Dr. Anna Bobak is a Senior Lecturer in Psychology at the University of Stirling, UK. She holds a PhD from Bournemouth University (2016) and joined Stirling as a Research Assistant on an EPSRC project under Peter Hancock before transitioning to her current role. Her primary research focuses on individual differences in unfamiliar face recognition, particularly developmental prosopagnosia, and the reliability of face-processing assessments. She also investigates neurodiversity in women, emphasizing lived experiences of autism and ADHD, including camouflaging behaviors and societal awareness. Research Interests: Face Recognition: Examines perceptual strategies, diagnostic criteria (e.g., Balanced Integration Score), and technological applications in forensic contexts. Neurodiversity: Explores gender-specific manifestations of autism and ADHD, societal perceptions, and support mechanisms. Cognitive Methodology: Advances psychometric rigor in face-processing studies and critiques measurement validity. Her work bridges theoretical research and real-world applications, such as evaluating automated face recognition technology’s biases and collaborating on initiatives like #ScienceForUkraine to aid displaced academics. She is affiliated with the Cognition in Complex Environments research group and contributes to global security and resilience themes at Stirling.
Lara A. Estroff is a Full Professor and the current Chair of the Department of Materials Science and Engineering at Cornell University's College of Engineering. She has been a faculty member since 2005 and served as Director of Graduate Studies from 2015 to 2019. Her academic leadership and research excellence position her at the forefront of bio-inspired materials and biomineralization research. Her educational background includes a B.A. in Chemistry from Swarthmore College (1997) and a Ph.D. in Chemistry from Yale University (2003), followed by an NIH-funded postdoctoral fellowship at Harvard University in the lab of Prof. George M. Whitesides. Dr. Estroff's research centers on the fundamental mechanisms of crystal growth, biomineralization, and pathological mineralization. She investigates how organisms control mineral formation and applies these principles to engineer synthetic materials with complex structures and functionalities. Her work spans biomaterials, tissue engineering, and energy materials—particularly hybrid organic-inorganic perovskites for photovoltaics. She employs advanced characterization techniques and has pioneered in situ methods to monitor crystallization dynamics. Her recent publications reveal a strong trend toward interdisciplinary research, integrating materials science with cancer biology, immunology, and machine learning. The articles emphasize bio-inspired synthesis, mineral-tissue interactions, and the development of functional crystalline materials for medical and energy applications. Faculty Early CAREER Award, National Science Foundation (2009) Fiona Ip Li '78 and Donald Li '75 Excellence in Teaching Award, Cornell College of Engineering (2007) Marilyn Emmons Williams Award, Cornell Undergraduate Research Board (2009) Keynote Speaker, Gordon Research Seminar on Biomineralization (2012) Lawrence Berkeley National Lab Affiliate (2013) Dr. Estroff leads a major DOE-funded project titled “Formulation Engineering of Energy Materials via Multiscale Learning Spirals,” a $3 million, three-year initiative using machine learning to optimize perovskite synthesis for solar cells. She has advised numerous graduate students and postdoctoral researchers, and her lab is known for fostering collaborative, cross-disciplinary research. She has also contributed to educational initiatives at Cornell, particularly in undergraduate research and materials education. Her research group operates at the intersection of chemistry, engineering, and biology, focusing on high-resolution characterization of biominerals, in situ crystal growth studies, and the design of in vitro models for cell-mineral interactions. The lab actively collaborates with institutions including Lawrence Livermore National Laboratory, National Renewable Energy Laboratory, and Johns Hopkins University.
Rayadurgam Srikant is the Fredric G. and Elizabeth H. Nearing Endowed Professor of Electrical and Computer Engineering at the University of Illinois Urbana-Champaign, affiliated with the Coordinated Science Lab. He co-directs the C3.ai Digital Transformation Institute, focusing on AI-driven solutions for global challenges. His research spans machine learning, communication networks, stochastic systems, and game theory. Srikant has authored influential textbooks including Communication Networks: An Optimization, Control and Stochastic Networks Perspective . He holds IEEE Fellow status and has received prestigious awards like the ACM SIGMETRICS Achievement Award (2021) and IEEE Koji Kobayashi Award (2019). Over 20 of his advisees hold faculty positions globally. Education: PhD (1991), MS (1988) in Electrical Engineering from UIUC; B.Tech (1985) from IIT Madras. He has taught advanced courses on optimization, stochastic systems, and game theory. His work bridges theory and practice, with contributions to congestion control, cloud computing, and reinforcement learning. Current projects include AI applications for pandemic response and digital transformation initiatives. Research highlights include foundational work on Lyapunov drift methods for network stability and distributed algorithms. He serves as Area Editor for Mathematics of Operations Research and has led editorial roles for IEEE/ACM Transactions on Networking. His lab collaborates with industry leaders like Microsoft and C3.ai, leveraging supercomputing resources for societal impact.
Dr. Matthew Jones is a Senior Lecturer in Criminology at Swinburne University of Technology’s School of Social Sciences, Media, Film and Education. With a career spanning institutions in the UK and Australia, Matthew’s research bridges interdisciplinary domains including policing, sociology, law, and organizational studies. He is a Senior Fellow of the Higher Education Academy, emphasizing evidence-informed pedagogy and curriculum innovation. Affiliation: Swinburne University of Technology (2022–Present) Previous Roles: The Open University (2017–2022), Northumbria University (2014–2017), Cardiff Metropolitan University (2013–2014). Research Themes: Matthew’s work focuses on three core areas: Policing: Police visibility, digital strategies, occupational culture, diversity, and leadership. LGBTQI+ Criminology: Workplace discrimination, hate crimes, community-police relations, and victimology. Digital Criminology: Technology’s role in policing, crime prevention, and digital victim support systems. Publications: His 15 most recent articles highlight evolving trends in digital policing, police visibility, academic standards in policing education, and LGBTQI+ workplace experiences. These works span qualitative studies, policy analysis, and interdisciplinary collaborations, reflecting his commitment to visual criminology and semiotics. Scientific Awards: Senior Fellow of the Higher Education Academy (2022) Fellow of the Higher Education Academy (2013) Professional Activities: Matthew served as Chair (2019–2022) and Board Member (2013–2019) of the British Society of Criminology’s Policing Network. He led curriculum development for police apprenticeships (2017–2019) and contributed to the first UK Subject Benchmark Statement for Policing (2022). He supervises PhD students on topics like community policing, child maltreatment resilience, and crime drama narratives. Education: He holds an LLB in Law, an MSc in Social Science Research Methods, a PhD in Socio-Legal Studies, a PGCert in Higher Education pedagogy, and an MBA in Leadership Practice.
Amol Deshpande is a Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley, College of Engineering. With over 160 publications spanning from 2000 to 2025, his research has significantly impacted the database systems community. His work bridges theoretical foundations with practical systems, evidenced by numerous publications in top-tier venues including SIGMOD, VLDB, and ICDE. Professor Deshpande's research focuses on database systems, with particular expertise in graph databases, data management, probabilistic databases, query optimization, and data provenance. His work addresses fundamental challenges in managing complex data, including efficient graph analytics, dataset versioning, streaming data processing, and privacy-preserving data management. Recent research directions include entity-relationship abstractions beyond traditional relations, standalone catalog engines for large data systems, and graph theoretical approaches to dataset versioning. His publication trends show a consistent focus on evolving database technologies, with early work on probabilistic databases and query optimization, transitioning to graph analytics and data provenance, and more recently addressing modern challenges in data cataloging, privacy-first data management, and serverless stream processing. His research spans both theoretical contributions (e.g., approximation algorithms for stochastic optimization) and practical systems building (e.g., RStore, TreeCat). Professor Deshpande has mentored numerous PhD students who have become active researchers in the database community, including Hui Miao, Souvik Bhattacherjee, and Konstantinos Xirogiannopoulos. His collaborative work spans across institutions, with frequent collaborations with researchers from MIT, University of Maryland, and other leading institutions. His research has been supported by major funding agencies and has influenced both academic research and industry practices in data management. The evolution of his work reflects the changing landscape of data management, from traditional relational systems to modern graph and streaming data challenges.
Abdon Pena-Francesch is an Assistant Professor in the Department of Materials Science and Engineering at the University of Michigan. He is also affiliated with the Macromolecular Science and Engineering Program, Chemical Engineering, and the Michigan Robotics Institute. His interdisciplinary research integrates biomaterials science, polymer chemistry, soft matter physics, and nanotechnology to develop programmable soft materials for applications in healthcare, robotics, and environmental science. Education: Ph.D. in Engineering Science and Mechanics, The Pennsylvania State University, 2017 M.Sc. in Chemical Engineering, Institut Químic de Sarrià (Barcelona, Spain), 2013 B.Sc. in Mechanical Engineering, Institut Químic de Sarrià (Barcelona, Spain), 2011 Research Interests: His work focuses on bioinspired materials , soft robotics , self-healing polymers , and biodegradable microrobots . By engineering molecular and nanoscale structures, his lab designs materials with programmable properties for soft robotic systems and biomedical devices. The group emphasizes both fundamental science and translational applications, including tissue repair, actuation, and environmental sensing. Awards and Honors: Humboldt Research Fellowship for Postdoctoral Researchers (2018–2020) Alumni Association Dissertation Award, Penn State University (2017) Rustum and Della Roy Innovation in Materials Research Award (2016) Materials Research Society Graduate Student Award (2016) First Prize, Penn State ESM Graduate Research Symposium (2015) AGAUR MOBINT Fellowship, Government of Catalunya (2012) Labs and Affiliations: He leads the Bioinspired Materials Lab , an interdisciplinary group within the University of Michigan’s Materials Science & Engineering Department. The lab collaborates with the Macromolecular Science & Engineering Program, Chemical Engineering, and the Michigan Robotics Institute.
Kathleen M. Carley is a full professor at Carnegie Mellon University's School of Computer Science with courtesy appointments in Engineering and Public Policy, Heinz School, and Electrical and Computer Engineering. As director of the Center for Computational Analysis of Social and Organizational Systems (CASOS) and the Center for Informed Democracy and Social-Cybersecurity (IDeaS) , she leads interdisciplinary research at the intersection of network science, cognitive modeling, and cybersecurity. Ph.D. in Sociology from Harvard University SB degrees in Economics and Political Science from MIT Her research focuses on Dynamic Network Analysis (DNA) and Social-Cybersecurity (SC) , developing tools like ORA (network analysis), AutoMap (semantic mining), Construct (influence simulation), and BotHunter (bot detection). She has over 400 publications and 15+ active research projects addressing disinformation, cognitive security, and organizational resilience. Recent work examines LLM-powered bots , multi-platform misinformation dynamics , and public health analytics . As an IEEE Fellow, she contributes to standards in computational social science while teaching courses on network analysis and complex socio-technical systems.
Professor Vinayak Dixit serves as the IAG Chair of Risk in Smart Cities and Director of the Research Centre for Integrated Transport Innovation (rCITI) at the University of New South Wales (UNSW), within the School of Civil and Environmental Engineering. With a distinguished career spanning academia and research leadership, Professor Dixit has established himself as a leading expert in transportation risk analysis and smart city infrastructure. Professor Dixit's research focuses on studying risk in transportation infrastructure systems, with particular emphasis on highway safety, travel time uncertainty, and resilience against natural and man-made disasters. His work integrates cutting-edge approaches including quantum computing applications for transportation network optimization, analysis of connected and automated vehicles, and development of models for transportation resilience. His research interests span multiple disciplines, bridging civil engineering, transportation science, risk analysis, and computational methods. Professor Dixit has secured significant research funding from prestigious organizations including the United States National Science Foundation, Federal Highway Administration, and the Strategic Highway Research Program of the Transportation Research Board. His leadership extends to directing the Research Centre for Integrated Transport Innovation (rCITI), where he oversees a comprehensive research program addressing critical transportation challenges in smart cities through multiple focus areas including Connected Mobility Services, Deep Data and Digitization, Engineering Smart Cities & Logistics, Human-Centred And Automated Systems Design, and Integrated Infrastructure Strategic Planning. Professor Dixit previously served as the Associate Director of Research for the Gulf Coast Centre for Evacuation and Transportation Resiliency at Louisiana State University, where he founded the Driving Simulator Laboratory in collaboration with other faculty members. This demonstrates his longstanding commitment to innovative research infrastructure development and interdisciplinary collaboration across engineering, economics, computer science, and urban planning to address complex transportation challenges in the 21st century.
Y. N. Singh is a Professor in the Department of Electrical Engineering at Indian Institute of Technology Kanpur . He holds a PhD and M.Tech from IIT Delhi and a B.Tech from REC Hamirpur . Education PhD, IIT Delhi (1997) M.Tech, IIT Delhi (1992) B.Tech, REC Hamirpur (1991) His research focuses on telecom networks , optical networks , complex networks , and wireless sensor networks , with significant contributions to network protection protocols, rumor propagation modeling, and optical memory design. His work on p-cycles has advanced telecom network survivability techniques. Selected publications include key papers in IEEE/OSA Journal of Lightwave Technology and Acta Physica Polonica B . He has received the AICTE Young Teacher Award (2002) and the Dr. M.N. Saha Memorial Award (2006). Contact details Office: EE/ACES, IIT Kanpur, UP, India-208016 Phone: 0512-[679/392/259]-7944 (O) Lab: 0512-[679/392/259]-7841, 7070 Fax: 0512-259-0063
Bryan K. Clark is an Associate Professor in the Department of Physics at the University of Illinois, with his office located in the Engineering Sciences Building. He leads the Clark Research Group, which works at the intersection of quantum information, condensed matter physics, machine learning, and computing. Clark's research spans four main areas: Quantum Computing , where his group develops quantum algorithms and collaborates with experimentalists on superconducting qubit systems; Quantum Many-Body Physics , where he applies computational methods to understand emergent behavior in strongly correlated systems; Algorithms for the Quantum Many-Body Problem , where his group has pioneered techniques like Neural Network Backflow (NNBF) that represent state-of-the-art accuracy for simulating fermions and frustrated magnetism; and Machine Learning for Experiment , where his group develops techniques to analyze experimental data like scanning transmission electron microscopy images. His publication record demonstrates consistent innovation in bridging theoretical quantum information science with practical applications. Recent work focuses on neural network approaches to quantum simulation, quantum error correction/mitigation, and novel qubit architectures like the Floquet Fluxonium Molecule. His research shows a clear trajectory from fundamental questions about the quantum-classical boundary to practical implementations in quantum hardware. Clark actively mentors graduate students, with recent thesis defenses by Faisal Alam, Matt Thibodeau, Chad Germany, James Allen, and Abid. His group has secured significant funding from the NSF and IBM's IIDAI institute to support research in quantum computing and machine learning applications for nano-photonics manufacturing and error mitigation. The Clark Research Group maintains strong connections with experimental teams, particularly in superconducting qubit development and materials characterization. They've developed computational tools like QOSY (Quantum Operators from SYmmetry) that are publicly available on GitHub and have gained recognition in the quantum information community.
Jeff Linderoth is the Harvey D. Spangler Professor in the Department of Industrial and Systems Engineering at the University of Wisconsin-Madison. His research focuses on large-scale numerical optimization, mixed-integer nonlinear programming, and stochastic programming, with applications in energy systems, global routing, and industrial processes. Education: BS in General Engineering (highest honors) from University of Illinois at Urbana-Champaign, MS in Operations Research from Georgia Institute of Technology, PhD in Industrial Engineering from Georgia Institute of Technology. Linderoth's work addresses theoretical and applied challenges in optimization, including developing algorithms for mixed-integer programming, analyzing knapsack polytopes, and creating tools like the Minotaur optimization toolkit. His recent publications explore integer programming techniques for subspace clustering, complementarity constraints, and customized coverage instrumentation. Selected trends in his research include advancements in stochastic programming, orbital branching for symmetric integer programs, and congestion analysis in power systems. His group contributes to optimization software and data-driven libraries like MIPLIB. Scientific Award: Harvey D. Spangler Professor.
Haipeng Shen is a Professor of Innovation and Information Management at HKU Business School, The University of Hong Kong, serving as Associate Dean (EMBA and IMBA) and holding the Patrick S C Poon Professorship in Analytics and Innovation. He chairs the Business Analytics and Innovation program and joined HKU in 2015 after previously holding a professorship at the University of North Carolina at Chapel Hill. His academic credentials include: PhD in Statistics, The Wharton School of Business, University of Pennsylvania, 2003 MA in Statistics, The Wharton School of Business, University of Pennsylvania, 2000 BS in Mathematics, School of Mathematical Sciences, Peking University, 1998 Professor Shen's research focuses on data-driven decision making under uncertainty, with expertise spanning big data analytics, business analytics, healthcare analytics, and service engineering. He develops advanced statistical and machine learning methodologies to solve complex operational problems in call centers, optimize stroke care protocols, and enhance financial risk modeling, emphasizing real-time applications in high-stakes environments. Analysis of his recent publications reveals a consistent interdisciplinary approach bridging operations research, statistics, and domain-specific knowledge. His work demonstrates strong methodological innovation in time-series forecasting for service systems, risk assessment frameworks for medical complications, and covariance structure analysis for financial markets, with direct translational impact on business operations and clinical outcomes. His scientific contributions have been recognized with prestigious awards including: Most Influential Publication Award from China Stroke Association (2018) Fellow of the American Statistical Association (2015) Best Advisor of the Year Award from Academy of Asian Business (2018) Elected Member of International Statistical Institute (2015) Cluster Chair for Big Data Analytics at INFORMS International (2015) As an academic leader, Professor Shen has secured significant research funding from organizations including The Xerox Foundation and National Institute on Drug Abuse. He serves as Associate Editor for Management Science, Journal of the American Statistical Association, and Technometrics, while mentoring graduate students in statistical methodology and applied analytics. His current initiatives position HKU Business School at the forefront of healthcare innovation through big data analytics, driving collaborations with medical institutions to transform stroke care and hospital operations in Asia.
Matt Nassar is an Associate Professor of Neuroscience and Assistant Professor of Cognitive and Psychological Sciences at Brown University. He leads the Learning, Memory and Decision Lab, which is part of the Department of Neuroscience and the Robert J. & Nancy D. Carney Institute for Brain Science. His research focuses on understanding how the brain flexibly processes information to achieve complex and adaptive behaviors through computational approaches that bridge cognitive psychology and neuroscience. Education: PhD, University of Pennsylvania (2012) BA, Colgate University (2004) Nassar's research examines how different cognitive systems—learning, memory, and perception—leverage common computational principles to optimize decision-making. His work particularly focuses on how the brain balances stability and flexibility in processing information, how uncertainty is represented and utilized in learning, and how neural computations underlie complex behaviors. Through computational modeling and empirical research, he investigates how modular information-processing systems impact decisions and complex behavior in dynamic environments. His research integrates methods from cognitive psychology, neuroscience, and computational modeling to address fundamental questions about human cognition. Analysis of Nassar's recent publications (2020-2024) reveals a strong focus on computational neuroscience applied to decision-making, learning, and psychiatric conditions. His work frequently employs Bayesian modeling approaches to understand belief updating, uncertainty processing, and structure learning. Key themes include the neural basis of flexibility in learning, computational mechanisms underlying psychiatric symptoms, and age-related changes in cognitive processing. His research bridges cognitive psychology, neuroscience, and computational modeling to provide insights into both healthy cognition and disorders such as depression and schizophrenia. Scientific Contributions: Developed computational models of belief updating and learning under uncertainty Investigated neural mechanisms of stability-flexibility tradeoffs in cognition Examined age-related differences in learning and memory processes Explored computational mechanisms underlying psychiatric conditions Studied the role of noise correlations in neural learning systems Investigated how prefrontal cortex representations shape decision processes Nassar actively mentors researchers in his lab, with recent announcements highlighting postdocs joining from prestigious institutions like Max Planck UCL and Freie Universität Berlin. His lab appears to receive significant research funding, supporting multiple postdoctoral positions and research projects. Collaborations span multiple departments at Brown University, particularly with researchers in Cognitive and Psychological Sciences, Neurology, and Psychiatry. The lab has produced numerous high-impact publications in top journals including Nature Human Behaviour, Brain, and eLife. The Learning, Memory and Decision Lab, led by Nassar, is an active research group that uses computational models to understand how the brain represents and stores information for effective decision making. Recent lab announcements (as of February 2025) indicate the lab is expanding with new postdoctoral researchers joining from Harvard, Max Planck UCL, and Freie Universität Berlin, suggesting strong research momentum and funding support. The lab appears to be well-integrated within Brown's neuroscience community, with collaborations spanning multiple departments and research centers.
Nazanin Tajik is an Assistant Professor in the Department of Industrial and Systems Engineering at Mississippi State University (MSU). She holds a Ph.D. from the University of Oklahoma, an M.S. from the University of Tehran, and a B.S. from Sharif University of Technology. Her research focuses on integrating artificial intelligence, machine learning, and social science concepts to develop cross-disciplinary frameworks for infrastructure resilience, smart transportation systems, and disaster management. Her academic journey includes a Ph.D. at the University of Oklahoma where she contributed to the Risk-Based Systems Analytics Laboratory. At MSU, she established a research center bridging AI/ML tools with socio-technical systems. Key research domains include cyber-physical-social infrastructure resilience, search-and-rescue planning, and game-theoretic robotic designs. Tajik's work emphasizes optimization algorithms for network vulnerability assessment, resource allocation in disaster scenarios, and adaptive recovery strategies. She is actively involved with professional organizations such as INFORMS, ISE, and POMS, reflecting her commitment to advancing operations research and systems engineering methodologies.
Albert Atserias is a Professor in the Department of Computer Science at the Universitat Politècnica de Catalunya (UPC), affiliated with the Faculty of Informatics of Barcelona (FIB) and the ALBCOM research group (Algorithms, Bioinformatics, Complexity, and Formal Methods). He is also associated with the Institut de Matemàtiques de la UPC-BarcelonaTech. His research is central to theoretical computer science, with a strong emphasis on logic and complexity. Atserias's research interests span Computational Complexity, Logic in Computer Science, Finite Model Theory, Proof Complexity, and Constraint Satisfaction Problems . His work explores the fundamental limits of computation, the expressive power of logical languages over finite structures, and the complexity of proving mathematical statements. He investigates the algebraic and combinatorial properties of proof systems, the limits of efficient algorithms for constraint solving, and the theoretical foundations of databases. His research often bridges logic, algebra, and combinatorics to provide deep insights into computational phenomena. The trends in his recent publications show a sustained focus on the logical and algebraic underpinnings of computational problems. Key themes include the consistency and complexity of database queries , the power and limitations of proof systems (like resolution and sum-of-squares), and the expressive power of homomorphism counts in graph theory. His work on the hardness of automating resolution and the development of circular proof systems are particularly significant contributions to proof complexity. The 2024 PODS Best Paper Award for work on relational consistency underscores the impact and timeliness of his research. Among his notable scientific awards are the prestigious ICREA Acadèmia , the PODS 2024 Best Paper Award , the Premi Extraordinari de Doctorat (Extraordinary Doctoral Prize), and the Kleene Award for Best Student Paper . These accolades reflect both the excellence of his early work and his continued leadership in the field. Atserias has been a principal investigator on numerous competitive research projects, including funding from the European Research Council (ERC) and the Spanish Ministry of Science. He has advised doctoral students, such as Toni Hakoniemi, whose thesis on proof complexity he supervised. His extensive collaborative network includes leading researchers like Phokion Kolaitis, Anuj Dawar, and Victor Dalmau. He has also served on the scientific committees of major conferences, contributing to the academic community. He is a core member of the ALBCOM research group , a leading team at UPC focused on theoretical aspects of computer science, which provides a vibrant environment for research in algorithms, complexity, and formal methods. His work is also connected to the broader Institut de Matemàtiques de la UPC, fostering interdisciplinary collaboration between computer science and mathematics.
