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Karthik Sankaranarayanan is an Assistant Professor in the Department of Agricultural & Biological Engineering at Purdue University. His research focuses on applying data science to enzymology, developing computational models for enzyme reaction chemistry to address manufacturing challenges in pharmaceuticals, agrochemicals, and food flavoring. He leads the KSankar Group, which emphasizes experimentally testable predictions for complex synthetic molecules. His work bridges computational methods with biological engineering. He holds a position in the College of Engineering at Purdue, with affiliations to the ADM Agricultural Innovation Center and the LORRE Integrative Center for Biotechnology & Engineering. His research interests include enzymatic synthesis optimization, computational modeling of biochemical pathways, and sustainable biomanufacturing processes. He collaborates with institutions like the Alfred Mann Institute for Biomedical Development through Discovery Park. Key areas of exploration include similarity-based retrosynthesis algorithms, tunable enzymatic production of immunomodulatory lipids, and the use of machine learning for predicting enzymatic reaction outcomes. His lab develops tools for efficient biomanufacturing and analyzes structure-activity relationships in biomolecules. Dr. Sankaranarayanan’s research is supported by facilities like the Maha Fluid Power Research Center and the Bindley Bioscience Center. His work aims to enhance manufacturing efficiency while addressing societal needs in healthcare and agriculture through interdisciplinary engineering approaches.

• Biological Engineering
• Data Science and Digital Agriculture
• Enzymatic Reaction Chemistry
• +4 more

David D. Gamarnik is a Professor of Operations Research at the MIT Sloan School of Management, affiliated with the Operations Research and Statistics Group. He holds a BA in Mathematics from New York University (1993) and a PhD in Operations Research from MIT (1998). Prior to joining MIT in 2005, he was a Research Staff Member at IBM T.J. Watson Research Center. His research focuses on discrete probability, optimization, quantum computing, machine learning, and stochastic processes. He serves as an Area Editor for Mathematics of Operations Research and has held editorial roles in journals such as Operations Research and Annals of Applied Probability . Education: Bachelor of Arts in Mathematics, New York University (1993) PhD in Operations Research, MIT (1998) His honors include the Erlang Prize, Best Publication Award from INFORMS’ Applied Probability Society, and recognition as a finalist for the Franz Edelman Prize. He is a Fellow of the Institute for Mathematical Statistics, Institute for Operations Research and Management Science, and the American Mathematical Society. His academic contributions span theoretical computing, algorithm design, and applications in healthcare (e.g., kidney exchange models). Recent work explores algorithmic barriers in optimization, quantum computing, and high-dimensional statistics.

• Discrete Probability
• Optimization and Algorithms
• Quantum Computing
• +3 more

Traian TULBURE is a Lecturer at the Department of Electronics and Computers within the Faculty of Electrical Engineering and Computer Science at the Technical University of Brașov. His research focuses on Very Large Scale Integrated Circuits, Digital Electronics, and reconfigurable architectures for structured ASIC technology. He has contributed to significant projects including the ATLAS experiment at CERN, particularly in developing readout controllers for muon spectrometers. Key research interests include optimizing testability in dynamic reconfigurable CPLD architectures and advancing structured ASIC applications. His work bridges microelectronics innovation with high-energy physics instrumentation, demonstrated through collaborations on particle detector systems. Publications emphasize both theoretical advancements (e.g., dynamic reconfiguration methodologies) and applied contributions to large-scale physics experiments. No scientific awards are explicitly mentioned, though his involvement in international collaborations like ATLAS highlights his professional standing. Advising activities and grant details are not detailed in the provided text. He has contributed to detector hardware development for the ATLAS New Small Wheel upgrade, reflecting his role in experimental physics instrumentation.

• Very Large Scale Integrated Circuits
• Digital Electronics
• Microelectronics
• +4 more

Hamed Hatami is an Associate Professor in the School of Computer Science at McGill University, with cross-appointment in Mathematics. His research lies at the intersection of theoretical computer science and mathematics, focusing on analytic methods in complexity theory, learning theory, analysis of Boolean functions, and additive combinatorics. Hatami received the IEEE Computer Society Technical Achievement Award in 2012 for his contributions. He has published extensively on graph theory, property testing, communication complexity, and combinatorial limits. Hatami teaches advanced courses in algorithm design, complexity theory, and analysis of Boolean functions, and mentors graduate students through research collaborations. Prior to McGill, he held positions at the Institute for Advanced Study and Princeton University.

• Theoretical Computer Science
• Complexity Theory
• Analysis of Boolean Functions
• +2 more

Vasileios Tenentes is an Associate Professor at the Department of Computer Engineering and Informatics, University of Ioannina, Greece. He has held research positions as a Postdoctoral Researcher at the University of Southampton (2014–2018) and a Research Engineer at ARM Ltd (2017–2018). His work focuses on reliability engineering and energy efficiency in embedded systems, with expertise in hardware/software co-design, fault modeling, and VLSI architectures. Education: B.Sc. in Computer Science, University of Piraeus, 2003 M.Sc. in Computer Science, University of Ioannina, 2007 Ph.D. in Computer Science and Engineering, University of Ioannina, 2013 Research Interests: He investigates design methodologies for testability, dependability (reliability, availability, maintainability, security), and energy efficiency in digital systems. His work addresses challenges in many-core architectures, signal/power reliability characterization, and wear-out effects in IoT applications. He contributed to the PRiME project (collaborating with Imperial College, Manchester, Newcastle, and industry partners like ARM Ltd) to optimize embedded system performance and reliability. Scientific Awards: Best Doctorate (Aristoukhos Didaktes) in Informatics, University of Ioannina (2013) Advising and Grants: Advises students via MS Teams under the 'student advisor' program. His Ph.D. research was funded by the Hercules II program, which accepted only 10% of proposals. He co-developed novel architectures for control systems and participated in the PRiME project, a collaborative effort to reduce energy consumption and improve reliability in multi-core embedded systems. Labs and Teams: Affiliated with the VLSI Systems and Computer Architecture Laboratory (VCAS) at the University of Ioannina, focusing on advanced system design and reliability assessment.

• Embedded Systems
• Hardware/Software Co-Design
• Electronic Design Automation
• +5 more

Igor Shinkar is an Associate Professor in the School of Computing Science at Simon Fraser University (SFU). His research focuses on theoretical computer science, discrete mathematics, and probability theory, with particular interests in computational complexity, property testing, and local-to-global phenomena in combinatorial objects. He holds a PhD in Mathematics and Computer Science from the Weizmann Institute of Science (2014), an MSc from the same institution (2009), and a BSc in Mathematics and Computer Science from Tel Aviv University (2005). Shinkar has taught numerous courses at SFU, including CMPT 125 (Introduction to Computing Science and Programming II), CMPT 225 (Data Structures and Programming), CMPT 405/705 (Design and Analysis of Algorithms), and CMPT 706 (Design and Analysis of Algorithms for Big Data). He has also co-taught courses at UC Berkeley and NYU, focusing on topics like coding theory and approximation algorithms. His research explores the interplay between theoretical computer science and mathematics, with contributions to areas such as randomized algorithms, approximation algorithms, and probabilistically checkable proofs. He actively seeks MSc and PhD students interested in theoretical computer science.

• Computational Complexity
• Property Testing
• Discrete Mathematics
• +3 more

Marcelino Santos is a Full Professor at the University of Lisbon's College of Engineering, affiliated with INESC-ID research institute. He teaches courses such as Testable Integrated Circuits, Microelectronics, and Integrated Circuit Systems. His research focuses on analog/mixed-signal design, power management systems, biomedical circuits, and IoT applications with an emphasis on reliability and energy efficiency. Recent work includes high-precision temperature sensors, power management unit automation, and fault-tolerant circuit design. Publications highlight advancements in integrated circuit design (e.g., voltage references, modulators), biomedical applications (electrodermal activity sensing), and renewable energy systems (agri-PV integration). He has contributed to over 50 peer-reviewed articles spanning 20+ years, demonstrating sustained innovation in both technical and biomedical domains. Collaborations include industry partnerships for EDA tools (ActivIC) and medical device development. No scientific awards are explicitly listed, but his prolific output indicates significant contributions to multiple engineering disciplines. His work often bridges theoretical circuit design with practical applications in healthcare and sustainable energy sectors.

• Integrated Circuits
• Microelectronics
• Power Management Systems
• +5 more

Nur Ahmed is an Assistant Professor at the Walton College of Business , University of Arkansas, and a Digital Fellow at MIT Sloan and MIT CSAIL. His research focuses on the intersection of innovation, artificial intelligence (AI), and responsible AI, examining how firms balance knowledge spillovers and resource acquisition to maintain competitive advantage. His work employs big data, machine learning (including NLP methods like word embeddings and transformers), and qualitative interviews to build testable models for managerial and policy decision-making. Prior to academia, he worked as a Software Engineer at Samsung R&D Lab in Bangladesh, India, and South Korea, informing his research on corporate R&D and technological dynamics. His publications span top-tier journals like Science and Strategic Management Journal , with working papers recognized at conferences including SMS, EGOS, and AMCIS. His research has been cited in prestigious AI reports such as the National Security Commission on Artificial Intelligence and the Stanford AI Index . Media outlets like the Financial Times , Nature , and The New York Times have covered his work. Best PhD Paper Prize (nominated, SMS) Max Boisot Award (finalist, EGOS) Best Paper in Strategy (won, AMCIS) Honorable Mention (ASAC) Best Paper Award (Israel Strategy Conference, finalist) Awardee of multiple grants and scholarships, including the Plan for Excellence Doctoral Fellowship (Ivey Business School) and Maurice Young Centre Travel Grant (UBC). He co-organizes professional development workshops at AOM and SMS conferences, including panels on machine learning in management research and corporate R&D disclosure strategies.

• Innovation
• Artificial Intelligence
• Responsible AI
• +3 more

Li-C. Wang is a Professor at the Department of Electrical and Computer Engineering, University of California, Santa Barbara, affiliated with the Robert Mehrabian College of Engineering. His research focuses on Artificial Intelligence for Design and Test, Data Analysis, and Machine Learning. Dr. Wang's work spans interdisciplinary domains including nanotechnology, biomedical engineering, and wireless networks. His recent publications highlight innovative approaches in: AI-driven design validation protocols Nanostructured biosensors for glucose detection Multi-RAT network interference analysis Advanced photonic waveguide systems Topological state manipulation in materials These works demonstrate his commitment to advancing engineering methodologies across multiple domains.

• Artificial Intelligence for Design and Test
• Data Analysis
• Machine Learning
• +4 more

Karen Kheruntsyan is Professor of Theoretical Physics at the University of Queensland's School of Mathematics and Physics. With a career spanning over 25 years at UQ, he progressed from postdoctoral researcher to full professor, holding key roles including ARC Future Fellow and Chief Investigator in the ARC Centre of Excellence for Quantum-Atom Optics. His research explores fundamental quantum phenomena in ultracold matter, with current focus on quantum thermodynamics, hydrodynamic modeling of quantum gases, and macroscopic quantum entanglement. Professor Kheruntsyan's research interests center on: Quantum thermodynamics of nanoscale heat engines Stochastic hydrodynamic approaches to non-equilibrium quantum dynamics Emergent phenomena in mesoscopic quantum transport Bell inequality tests with ultracold atomic systems His work bridges theoretical physics with experimental quantum science, particularly in atomic Bose-Einstein condensates and degenerate Fermi gases. His recent publications demonstrate strong focus on quantum thermodynamics (50% of 2020-2025 papers) and hydrodynamic modeling of 1D quantum gases (30%), with applications in quantum engine design, entanglement verification, and non-equilibrium dynamics. The research consistently combines mathematical rigor with experimental testability, featuring high-impact results in Physical Review Letters and Nature-family journals. Professor Kheruntsyan actively mentors graduate researchers, with current projects including quantum hydrodynamics and ultracold gas thermodynamics. He has secured continuous ARC funding since 2003, including Discovery Projects and Fellowships totaling over $3M. His laboratory activities involve theoretical and computational work, with strong ties to experimental quantum gas groups worldwide.

• Quantum Thermodynamics
• Ultracold Atomic Gases
• Quantum Hydrodynamics
• +7 more

Britney Arce serves as Clinical Assistant Professor in the Community & Health Systems Department at Indiana University School of Nursing Bloomington Campus, where she has advanced rural health initiatives since 2019. Her interdisciplinary expertise bridges clinical nursing practice, public health research, and community engagement to address critical healthcare gaps in underserved populations. Dr. Arce's academic credentials include: PhD in Public Health from Indiana University MBA from Indiana Wesleyan University MSN from Indiana Wesleyan University BSN from Arkansas State University Her research program centers on developing context-appropriate preventative care models for rural communities, with dual emphasis on community health screening systems and nursing education integration. Dr. Arce pioneered a community-as-classroom approach where students deliver health services while collecting real-world data on rural health disparities. Her work specifically targets chronic disease prevention through culturally adapted interventions that overcome geographic and resource barriers. Dr. Arce's publication record demonstrates consistent focus on rural health innovation, exemplified by her 2021 telehealth childhood obesity study which established methodological frameworks for family-centered interventions in resource-limited settings. Her research consistently translates clinical observations into testable community solutions. Dr. Arce maintains active research funding through Indiana University's Center for Rural Engagement, with her current project optimizing community-nursing student partnerships for preventative care delivery. She strategically leverages these grants to create sustainable pipelines between academic training and community health needs. Through Agape Kids House of Lawrence County and campus-community partnerships, Dr. Arce cultivates practical learning environments where nursing students gain field experience while addressing urgent rural health needs. Her leadership fosters collaborative teams that include community health workers, nursing faculty, and local stakeholders in co-designing interventions.

• Rural Health
• Preventive Care
• Community Health
• +3 more

Dr. Borysław Paulewicz is an Assistant Professor in the Department of Experimental Psychology at Jagiellonian University's Faculty of Philosophy. Previously taught cognitive psychology and psychometrics at SWPS University for a decade. His work bridges cognitive psychology, mathematical methodology, and meta-theoretical foundations of psychology, with a focus on metacognition and causal inference. Current affiliations: Jagiellonian University, Faculty of Philosophy, Department of Experimental Psychology Past affiliations: SWPS University of Social Sciences and Humanities Research Interests: Specializing in causal/Bayesian inference applications, development of hierarchical generalized linear models, and formal definitions of measurement invariance. His projects explore metacognition, consciousness, and placebo mechanisms through National Science Centre grants (MAESTRO, HARMONIA, SONATA BIS/SONATA programs). Academic Contributions: Created R libraries for psychometric models, formulated causal-theoretical measurement frameworks, and authored a GitHub-hosted book on mathematics for psychologists. Published extensively in metacognition, signal detection theory, and causal modeling. Projects: Key investigator in studies on pain unlearning, metacognitive mechanisms, and cognitive control. Collaborated with Agata Blaut on emotional disorder biases, Michał Wierzchoń on consciousness, and Marta Siedlecka on metacognition.

• Cognitive Psychology
• Psychological Research Methodology
• Applications of Mathematics in Psychology
• +7 more

Thierry Bollon serves as a Senior Lecturer at the University of Savoie Mont Blanc in Chambéry, France, where he bridges social psychology and computational modeling through innovative connectionist approaches. His research fundamentally challenges traditional social psychology frameworks by simulating how social context shapes cognitive processing. Bollon's work centers on three interconnected domains: Social learning mechanisms in motor task acquisition Computational modeling of social norm internalization (exemplified by the NetNorm framework) Emotion-decision interactions, particularly how uncertainty appraisal modulates risk-taking in tasks like the Iowa Gambling Task His methodology uniquely integrates neural network architectures with social psychological theory to model contextual influences on cognition. Analysis of his 1995-2013 publications reveals consistent emphasis on computational social psychology, with recent work (2008-2013) increasingly focusing on emotion-cognition dynamics. Key themes include appraisal theory applications, social context simulation, and the development of specialized connectionist models for social phenomena. His research demonstrates how computational approaches can generate testable predictions about social behavior. While grant details aren't specified in available materials, Bollon maintains active collaborations with researchers including Virginie Bagneux, Pascal Pansu, and Adeline Paignon across multiple publications. His advising appears to focus on computational social psychology methods, with co-authors suggesting mentorship of junior researchers in neural network modeling applications.

• Social Psychology
• Neural Networks
• Learning
• +3 more