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Mohamad AwadaView profile

Assistant Professor

• Machine Learning
• Data Science
• Sustainable Infrastructure
• +5 more

Mohamad Awada is an Assistant Professor in the Department of Civil and Urban Engineering at New York University (NYU Tandon School of Engineering) . His research focuses on integrating sensing systems , immersive technologies , and artificial intelligence to enhance human livability in built environments. Education Ph.D. in Civil Engineering, University of Southern California (USC), 2024 M.Sc. in Computer Science, University of Southern California (USC), 2021 M.Sc. in Civil Engineering (Construction Engineering and Management), American University of Beirut, 2018 B.S. in Civil and Environmental Engineering, American University of Beirut, 2016 His work spans machine learning , data science , and human-building interaction , with applications in sustainable infrastructure and smart cities . Recent research trends emphasize occupant health , stress detection , and AI-driven environmental optimization in office and residential settings. Scientific Awards : University of Southern California PhD Achievement Award – 2024 Sonny Astani CEE Outstanding Department Service Award - 2023 Sonny Astani CEE Outstanding Research Assistant Award - 2022 Awada leads the Smart Innovations in Built Environment Research (SiBER) Lab , where he explores automation, control systems, and AI applications to create healthier, energy-efficient infrastructure. He actively seeks Ph.D. candidates with coding and ML/data science experience for Fall 2025.

• Real Algebraic Geometry
• Convex Algebraic Geometry
• Sum of Squares
• +3 more

Rainer Sinn is a University Professor (on leave) at Leipzig University, specializing in Applied Algebra within mathematics. His research centers on real algebraic geometry, convex optimization, and sums of squares, with significant contributions to spectrahedra, amplituhedra, and nonnegativity certificates. His primary research interests include real algebraic geometry (focusing on nonnegative polynomials and quadratic forms), convex algebraic geometry (studying convex hulls of algebraic varieties), and combinatorial applications in optimization. He explores geometric structures like amplituhedra in theoretical physics and investigates algebraic solutions to optimization problems. Recent publications (2022-2025) demonstrate a cohesive focus on algebraic approaches to optimization, with recurring themes in nonnegativity certificates, tropical geometry, and combinatorial aspects of algebraic varieties. His German-language works also address the philosophy and public understanding of mathematics, highlighting interdisciplinary impact. No scientific awards were documented in the provided sources. Details regarding academic advising, research grants, laboratories, or collaborative teams were not specified in the available information.

Vijayaraghavan AravindanView profile

Associate Professor

• Theoretical Computer Science
• Machine Learning
• Quantum Information
• +3 more

Dr. Vijayaraghavan Aravindan is an Associate Professor in the Department of Computer Science at Northwestern University, with a courtesy appointment in Industrial Engineering & Management Sciences. He is a core member of the Theory CS Group and serves as the Site Director for the NSF-funded Institute for Data, Economics, Algorithms, and Learning (IDEAL). His research focuses on theoretical computer science, particularly algorithmic foundations of machine learning, quantum information, and beyond worst-case analysis. Education: Ph.D. and M.A. in Computer Science, Princeton University B.Tech. in Computer Science and Engineering, Indian Institute of Technology Madras Research Interests: His work bridges theoretical computer science and machine learning, emphasizing efficient algorithms for high-dimensional data, quantum entanglement certification, and adversarial robustness. He explores paradigms such as smoothed analysis and stability-based approaches to provide practical algorithmic guarantees. Awards & Grants: NSF CAREER Award Google Research Scholar NSF AITF Grants (CCF-1637585, CCF-2154100) Advising & Teaching: He advises PhD students on topics like quantum computing, robust machine learning, and optimization. Courses taught include graduate algorithms, theoretical foundations of data science, and quantum computation. His lab collaborates on projects at IDEAL and with institutions like Carnegie Mellon University and TTI Chicago. Professional Leadership: Served as FOCS 2024 General Chair and organizes the McCormick Theory Workshops. Active on program committees for ICML, COLT, and NeurIPS.

Jennifer L. SkeemView profile

Professor

• Behavioral Science
• Criminal Justice
• Violence Prevention
• +4 more

Professor Jennifer L. Skeem is a distinguished Professor of Social Welfare and Public Policy at the University of California, Berkeley, where she directs the Risk Resilience lab. Her work bridges behavioral science and criminal justice, focusing on violence prevention, decision-making within justice systems, and equitable justice reform. She has established herself as a leading expert in risk assessment, mental health policy, and interventions for justice-involved populations. Her research interests span three interconnected domains: Promoting Safety and Wellbeing through improved violence prediction and prevention; Transforming Services and Supervision by analyzing justice system decisions and identifying effective supervision and treatment approaches; and Reducing Inequality by studying how institutional responses to crime deepen disparities and developing evidence-based solutions. She has pioneered innovative correctional services for people with mental illness and developed the videogame-supported afterschool program Playing for Change to prevent aggression among at-risk youth. An analysis of her recent publications reveals a consistent focus on addressing racial disparities in criminal justice, particularly in pretrial detention and risk assessment. Her work increasingly integrates data science methods with behavioral science to develop more accurate prediction models while ensuring fairness. She has made significant contributions to understanding how environmental factors influence violence in institutional settings and how prosocial identity can serve as a target for youth justice interventions. Scientific Awards: 2025-26 Faculty Research Award by UC Berkeley's Institute for Research on Labor and Employment (IRLE) Gold at the 2024 Serious Play Awards for un-TILTED! game Past President of the American Psychology-Law Society Member of the MacArthur Foundation Research Network on Mandated Community Treatment Professor Skeem has delivered congressional briefings on violence prevention and consults with local and federal agencies on criminal justice reform. Her lab currently manages multiple significant projects including Playing for Change (funded by Arnold Ventures), psychiatric hospital safety initiatives (funded by the California Department of State Hospitals), and pretrial reform research (in partnership with Pretrial and Probation Services). She has mentored numerous students who have gone on to become researchers and practitioners in criminal justice and social welfare fields. The Risk Resilience lab, under her leadership, has established strong partnerships with the California Department of State Hospitals, Seneca's Unconditional Education Program, Bay Area schools, and federal judicial entities. The lab operates with a mission to 'build resilience, reduce risk, promote justice' through interdisciplinary research that directly informs policy and practice.

Chia-Yi ChiuView profile

Associate Professor

• Educational Measurement
• Nonparametric Cognitive Diagnosis
• STEM Assessments
• +3 more

Chia-Yi Chiu is an Associate Professor in Applied Statistics at Teachers College, Columbia University. She specializes in educational measurement and psychometrics, focusing on nonparametric cognitive diagnosis and small data applications in STEM assessments. Her work emphasizes real-time classroom monitoring and innovative algorithms for diagnostic testing. PhD: Quantitative Methodology, Measurement, and Evaluation (University of Illinois at Urbana-Champaign) MS: Statistics (University of Illinois at Urbana-Champaign) MA: Mathematics in Teaching (University of Northern Colorado) BS: Mathematics (National Taiwan Normal University) Her research advances classroom-level assessments through methods like the restricted DINA model and nonparametric CD-CAT, addressing challenges in small-sample settings. She collaborates widely, with notable contributions to Q-matrix theory and cognitive diagnosis algorithms. Recent publications explore method selections in cognitive diagnosis and Q-matrix optimization. Awards: John P. Yackel Professorship (2022-2024), Chancellor’s Scholar Award (2017-2022), and NCME Dissertation Award (2010). Dr. Chiu advises students on topics like diagnostic testing algorithms and adaptive assessment systems. Her grants include innovations in education technology and small-data psychometric methods.

Keenan CraneView profile

Associate Professor

• Computer Science
• Mathematics
• Scientific Computing
• +7 more

Keenan Crane is the Michael B. Donohue Associate Professor of Computer Science and Robotics at Carnegie Mellon University , with membership in the Center for Nonlinear Analysis and mentorship in the Geometry Collective . His research bridges differential geometry and computer science to develop fundamental algorithms for geometric data processing. Education : BS from University of Illinois at Urbana-Champaign, PhD from Caltech Fellowships : Google PhD Fellow, NSF Mathematical Sciences Postdoctoral Fellow Research focuses on Discrete Differential Geometry , addressing PDE solutions, mesh processing, and geometric modeling through methods like: Walk on Spheres for PDEs Intrinsic Triangulations for robust geometry Repulsive Energy formulations for collision avoidance Recent publications span 2025–2021 , emphasizing grid-free algorithms , anisotropic mesh generation , and differentiable systems . Scientific accolades include Packard Fellowship and NSF CAREER Award . Students include Nicole Feng , Olga Gutan , and Zoë Marschner . During his 2024 sabbatical at Roblox , he does not accept new researchers. Key software contributions include Penrose (math diagram generation) and I♥Mesh (domain-specific language for mesh algorithms).

Diptarka ChakrabortyView profile

Assistant Professor

• Theoretical Computer Science
• Algorithms on large data sets
• Sublinear algorithms
• +5 more

Diptarka Chakraborty is an Assistant Professor in the Department of Computer Science at the School of Computing, National University of Singapore (NUS). He obtained his PhD from the Indian Institute of Technology Kanpur in 2016 and held postdoctoral positions at Charles University, Prague, and the Weizmann Institute of Science, Israel. His educational background includes: PhD in Computer Science and Engineering, Indian Institute of Technology Kanpur, 2016 Dr. Chakraborty's research lies at the intersection of Theoretical Computer Science and Algorithms , specializing in sublinear algorithms , approximation algorithms , graph algorithms , and data structures for massive datasets. His work addresses computational hardness in edit distance, rank aggregation, and fault-tolerant graph structures, with recent emphasis on algorithmic fairness and streaming applications. Analysis of his 2020-2025 publications reveals dominant trends in fairness-constrained algorithms (e.g., fair rank aggregation) and scalable techniques for massive data (e.g., clustering permutations in streaming models), alongside foundational contributions to equivalence testing, model counting, and reachability under failures. His scientific contributions have been recognized with: Faculty Teaching Excellence Award 2024 Google South & Southeast Asia Research Award 2022 Best Paper Award at FOCS 2018 IBM Research India Outstanding Ph.D. Student Award 2015-16 Dr. Chakraborty actively mentors two PhD students and multiple research fellows while leading three major research projects: massive data handling under edit metrics, fair rank aggregation, and fault-tolerant graph structures. His Google Research Award supports investigations into computational challenges of fairness. He maintains an active research group with international collaborations and regular seminar participation across institutions including IITs, TIFR, and UC campuses.

Kasper WelbersView profile

Associate Professor

• Computational Communication Science
• Political Communication
• Journalism Studies
• +7 more

Kasper Welbers is an Associate Professor at the Department of Communication Science, Faculty of Social Sciences, Vrije Universiteit Amsterdam. He holds additional appointments at the Network Institute and the Communication Choices, Content and Consequences (CCCC) research group. He is co-Director of The Societal Analytics Lab, Vice Chair of the Computational Methods Division at the International Communication Association (ICA), and a scientific representative of the OPINION COST Action network. His research focuses on computational communication science, journalism, and political communication, particularly exploring how news spreads and the role of gatekeepers in media systems. He develops open-source tools for research and advocates for reproducible computational methods. Education and Academic Background: While formal education details are not explicitly provided, his academic trajectory is evident through his faculty role and publications. Research Interests: Welbers combines methodological innovations with substantive research on news diffusion, media gatekeeping, and computational text analysis. His work addresses topics like dark platforms' agenda-setting roles, automated content analysis techniques, and the impact of platform policies on data donation studies. He emphasizes ethical research practices and open-source infrastructure development. Grants and Awards: He received the Faculty of Social Sciences Dissertation Award (2017) and Teacher Talent Award (2019), highlighting both research and pedagogical excellence. Labs and Collaborations: His work is anchored in The Societal Analytics Lab, fostering interdisciplinary research on societal challenges through computational methods. Collaborations span global networks like the OPINION COST Action, addressing cross-cultural media dynamics. Teaching: He teaches courses such as Computational Analysis of Digital Communication and contributes to curriculum development in AI ethics and data science for societal issues.

Robert JacobView profile

Professor

• Human-Computer Interaction
• Brain-Computer Interfaces
• Implicit Interaction Techniques
• +3 more

Robert Jacob is a Professor of Computer Science at Tufts University's School of Engineering, where he leads research in human-computer interaction with particular focus on implicit brain-computer interfaces. His work bridges computer science, cognitive science, and interface design to create adaptive systems that respond to users' cognitive states without explicit input. His educational background includes a Ph.D. from Johns Hopkins University. Professional milestones include: ACM CHI Academy membership (2007) ACM Fellow designation (2016) Leadership roles as ACM SIGCHI Vice President and conference chair for CHI, UIST, and TEI Professor Jacob's research centers on implicit interaction techniques, particularly using fNIRS brain sensing to create adaptive interfaces. His work has evolved from foundational studies in reality-based interaction and tangible programming to current neuroadaptive systems that measure cognitive workload in real-time. This research spans domains including music learning, museum education, and general user interface adaptation. His publications reveal consistent focus on brain-computer interfaces since 2012, with increasing sophistication in physiological measurement and machine learning techniques. Recent work integrates multiple physiological signals beyond brain data to create comprehensive user state models. Major recognitions include: CHI 2016 Best Paper Award for music learning research CHI 2014 and 2012 Best Paper Honorable Mentions Keynote addresses at major conferences including Neuroadaptive Technology Conference (2017) Extensive media coverage in New Scientist, IEEE Computer, and Boston Globe Professor Jacob has mentored 17 PhD students who now hold faculty positions at institutions including Worcester Polytechnic Institute, Northwestern University, and Carleton University. His HCI Lab, located in the Joyce Cummings Center, receives funding from NSF and other sources supporting neuroadaptive interface research. Current projects focus on broadening implicit interaction to include multiple physiological measurements while maintaining user privacy and system transparency.

Marco DonatoView profile

Assistant Professor

• Hardware for Machine Learning
• Non-Volatile Memories
• SoC Design
• +5 more

Marco Donato is an Assistant Professor in both the Department of Electrical and Computer Engineering and the Department of Computer Science at Tufts University. He leads the TECS Lab (Testchip, Embedded Computing Systems) focused on hardware design for emerging applications. Prior to joining Tufts, he was a postdoctoral fellow at Harvard University's John A. Paulson School of Engineering and Applied Sciences. Dr. Donato received his academic training from prestigious institutions: Ph.D. in Electrical Sciences and Computer Engineering from Brown University (2016) M.Sc. in Electrical Engineering from Università di Roma La Sapienza, Rome, Italy (2010) B.Sc. in Electrical Engineering from Università di Roma La Sapienza, Rome, Italy (2008) Dr. Donato's research primarily focuses on designing reliable and energy-efficient hardware systems leveraging emerging technologies. His work centers on co-design methodologies for building specialized architectures for machine learning applications that utilize dense, fault-prone embedded non-volatile memories. He investigates noise modeling and reliability aspects of next-generation memory technologies, with particular emphasis on how these can be effectively integrated into system-on-chip (SoC) designs for edge computing and IoT applications. His research bridges the gap between circuit-level design and system-level architecture to create holistic solutions for hardware acceleration of machine learning workloads. Analysis of Dr. Donato's publication record reveals a strong focus on hardware acceleration for machine learning, particularly through innovative memory system designs. His work spans multiple domains including non-volatile memory technologies, energy-efficient circuit design, and flexible SoC architectures. A notable trend is his exploration of how emerging memory technologies can be leveraged to create more efficient implementations of deep neural networks, with particular attention to the trade-offs between reliability, density, and energy consumption. His research often involves full-stack approaches that consider everything from device physics to system architecture. Dr. Donato is actively involved in mentoring and has indicated he is "looking for Ph.D. students." His work has been supported by significant research grants that have enabled the fabrication of multiple test chips, as evidenced by his extensive publication record in top-tier venues including IEEE Journal of Solid-State Circuits, ISSCC, and MICRO. He leads the TECS Lab at Tufts University, which focuses on testchip development, embedded computing systems, and hardware acceleration. The lab appears to maintain connections with researchers at Harvard University and other institutions, reflecting Dr. Donato's collaborative approach to research. The lab's work emphasizes practical, real-world implementations of novel hardware concepts through actual silicon fabrication, which is relatively rare in academic settings.

Teng WuView profile

Professor

• Wind Engineering
• Hurricane Engineering
• Structural Engineering
• +8 more

Teng Wu is a Professor and Director of Graduate Studies in the Department of Civil, Structural and Environmental Engineering at the School of Engineering and Applied Sciences, University at Buffalo . His research focuses on wind engineering, hurricane risk assessment, and climate change adaptation in infrastructure systems. PhD, Civil Engineering, University of Notre Dame (2013) MS, Civil Engineering, University of Notre Dame (2012) MS, Bridge Engineering, Tongji University (2010) BS, Civil Engineering, Tongji University (2007) Minor, Financial Engineering, Fudan University (2006) Teng Wu's research spans multiple domains including Wind Engineering , Hurricane Engineering , Structural Engineering , and Climate Change Adaptation . He specializes in nonlinear aerodynamics, performance-based wind design, and computational fluid dynamics applications to infrastructure resilience. His recent publications focus on machine learning applications for storm surge prediction, wind-induced structural response analysis, and climate change-informed infrastructure recovery frameworks. Key methodologies include hierarchical deep neural networks, knowledge-enhanced models, and reinforcement learning-based control systems. Contact: tengwu@buffalo.edu | Office: 226 Ketter Hall, Buffalo, NY 14260

Karl GregoryView profile

Associate Professor

• Bootstrap Methods
• High-Dimensional Regression
• Nonparametric Regression
• +3 more

Karl Gregory is an Associate Professor in the Department of Statistics at the University of South Carolina, part of the McCausland College of Arts and Sciences. He holds a BS from Central Michigan University, and MS and PhD in Statistics from Texas A&M University, followed by a postdoctoral position at the University of Mannheim. His research focuses on bootstrap methods for high-dimensional regression, nonparametric regression, and sparse linear regression models. He currently serves as an Associate Editor for The American Statistician. Dr. Gregory’s work emphasizes methodological advancements in statistical inference, particularly in handling complex data structures such as group testing and high-dimensional settings. His contributions bridge theoretical statistics with practical applications in epidemiology and biomedical research. He is also actively involved in teaching, contributing to courses like STAT 516 and STAT 513. His research trends highlight interdisciplinary collaborations, integrating computational statistics with real-world challenges in health and data science. While no awards are explicitly listed, his editorial role underscores his impact on the statistical community. He maintains a lab focused on developing scalable statistical tools for modern datasets through his website and published works.

• Probabilistic Combinatorics
• Extremal Combinatorics
• Random Combinatorial Structures
• +3 more

Guillem Perarnau is an Associate Professor in the Department of Applied Mathematics at Universitat Politècnica de Catalunya (UPC), affiliated with CRM, IMTech, and BGSMath. He holds a PhD from UPC (advised by Oriol Serra) and was a CARP Postdoc Fellow at McGill University (2013–2015). From 2016 to 2019, he was a Lecturer at the University of Birmingham. His research focuses on Probabilistic and Extremal Combinatorics, Random Combinatorial Structures, and Discrete Stochastic Processes. Education: Bachelor, Master, and PhD in Mathematics at UPC. His academic journey includes postdoctoral research at McGill University and a Lecturer role at the University of Birmingham. Research interests include Percolation Theory, Random Graphs, Stochastic Processes, and Algorithmic Combinatorics. His recent work explores topics like percolation on dense graphs, random walk mixing times, and synchronization in automata. He is co-PI of the COCOA grant and coordinates the Spanish Discrete and Algorithmic Mathematics Network. He participates in the RandNET MSCA Exchange Programme. Key contributions include studies on giant components in directed graphs, phase transitions in Glauber dynamics, and extremal problems in graph colorings. His publications span top journals like Annals of Applied Probability , SIAM Journal on Discrete Mathematics , and Random Structures & Algorithms .

Lisa DuizerView profile

Professor

• Sensory Science
• Food for Older Adults
• Texture Analysis
• +3 more

Lisa Duizer serves as Professor and Department Chair in the Department of Food Science at the University of Guelph, where she has been faculty since 2009 after previous appointments at Acadia University's School of Nutrition and Dietetics and Massey University's Institute of Food Nutrition and Human Health. Her research addresses critical challenges in food for aging populations, focusing on sensory properties of foods designed for older adults with dysphagia or malnutrition. Education BASc in Applied Human Nutrition, University of Guelph (1990) MSc in Food Science, University of Guelph (1992) PhD in Food Technology, Massey University (2003) Research Interests Professor Duizer's work centers on sensory science applications for geriatric nutrition, investigating age-related changes in taste and texture perception. She develops evidence-based strategies for improving nutritional quality and acceptability of foods for elderly populations, including pureed food optimization, nutrient fortification techniques, and 'supermenu' development for long-term care settings. Her research integrates human subjects testing with rheological and tribological analysis to establish science-based standards for modified texture foods. Publication Trends Her recent publications (2022-2025) demonstrate expanding interdisciplinary scope, with increasing focus on emotional food responses, space food sensory perception, and plant-based alternatives. The predominant themes involve texture modification for dysphagia management, salt reduction strategies for older adults, and stakeholder-driven interventions for malnutrition in long-term care. This trajectory reflects growing integration of sensory science with healthcare delivery systems and innovative food product development. Scientific Awards No scientific awards were documented in the provided source materials. Advising and Grants Lisa Duizer mentors graduate students with rigorous emphasis on ethical conduct in human subjects research, requiring comprehensive understanding of participant interaction protocols. Her students from nutrition and food science backgrounds have pursued careers as registered dietitians, food product developers in multinational corporations, and research scientists. Her work with the Agri-Food for Healthy Aging group and University Health Network affiliation indicates substantial grant funding from health and agriculture sectors for projects targeting malnutrition intervention in aging populations. Labs and Teams She leads sensory evaluation laboratories within the Department of Food Science and serves as Research Scientist for the Agri-Food for Healthy Aging initiative. Her collaborative network includes the University Health Network, Ontario Ministry of Health and Long-Term Care, long-term care home operators, food distributors, and agricultural producers, facilitating translational research from laboratory to institutional meal service implementation.

Gholamreza AminView profile

Associate Professor

• Performance Measurement and Management
• Mathematical Programming
• Productivity and Efficiency Analysis
• +5 more

Gholamreza Amin is an Associate Professor of Management Science in the Faculty of Business at the University of New Brunswick (UNB), Canada. Previously, he taught at Sultan Qaboos University (Oman), where he received the Distinguished Research Award. He holds a Ph.D. in Operations Research from Azad University (2003), Iran. His roles include Associate Editor of the IMA Journal of Management Mathematics and SN Business and Economics , and participation in organizing international conferences. Dr. Amin's research focuses on Performance Measurement, Mathematical Programming, and Productivity Analysis using Data Envelopment Analysis (DEA), Ordered Weighted Averaging (OWA) Operators, and Inverse Optimization. He has published over 90 articles in top journals like European Journal of Operational Research and Annals of Operations Research , with over 3,200 citations. His recent work emphasizes DEA applications in mergers, efficiency evaluation, and environmental sustainability. Awards: UNB Merit Award (2019-2020), Teaching Excellence Award (2024), and Distinguished Research Award (Sultan Qaboos University). His teaching spans Operations Research courses including Management Science, Quantitative Methods, and Operations Management. He contributes to editorial work and conference organization, advancing the field's theoretical and applied dimensions.