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Adi SunderamView profile

Professor

• Corporate Finance
• Asset Pricing
• Financial Intermediation
• +3 more

Adi Sunderam is the Willard Prescott Smith Professor of Corporate Finance at Harvard Business School (HBS), a Research Associate at the National Bureau of Economic Research (NBER), and a Faculty Affiliate of Harvard's Economics Department. He teaches Finance 2 in the MBA curriculum and Ph.D. courses in Corporate Finance and Empirical Methods. His research focuses on corporate finance, asset pricing, and financial intermediation, particularly examining how financial market structures influence asset prices and corporate investment decisions. He holds a Ph.D. and A.B. in Business Economics and Computer Science/Economics from Harvard University. From 2009–2010, he served in the U.S. Treasury Department as a special assistant and liaison to the White House National Economic Council. His academic leadership includes co-editing the Journal of Finance and serving as an Associate Editor for the Quarterly Journal of Economics . Sunderam’s work has been published in leading journals like the Quarterly Journal of Economics , American Economic Review , and Review of Financial Studies . His research has received prestigious awards, including the RFS Young Researcher Prize (2012) and the Ieke van den Burg Prize for Systemic Risk Research (2020). His research spans topics such as monetary policy perceptions, banking evolution post-2008, minority representation in mortgage lending, and the impact of financial crises on market structures. Sunderam has contributed to policy discussions on banking regulation, crisis response mechanisms (e.g., Paycheck Protection Program analysis), and systemic risk mitigation. His pedagogical contributions include case studies on financial crises, asset management, and corporate finance decision-making. Education: Ph.D. and A.B. in Business Economics from Harvard University Key Roles: Co-Editor of Journal of Finance, NBER Research Associate, HBS Faculty Grants and Funding: Extensive research supported by academic and policy institutions His advising and mentorship have shaped future generations of finance scholars, though specific advisee names are not listed in the provided texts. Sunderam’s work integrates theoretical frameworks with empirical analysis, addressing both academic and policy-relevant questions in modern finance.

Angela KedgleyView profile

Associate Professor

• Biomedical Engineering
• Orthopaedic Biomechanics
• Human Movement and Sports Sciences
• +5 more

Dr. Angela Kedgley is a Reader in Orthopaedic Biomechanics at the Department of Bioengineering, Faculty of Engineering, Imperial College London. Her research focuses on inter-disciplinary approaches to understanding joint motion mechanics and improving diagnostics/treatments for musculoskeletal pathologies, particularly in the hand/wrist through collaborations with clinical partners. She leads the Kedgley Lab, which develops engineering solutions for upper/lower limb injury rehabilitation. Key affiliations include the AI for Healthcare Network, Musculoskeletal Medical Engineering Centre, and Robotics Forum. Her work integrates biomechanics with advanced technologies like AI, 3D printing, and wearable sensors. Recent studies involve orthosis compliance monitoring, electromyography analysis for osteoarthritis, and clinical applications of statistical shape modeling. She actively engages in military trauma outcome research and ballet injury epidemiology. Her publications since 2020 reflect a focus on translational research bridging engineering and clinical practice, with emphasis on surgical instrumentation validation, musculoskeletal modeling, and rehabilitation technology development. The lab welcomes postgraduate researchers and interdisciplinary collaborations aimed at advancing clinical solutions.

• Computer Science
• Medical Imaging
• Traffic Engineering
• +3 more

PAUL BAKAKI is a Lecturer in Computer Science with research focused on interdisciplinary applications of computational methods in medical imaging and traffic dynamics. His work integrates graph theory, shape analysis, and machine learning to address challenges in surgical outcome assessment and transportation systems. He is affiliated with the Centre for Intelligent Visual Computing and Complex Systems Research Centre. Education: PhD in Computer Science (2024) – Thesis: 'Computational Approach for an Automatic Facial Appearance Outcome Measure of Cleft Lip Surgical Repair Using Digital Images' Research Interests: His primary areas include: - Development of computational tools for medical aesthetics evaluation - High-order graph models for traffic system analysis - Application of shape analysis in surgical outcomes assessment - Expander graphs and their dynamic modeling capabilities - Image processing techniques for partially occluded facial reconstructions Research Trends: His publications highlight a dual focus on medical imaging applications (e.g., cleft lip repair assessment) and traffic engineering innovations using advanced graph theory. The 2024 work pioneers high-order evolving graphs for traffic dynamics, while the 2021-2022 studies establish novel methods for automated surgical outcome evaluation. Labs/Teams: Active in the Centre for Intelligent Visual Computing Research and Complex Systems Research Centre, collaborating on projects blending computational models with real-world clinical and transportation challenges.

Stanislav SokolinskiView profile

Assistant Professor

• Sustainable Finance
• Asset Pricing
• Financial Investments
• +3 more

Stanislav Sokolinski is an Assistant Professor of Finance at the Department of Finance, Broad College of Business, Michigan State University. His research focuses on how asset management intersects with asset pricing, examining the role of institutional investors, financial advisors, and fintech wealth managers in shaping markets. His work employs both empirical and theoretical methods to explore delegation effects on asset prices and investor outcomes. Key research areas include sustainable finance, anomaly returns driven by investor heterogeneity, and the impact of team dynamics on fund manager compensation. Sokolinski has published in top-tier journals such as the Journal of Financial Economics and Review of Finance , with recent work analyzing the effects of robo advice on wealth management access and strategic borrowing in passive investing. His research presentations include conferences at the Swiss Society for Financial Market Research, Yale Initiative on Sustainable Finance, and HEC-McGill Winter Finance Workshop. Sokolinski’s contributions span asset pricing theory, institutional investor behavior, and fintech-driven financial inclusion.

Rosy BoardmanView profile

Associate Professor

• Social Justice
• Digital Technologies
• AI and Metaverse Applications
• +4 more

Dr Rosy Boardman is a Reader in Fashion Business at the University of Manchester's Department of Materials. Her research focuses on social justice and UN Sustainable Development Goals, with a particular emphasis on digital technologies, AI, and metaverse applications for inclusion. She explores how technology can advance social and environmental sustainability, including projects on age-inclusive shopping, combating online misogyny, and circular economy initiatives. Rosy has secured grants from Leverhulme UK, Manchester City Council, and private entities, and collaborates globally with institutions in India, the USA, China, and more. She has supervised 15 PGR students, with 5 completed, and holds editorial roles at journals like the Journal of Fashion Marketing & Management and guest editorships for Qualitative Market Research and Management Decision . Her work bridges academia and industry, exemplified by her consultancy for TV production companies and media engagements with BBC and New Statesman. Key Research Areas: Consumer Behavior, Retail Marketing, Sustainable Fashion, Digital Inclusion Grants: Leverhulme UK (2024), Manchester City Council, Private Sponsors Global Collaborations: Universities in India, USA, Malaysia, China, France, Denmark, Italy, Germany, Ireland Her research extends beyond academia through public engagements like the Manchester Institute for Collaborative Research on Ageing (MICRA) presentation (attended by 100+ stakeholders) and the 2021 fashion & technology talk with Salford University (157 live viewers). She advocates for translating research into societal impact, addressing issues like gender-based violence in online spaces and age-inclusive retail design. Recent articles highlight innovations in metaverse fashion, digital product acceptance, and the role of AI in marketing. Her work emphasizes ethical technology use and sustainable practices across digital and physical retail landscapes.

• Computer Graphics
• 3D Modeling
• Computer Animation
• +7 more

Marie-Paule Cani is a Professor of Computer Science at École Polytechnique (Institut Polytechnique de Paris), where she serves as Dean of the Master of Science and Technology program and leads the STREAM research team at LIX Laboratory (CNRS/IP Paris). Her research bridges computer graphics, HCI, and AI, focusing on expressive 3D modeling, animation, and virtual environments. Education: 1987: M.Sc. in Computer Science, École Normale Supérieure & University Paris XI 1990: Ph.D. in Computer Graphics, University Paris XI 1995: Habilitation in Computer Science, Institut National Polytechnique de Grenoble Research Interests: She pioneers user-centered tools for 3D content creation, combining gesture-based interfaces (sketching, sculpting) with knowledge-enhanced procedural models. Key areas include implicit surfaces, physical simulation, AI-driven animation, and accessible tools for non-experts. Her work enables intuitive design of complex shapes, terrains, and dynamic scenes. Publication Trends: Recent work (2023–2025) emphasizes AI-integrated graphics, including controllable natural phenomena (volcanoes, erosion), sketch-based bio-visualization, reinforcement learning for animation, and real-time character control. Cross-disciplinary applications span geology, biology, and cinematography. Awards & Honors: ACM Steven A. Coons Award (2023) French Academy of Sciences (2020) ACM SIGGRAPH Academy (2019) ERC Advanced Grant EXPRESSIVE (2012–2017) CNRS Silver Medal (2012) Leadership & Grants: She founded research teams (STREAM, Imagine, Evasion) and secured major funding: ERC Advanced Grant, Google-École Polytechnique AI Chair (2018–2021), Horizon 2020 project CLIPE (2020–2024), and Hi!Paris Creative AI Fellowship (2021–2024). She mentors researchers in geometric modeling, animation, and AI. Labs & Teams: Directs the Modeling, Simulation & Learning pole at LIX (2019–2023). Founded STREAM (Structure Representation for Animation/Modeling) and Imagine (Inria/Grenoble teams), advancing real-time graphics and procedural content.

Xin ChenView profile

Associate Professor

• Medical Image Analysis
• Computer Vision
• Machine Learning
• +7 more

Xin Chen is an Associate Professor of Computer Science at the University of Nottingham, School of Computer Science. He is an IEEE Senior Member with expertise in medical image analysis, computer vision, and machine learning. His educational background includes BEng and PhD degrees, though specific institutions are not mentioned in the provided text. Chen's research focuses on medical image processing with particular emphasis on image segmentation, registration, and motion analysis. His work applies computer vision and machine learning techniques to solve medical challenges in areas such as wrist injury analysis, diabetes care, breast cancer screening, and MRI reconstruction. His team develops algorithms for 2D/3D image segmentation, image registration, statistical shape/motion modeling, and CT & MRI image reconstruction. His recent publications (2023-2025) show a strong focus on deep learning approaches for medical image analysis, particularly segmentation and registration tasks. These works often incorporate innovative techniques like fuzzy systems, transformer networks, and uncertainty modeling to address challenges in medical image analysis. IEEE Senior Member Chen has successfully supervised multiple PhD students to completion and currently mentors several PhD candidates. His research is supported by significant grants from Wellcome Leap, MRC, ERC, NIHR/NHSx, Weston Brain Institute, British Heart Foundation, and Precision Imaging Beacon. His team, the Intelligent Modelling & Analysis Group, focuses on translating computer vision and machine learning research into practical medical applications. The group maintains active collaborations with medical researchers and clinicians, developing software tools for 2D/3D interactive image segmentation and automatic nerve fiber quantification in corneal confocal microscopy images.

• Medical Imaging
• Artificial Intelligence
• Machine Learning
• +5 more

Jiliu Zhou is an active academic researcher with a focus on medical imaging, artificial intelligence, and biomedical engineering. His work spans topics such as PET image reconstruction, radiotherapy dose prediction, and domain adaptation, often employing advanced deep learning techniques like GANs, Transformers, and diffusion models. Collaborations with experts like Yan Wang, Xi Wu, and Dinggang Shen highlight his interdisciplinary approach. Research interests include Medical Imaging: Developing algorithms for PET and MRI reconstruction. Deep Learning: Applying GANs, Transformers, and diffusion models to medical data. Domain Adaptation: Enhancing robustness in multi-source environments. Image Processing: Innovating segmentation and super-resolution methods. His recent publications demonstrate trends in semi-supervised learning , contrastive strategies , and multi-task frameworks for medical applications. Despite no explicit awards or grants mentioned, his prolific output in top journals and conferences underscores significant contributions to biomedical image analysis.

• Radiation Oncology
• MRI-guided Radiotherapy
• Adaptive Radiation Therapy
• +8 more

Claus Belka is a Professor in the Department of Radiation Oncology at the Faculty of Medicine, Ludwig-Maximilians-Universität München. His work focuses on advancing MRI-guided radiotherapy, adaptive treatment strategies, and the integration of artificial intelligence in radiation oncology. Research Interests: His research spans radiation oncology, deep learning applications in medical imaging, MRI-guided and adaptive radiotherapy, proton therapy, and dosimetric optimization. He investigates real-time tumor tracking, auto-segmentation, motion prediction, and radiomics for prognostic modeling in cancers of the lung, head and neck, prostate, and liver. The recent publications reflect a strong trend toward leveraging deep learning for segmentation, motion prediction, synthetic imaging, and outcome modeling in MRI- and PET-guided radiotherapy. Key themes include uncertainty calibration, patient-specific modeling, low-field MR-Linac applications, and dose accumulation in adaptive settings. Scientific Awards: Advising and Grants: While specific students and funding sources are not listed in the provided text, his extensive publication record across multi-institutional and technical-clinical domains suggests active mentorship and leadership in major research initiatives, including clinical trials like the PUMA trial for pulmonary MR-guided radiotherapy. Labs and Teams: He is associated with a leading radiation oncology research group at LMU Munich, focusing on medical physics innovation, AI integration, and clinical translation of advanced radiotherapy techniques.

Rahman AttarView profile

Lecturer

• Artificial Medical Intelligence
• Computer Science
• Medical Image Computing
• +2 more

Dr. Rahman Attar is a Lecturer (tenured Assistant Professor equivalent) at the University of Southampton's Department of Electronics and Computer Science, Faculty of Engineering. He leads the ATTAR Lab and serves as an academic integrity officer, with prior postdoctoral experience at Imperial College London. His academic credentials include BEng, MPhil, and PhD degrees in Computer Science and Engineering from the University of Sheffield and University of Leeds. Royal School of Mines, South Kensington Campus University of Southampton Imperial College London Research Focus: Developing ethical AI technologies for healthcare, particularly in: Artificial Medical Intelligence Medical Image Computing Biomedical Image Analysis Data Science in Medicine Publication Trends: His work spans AI applications for skin disease severity assessment, cardiac imaging analysis, retinal biomarkers for heart disease, and advanced 3D reconstruction techniques, often leveraging UK Biobank data. Awards & Memberships: Senior Member IEEE (SMIEEE) Member IET (MIET) Fellow of HEA (FHEA) Academic Leadership: Supervising PhD students in computer science and biological sciences, with over 30 peer-reviewed publications and editorial/reviewer roles in top journals/conferences.

• Economics
• Finance
• Industrial Organization
• +7 more

Heski Bar-Isaac is a Professor at the Joseph L. Rotman School of Management , University of Toronto, where he holds the Distinguished Professor of Economics and Finance chair. He has held visiting roles at London Business School (2016-2017) and MIT Sloan (2011). As Managing Editor of the Economic Journal (2020-present) and former Co-Editor of the International Journal of Industrial Organization , his editorial leadership spans major economics journals. PhD in Economics (University of London, 1999-2004) MSc in Econometrics (London School of Economics, 1995-1997) BA in Mathematics (Oxford University, 1992-1995) His research spans Industrial Organization , Labor Economics , and Reputation Dynamics , with a focus on how information asymmetry shapes organizational behavior, market structures, and career incentives. Key themes include: Strategic product design in competitive markets Economics of online platforms and algorithmic steering Reputational incentives in teams and corporate governance Labor market dynamics with endogenous information Credit rating agencies and business cycle effects Organized crime structure and law enforcement His recent work analyzes marketplace algorithms , certification economies , and monopsony power in talent acquisition . Papers often intersect Game Theory with Empirical Industrial Organization . Scientific Awards & Editorial Roles: Robert F. Lanzilotti Prize (2024) University of Toronto Excellence in Teaching Awards (2020, 2019) CEPR Research Fellow (2016-2022) Associate Editor, RAND Journal of Economics (2020-present) Former Editorial Board Member, American Economic Review (2016-2025) As Special Economic Advisor to Canada's Competition Bureau (2024-2025), he advises on Digital and Platform Markets . His work appears in top journals like American Economic Review , Journal of Political Economy Microeconomics , and Review of Economic Studies .

Juan Jose ManfrediView profile

Assistant Professor

• Radiation Detection
• Nuclear Physics
• Computational Modeling
• +4 more

Dr. Juan Jose Manfredi is an Assistant Professor of Nuclear Engineering in the Department of Engineering Physics at the Air Force Institute of Technology (AFIT). He focuses on radiation detection, nuclear physics, and computational modeling, with projects involving additive manufacturing of scintillators, nuclear data measurements with the GENESIS collaboration, and terrestrial gamma flash detection systems aboard "Hurricane Hunter" aircraft. Education: Ph.D. in Physics, Michigan State University (2018) M.S. in Physics, Michigan State University (2015) B.A. in Mathematics and Physics, Washington University in St. Louis (2012, Summa cum laude) Research Interests: Nuclear Reactions on Exotic Isotopes Scintillator Development and Characterization Neutron Imaging and Radiation Detection Nuclear Structure and Astrophysics Natural Language Processing for Nuclear Science Publication Trends: Recent works emphasize radiation detection technologies (scintillators, neutron imaging), nuclear reactions on exotic isotopes, symmetry energy studies in heavy-ion collisions, and detector calibration using cosmic rays. Scientific Awards: DOE NNSA Stewardship Science Graduate Fellowship (2013-2017) NSSC Postdoctoral Fellowship (2020-2021) MSU Dissertation Completion Fellowship (2017) NSCL Fellowship (2012-2017) Washington University Scholarships (Eliot & Robert Levis Family, 2008-2012) Professional Collaborations: Projects involve cross-institutional efforts with the Department of Energy, Department of Defense, Lawrence Berkeley National Laboratory, and Sandia National Laboratories.

Jeffrey ZacksView profile

Professor

• Cognitive Psychology
• Neuroscience
• Memory Research
• +6 more

Jeffrey Zacks is the Edgar James Swift Professor in Arts & Sciences and Chair of the Department of Psychological & Brain Sciences at Washington University in St. Louis. His research focuses on perception, memory, and event cognition, particularly in naturalistic contexts like film processing and navigation. He uses neuroimaging, eye-tracking, and computational models to study how cognition changes with aging and neurological disorders. Zacks holds a PhD and MA from Stanford University and a BA from Yale University. Research Interests: Zacks investigates how the brain segments and remembers everyday activities, the impact of aging on cognitive processes, and the neural mechanisms underlying spatial and temporal cognition. His work bridges psychology, neuroscience, and media studies, with applications to film analysis and data visualization. Key Achievements: Named to the American Academy of Arts & Sciences (2024), recipient of the Psychonomic Society’s Mid-Career Award (2021), and former president of the Association for Psychological Science. His lab, the Dynamic Cognition Laboratory, develops tools like the META stimulus set for studying event cognition. Grants & Collaborations: His research is supported by NIH grants focusing on event cognition in aging populations. Collaborations include interdisciplinary projects with neuroimaging experts and computational modelers. Labs & Teams: Directs the Dynamic Cognition Laboratory, which employs behavioral experiments, fMRI, and eye-tracking to explore temporal and spatial cognition. The lab also engages in media studies, analyzing how films shape perception and memory.

Pieter PeersView profile

Associate Professor

• Relighting
• Appearance Modeling
• Neural Rendering
• +7 more

Pieter Peers is an Associate Professor in the Department of Computer Science at the College of William & Mary. Prior to this, he was a senior researcher at the Graphics Laboratory at the University of Southern California's Institute for Creative Technologies and a Research Assistant Professor at USC's Viterbi School of Engineering. His educational background includes a Ph.D. in Computer Science from K.U.Leuven (Belgium) in 2006 and a BS/MS in the same field from the same institution in 2000. Peers' research focuses on relighting, appearance modeling, and neural rendering—central topics in computer graphics and vision. His work bridges traditional rendering techniques with modern deep learning, particularly in inverse rendering, material modeling, and image-based relighting. He has made significant contributions to subsurface scattering, photometric stereo, and neural rendering systems. His recent publications, including work in SIGGRAPH, CVPR, and ICCV, demonstrate a strong trend toward integrating diffusion models and transformer architectures into appearance modeling and relighting tasks. These works often address real-world challenges such as single-image relighting, material estimation, and lighting control with minimal user input. RenderFormer (SIGGRAPH 2025): Transformer-based neural rendering with global illumination. ScribbleLight (CVPR 2025): Scribble-driven indoor relighting using diffusion models. MatFusion (SIGGRAPH Asia 2023): Diffusion-based SVBRDF capture. He has mentored and collaborated with numerous students and researchers, including Chong Zeng, Jun Myeong Choi, and James Bieron, indicating active involvement in advising and research supervision. While no formal grants or awards are listed, his consistent publication record in top venues suggests sustained research funding and recognition in the graphics community. Peers has led research in advanced rendering systems, including work on compressive light transport sensing, wavelet environment matting, and compact representations of heterogeneous subsurface scattering. His early work at USC and K.U.Leuven laid the foundation for his current research in neural appearance modeling and inverse rendering.

• Business Analytics
• Information Systems
• Data Science
• +5 more

Wendy A. Ceccucci is a Professor and chair of the Computer Information Systems Department at Quinnipiac University, where she has served for 26 years. Her expertise spans Business Analytics, Information Systems, and Data Science, with leadership in curriculum development for business analytics programs including the Masters of Business Analytics. She teaches core courses such as Data Visualization, Predictive Modeling, and Optimization while maintaining active research in healthcare analytics and IT workforce dynamics. Her academic credentials include: BS in Math-Computer Science from Union College MA from Virginia Polytechnic Institute & State University PhD from Virginia Polytechnic Institute & State University Dr. Ceccucci's research focuses on the intersection of data science and human factors, particularly in IT workforce studies and healthcare applications. Her work examines developer job satisfaction, mental health impacts on compensation, blockchain education, and AI tool adoption. She develops predictive models for critical healthcare outcomes like post-surgical falls while advancing analytics education through textbook authorship and conference leadership. Her research consistently bridges technical analytics with real-world business and healthcare challenges. Analysis of her 15 most recent publications (2021-2024) reveals three dominant research trajectories: (1) IT workforce studies examining job satisfaction, mental health, and compensation across global developer populations; (2) Healthcare analytics applying machine learning to predict and prevent adverse outcomes like post-surgical falls; and (3) Emerging technology education focusing on blockchain curriculum development and AI tool adoption in business contexts. Her methodology combines quantitative analysis with practical case studies, often collaborating across disciplines including healthcare and economics. Her significant recognitions include: Induction as EDSIG-AITP Fellow (2018) for contributions to information systems education Tableau Desktop Specialist certification validating technical expertise Dr. Ceccucci actively shapes her field through editorial service on the Information Systems Education Journal and leadership in the EDSIG conference community. She mentors students through courses like BAN 650 Data Visualization for Managers and PMBA 626 Data Driven Decisions, while developing innovative teaching cases such as the award-winning healthcare management predictive analytics case. Her current grant activities focus on integrating blockchain and AI into business analytics curricula, with upcoming presentations at ISCAP 2024.