Kent, since August 2021. He previously held positions at the University of Aberdeen and the University of Bradford and earned his PhD from the University of Aberdeen. Research interests: Financial derivatives, option pricing, and quantitative finance. His work focuses on volatility modeling, risk-neutral density estimation, and computational finance, with publications in journals such as the European Journal of Finance, Journal of Futures Markets, and Economics Letters. Teaching: Covers financial markets, derivatives, econometrics, and quantitative methods at undergraduate and postgraduate levels. Scientific awards: Fellow (FHEA) of Higher Education Academy Advising: Offers PhD supervision in topics aligned with his research interests, including financial derivatives and quantitative finance. He emphasizes collaboration on research ideas directly related to his expertise. Publications: Recent work explores volatility dynamics in VIX/VXX options, risk-neutral density extraction, and implied volatility forecasting, leveraging computational methods and empirical finance techniques.
Kimon Fountoulakis is an Associate Professor at the University of Waterloo. His research focuses on Machine Learning on Graphs and Numerical Optimization, with a strong emphasis on algorithmic methods for graph-structured data. He holds a Ph.D. from The University of Edinburgh (2015), an M.Sc. from The University of Edinburgh (2010), and a B.Sc. from Athens University of Economics and Business (2009). His work spans theoretical foundations and practical applications in graph algorithms, optimization, and machine learning. Research interests include graph neural networks, local graph clustering algorithms, and algorithmic reasoning. His contributions address challenges in graph representation learning, message-passing architectures, and scalable optimization methods. Notable themes in his publications include improving counting abilities of vision-language models, analyzing graph convolutions, and developing flow-based clustering techniques with statistical guarantees. His work often bridges theory and practice, with applications in network analysis, pandemic containment strategies, and high-performance computing. While no specific grants or awards are listed, his research demonstrates significant contributions to graph-based machine learning and optimization. He maintains a research group at the University of Waterloo, with a focus on developing open-source tools and frameworks for graph algorithms. His lab’s work emphasizes local graph clustering methods and their scalability in real-world networks.
Alexander Trubowitz is a Postdoctoral Prize Research Fellow in Politics at Nuffield College, University of Oxford, conducting research in comparative and historical political economy. He completed his Ph.D. in Political Science at Yale University in 2025, focusing on labor market transformations in Western Europe. Education: B.A. in Classics and Political Science, Tufts University, 2017 (summa cum laude) Ph.D. in Political Science, Yale University, 2025 His research examines political responses to labor market risks from technological change in 20th-century Western European manufacturing, combining archival analysis of UK/Germany cases with quantitative studies of worker behavior during economic shocks and institutional drivers of inequality-reducing policies. His work bridges historical institutionalism and contemporary political economy. Recent publications demonstrate expertise in European political development, particularly through award-winning analysis of electoral system impacts in interwar France, reflecting methodological versatility across qualitative and quantitative approaches. Scientific Awards: 2024 Best Paper Award from APSA European Politics and Society section 2024 CQ Press Award from APSA Legislative Studies section Holds a competitive Postdoctoral Prize Fellowship at Oxford, with prior research appointments at the Cluster on the Politics of Inequality (University of Konstanz, 2021-2022) and Chair of Political Economy (University of Bonn, Fall 2023), indicating sustained engagement with leading European political economy research networks.
David W. Jacobs is a Professor in the Department of Computer Science at the University of Maryland, with a joint appointment at the University of Maryland Institute for Advanced Computer Studies (UMIACS). He also served as the interim Director of the University of Maryland Center for Machine Learning starting in 2018. University: University of Maryland School: College of Computer, Mathematical, and Natural Sciences Department: Department of Computer Science Academic Rank: Professor Education: He received his B.A. from Yale University, and M.S. and Ph.D. in Computer Science from MIT. Research Interests: His research primarily focuses on computer vision and machine learning, particularly visual object recognition, lighting variation modeling, 3D reconstruction, perceptual organization, motion understanding, and the integration of vision with graphics and human-computer interaction. A major applied contribution is the development of Leafsnap , an electronic field guide app for plant identification, which has been downloaded over 1.5 million times and used in biodiversity and educational contexts. Publication Trends: His recent scholarly output centers on deep learning, convolutional networks, residual architectures, generative models (especially GANs), and interpretability. His work often bridges theoretical insights with practical applications in vision and AI. Scientific Awards: Honorable Mention, Best Paper Award, CVPR 2000 Best Student Paper Award, UIST 2003 Best Paper Award, Eurographics 2016 2011 Edward O. Wilson Biodiversity Technology Pioneer Award for Leafsnap Teaching and Advising: He has taught advanced courses such as CMSC 422 (Introduction to Machine Learning) and CMSC 828L (Deep Learning). He mentors students through course projects and research, though specific advisees are not listed. He has collaborated with institutions like Columbia University and the Smithsonian on impactful interdisciplinary projects. Labs and Teams: He is affiliated with UMIACS and leads research efforts in vision and learning, contributing to the University of Maryland Center for Machine Learning. His team has developed several mobile applications including Leafsnap, Birdsnap, and Dogsnap, demonstrating a strong focus on real-world deployment of vision technology.
Prof. Dr. Barbara Mittler is Professor of Sinology at Heidelberg University and founding co-director of the Heidelberg Centre for Transcultural Studies (CATS) and the Cluster of Excellence 'Asia and Europe in a Global Context.' Her research explores Chinese cultural policy, print media, musicology, visual culture, and transcultural studies. Mittler's scholarly contributions include groundbreaking work on Cultural Revolution culture, early Chinese print media (particularly women's magazines), and the transcultural flows of cultural production. Her current projects examine Mao as a global icon and comparative studies of Asian 'fathers of the nation.' Her publication record demonstrates consistent focus on cultural transmission mechanisms, with recent research developing transcultural frameworks for understanding cultural exchange and historical analysis. Publications reflect an interdisciplinary approach combining visual analysis, media studies, and historical methodology. Mittler has received numerous honors including the Fairbank Prize, Heinz-Maier-Leibnitz Prize, and memberships in the Leopoldina and Heidelberg Academy of Sciences. She leads major initiatives like the China-Schul-Akademie developing educational materials about China for German schools.
Stephen Redmond is an Associate Professor at the School of Electrical and Electronic Engineering at University College Dublin (UCD), where he leads the Biomedical Sensors and Signals Research Group. He completed his Bachelor of Electronic Engineering at UCD in 2002, followed by a PhD in biosignal processing in 2006 on at-home sleep staging. After spending 10 years at the University of New South Wales in Sydney, he returned to UCD in 2018. His educational background includes: BE Electronic Engineering, University College Dublin (2002) PhD Biosignal Processing, University College Dublin (2006) Redmond's research focuses on the intersection of signal processing, pattern recognition, and novel sensing hardware to enable longitudinal health monitoring in home environments. His group has developed expertise in wearable sensor systems for human movement analysis, robust physiological signal measurement in unsupervised settings, tactile physiology and sensing, and the application of deep neural networks for medical image segmentation and robotic manipulation. His work bridges biomedical engineering with practical applications in healthcare and robotics. His recent publications demonstrate a strong trend toward integrating tactile sensing with machine learning for robotic applications, particularly in slip detection and dexterous manipulation. His research spans multiple disciplines including biomedical engineering, robotics, computer vision, and artificial intelligence, with a particular emphasis on practical applications that bridge the gap between laboratory research and real-world implementation. His notable recognition includes: Science Foundation Ireland President of Ireland Future Research Leaders Award for his project on tactile sensing As a research leader, Redmond mentors multiple doctoral students and postdoctoral researchers, securing significant research funding to support his team's work in tactile sensing and robotic manipulation. His research group has established strong industry connections, notably through the co-founding of Contactile, a tactile sensor company. The group maintains active collaborations with both academic and industry partners to translate research into practical applications. The Biomedical Sensors and Signals Research Group operates a well-equipped laboratory featuring advanced robotics platforms including a UR5e six-axis arm, Physik Instrumente Hexapods, ATI force/torque sensors, multiple 3D printers, and specialized tactile sensing equipment including Contactile Dev Kits and Meta Digit tactile sensors. This infrastructure supports their research in tactile physiology, sensor development, and intelligent robotic manipulation.
Afonso S. Bandeira is a Professor in the Department of Mathematics (D-MATH) at ETH Zurich, where he conducts research at the intersection of mathematics, statistics, and computer science. He maintains strong affiliations with several interdisciplinary research centers including the Institute For Operations Research (IFOR), the Max Planck ETH Center for Learning Systems, the ETH Foundations of Data Science, and the ETH AI Center, with a courtesy appointment at D-ITET. Bandeira actively teaches courses including Mathematics of Signals, Networks, and Learning, and Mathematics of Data Science. His research focuses on High Dimensional Probability, Random Matrices, Mathematical Statistics, Theoretical Computer Science, Combinatorics, and Mathematical Optimization. Bandeira's work often explores the theoretical foundations of data science, examining phase transitions in statistical problems, computational barriers, and the geometry of high-dimensional spaces. He maintains a research group blog called Randomstrasse101 that emphasizes open problems in his field. Analysis of his recent publications reveals a strong focus on matrix and tensor concentration inequalities, synchronization problems, nonconvex optimization landscapes, and computational-statistical tradeoffs in high-dimensional inference. His work bridges theoretical mathematics with practical applications in machine learning and data analysis, particularly examining where computational limitations arise in statistical problems. Bandeira actively mentors students and researchers, currently supervising several doctoral candidates including Daniil Dmitriev, Konstantin Donhauser, Anastasia Kireeva, Kevin Lucca, Chiara Meroni, Gil Kur, Petar Nizic-Nikolac, and Almut Roedder. He emphasizes that students working with him should participate in the DACO seminar and group meetings, and encourages prospective students to have completed his Mathematics of Signals, Networks, and Learning or Mathematics of Data Science courses. He leads a research group focused on the mathematics of data science, with regular group meetings and seminars. Bandeira has developed comprehensive lecture notes including 'A Tour Through the Mathematics of Signals, Learning, and Networks' (2025) and 'Mathematics of Machine Learning' (2021), and previously authored 'Ten Lectures and Forty-Two Open Problems in the Mathematics of Data Science' (2015).
Cecilie Schou Andreassen is a Professor at the Department of Clinical Psychology, University of Bergen . She specializes in behavioral addictions , including workaholism, social media addiction, and substance use interactions. Email: cecilie.andreassen@uib.no Visitor Address: Alrek Health Cluster, Årstadveien 17, 5009 Bergen Research Focus: Andreassen's work examines the psychosocial antecedents of addictive behaviors, with emphasis on personality traits , workplace dynamics , and digital platform interactions . Her research spans cross-cultural validation studies and longitudinal health outcomes related to technology and substance misuse. Article Trends (2017-2022): Analyzing 15 recent publications reveals a consistent focus on addiction scale validation , social media epidemiology , and workaholism's health impacts . Key themes include measurement invariance , digital-chemical addiction comorbidity , and psychological withdrawal mechanisms . Scientific Contributions: Co-developer of Bergen Work Addiction Scale Creator of Bergen-Yale Sex Addiction Scale Leadership in behavioral addiction epidemiology Recipient of Norwegian Research Council grants Advising & Collaborations: While no direct advisees are listed, Andreassen collaborates extensively with international researchers in work psychology , social media studies , and public health . Her work involves partnerships with Nottingham Trent University , Yale University , and National Competence Service for Sleep Disorders . Labs & Teams: Affiliated with the Department of Clinical Psychology and previously with the Department of Social Psychology at University of Bergen, she contributes to national health registry studies and cross-occupational research on addiction patterns.
Florian Kerschbaum is a Professor and NSERC/RBC Industrial Research Chair in Data Security at the Cheriton School of Computer Science, University of Waterloo. His research focuses on data security and privacy, applied cryptography, and confidentiality in data science. Research interests span data collection/preparation management, secure multi-party computation, homomorphic encryption, differential privacy, and machine learning robustness/privacy. His work develops cryptographic solutions for practical data management challenges in distributed systems.
Amit Kumar is the Jaswinder and Tarwinder Chadha Chair Professor in the Department of Computer Science and Engineering at IIT Delhi. His research focuses on combinatorial optimization, online algorithms, and algorithmic fairness. He has taught courses such as Approximation Algorithms (COL 754), Design and Analysis of Algorithms (COL 351), and Numerical Analysis (COL 726). His work spans theoretical computer science with applications to clustering, scheduling, and fairness in evaluation processes. Research Interests Kumar's research emphasizes developing efficient algorithms for online and dynamic settings, particularly in constrained optimization and biased evaluation systems. He explores theoretical foundations of clustering, load balancing, and resource allocation, with recent contributions to fair food delivery systems and coreset constructions. Publications His recent work includes advancements in online convex paging (STOC 2025), consensus clustering (SODA 2025), and fairness-aware algorithms (AAAI 2024). Over 70 papers across top venues like STOC, SODA, and ICML reflect his expertise in algorithm design and analysis. Awards Best Paper Award at ISAAC 2023 for 'Clustering What Matters in Constrained Settings' Teaching & Mentorship Kumar instructs graduate and undergraduate courses in algorithms, data structures, and numerical methods. He advises students through these courses and collaborates with researchers on NSF-funded projects related to approximation algorithms and streaming systems.
Dr. Mihai Pop is a Professor of Computer Science and Director of the University of Maryland Institute for Advanced Computer Studies (UMIACS). He holds appointments in the Department of Computer Science, UMIACS, and the Center for Bioinformatics and Computational Biology (CBCB). His research focuses on computational biology, metagenomics, and algorithm development for genomic data analysis. He received a Ph.D. in Computer Science from Johns Hopkins University (2000), followed by work at The Institute for Genomic Research (TIGR) developing genome assembly algorithms. Education: Ph.D., Computer Science, Johns Hopkins University, 2000. Research Interests: Bioinformatics, genomics, metagenomics, computational geometry, software testing. His lab develops tools for analyzing microbial communities and has pioneered methods for metagenomic assembly and analysis. Notable tools include the AMOS genome assembly toolkit. Recent Article Trends: Recent work emphasizes long-read sequencing, metagenomic profiling (e.g., TIPP3), and strain-level analysis (e.g., Strainy). He addresses challenges in scaling sequence-based searches and improving taxonomic resolution in large datasets. Awards: ACM Fellow (2019), ISCB Fellow (2022), UMD Excellence in Teaching Award (2015). Grants & Leadership: Co-leader of the Human Microbiome Project data analysis group. Active in diversity initiatives to promote inclusivity in computational fields. Labs/Teams: Pop Lab (pop-lab.org) focuses on computational methods for microbial genomics and metagenomics.
Vicente Ordóñez-Román is an Associate Professor in the Department of Computer Science at Rice University, part of the George R. Brown School of Engineering. His research focuses on the intersection of computer vision, natural language processing, and machine learning, with an emphasis on fair, transparent, and interpretable AI. He leads the Vision, Language, and Learning Lab and contributes to the Ken Kennedy Institute's Closed-loop Computer Vision research cluster. Education: PhD in Computer Science (UNC Chapel Hill, 2015), MS in Computer Science (Stony Brook University), and Engineering (Escuela Superior Politécnica del Litoral, Ecuador). Prior roles include Assistant Professor at the University of Virginia (2016-2021) and visiting positions at Adobe Research, the Allen Institute for AI, and Amazon. Research Interests : Developing multimodal AI systems that integrate visual and textual data, mitigating biases in AI, and advancing generative models. His work emphasizes ethical AI and societal impact, as seen in his contributions to the whitepaper advocating for federal regulation of facial recognition technologies. Awards & Recognition : NSF CAREER Award (2021), Marr Prize (ICCV 2013), Best Paper at EMNLP 2017, and multiple industry grants from Google, Amazon, and Facebook. His research has been featured in media outlets like WIRED, The New York Times, and Bloomberg News. Advising & Grants : Supervises a diverse research group spanning PhD, MS, and undergraduate students. Secured over $1.8 million in external funding, including NSF grants, Amazon FAI awards, and Google Cloud credits. Leads initiatives on bias mitigation, AI ethics, and multimodal learning. Labs & Collaborations : Directs the Vision, Language, and Learning Lab (vislang.ai), collaborating with industry partners like Adobe, Amazon, and SAP. Engages in interdisciplinary projects at the Ken Kennedy Institute, focusing on closed-loop computer vision systems.
