Christopher M. Overall is a Full Professor at the University of British Columbia in the Faculty of Dentistry, Department of Oral Biological and Medical Sciences . He is also a Principal Scientist at the Centre for Blood Research and holds associate memberships in UBC's Biochemistry & Molecular Biology , Obstetrics and Gynecology , and Bioinformatics Graduate Program departments. As a Canada Research Chair Laureate , he pioneered the field of degradomics to study proteases in vivo. B.D.S., University of Adelaide Ph.D., University of Toronto Postdoctoral Fellowship, UBC (with Nobel Laureate Michael Smith) Dr. Overall’s research focuses on protease proteomics and systems biology , particularly degradomics to analyze protease substrates in diseases like COVID-19 and immunodeficiency . His work on matrix metalloproteinases has revealed new therapeutic strategies for inflammatory diseases and cancer . His 15 most recent articles (2015–2008) demonstrate expertise in TAILS proteomics , protein terminomics , and protease network analysis with applications in arthritis , antiviral immunity , and precision medicine . Scientific Awards 2022 Helmut Holzer Award 2018 Royal Society of Canada Fellow 2014 Tony Pawson Canadian Proteomics Award 2013 IADR Distinguished Scientist Award Dr. Overall has mentored 61 trainees , including 9 full professors with department chairs, and received the UBC John McNeill Mentorship Award (2023). He leads the HUPO Chromosome-centric Human Proteome Project and consults for Genentech and Novartis .
Soumya Dutta is an Assistant Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Kanpur (IITK), where he leads the INSIGHT: Intelligent Scientific and Visual Computing of Big Data Research Group. He joined IIT Kanpur in October 2022 after working as a Scientist II at Los Alamos National Laboratory (LANL) from July 2019 to August 2022, and previously as a Postdoctoral Research Associate at LANL from June 2018 to July 2019. His educational background includes a Ph.D. and M.S. in Computer Science and Engineering from The Ohio State University (2011-2018), where he was part of the GRAVITY research group, and a B.Tech. in Electronics and Communication Engineering from West Bengal University of Technology, India (2005-2009). Research Interests: Dr. Dutta's research focuses on the intersection of machine learning, visual computing, big data, and high-performance computing. His primary research areas include Machine Learning for Visual Computing and Image Analysis, Big Data Visualization and Analytics, Data Science and HPC, Machine Learning for Scientific Computing, and Explainability and Interpretability of AI Models. His work addresses various big data characteristics including the 5 Vs: Volume, Velocity, Variety, Veracity, and Value. He develops techniques that make complex machine learning models more interpretable and explainable, enabling their effective adoption in real-life applications across scientific domains, social media, IoT, healthcare, and industry applications. Dr. Dutta's research group has secured multiple funded projects including: DAVi: An Intelligent Data Analytics and Visualization Framework (funded by ISRO), Intelligent Visual Computing of Extreme-scale Data for Accelerating Scientific Discovery (IIT Kanpur Initiation Grant), Enabling Interactive Big Data Analytics and Visualization at Exascale (SERB), Development of AI-Enabled National Portal for Efficient Search of Missing People (C3iHub), and Proactive and Generalized Deepfake Defense Mechanisms (C3iHub). Best Reviewer, Honorary Mention Award for IEEE Transactions on Visualization & Computer Graphics (TVCG), 2021 Best Paper Award at ISAV 2021, co-located with Supercomputing (SC) LAAP Award at Los Alamos National Laboratory, 2021 Best Paper Award at TopoInVis 2019 Best Paper Award at ISAV 2018, co-located with Supercomputing (SC) Best Poster Award in 12th Annual CSE Student Poster Exhibition, The Ohio State University, 2018 Best Poster Award in 11th Annual CSE Student Poster Exhibition, The Ohio State University, 2017 Best Paper Honorable Mention Award at IEEE Visualization Conference (IEEE VIS) 2016 Dr. Dutta actively mentors a large group of students including Ph.D., M.Tech., and B.Tech. students. His current Ph.D. students include Shanu Saklani, Sankhadeep Bhowmick, Ananya Chaturvedi, Arpita Santra, Anubhav Dixit (co-supervised), and Robin Shah. He has supervised numerous M.Tech. students with thesis topics ranging from uncertainty-aware neural networks to deepfake detection. Dr. Dutta currently teaches courses including CS360 - Introduction to Computer Graphics and CS661 - Big Data Visual Analytics. The INSIGHT research group collaborates internationally with researchers from Meta, Oak Ridge National Laboratory, and National Taiwan Normal University. The group's work focuses on building machine learning and data science-based solutions to analyze large-scale multifaceted data in a scalable way, enabling interactive and interpretable analytics of complex data from scientific simulations, social media, IoT, healthcare, and other application domains.
Witold J. Henisz serves as Vice Dean and Faculty Director of the Impact, Value, and Sustainable Business Initiative at The Wharton School, University of Pennsylvania, holding the Deloitte & Touche Professorship in Management. His research centers on political and social risk identification, ESG impact materiality, and corporate diplomacy frameworks that transform stakeholder relationships into strategic assets. His educational background includes a Ph.D. in Business and Public Policy from UC Berkeley's Haas School and an M.A. in International Relations from Johns Hopkins SAIS. Henisz has served as Departmental Editor at Journal of International Business Studies and Associate Editor at Strategic Management Journal , reflecting his scholarly influence. Research interests converge on geopolitical risk quantification, stakeholder engagement optimization, and sustainable business model innovation. His work demonstrates how political hazards materially impact firm valuation and how corporate diplomacy can mitigate conflict through strategic stakeholder alignment. Recent publications leverage massive media datasets (4+ billion articles) to model populism dynamics, indigenous land conflicts, and ESG-credit risk linkages. Award highlights include the Aspen Institute Ideas Worth Teaching Award (2020), Iron Prof recognition (2019), and Academy of International Business Fellowship. His geostrategy framework Geostrategy by Design (2024) provides executives with actionable methods for navigating geopolitical volatility. Aspen Institute Ideas Worth Teaching Award (2020) Iron Prof recognition (2019) Academy of International Business Fellow (2014-present) Multiple Wharton Excellence in Teaching Awards (2006-2021) Industry Studies Association Best Paper Award (2019) Henisz consults through PRIMA LLC for multinational firms (Rio Tinto, Shell), financial institutions (Eaton Vance, World Bank), and NGOs. His executive education programs train leaders in ESG integration and geopolitical risk management, while his KEROVKA crisis simulation develops real-time strategic response capabilities. Media frequently cites his expertise on ESG controversies, with 50+ major publications featuring his analysis since 2022 including Financial Times , Bloomberg , and Wall Street Journal .
Professor Heather Ferguson is a distinguished cognitive psychologist at the University of Kent's School of Psychology, where she has built an impressive academic career since her appointment as Lecturer in 2009. Promoted to Professor in 2018, she leads groundbreaking research examining the cognitive basis of social communication across the lifespan. Her work bridges cognitive psychology, neuroscience, and social cognition, with particular expertise in perspective-taking, theory of mind, and the interface between cognitive processes and social interaction. Her research primarily investigates how we access and represent other people's perspectives during communication, using methodologies including eye-tracking, EEG, and reaction time measurements. Key questions driving her work include: How do people understand and predict events based on others' mental states? What happens when these conflict with our own knowledge? How do social abilities relate to cognitive skills like memory and inhibitory control? How does aging affect social cognition? Her work has significant implications for understanding autism spectrum disorder, social development, and cognitive aging. Professor Ferguson's recent publications reveal a strong focus on perspective-taking across the lifespan, social cognition in autism, the cognitive effects of fiction reading, and the neural mechanisms underlying empathy. Her research demonstrates consistent methodological rigor through pre-registered studies and meta-analyses, with particular attention to developmental trajectories from adolescence through older adulthood. Psychonomic Society Early Career Award (2019) Open Science Framework Pre-registration Challenge award (2018) Cognitive Neuroscience Society Postdoctoral Fellow Award (2018) University of Kent Prize for Research (2016) Kent Union Teaching Award for 'Best Teacher' (2012) Professor Ferguson actively supervises multiple PhD students and has secured substantial research funding, including a €1.5 million ERC Starting Grant and multiple Leverhulme Trust grants. Her work with the Cognitive basis of Social Communication and Ageing (COGSOCOAGE) research group has produced influential findings on social cognition across the lifespan. She serves as Associate Editor for the Journal of Experimental Psychology: Learning, Memory & Cognition and on the editorial board of Cognition, while also mentoring early-career researchers through the Eastern Arc Mentoring Scheme.
Ilker Yildirim is an Assistant Professor of Psychology at Yale University, where he leads the Cognitive and Neural Computation Lab (CNCL). He received his Ph.D. from the University of Rochester in 2014. His research focuses on understanding how perception transforms raw sensory signals into meaningful representations of objects and scenes through computational modeling approaches. His research interests include: Computational modeling of visual perception and cognition Intuitive physics and physical reasoning Bayesian inference and probabilistic models in cognition Integration of graphics and physics engines in cognitive modeling Neural mechanisms of object representation Attention and resource allocation in dynamic scenes Yildirim's lab develops computational frameworks that bridge cognitive processes with neural mechanisms, using tools including probabilistic models, simulation engines, and deep learning. His work often involves testing these models through behavioral and neural experiments to create unified accounts of perception and cognition. His recent publications reveal a strong focus on how humans perceive physical properties of objects, particularly soft materials and liquids, and how attention dynamically allocates resources during scene perception. Among his notable achievements is receiving an NSF CAREER award for his project "CAREER: CompCog: Reverse-engineering neural mechanisms of object cognition with multilevel computational modeling," which will run from 2025 to 2030. His Nature Human Behaviour paper by Qi Lin was featured on CBS News, Germany's NZZ, and Wired Italy. Yildirim mentors a diverse group of graduate students and postdocs working on various aspects of perception, cognition, and computational modeling. His students have received prestigious awards including the Jane Olejarczyk Award and the Leonard J. Savage Prize.
Dr. Feng Cheng is a Senior Researcher and head of the IT Security Engineering (Sec-Eng) Team at the Hasso Plattner Institute (HPI), Germany, and serves as the Representative of the Chair "Internet Technologies and Systems" at the Digital Engineering Faculty, University of Potsdam. He holds a PhD from the University of Potsdam, an MEng from Beijing University of Technology, and a BEng from Beijing University of Aeronautics and Astronautics. His research is centered on data-driven security engineering, security analytics, network security, cloud security, firewall, IDS/IPS, attack modeling, and penetration testing. Dr. Cheng's research interests span a broad range of cybersecurity domains, with a strong emphasis on security analytics , data-driven threat detection , cloud and network security , and authentication . His work integrates advanced data engineering and machine learning techniques to enhance security operations, including intrusion detection, threat intelligence, and security information and event management (SIEM). Key application areas include IoT security, mobile security, and web security. The recent publications of Dr. Cheng and his team reflect a significant trend towards leveraging big data analytics , machine learning , and graph-based methods for cybersecurity. Their work focuses on developing frameworks for blockchain-based public key infrastructures, advanced SIEM analytics, malware detection using NLP, and chaos engineering for cloud security. This demonstrates a strong commitment to creating practical, scalable, and intelligent solutions for modern security challenges in complex, distributed environments. Dr. Cheng is actively involved in the academic community as a member of ACM and IEEE, a program committee member for numerous international conferences, and an organizer of workshops on cloud computing and cybersecurity. He has also served on editorial boards for international journals. He supervises a large group of PhD, master's, and bachelor's students, guiding research on topics such as security analytics, cyber threat intelligence, digital credentials, and behavioral authentication. He leads the Sec-Eng team, which conducts research on a diverse portfolio including the Security Analytics Lab, HPI-VDB (vulnerability database), Lock-Keeper (physical separation technology), and the Security Lab Generator for scenario-driven security training.
Professor Peter F. Driessen is a faculty member in the Department of Electrical and Computer Engineering at the University of Victoria, with a cross-appointment in the School of Music. He holds a BSc and PhD from the University of Victoria and is a Professional Engineer (PEng). His research focuses on communication systems, signal processing, control, and interdisciplinary projects in computer music and wireless technologies. Key areas include audio/video signal processing, radio propagation, sound recording, and multimedia systems. He leads the University of Victoria Propagation Laboratory, which explores radio wave propagation and Amateur radio integration with engineering education. His work spans theoretical research and applied projects like ECOSat satellite systems, software-defined radio (SDR), and innovative musical instruments such as the Radio Drum. He supervises undergraduate and graduate projects in these domains through ELEC 499 courses. Notable contributions include the APEGBC Editorial Board Award for Best Paper (2002) and patents in wireless networking and signal processing. His teaching includes courses in signal analysis and electromagnetics, and he collaborates on interdisciplinary programs like the Music/Computer Science degree. Education: BSc in Electrical Engineering, University of Victoria PhD in Electrical Engineering, University of Victoria Research Interests: Audio and video signal processing for music and media Software-defined radio and Amateur radio technologies Satellite communication and ground station development Gesture-based interfaces and musical instrument design Error mitigation in streaming audio/video Optical and microwave-photonic systems Labs & Collaborations: Propagation Laboratory (radio wave research) UVic Experimental Radio Group (Amateur radio club) UVic Satellite Design Team (ECOSat projects) UVic Centre for Aerospace Research Grants & Awards: APEGBC Editorial Board Award (2002) Multiple US patents in wireless systems and signal processing
Professor Wayne Luk is a Professor of Computer Engineering at the Department of Computing, Faculty of Engineering, Imperial College London. He leads the Programming Languages and Systems Section and the Custom Computing Research Group, and directs the EPSRC Centre for Doctoral Training in High-performance Embedded and Distributed Systems and the Centre for Advanced Financial Engineering. He previously served as a Visiting Professor at Stanford University from 2006 to 2009. His research spans FPGA acceleration, quantum computing, deep learning optimization, and algorithm-hardware co-design, with affiliations to the CRUK Convergence Science Centre and the Engineering Secure Software Systems group. His research interests include computational modeling for particle physics, causal discovery in agent-based systems, and high-throughput digital electronics. Notable contributions include FPGA-accelerated algorithms for neural networks, quantum circuit simulation, and Bayesian optimization frameworks. His work emphasizes practical applications of reconfigurable hardware in fields like medical imaging, high-energy physics, and financial systems. Professor Luk is a Fellow of the Royal Academy of Engineering, IEEE, and BCS. His publications focus on advancing hardware-aware machine learning, FPGA-based acceleration techniques, and scalable design methodologies. His research bridges theoretical computer science with applied engineering, addressing challenges in real-time systems, embedded computing, and next-generation computing architectures. His academic leadership includes directing interdisciplinary centers and training programs, fostering collaboration across computing, engineering, and physics. Current projects explore quantum computing tools, causal inference systems, and high-performance graph neural networks for particle physics applications.
Huiyan Sang is a Professor and Director of the Undergraduate Program in the Department of Statistics at Texas A&M University (College of Arts & Sciences). She earned her Ph.D. in Statistics from Duke University and a B.Sc. in Mathematics and Applied Mathematics from Peking University. Her research focuses on spatial statistics, Bayesian nonparametric methods, machine learning, computational statistics, and applications in environmental sciences, geosciences, urban planning, and biomedical research. Her interdisciplinary work integrates statistical methodologies with real-world challenges, such as analyzing extreme environmental events, optimizing urban infrastructure, and modeling complex systems like human mobility during pandemics. She has contributed to advancing spatio-temporal modeling, Gaussian processes, and Bayesian hierarchical frameworks for large datasets. Recent publications highlight innovations in nonparametric regression, spatial functional data analysis, and stochastic frontier analysis, often leveraging computational efficiency and scalability. Her work addresses critical societal issues, including the impact of community design on public health and environmental monitoring through remote-sensing data. No scientific awards are explicitly listed in the provided texts. She advises no students or grants in the current dataset but collaborates widely on interdisciplinary projects. Her research lab focuses on developing cutting-edge statistical tools with applications in engineering, public health, and environmental science.
Dr. Yolanda Gil is a Research Professor in Computer Science and Spatial Sciences at the University of Southern California, where she serves as Principal Scientist and Senior Director for Strategic Initiatives in Artificial Intelligence and Data Science at the Information Sciences Institute (ISI). She is also the Director of AI and Data Science Initiatives in the Viterbi School of Engineering and leads the USC Center for Knowledge-Guided Interdisciplinary Data Science (CKIDS). Dr. Gil received her Licenciatura in Computer Science from the Polytechnic University of Madrid and her M.S. and Ph.D. in Computer Science from Carnegie Mellon University, with a focus on artificial intelligence and cognitive science. Her research focuses on developing AI approaches that use knowledge to accelerate scientific discovery processes. Her key research interests include knowledge capture and representation, semantic workflows, ontology tools, scientific discovery methods, task-based collaboration, provenance tracking, knowledge networks, reproducibility in science, and machine learning for data analysis. She collaborates with scientists across multiple domains to improve how scientific knowledge is created, shared, and used. Dr. Gil's work has significant impact across multiple scientific domains including climate science, neuroscience, and omics research. Her projects demonstrate her commitment to building knowledge-guided systems that transform how scientists conduct research. She has pioneered approaches to capture the provenance of scientific experiments and to automate the analysis of complex scientific data. Fellow of the Association for Computing Machinery (ACM) Fellow of the Association for the Advancement of Science (AAAS) Fellow of the Institute of Electrical and Electronics Engineers (IEEE) Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) 24th President of the Association for the Advancement of Artificial Intelligence Co-chair of the CRA/AAAI 20-Year Artificial Intelligence Research Roadmap for the US Initiator and leader of the W3C Provenance Group that resulted in a widely-used standard for web trust As an educator and leader, Dr. Gil directs the Data Science Program in Computer Science and serves as Co-Director of multiple joint MSc programs including Communication Data Science, Spatial Data Science, Environmental Data Science, Public Policy Data Science, and Healthcare Data Science. She also leads the new dual degree USC-Tsinghua University on Communication Data Science. Her leadership extends to mentoring numerous students and researchers in AI and data science. Through the USC Center for Knowledge-Guided Interdisciplinary Data Science (CKIDS), Dr. Gil organizes DataFest events each semester, fostering collaboration and innovation in data science across disciplines.
Habib Ullah is an Associate Professor in Data Science at the Norwegian University of Life Sciences (NMBU), Norway, where he conducts research at the intersection of computer vision and machine learning. He is affiliated with the Institute of Data Science under the Faculty of Science and Technology. He has previously held academic positions at COMSATS University Islamabad, Pakistan, and the University of Ha'il, Saudi Arabia, and served as a postdoctoral researcher at The Arctic University of Norway. Educational Background: PhD in Information and Communication Technology (Computer Vision), University of Trento, Italy (2011–2015) MSc in Electronics and Computer Engineering, Hanyang University, South Korea (2007–2009) BSc in Computer Systems Engineering, NWFP University of Engineering and Technology, Pakistan (2002–2006) Habib Ullah's research is primarily focused on computer vision and machine learning, with applications in aquaculture, agriculture, and human behavior analysis. He investigates underwater fish feeding sounds using audio classification, develops zero-shot learning models for recognizing unseen classes, and applies deep learning to detect stress in salmon via skin dot patterns. He also explores AI-driven controlled environment agriculture, leveraging sensors and automation for optimal crop growth. His work emphasizes practical AI solutions for real-world challenges in environmental and biological domains. The recent publications highlight a strong trend in leveraging deep learning for zero-shot and semi-supervised learning, particularly in computer vision tasks such as sea ice classification, crowd anomaly detection, and agricultural monitoring. His research spans remote sensing, biomedical signal processing, and human activity recognition, demonstrating interdisciplinary versatility. The keywords reflect a focus on robust feature representation, knowledge transfer, and model generalization. Scientific Awards and Funding: Industrial PhD grant 'Advancing Controlled Environment Agriculture AI' from The Research Council of Norway (Project number 354125, 2 million NOK, 2024) Team member (Coordinator-Participant) in the Battery Cell Assembly Twin (BatCAT) project funded by Horizon Europe (7 mEuro, 2023–2027) Development of an AI-Based Image Analysis System for Monitoring Plant Status (Funding: 1.8 mNOK, starting 2025) Habib Ullah actively supervises PhD projects and contributes to academic service through editorial and organizational roles. He has served as an Associate Editor for IEEE Access, Guest Editor for MDPI Remote Sensing, and Editor of the Springer book Machine Learning Techniques and Sensor Applications for Human Emotion, Activity Recognition, and Support (ML-SHEARS) . He has also been a Track Chair and Program Committee Member for several international conferences, reflecting his leadership in the academic community. His research is supported by significant grants and collaborative projects, indicating strong institutional and international engagement. He is involved in multiple research teams and projects, including the BatCAT project on battery manufacturing and AI applications in controlled environment agriculture with RIFT LABS AS. His lab work integrates deep learning, sensor fusion, and data analytics for environmental and biological monitoring systems.
Freek Persyn is a Full Professor of Architecture and Urban Transformation at ETH Zurich, where he has held the NEWROPE Professorship since 2019. He leads the Design in Dialogue Lab within the Department of Architecture, focusing on spatial design and social innovation. Persyn co-founded the spatial planning firm 51N4E in 1998, which specializes in urban and social transformation across Europe. Persyn's research centers on the concept of 'Design in Dialogue,' which emphasizes transdisciplinary collaboration to address the complexity of contemporary urban challenges. His work explores innovative solutions for European urban spaces, aiming to rediscover Europe as a habitable terrain and shared living space. Key research areas include urban design, spatial planning, social transformation, and the intersection of architecture with social innovation. The NEWROPE initiative seeks to examine diverse urban practices that shape Europe, fostering radical openness and learning from various urban practitioners through transdisciplinary projects. His scholarly output demonstrates a strong focus on reimagining urban spaces through dialogue-based approaches. Persyn's work examines European urban transformation, rural-urban connections, industrial heritage sites, and innovative design methodologies. Key themes across his publications include sustainable approaches to building, the social dimensions of space, and transformative design processes that challenge conventional architectural practices. His research often bridges theoretical frameworks with practical design applications, emphasizing participatory methods and material investigations. Persyn delivered his inaugural lecture at ETH Zurich on October 20, 2021, titled 'Wouldn't it be nice if architects started dreaming about building less?' The lecture explored his professional background, inspiration, and future plans for the NEWROPE Chair. The event featured collective sensing exercises developed by students of Studio Seebach with theater and dance maker Manuela Runge, highlighting his commitment to experiential and collaborative approaches to architectural education and practice. The Design in Dialogue Lab serves as a hub for exploring innovative approaches to urban transformation through projects across European cities including Brussels, Piraeus, Basel, and Plovdiv. Current initiatives examine topics such as urban spaces for more than human communities, collaborative processes, design approaches, and hybrid applications for European urban environments. Persyn's work consistently emphasizes transdisciplinary collaboration, bringing together spatial and strategic knowledge with other disciplines and urban practitioners to address complex contemporary challenges.
Ning Nan is an Associate Professor at the University of British Columbia's Sauder School of Business, specifically within the Department of Accounting and Information Systems. With a BA from Peking University, an MA from the University of Minnesota, and a PhD from the University of Michigan, Dr. Nan focuses on digital business strategy, evolvable IT infrastructure, and complex adaptive systems. His research explores blockchain applications, agent-based modeling, and the intersection of AI with societal challenges like sustainability. Education: PhD in Information Systems, University of Michigan MA in Information Systems, University of Minnesota BA in Computer Science, Peking University Research Interests: Dr. Nan investigates how technology shapes organizational and societal systems, with a focus on AI ethics, sustainability through personalized recommendations, and blockchain's role in decentralized supply chains. He employs agent-based modeling to simulate complex phenomena like innovation diffusion and online community dynamics. Recent work addresses explainable AI in smart cities and carbon-reduction gamification strategies. Teaching: In 2024-2025, he teaches Data Management for Business Analytics , emphasizing practical applications of data-driven decision-making. Key Research Themes: His articles highlight trends in AI sustainability (e.g., carbon footprint impacts of recommendation systems), digital business strategy (blockchain governance, app update strategies), and foundational IS theory (hyperturbulence and IS strategy).
John C. Butler is a Clinical Associate Professor in the Finance Department at the McCombs School of Business, University of Texas at Austin. He holds leadership roles as Academic Director of the Kay Bailey Hutchison Energy Center, Director of the MS Finance Program, and Director of the Energy Management Minor. His academic journey includes a PhD in Management Science and Information Systems from UT Austin (1998) and a BBA from Texas A&M University (1991). His research focuses on applications of decision analysis across domains including operations, finance, and information systems. Key areas include risk analysis, optimization, multi-attribute utility theory, and energy finance. His work integrates theoretical modeling with empirical validation to address complex decision-making challenges in both public and private sectors. Butler's publications demonstrate a consistent focus on decision modeling methodologies, with recent work emphasizing risk quantification and utility theory applications. His articles frequently intersect operations research, behavioral economics, and systems optimization, reflecting interdisciplinary approaches to solving managerial and policy problems. Awards and Honors: MBA Applause Award (2008, 2011) Finalist, INFORMS Franz Edelman Award (2004) INFORMS Decision Analysis Society Practice Award (2000) Fred Moore Teaching Award Dean's Research Fellowship, Ohio State University (2004) Leadership & Advising: Butler has supervised 11 PhD students to completion and secured significant grants including DOE funding for nuclear terrorism risk analysis. He directs multiple energy finance initiatives and serves on editorial boards for Decision Analysis and previously Decision Support Systems . Centers & Programs: As Academic Director of the Kay Bailey Hutchison Energy Center, he leads interdisciplinary energy research. He also developed the Energy Finance concentration and redesigned the MS Finance curriculum to incorporate quantitative energy market analysis.
Adam B. Badawi is a Professor of Law at the University of California, Berkeley - School of Law, where he maintains an active research agenda at the intersection of law, finance, and corporate governance. His scholarly work has established him as a leading expert in contract law, M&A transactions, securities litigation, and ESG compliance. Professor Badawi's research interests span multiple dimensions of corporate law and legal theory. His work on contractual complexity, particularly in debt agreements and M&A transactions, has provided significant insights into how legal drafting affects economic outcomes. He has extensively examined the business judgment rule, board authority, and the role of courts in corporate governance. More recently, his research has expanded into ESG-related topics, analyzing how environmental, social, and governance considerations intersect with traditional corporate law frameworks. His methodological approach often combines legal analysis with empirical techniques, including text analysis of legal documents and event studies of litigation outcomes. Professor Badawi's scholarly output demonstrates consistent high-impact research across multiple domains of corporate law. His early work focused on foundational aspects of contract theory and interpretation, while his more recent publications reflect growing interest in ESG metrics, executive compensation structures tied to sustainability goals, and the evolving regulatory landscape. The trajectory of his research shows a progression from theoretical legal analysis toward increasingly empirical and interdisciplinary approaches that bridge law with finance and data science. Professor Badawi has collaborated with numerous scholars across different institutions, including Duke University, Stanford Law School, and the University of Chicago Law School, reflecting the interdisciplinary nature of his work and its relevance to multiple academic communities.
