Tim Conley is Professor and Chair at the Department of Economics, University of Western Ontario. He holds a Ph.D. from the University of Chicago (1996). His research focuses on applied econometrics with emphasis on spatial dependence, cross-sectional analysis, and empirical industrial organization. His primary research interests include methodological development in econometrics, particularly around dependence modeling in cross-sectional data and spatial analysis techniques. He has made significant contributions to understanding technology adoption in developing economies and detection of collusion in market mechanisms. Professor Conley's publications demonstrate consistent focus on developing robust statistical methods for economic applications, with recent work emphasizing practical applications in policy evaluation and market analysis. His methodological innovations have been implemented in statistical software packages used by researchers worldwide.
Dr. Scarlett Raine is a Lecturer in the School of Electrical Engineering and Robotics at Queensland University of Technology (QUT), where she serves as a Chief Investigator at the QUT Centre for Robotics and an Associate Investigator at the QUT Centre for Data Science and QUT Centre for Environment and Society. Her work focuses on applying Artificial Intelligence to automate underwater image analysis for marine ecosystem monitoring.
Ron H.J. Peerlings is Associate Professor in the Department of Mechanical Engineering at Eindhoven University of Technology (TU/e) , where he leads the Mechanics of Materials research group. Promoted to Associate Professor in 2007 after joining as Assistant Professor in 2000, he has built an extensive portfolio in theoretical and computational mechanics of materials. Education: PhD (1999) – Eindhoven University of Technology, thesis: Enhanced damage modelling for fracture and fatigue Post-doctoral research (1999–2000) – University of Cambridge, Engineering Department Research interests revolve around micromechanics , micro-plasticity , multiscale modelling , homogenisation , damage and fracture , and enriched continuum theories . His work spans advanced high-strength steels, composites, paper and fibrous networks, with strong emphasis on coupling rigorous theoretical developments to industrially motivated problems. His recent publications (2023-2025) demonstrate a clear trajectory towards integrating advanced experimental techniques (e.g., digital image correlation, micro-mechanical testing) with high-fidelity computational frameworks such as crystal-plasticity finite-element modelling, FFT-based solvers and micromorphic homogenisation. Dominant themes include: Deformation and fracture in lath martensite and dual-phase steels Hygro-mechanics of paper and fibrous networks Pattern-transforming mechanical metamaterials Discrete-to-continuum scale bridging methods Scientific awards are not explicitly listed in the provided material; however, his prolific output (294 research items, >6500 citations) attests to significant peer recognition. Teaching & supervision: He delivers courses on Computational Mechanics – Numerical Methods for Fluids and Solids and Fracture Mechanics – Theory and Application , and has supervised >80 student works and numerous PhD candidates whose names appear on joint publications. Laboratory & teams: He heads the Group Peerlings within the Mechanics of Materials cluster, maintaining close collaboration with the Mechanics of Materials Group Geers and extensive national/international experimental and computational networks.
Wooyong Lee is a Lecturer in the Economics Discipline Group at the UTS Business School, University of Technology Sydney. He holds a PhD in Economics from the University of Chicago (2020), an MS in Statistics from the University of British Columbia (2014), and a BA in Economics and Statistics from Korea University (2012). His research focuses on econometrics and applied microeconomics, specializing in panel data methods, difference-in-differences frameworks, and dynamic models. He has developed methodologies addressing spillover effects in staggered DiD designs and partial identification in heterogeneous coefficient models. His work applies to real-world issues like lifecycle earnings dynamics and policy evaluation. Lee teaches econometrics at undergraduate and postgraduate levels and supervises research students. His publications appear in venues such as Statistical Inference for Stochastic Processes and peer-reviewed working papers. Research interests emphasize causal inference techniques, with contributions to handling unobserved heterogeneity and measurement errors in economic data. Ongoing work explores dynamic treatment choice models where treatment decisions respond to outcome shocks, challenging traditional parallel trends assumptions.
Dr. Stan Ahalt serves as the inaugural Dean of the UNC School of Data Science and Society (SDSS) and holds a professorship in the Department of Computer Science at UNC-Chapel Hill. He previously directed the Renaissance Computing Institute (RENCI) and currently acts as its executive advisor. Additionally, he is the associate director of informatics and data science at NC TraCS, the translational research institute at UNC. His academic journey includes a 22-year tenure as professor of electrical and computer engineering at Ohio State University and leadership roles at the Ohio Supercomputer Center. Ahalt’s research focuses on applying data science to societal challenges such as healthcare, climate, and judicial reform. Key initiatives include the National Consortium for Data Science and the iRODS project, which advanced enterprise data management systems. Ahalt has secured over $34.5M in grants to lead high-impact projects like the NHLBI BioData Catalyst (unifying biomedical data), the NCATS Translator (accelerating clinical discovery), and the NIH HEAL Initiative (ensuring FAIR data standards). He mentors students across doctoral and master’s programs, teaching data science fundamentals in courses like COMP 116: Introduction to Scientific Programming. His work emphasizes interdisciplinary collaboration, evidenced by roles in RENCI, NC TraCS, and the SDSS. Ahalt’s contributions span cyberinfrastructure development, educational innovation, and translational research, positioning UNC as a national leader in data science.
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.
Colin Jones is an Associate Professor at the École Polytechnique Fédérale de Lausanne (EPFL) in the Automatic Control Laboratory, School of Engineering. He earned his BASc and MASc in Electrical Engineering and Mathematics from the University of British Columbia (1994-2002) and a PhD in Control Theory from the University of Cambridge (2002-2005). Prior to EPFL, he was an assistant professor there and a senior researcher at ETH Zürich. Current role: Director of the Robotics, Control, and Intelligent Systems Doctoral Program at EPFL Research focus: Optimization-based and model predictive control (MPC) for renewable energy systems, green energy management, and data-driven control methods His recent work (2023-2025) spans high-speed predictive control , smart grid optimization , and physically consistent neural networks , with applications to buildings, hovercrafts, and power systems. He has secured an ERC Starting Grant for his research on optimal control of building networks. Publications include over 200 papers in journals like Automatica , IEEE Transactions , and Energy and Buildings . Notable article trends include distributed optimization , data privacy in energy systems , and nonlinear MPC for autonomous vehicles . Scientific Awards : ERC Starting Grant for optimal control of building networks Advising : Supervises 10 current PhD students and has advised 19 past PhD students, including Alessandretti Andrea and Diwale Sanket Sanjay. Grants and projects emphasize smart energy systems , predictive demand response , and nonlinear control .
Perla Maiolino serves as an Associate Professor in Engineering Science at the University of Oxford and Principal Investigator of the Soft Robotics Lab (SRL) within the Oxford Robotics Institute. Her academic foundation includes BEng, MEng, and PhD degrees in Robotics and Automation from the University of Genoa, where she pioneered CySkin technology for distributed tactile sensing in robots—later exhibited at the Science Museum in London. She expanded her expertise during a 2017-2018 postdoctoral fellowship at Cambridge University's Biologically Inspired Robotics Lab, focusing on soft robotics and tactile perception. Dr. Maiolino's research centers on developing artificial skin systems, soft robotic actuators, and distributed sensing architectures. Her work bridges biological inspiration with engineering innovation to create robots capable of safe human interaction and dexterous manipulation in unstructured environments. Key contributions include compliant beaded-string jamming mechanisms for anthropomorphic fingers, monolithic 3D-printed soft pneumatic arms (JAMMit!), and distributed time-of-flight sensor networks for robotic self-awareness. Recent publications (2024-2025) reveal a strong convergence of tactile sensing with machine learning, featuring optical flow for gesture recognition, diffusion models for artificial skin simulation, and zero-shot sim-to-real transfer techniques. Her team has made significant advances in multi-modal sensing integration, variable stiffness actuation, and scene flow estimation for robots operating in dynamic surroundings. Scientific Awards No specific awards were documented in the provided institutional materials. Advising and Grants While her leadership of the Soft Robotics Lab implies active student supervision and grant management, detailed information about advisees or funded projects was not included in the source documentation. Labs and Teams As Principal Investigator of the Soft Robotics Lab at Oxford Robotics Institute, Dr. Maiolino directs research on tactile perception systems, soft actuation mechanisms, and sensor-integrated robotic structures. The lab's work focuses on applications requiring safe physical interaction, including healthcare robotics and human-robot collaboration scenarios, with emphasis on multi-material 3D printing and embedded sensing technologies.
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.
Manu Magar is a Researcher at the School of Biological Sciences , University of Western Australia , with a focus on plant genetics and climate resilience. They also hold an external position as a Biotechnologist at the National Biotechnology Research Center, Nepal Agricultural Research Council since May 2014. M.Sc.Ag. in Agricultural Science from Acharya N G Ranga Agricultural University (2012) B.Sc.Ag. in Horticulture from Tribhuwan University (2009) Research Interests Their work centers on plant breeding , gene expression , and crop genomics to combat heat stress in wheat. Key areas include transcription factor analysis , ERF gene regulation, and drought tolerance mechanisms. Article Trends Magar’s publications (2022–2025) emphasize AP2/ERF superfamily genes in wheat, heat tolerance pathways, and ethyline signaling during grain filling. Their work bridges genomic studies and transcription factor roles in stress response. Collaborations & Research Active in international collaborations between Australia, Nepal, and India, Magar contributes to crop improvement via genomic analysis and biotechnology .
Professor Aleksandra Cichocka is a Professor of Political Psychology and Deputy Head of the School of Psychology at the University of Kent. She holds a PhD in Psychology from the University of Warsaw and was a Fulbright Fellow at New York University. Her research focuses on political psychology, particularly collective narcissism, self-worth dynamics, and the psychological underpinnings of political attitudes. She leads the Political Psychology Lab and has been on leave until Summer 2025. Education: PhD in Psychology (University of Warsaw, 2013), Fulbright Fellowship (New York University, 2009-2013). Research interests include links between narcissism and political behavior, collective narcissism's social consequences, and psychological factors driving political engagement. Her work has been published in top journals like Journal of Personality and Social Psychology and Political Psychology . Key achievements include major grants from the Leverhulme Trust and Nuffield Foundation, and awards such as the Jos Jaspars Medal (2017) and Andrzej Malewski Award (2016). She supervises current and past PhD students in topics like conspiracy theories and identity politics. Professional roles include editorial positions at PNAS Nexus and European Journal of Social Psychology . Her research spans global collaborations on topics like pandemic responses and nationalism.
Michele Zorzi is a Professor of Telecommunications at the School of Engineering, University of Padova, Italy, where he has held a faculty position since 2003. He leads the SIGNET (Signal processing and Networking) Research Group, focusing on cutting-edge wireless networking challenges including mmWave communications and underwater networks. His extensive publication record exceeds 600 papers in top-tier journals and conferences, reflecting significant contributions to the field through both theoretical and experimental work. He received his Laurea Degree (1990) and Ph.D. (1994) in Electrical Engineering from the University of Padova. Prior academic appointments include Politecnico di Milano (1993-1996), University of California San Diego (1995-1998), and University of Ferrara (1998-2003), where he progressed from Associate Professor to full Professor. His educational trajectory demonstrates deep roots in Italian academia with international exposure. Professor Zorzi's research spans wireless communications and networking, with current emphases on mmWave networking for vehicular systems, underwater acoustic/optical communications, non-terrestrial networks, and AI-driven networking solutions. His group conducts experimental validations including at-sea trials for underwater systems and testbeds for vehicular networks. Key projects include PRATA for predictive QoS in autonomous driving and IoT-based environmental monitoring of the Venice Lagoon, demonstrating practical applications of theoretical work. Analysis of his 2022-2025 publications reveals strong trends in applying artificial intelligence to networking challenges across diverse environments. There is significant emphasis on vehicular networks (predictive QoS, teleoperated driving), underwater systems (acoustic/optical communications, AUV swarms), and satellite networks (Starlink integration, NTN security). Experimental validation in real-world scenarios like the Venice Lagoon monitoring project and underwater sea trials characterizes his applied research approach. His scientific accolades include: IEEE Fellow (2007) IEEE Communications Society Best Tutorial Paper Award (2008, 2019) Stephen O. Rice Best Paper Award (2018) Multiple best paper awards at IEEE conferences (2005-2020) As principal investigator for numerous European and US research projects plus 20+ industry-funded initiatives, Professor Zorzi has mentored over 35 PhD students and post-docs. Graduates now hold prominent positions at institutions including Stanford, UCSD, CTTC, and Huawei. His SIGNET group maintains active international collaborations and contributes to open-source networking tools via GitHub, demonstrating commitment to community engagement. The SIGNET Research Group, housed within the Department of Information Engineering, operates specialized experimental facilities for mmWave and underwater communications. Current initiatives include AI-based predictive QoS frameworks for vehicular networks, underwater optical communication systems using ultraviolet light, and large-scale IoT deployments for environmental monitoring. The group's GitHub presence indicates strong open-science practices, while recent sea trials confirm hands-on experimental capabilities beyond theoretical work.
Yuriy Rogovchenko is a Professor in the Department of Mathematical Sciences at the University of Agder. His research spans differential equations, mathematical modeling, and education innovation, with applications in biology, social sciences, and engineering. Rogovchenko has contributed extensively to mathematics education through projects like PLATINUM (Erasmus+ Strategic Partnership) and CPEA-ST-2019/10067 (Eurasia project). PhD in differential equations (Institute of Mathematics, Kyiv, 1987) Regular Associate at Abdus Salam ICTP, Trieste (2004-2011) Editor for 11 international journals Referee for over 70 journals Research Interests: Qualitative theory of differential equations, perturbation methods, mathematical modeling in interdisciplinary contexts. He focuses on enhancing conceptual understanding through inquiry-based learning and nonstandard problems. Publications: Recent works include advancements in linear system observability, parameter identification methods, and educational studies on exact differential equations. His collaborations with Svitlana Rogovchenko and Matthias Pätzold highlight applications in engineering and biology. Awards: Sørlandet kompetansefonds research award (2016).
Michael Baldea is an Associate Professor in the Department of Chemical Engineering at the University of Texas at Austin . He holds a Ph.D. in Chemical Engineering from the University of Minnesota (2006), with prior degrees from 'Babeş-Bolyai' University in Romania (M.Sc. 2001, Diploma 2000). His research group develops theoretical and computational methods for Process and Energy Systems Engineering , focusing on integrated decision-making, performance optimization, and process intensification with industrial validation. Education: Ph.D., Chemical Engineering, University of Minnesota (2006) M.Sc., Interface Process Engineering, 'Babeş-Bolyai' University (2001) Diploma, Chemical Engineering, 'Babeş-Bolyai' University (2000) Research Thrusts: Integrated decision-making in chemical/energy supply chains Process performance monitoring and optimization Process integration and intensification Key applications include grid-responsive chemical plants, intensified distillation/column designs, and renewable energy integration for building systems. Scientific Awards: Frank A. Liddell, Jr. Fellowship NSF CAREER Award (2015-2020) Moncrief Grand Challenges Faculty Award (2014) AIChE Outstanding Young Researcher Award (2017) Implementation : His group has translated research into commercial tools through partnerships with industrial test beds and is working to integrate methods into commercial simulators. They explore predictive approaches for building energy management and strategic capital investment analysis in next-generation energy systems.
Mark Gales is Professor of Information Engineering at the University of Cambridge and an Official Fellow at Emmanuel College. He is currently on sabbatical leave for the 2024/25 academic year. Prior to his academic career, he worked as a consultant at Roke Manor Research Ltd, developing radar systems, before transitioning to speech and language processing. PhD in 'Model-Based Techniques for Robust Speech Recognition' (University of Cambridge, 1995) BA in Electrical and Information Sciences (University of Cambridge, 1988) His research focuses on speech and language processing , particularly in automated language assessment and low-resource speech technology . He leads the Automated Language Teaching and Assessment (ALTA) Institute , which collaborates with Cambridge University Press & Assessment (CUP&A) to develop commercial tools like Linguaskill and Speak & Improve . These platforms provide automated spoken/written assessment for millions of users globally. Recent publications highlight his work in LLM-driven speech processing , including adversarial attacks on foundation models, end-to-end spoken error correction, and uncertainty estimation frameworks. His team's research spans multilingual capabilities, with deployments in languages ranging from Dholuo to Tok Pisin . Awards : IEEE Fellow, ISCA Fellow Leadership : Fellows' Steward at Emmanuel College Mark has contributed extensively to Hidden Markov Model (HMM) applications in speech recognition, which underpinned early automatic speech systems. His work now bridges LLM-based language assessment with cross-lingual transfer learning and robustness testing for real-world deployments.
