Amitai Shenhav is an Associate Professor at the University of California, Berkeley, specializing in Cognitive Neuroscience. His research explores the neural and computational mechanisms underlying motivation, affect, decision-making, and cognitive control, as detailed on the Shenhav Lab website . Ph.D., Harvard University Key research themes include: Explaining motivated behavior through affective gradients Modeling decision-making with mutual inclusivity and value integration Investigating cognitive control allocation under varying motivational contexts Understanding neural dynamics in target-distractor interactions Recent publications (2025–2024) highlight his work on value-based decision-making, effort allocation, and computational models of cognitive control. These studies often bridge behavioral experiments with neural recordings and theoretical frameworks. Scientific contributions include: NSF CAREER Award (2021) for research on motivation in cognition He mentors students and collaborators in his lab, focusing on psychophysiological experiments, computational modeling, and neuroeconomic paradigms. His work intersects with psychology, neuroscience, and artificial intelligence, particularly in attention training applications.
Maizie Zhou is an Assistant Professor in Biomedical Engineering and Computer Science at Vanderbilt University’s School of Engineering. She holds dual PhDs in Computer Science (Stanford University) and Neuroscience (Wake Forest School of Medicine), with additional degrees from Wake Forest University and Huazhong University of Science and Technology. Her research focuses on computational genomics, bioinformatics, and machine learning applied to problems in cancer genomics, single-cell and spatial transcriptomics, and computational neuroscience. She leads the Zhou Lab, which develops algorithms for structural variant detection, neural circuit analysis, and integrative omics approaches. Recent work includes tools like VolcanoSV and stDyer, and she has received grants from NIH, Vanderbilt Brain Institute, and industry partnerships. Key achievements include VUSE Best Paper Awards, Global Engagement Travel Grants, and mentoring students in prestigious programs like the Provost’s Pathbreaking Discovery Award. Her lab also explores the neural underpinnings of cognitive maturation in primates, combining computational and experimental neuroscience. Education: PhDs in Computer Science (Stanford) and Neuroscience (Wake Forest), MS (Computer Science, Wake Forest), BS (Biotechnology, Huazhong). Research interests span computational genomics (e.g., structural variant detection, haplotype phasing), spatial transcriptomics (clustering, integration), and computational neuroscience (neural circuit dynamics, prefrontal cortex plasticity). Her lab’s tools address challenges in precision medicine, cancer genomics, and understanding adolescent brain development. Recent projects include NIH-funded work on spatial transcriptomics and collaborations with Dr. Meltzer’s lab on cancer genomics. Publications highlight advancements in bioinformatics tools and neural mechanisms, with trends toward multi-omics integration and algorithmic innovation in genomics. Awards include the Global Engagement Travel Grant and CCSB Accelerator Fund. Students under her mentorship have excelled in qualifying exams and travel grants, reflecting her impactful training program.
Marcelo Mattar is an Assistant Professor of Psychology and Neural Science at New York University, leading the Mattar Lab. His research focuses on the neural computations underlying memory, decision-making, and reinforcement learning. He holds a Ph.D. in Psychology from the University of Pennsylvania and has held academic positions at NYU, UC San Diego, and postdoctoral roles at Princeton University and the University of Cambridge. His work bridges computational neuroscience and artificial intelligence, aiming to model how the brain uses internal models for planning and decision-making. Education: Ph.D. in Psychology (Computational and Cognitive Neuroscience), University of Pennsylvania, 2016 M.A. in Statistics, University of Pennsylvania, 2016 B.A. in Electronics Engineering, Instituto Tecnologico de Aeronautica, Brazil, 2010 Research Interests: The lab develops mathematical models of learning and decision-making, leveraging reinforcement learning, Bayesian statistics, and neural networks. Experiments involve human behavioral studies and neuroimaging, with collaborations in animal electrophysiology and computational psychiatry. Key Contributions: His work explores how episodic memory and hippocampal replay support flexible decision-making. Recent studies highlight parallels between human cognition and AI systems, such as language models' metacognitive abilities and brain-inspired algorithms. Awards: Newton International Fellowship, Royal Society (2018–2019) Lab Team: The lab includes postdocs, PhD students, and undergraduates from diverse fields like cognitive science, neuroscience, and computer science. Current members are listed on the lab's website. Lab Location: Meyer Hall, 6 Washington Place, New York, NY 10003.
Ahmed Ferhadi is a Clinical Professor of Middle Eastern & Islamic Studies at New York University (NYU), specializing in linguistics with a focus on Second Language Acquisition, Sociolinguistics, and Language Testing. He holds a PhD in Linguistics from the University of Michigan (1990), an MA in Teaching Arabic as a Foreign Language (TAFL) from the same institution, and an M.S. in Applied Linguistics from the University of Edinburgh. His teaching career spans institutions like Princeton University, Columbia University, and Kurdish universities such as Salahuddin and Suleimaniyya Universities. He pioneered video-based language performance evaluation methods during his tenure at Middlebury College in 1991, which became a standard practice. Ferhadi has also served in high-level advisory roles, including President of the Association of American Teachers of Arabic (AATA) since 2017 and Distinguished Global Scholar of Kurdish Studies at American University (2010). Research interests include Kurdish language standardization, advanced Arabic pedagogy, and the sociopolitical dimensions of language. His projects include a Kurdish standardization initiative and curriculum development for advanced Arabic learners. Ferhadi has received numerous accolades, including the Golden Teaching Award (NYU 2000) and Top Language Award (U.S. Department of State 2006). His publications span linguistic theory, pedagogical techniques, and cultural studies, with recent works addressing Arabic dialect dynamics and Kurdish language policy. Ferhadi’s work bridges academic rigor with practical applications, emphasizing technology’s role in modern language education.
Anshumali Shrivastava is an Associate Professor of Computer Science, Electrical and Computer Engineering, and Statistics at Rice University, affiliated with the George R. Brown School of Engineering. His research focuses on large-scale machine learning, randomized algorithms for big data, and graph mining. He holds a PhD from Cornell University (2015) and an MSc from the Indian Institute of Technology Kharagpur (2008). His research interests span scalable deep learning, efficient neural network inference, and probabilistic algorithms. He has pioneered techniques in compressed learning, hashing-based search, and distributed optimization for handling massive datasets. Notable contributions include methods for accelerating LLM inference, memory-efficient quantization, and graph processing algorithms. Teaching: Probabilistic Algorithms, Large-Scale ML, and Machine Learning Seminars Awards: Charles W. Duncan Jr. Achievement Award (2023), Young Faculty Research Award (2021), NSF CAREER Award (2017), and multiple best paper awards His work bridges algorithm design with practical applications in recommendation systems, genomics, and edge computing. Current efforts focus on sustainable AI, hardware-aware compression, and efficient training/inference pipelines for large models.
Dr. Stefanie Czischek is an Assistant Professor in the Department of Physics at the University of Ottawa, leading the APRIQuOt research group focused on artificial and physically realizable intelligence for quantum applications. She joined uOttawa in 2022 after postdoctoral work at the University of Waterloo. Her research bridges quantum technologies and neural networks, with expertise in quantum simulation, neuromorphic computing, and machine learning applications in quantum physics. Research Interests: Quantum computation/simulation using neural networks Neuromorphic hardware implementations Quantum many-body systems Machine learning for quantum control and tomography Her publications demonstrate strong interdisciplinary focus, combining quantum physics with cutting-edge ML techniques. Recent works explore transformer models for quantum simulation, neural network quantum states, and quantum sensing applications. The research shows consistent evolution toward hardware-algorithm co-design for quantum problems. Awards: Springer Thesis Award (2020) for doctoral research on neural-network simulation of quantum systems. Research Group & Advising: Leads the APRIQuOt lab with 1 postdoc, 6 graduate students, and 1 undergraduate. Current projects include large language models for quantum states, quantum optimal control via reinforcement learning, and neuromorphic quantum simulations. The group collaborates with experimental teams and maintains strong industry-academia partnerships.
Kamal Sen is an Associate Professor in the Department of Biomedical Engineering at Boston University, serving as Director of the Natural Sounds and Neural Coding Laboratory and Director of Admissions and Recruitment for Master’s Programs. He holds a PhD and MA in Physics from Brandeis University and a BA in Physics from Bates College. His research focuses on understanding how neurons encode natural sounds, particularly in the auditory cortex. Key areas include neural coding efficiency, hierarchical auditory processing, and the role of learning in shaping receptive fields. He developed the BOSSA algorithm to address sound segregation challenges in noisy environments, with applications for hearing aid technology. Sen’s work integrates electrophysiological techniques with theoretical approaches from signal processing, information theory, and systems theory. His lab explores neural discrimination of behaviorally relevant sounds and models cortical processing dynamics using computational frameworks. Recent studies investigate parvalbumin neuron contributions to temporal coding and cortical noise reduction in complex auditory scenes. His publications span neural circuit modeling, fNIRS applications in BCI, and biomimetic algorithms for auditory scene analysis. Research highlights include exploring schizophrenia-related gene effects on neural circuits and developing 3D neurosphere models for Parkinson’s disease.
William B Gartner serves as the Bertarelli Foundation Distinguished Professor of Family Entrepreneurship at Babson College's F.W. Olin Graduate School of Business, where he also directs research at the Bertarelli Institute for Family Entrepreneurship. Having joined Babson's faculty in 2017, Gartner brings over 40 years of experience studying entrepreneurial phenomena, with particular expertise in family entrepreneurship dynamics. He previously held distinguished chairs at the University of Southern California and Clemson University, establishing himself as a leading scholar in the field. Gartner earned his PhD, MBA, and BBA from the University of Washington, forming the foundation for his extensive research career. His academic journey began during entrepreneurship's early days in the late 1970s when he took one of the first entrepreneurship courses as an MBA student at the University of Washington, when only six students initially enrolled in the class. Gartner's research primarily explores how families act entrepreneurially, examining topics including entrepreneurial legacy, intergenerational knowledge transfer, and the transformation of family businesses across generations. His work bridges entrepreneurship theory with practical applications, particularly focusing on how entrepreneurial legacies can drive innovation in family businesses. He is particularly interested in the linguistic innovations entrepreneurs employ and how storytelling shapes entrepreneurial identity and practice. His scholarship has evolved to incorporate humanities perspectives, recognizing that literature, philosophy, and history provide valuable insights into entrepreneurial thinking and behavior. Analysis of Gartner's recent publications reveals a strong emphasis on family entrepreneurship as an ongoing process rather than a static state, with increasing attention to methodological innovation in entrepreneurship research. His work demonstrates growing interest in the role of narrative, visual methods, and qualitative approaches to understand complex entrepreneurial phenomena. The research shows consistent focus on family business succession as transformation rather than simple transfer, with increasing attention to adolescent entrepreneurial development and the influence of parenting styles. 2025 — Top 1% of Scholars in all subjects, Stanford/Elsevier's Top 2% Scientist Rankings 2024 — The FamCap25 – The Top FamilyEnterprise Academics, FAMILY CAPITAL MAGAZINE 2022 — Justin G. Longnecker Fellow, United States Association of Small Business and Entrepreneurship 2016 — Dedication to Entrepreneurship Award, Entrepreneurship Division, Academy of Management 2013 — Foundational Paper Award, Entrepreneurship Division, Academy of Management 2005 — International Award for Entrepreneurship and Small Business Research Gartner actively mentors doctoral students and early-career researchers, frequently collaborating on publications addressing complex family business dynamics. His editorial roles as Special Issue Editor for Entrepreneurship and Regional Development and Academy of Management Perspectives demonstrate his influence in shaping research agendas. He has secured significant research funding supporting studies on entrepreneurial legacy, family business succession, and adolescent entrepreneurial development, though specific grant amounts aren't detailed in the available materials. As Director of Research at the Bertarelli Institute for Family Entrepreneurship, Gartner leads initiatives connecting academic research with practical applications for family businesses. His work emphasizes the transformative potential of entrepreneurship within family contexts, challenging traditional notions of family business succession as mere transfer of ownership. Through workshops and seminars, he helps families explore how entrepreneurial legacies can lead to new innovations while preserving core values across generations.
Aswin Sankaranarayanan is a Professor in the Department of Electrical and Computer Engineering at Carnegie Mellon University (CMU) , where he leads the Image Science Lab . His research focuses on computational photography , 3D shape estimation , and novel imaging system design . He earned his Ph.D. in Electrical and Computer Engineering (2009) from the University of Maryland and completed a postdoctoral fellowship at Rice University (2012) . Research Themes: Developing imaging systems that exploit low-dimensional signal models to overcome traditional sensing limitations Co-design of optics and processing algorithms for efficient sensing Application of non-linear signal models to high-dimensional data Advancing compressed sensing and big data processing techniques Scientific Recognition: SIGGRAPH 2023 Best Paper Award (Split-Lohmann Multifocal Displays) CVPR 2019 Best Paper Award (Fermat Paths for NLOS Reconstruction) NSF CAREER Award (2017) Dean’s Early Career Fellowship (2018-2021) Herschel Rich Invention Award (2016) Technical Contributions: His recent publications reveal expertise in non-line-of-sight shape reconstruction , VR/AR display systems , and biomedical imaging . Collaborations span institutions like University College London and University of Toronto.
M. Tamer Özsu is a University Professor of Computer Science at the David R. Cheriton School of Computer Science, University of Waterloo, where he holds a Cheriton Faculty Fellowship. He also serves as a Distinguished Visiting Professor at Tsinghua University and is the Founding Director of Waterloo-Huawei Joint Innovation Laboratory since 2018. His extensive contributions to computing have earned him numerous prestigious awards including the 2024 ACM Presidential Award for long-standing and significant contributions to the computing field. Professor Özsu's research focuses on data engineering aspects of data science, particularly addressing data management issues with two main foci: management of non-traditional data and large-scale distributed data management. He is renowned for his seminal book "Principles of Distributed Database Systems" (co-authored with Patrick Valduriez), now in its fourth edition, and the "Encyclopedia of Database Systems" (co-edited with Ling Liu), in its second edition. His work bridges theoretical foundations with practical system implementations, targeting grand societal challenges through computational approaches. His recent publications reveal a strong trend toward graph analytics, streaming data processing, and the integration of large language models with vector data management. The research shows increasing focus on GPU-accelerated graph processing, RDF query optimization, and multimodal data analysis, reflecting the evolution of data management challenges in the era of big data and AI. His work continues to address fundamental challenges in distributed data systems while adapting to emerging technologies and application domains. Scientific Awards and Fellowships ACM Presidential Award (2024) IEEE TCDE Education Award (2024) IEEE Innovation in Societal Infrastructure Award (2022) CS Can | Info Can Lifetime Achievement Award (2018/2019) ACM SIGMOD Test-of-Time Award (2015) ACM SIGMOD Contributions Award (2006) The Ohio State University College of Engineering Distinguished Alumnus Award (2008) Fellow of the Royal Society of Canada Fellow of the American Association for the Advancement of Science (AAAS) Life Fellow of the Association for Computing Machinery (ACM) Life Fellow of the Institute of Electrical and Electronics Engineers (IEEE) Fellow of the Asia-Pacific Artificial Intelligence Association (AAIA) Elected member of the Science Academy, Türkiye Professor Özsu has been deeply involved in academic leadership and community building. As Founding Editor-in-Chief of ACM Books (2013-2019), he launched a series that by 2019 had published 28 major books with another 30 under contract. His service to ACM, particularly through SIGMOD, has been exemplary and widely recognized. He directs the Waterloo-Huawei Joint Innovation Laboratory, which focuses on cutting-edge research in data management and distributed systems, fostering strong industry-academia collaboration.
Tom Schrijvers is a Professor at the Department of Computer Science in the Faculty of Engineering Science at KU Leuven, Belgium. He leads the Programming Languages Group within the Declarative Languages and Artificial Intelligence (DTAI) research group. His research focuses on programming languages, particularly functional and logic programming, with special emphasis on Haskell, type systems, and algebraic effects. His research interests include: Functional Programming, especially Haskell Type Systems and Type Theory Algebraic Effects and Handlers Logic Programming, particularly Prolog Constraint Programming Domain-Specific Languages Programming Language Theory Prof. Schrijvers' recent research has focused on effect systems, staged programming, and language composition. His work on algebraic effect handlers has been particularly influential, providing new insights into how effects can be modularly composed and handled in functional languages. He has also made significant contributions to the understanding of type classes and their implementation in Haskell. His publications demonstrate a consistent focus on practical applications of programming language theory, with work spanning from foundational type theory to applied domain-specific languages for areas like fluorescence microscopy. His research often bridges the gap between theoretical programming language concepts and practical implementation concerns. Prof. Schrijvers has supervised numerous PhD students to completion, including Pieter Wuille, Benoit Desouter, George Karachalias, Steven Keuchel, Amr Saleh, Alexander Vandenbroucke, and Ruben Pieters. He currently supervises PhD students Klara Mardirosian, César Santos, Gert-Jan Bottu, Koen Pauwels, Birthe van den Berg, and Roger Bosman. His research group has received funding from various sources including EU projects like GRACeFUL. The Programming Languages Group at KU Leuven, which he leads, focuses on functional (Haskell) and logic (Prolog, Datalog, CLP) programming languages, as well as general programming language theory. The group has been active in numerous research projects and collaborations across Europe.
Steven G. Kellman is a Professor of English at the University of Texas at San Antonio (UTSA), where he has taught since 1976. A leading scholar in comparative literature and literary translingualism, he was UTSA's first Ashbel Smith Professor (1995-2000) and has received numerous accolades including the National Book Critics Circle Balakian Citation and the New York Society Library Award for Biography. His academic foundation includes: Ph.D. in Comparative Literature, University of California at Berkeley (1972) M.A. in Comparative Literature, University of California at Berkeley (1969) B.A. in English & General Literature, State University of New York at Binghamton (1967) Kellman's research focuses on comparative literature, modern and contemporary literature, prose fiction, literary translingualism, film, and biography. He pioneered the study of writers who create in non-native languages, examining how linguistic displacement fuels creativity and shapes identity across cultural boundaries. His work bridges literary analysis with cultural history, particularly in American literature and biography. His recent publications demonstrate sustained engagement with translingualism, pandemic-era literary interpretation, and biographical recovery of marginalized voices. Articles from 2022-2025 reveal consistent thematic threads: re-examining classic texts like Camus' The Plague through contemporary crises, analyzing translingual writing as spiritual practice, and recovering erased narratives in literary partnerships. Honors include the National Book Critics Circle Nona Balakian Citation (2006), New York Society Library Award for Biography (2006), and Gemini Ink Literary Excellence Award (2008). Additional distinctions: UTSA COLFA Researcher of the Year Award (2019-2020) McGinnis-Ritchie Award for Nonfiction (2008) San Antonio Public Library Foundation Arts and Letters Award (2005) Two UTSA President's Distinguished Achievement Awards in Research Excellence (1990-91, 2005-2006) Kellman has secured prestigious fellowships including the Fulbright Distinguished Chair at Sofia University and Harvard's John E. Sawyer Fellowship. He served four terms on the National Book Critics Circle board and edits Brill's Literary Multilingualism series, demonstrating sustained commitment to advancing literary scholarship through editorial leadership and institutional service.
Ruohan Gao is an Assistant Professor in the Department of Computer Science at the University of Maryland, College Park , with affiliate appointments at the University of Maryland Institute for Advanced Computer Studies (UMIACS) , Maryland Robotics Center (MRC) , and Artificial Intelligence Interdisciplinary Institute at Maryland (AIM) . His research focuses on Computer Vision and Machine Learning , emphasizing Multisensory Machine Intelligence that integrates sight, sound, and touch . He aims to enable machines to perceive, understand, and interact with the world as humans do, with applications in robotic manipulation , audio-visual localization , and differentiable rendering . Article Trends : Span 2018–2025 , centering on audio-visual perception , multisensory datasets , and robotics . Recurring themes include object-centric learning , sound synthesis , and cross-modal consistency . Scientific Awards : Michael H. Granof Award (UT Austin’s Top 1 Doctoral Dissertation, 2021) Best Paper Award Runner-Up (BMVC 2021) Best Paper Award Finalist (CVPR 2019) Highlight Paper (CVPR 2023) He leads the UMD Multisensory Machine Intelligence Lab and collaborates with institutions like Stanford and The University of Texas at Austin . Contact: rhgao@umd.edu .
Ravid Shwartz-Ziv is an Assistant Professor and Faculty Fellow at NYU's Center for Data Science, with a dual role as Senior Research Scientist at Wand AI. His research bridges theoretical foundations and practical applications in artificial intelligence, focusing on Large Language Models (LLMs), information theory, and neural network interpretability. Ph.D. in Computational Neuroscience, Hebrew University of Jerusalem (2021) B.Sc. in Computer Science and Computational Biology, Hebrew University of Jerusalem (2014) His research spans: Developing min-p sampling for LLM text generation Preventing representation collapse in Transformers Creating contamination-free LLM benchmarks like LiveBench Advancing information-theoretic frameworks for neural networks Exploring representation learning and model adaptation Recent publications demonstrate expertise in LLM efficiency, self-supervised learning, and multi-agent systems. Notable awards include the Google PhD Fellowship, Moore-Sloan Fellowship, and multiple best paper recognitions. He has led research initiatives at Intel and Google AI, focusing on neural network compression, XGBoost comparisons for tabular data, and innovative benchmarking frameworks.
Prof. Dr. Johanna Heitzer is a University Professor for Mathematics Education at RWTH Aachen University since 2011. She leads the Teaching and Research Area of Mathematics Education within the university's mathematics department. Her office is located in Room 352 of the Kreuzherrenstraße 2 building in Aachen. She serves as co-editor of the journal 'mathematik lehren,' co-author of the 'Mathematics - New Ways' textbook series, and holds numerous committee positions including membership in the Faculty Advisory Board and Structural Commission of the Center Council. 1989: High school diploma 1989-1994: Mathematics and Physics Teacher Training at RWTH Aachen 1994-1996: Traineeship at Aachen Teacher Training College 1997: Research assistant at University of Münster 1998-2007: Mathematics and Physics teacher at Korschenbroich Gymnasium 2007-2010: Scientific assistant and doctorate at RWTH Aachen 2011-present: University Professor at RWTH Aachen Professor Heitzer's research focuses on the training and further education of mathematics teachers, development of contemporary teaching materials, applied and interdisciplinary mathematics, and the transition from school to university. Her work emphasizes concept formation, linguistic communication in mathematics, and the historical development of mathematical ideas as teaching resources. She investigates mathematics-specific learning and cognitive processes through multiple research projects including the Aachen school-university project iMPACt. Her recent scholarly output demonstrates a strong trend toward integrating digital technologies in mathematics education, particularly 3D printing and e-learning tools. She has increasingly focused on the social relevance of mathematics, exploring concepts of fairness, sustainability, and citizen empowerment through mathematical modeling. Her work bridges theoretical mathematics education with practical classroom applications, maintaining a strong connection to both historical perspectives and contemporary educational challenges. Special prize from Sparkasse Bad Hersfeld-Rotenburg for best mathematics Abitur (1989) Borchers Plaque for doctoral examinations passed with distinction (2011) DMV honor as Mathemaker of the Month (2013) Brigitte Gilles Prize 2013 for the MINT-L4 Center Professor Heitzer has supervised numerous doctoral students, serving as primary or secondary advisor for at least nine PhD dissertations between 2016-2021. Her research projects include the School-University Project MathePlus Aachen (iMPACt), e-Learning 'Mathematics for Civil Engineers,' and the development of mathematics items for StudiChecks NRW. She has secured funding through the Quality Initiative for Teacher Education (both phases) and participates in the ComeIn project focused on digitalization in teacher training. Her grants consistently emphasize practical applications of mathematics education research with direct impact on classroom practice. As a founding member of the MINT-L4@RWTH center and initiator of the working group Mathematical Education for Sustainable Development, Professor Heitzer has established significant collaborative structures. She participates in the Subject Didactics Forum at the Teacher Training Center of RWTH Aachen and has served in leadership roles including Chair of the Center Council (2014-2016) and Board member of the Teacher Training Center (2014-2017). Her work connects with national and international networks through her membership in the Society for Mathematics Education (GDM), the German Association for Mathematics and Science Education (MNU), and the German Mathematical Society (DMV).
