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Tom SchrijversView profile

Professor

• Programming Languages
• Functional Programming
• Type Systems
• +6 more

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.

• Computer Learning
• Fuzzy Sets and Systems
• Decision Support Systems
• +3 more

Prof. Sadettin Emre Alptekin is a full Professor of Industrial Engineering at Galatasaray University, Faculty of Engineering and Technology, where he also serves as Vice Dean. Since joining the university as a research assistant in 2000, he has steadily advanced through the academic ranks, becoming an Assistant Professor (2006–2010), Associate Professor (2010–2023), and finally Professor in 2023. Education: PhD (Dr), Industrial Engineering, Istanbul Technical University, Institute of Science and Technology, 2001–2006 MSc, Industrial Engineering, Galatasaray University, Faculty of Engineering and Technology, 1999–2001 BSc, Industrial Engineering, Istanbul Technical University, Faculty of Management, 1995–1999 Languages: Advanced English (C1), Upper-Intermediate French (B2), Advanced German (C1) Research Interests: Prof. Alptekin’s research focuses on Computer Learning , Fuzzy Sets and Systems , and Decision Support Systems . His work integrates artificial intelligence, machine learning, and soft-computing techniques to solve complex industrial and managerial problems in areas such as supply chain management, quality function deployment, blockchain adoption, and mental-health prediction. Publication Trends: Across more than 50 refereed publications, Prof. Alptekin has consistently explored hybrid intelligent models that combine fuzzy logic, machine learning, and multi-criteria decision-making. Recent articles emphasize deep-learning-based anomaly detection in industrial time-series data, blockchain adoption in supply chains, and machine-learning applications in subjective well-being and mental-health modeling. Scientific Awards & Honors: No specific awards or medals are listed in the provided documents. Research Leadership & Funding: Since 2008 he has been the principal investigator (executive) of 12 nationally funded projects, covering topics such as Industry 4.0 sub-system design, Internet of Things applications, artificial neural networks in organizational decision-making, big-data analytics, and strategic decision processes. Graduate Advising: He has formally supervised at least 8 master’s theses and numerous undergraduate projects. Representative thesis titles include Gaussian-process-regression-based man-hour prediction, machine-learning-driven human-behavior modeling, recommender-system design for e-commerce, thyroid-nodule diagnosis from scintigraphic images, software-effort estimation via neural networks, spreadsheet heuristics for joint-replenishment problems, cross-selling decision systems in insurance, and profitability analyses of Turkish banks under disinflation. Laboratories & Teams: While no dedicated laboratory name is disclosed, his continuous role as Vice Dean and principal investigator implies active leadership of the Industrial Engineering department’s research clusters in intelligent systems and decision support technologies.

• Embedded Systems
• Cyber-Physical Systems
• Computer Architecture
• +5 more

Aviral Shrivastava is a Professor at the School of Computing and Augmented Intelligence, Arizona State University, leading the Make Programming Simple Lab. He holds a Ph.D. and M.S. from the University of California-Irvine (2006, 2002) and a Bachelor’s from IIT Delhi (1999). His research focuses on making programming simple for embedded and cyber-physical systems, with a particular interest in manycore and accelerated computing, software for CPS, and resilient/fault-tolerant computing. He has co-authored over 120 publications in top venues like DAC, ESWEEK, and ACM TECS, with more than 3000 citations and 5 granted patents. His work has been recognized with multiple awards, including the 2010 NSF CAREER award and best paper nominations. Research Areas: Embedded and Cyber-Physical Systems Compiler Design for Modern Architectures Resilient and Fault-Tolerant Computing Scientific Awards: 2010 NSF CAREER award DAC 2017 Best Paper Award Candidate VLSI 2016 Best Student Paper Award LCTES 2010 Second Highest Ranked Paper ASPDAC 2008 Best Paper Candidate Advising & Grants: He has mentored 9 Ph.D. and over 20 Masters students. His research has been funded by NSF, DOE, NIST, SFAZ, and industry partners, totaling $3.5M. He teaches courses on computer organization, architecture, and embedded systems, with student evaluations averaging over 4/5. He also serves as General Chair of Embedded Systems Week (ESWEEK) and holds editorial roles in IEEE ESL, ACM TCPS, and ACM TECS.

Conrad WattView profile

Assistant Professor

• WebAssembly
• Formal Verification
• Programming Languages
• +4 more

Conrad Watt is an Assistant Professor at Nanyang Technological University (NTU), Singapore , specializing in WebAssembly, formal verification, and concurrency. He previously served as a Research Fellow at Peterhouse, University of Cambridge, and earned his PhD under Peter Sewell. Co-chair of the W3C WebAssembly Community Group Active in WebAssembly standards development, including concurrency specifications Developed mechanizations in theorem provers like Isabelle/HOL Collaborator with industry (wasmtime engine) and academic teams on verification tools Research Focus: Formal verification of low-level languages, concurrency models, and security mechanisms for WebAssembly. His work bridges theoretical rigor with practical applications, including WasmRef-Isabelle and threads projects. Recent Trends: 2025 publications explore separation logic automation and concurrency experiments, while 2024-2023 work emphasizes specification toolchains (SpecTec), verified interpreters, and memory-safe execution techniques. Scientific Awards ACM Doctoral Dissertation Award Honorable Mention EAPLS Best Dissertation Award Advising: Supervises PhD students Qiyuan Xu and Antanas Kalkauskas. Collaborates with researchers like Philippa Gardner and Jean Pichon-Pharabod.

Mahesh TripunitaraView profile

Professor

• Information Security
• Authorization and Access Control
• Cryptographic Key Transport
• +7 more

Dr. Mahesh Tripunitara is a Professor in the Department of Electrical and Computer Engineering at the University of Waterloo, serving as Associate Chair for Undergraduate Studies. He holds a PhD (2005) and Master's (1995) in Computer Science from Purdue University, along with a BSc (1993) in Computer Science from Dalhousie University. His research focuses on information security, authorization mechanisms, cryptographic key management, and hardware security, with industry experience at Motorola's R&D labs and Silicon Valley. His work spans theoretical advancements like access control policy analysis and practical applications such as secure payments systems and IoT device reliability. Notable awards include the Best Student Paper at Usenix Security 2013 and Best Paper at ACM SACMAT 2013. He actively serves on program committees for major security conferences including CCS, CODASPY, and SACMAT. Recent publications highlight innovations in cellular security (SUCI-Catchers defense), role-mining optimization, and blockchain smart contract auditing. Teaching includes advanced algorithm design courses (ECE 406/606) and digital computation (BME 121). His research emphasizes balancing security rigor with usability in authorization systems and hardware protection mechanisms.

Zhendong SuView profile

Professor

• Programming Languages
• Compilers
• Software Engineering
• +3 more

Zhendong Su is a full professor in the Department of Computer Science at ETH Zurich since August 2018. Previously, he held a full professorship at UC Davis from 2003 until June 2019. He earned his Ph.D. in Computer Science from UC Berkeley and dual Bachelor’s degrees in Computer Science and Mathematics from UT Austin in 1995. Affiliations: ETH Zurich: Full Professor (since 2018) UC Davis: Full Professor and Chancellor’s Fellow (2003–2019) IEEE Fellow, ACM Fellow, and Member of Academia Europaea His research focuses on programming languages, compilers, software engineering, computer security, and education technologies . Key contributions include compiler validation (e.g., Project Yin-Yang for SMT solvers and DBMS testing), testing tools like SQLancer, and educational innovations such as the Algot visual programming language. Recent work emphasizes secure AI (e.g., CipherSteal for TEE-shielded models) and compiler reliability (e.g., Artemis/Apollo for JIT validation). He has pioneered techniques like metamorphic testing and equivalence modulo inputs (EMI) for compiler validation, uncovering thousands of bugs in GCC/LLVM and SMT solvers. Awards: ICSE MIP Award (2022), ACM SIGSOFT Impact Paper (2018), NSF CAREER Award, and multiple industrial awards. His students have won IEEE TCSE Rising Star and SIGSOFT Impact Paper awards, securing roles at top universities and companies like Google and NVIDIA. Service: Steering committee member of ISSTA and ESEC/FSE, ACM Distinguished Speaker, and Associate Editor for ACM TOSEM. Program chaired ISSTA 2012 and co-chaired FSE 2016. Labs/Teams: Leads research groups on compiler validation, secure AI, and education technologies. Projects include Yin-Yang (SMT testing), SQLancer (DBMS fuzzing), and Algot (visual programming for education).

Ross ThyerView profile

Assistant Professor

• Synthetic Biology
• Protein Engineering
• Genetic Code Expansion
• +6 more

Ross Thyer is an Assistant Professor in the Department of Chemical and Biomolecular Engineering at Rice University. He holds a BSc (Hons) from the University of Western Australia and a PhD from the Harry Perkins Institute of Medical Research under Drs. Rackham and Filipovska. His postdoctoral training at the University of Texas at Austin with Prof. Andrew Ellington focused on engineered biosynthesis pathways and non-canonical amino acids. He co-founded GRO Biosciences, a Boston-based biotech startup, and leads the Thyer Lab at Rice. His research bridges synthetic biology, protein engineering, and molecular programming to address global challenges. Key areas include expanding genetic codes for therapeutics, engineering biosynthetic pathways via genetic circuitry, and developing microbial systems for environmental bioremediation. Core technologies include deep learning for protein design, modular DNA assembly, and high-throughput selections. The lab also develops tools like MutCompute for enzyme engineering and domesticates non-model bacteria for bioproduction. His work emphasizes technology innovation, with recent advances in selenocysteine incorporation, L-DOPA sensing systems, and actinobacteria toolkits. The Thyer Lab actively collaborates on biocatalyst development and translational applications in healthcare and industry.

KHOO Siau ChengView profile

Associate Professor

• Programming Languages
• Code Analytics
• Software Engineering
• +7 more

KHOO Siau Cheng is an Associate Professor in the Department of Computer Science at the National University of Singapore (NUS) School of Computing. He also serves as Co-Director of the NUS Business Analytics Centre, established in 2013 in collaboration with the Economic Development Board of Singapore and IBM. His academic journey began with a Ph.D. in Computer Science from Yale University in 1992. Professor Khoo's research focuses on improving software developer productivity through advanced programming language theories and software engineering techniques. His work spans multiple areas including static program analysis, dynamic program optimization, code analytics, and specification mining. He has applied his expertise to develop domain-specific languages for financial data analysis and has pioneered techniques for discovering dynamic program behaviors via data-mining approaches. Research Interests: Programming Languages and Software Engineering Code Analytics and Program Analysis Specification Mining and Bug Signature Discovery Static and Dynamic Program Analysis Program Transformation and Optimization Domain-Specific Languages Professor Khoo's publications demonstrate a consistent focus on improving software quality and developer productivity. His recent work emphasizes scalable approaches to refactoring detection, bug signature mining, and specification inference, showing an evolution from theoretical foundations to practical applications in software maintenance and quality assurance. As an educator and mentor, Professor Khoo has supervised numerous graduate students to completion of their M.Sc. and Ph.D. degrees. His research projects have provided valuable training opportunities for students while addressing important challenges in software development. He has also contributed significantly to academic administration, having served as Vice Dean (Undergraduate Studies) in the School of Computing from 2005 to 2011 and currently as Co-Director of the Master of Science (Business Analytics) Programme.

Sharad MalikView profile

Professor

• Computer Science
• Hardware Security
• Digital Systems Design
• +5 more

Sharad Malik is the George Van Ness Lothrop Professor of Engineering at Princeton University's Department of Electrical and Computer Engineering. His research focuses on designing functionally correct and secure computing systems, combining system design with mathematical modeling for verification. He pioneered the Instruction-Level Abstraction (ILA) model for SoC verification and has contributed extensively to Boolean satisfiability (SAT) solvers. Education: PhD (1990), M.S. (1987) in Computer Science from UC Berkeley; B.Tech. (1985) in Electrical Engineering from IIT Delhi. Research Interests: Formal Verification of Digital Systems Hardware Security and Trust Boolean Satisfiability Solvers System-on-Chip (SoC) Design Accelerator-rich Platform Architectures Notable Achievements: IEEE CEDA A. Richard Newton Technical Impact Award (2017) 2013 IEEE/ACM DAC Most Cited Paper Award Princeton President’s Distinguished Teaching Award (2009) Advising & Labs: Leads the Malik Group, advising over 50 graduate students and postdocs. Active in postdoc recruitment and mentorship programs.

Muhammad Ali GulzarView profile

Assistant Professor

• Fault Localization
• Big Data systems
• Data Intensive scalable computing
• +5 more

Muhammad Ali Gulzar is an Assistant Professor in the Computer Science Department at Virginia Tech and an Amazon Scholar at Amazon Web Services. His research focuses on improving developer productivity through automated debugging and testing for applications in emerging domains, including data-intensive software such as dataflow programs, ML/AI applications, and computational notebooks. Education Ph.D. in Computer Science from University of California, Los Angeles (Google Ph.D. Fellow 2017-2020) Research Interests Gulzar's research spans three primary areas: (1) automated tracking-code localization techniques in web applications, (2) re-engineering testing and debugging for data-intensive applications, and (3) advancing current testing and debugging practices in Federated Learning Applications. His work addresses the challenges of debugging in complex systems where traditional approaches fail due to the scale and distributed nature of modern applications. His research has significant implications for improving software quality, developer productivity, and accessibility in web applications. Research Trends Recent publications demonstrate a strong focus on debugging and testing challenges in emerging application domains. His work bridges traditional software engineering with machine learning, data-intensive systems, and web technologies. Notably, he has made significant contributions to Federated Learning debugging (FedDebug), accessibility challenges in ad-driven web applications, and semantic caching for Large Language Models. His approach often combines novel algorithmic insights with practical implementations that address real-world challenges in software development and maintenance. Scientific Awards Google Ph.D. Fellow (2017-2020) $1.1 million NSF award for Federated Learning research ACM CCS 2024 Distinguished Artifact Award Advising and Grants Gulzar leads a productive research group with multiple students contributing to publications in top-tier venues. His NSF-funded research on Federated Learning demonstrates his ability to secure competitive funding for innovative projects. His advising style appears to emphasize practical impact alongside theoretical contributions, with students often taking lead roles in publications. Current research directions include debugging techniques for Large Language Models, accessibility challenges in modern web applications, and novel testing approaches for distributed data processing systems.

Ana Paulina LeeView profile

Assistant Professor

• Literature and the social imagination in the Americas
• Slavery, emancipation, and the legacies of colonialism
• Magic and the state in Latin American cultural texts
• +3 more

Ana Paulina Lee is an Assistant Professor of Writing and Digital Humanities in the Comparative Literature, English, and Creative Writing Department at the American University of Paris. She joined AUP in 2025 after teaching Latin American literature and culture at Columbia University for eight years and serving as a Mellon Postdoctoral Fellow at Tulane University. Her interdisciplinary scholarship bridges literature, history, law, and urban studies with particular focus on marginalized narratives in the Americas. Ph.D. from University of Southern California M.A. from New York University B.A. from Binghamton University Professor Lee's research examines how stories—archival, fictional, oral—shape legal imagination, social life, and urban experience in the Americas. She specializes in the intersections of magic, law, and literature in Latin America, with particular focus on Asian and African diasporas in Brazil. Her work traces how narrative functions as both historical record and catalyst for social transformation, exploring themes of slavery, emancipation, colonial legacies, and urban memory. She approaches these topics through interdisciplinary lenses combining literary analysis with histories of labor, migration, and performance. Lee's recent publications reveal a consistent focus on marginalized narratives and their impact on social structures. Her scholarship connects legal history with cultural production, showing how magical thinking and narrative practices shape urban spaces and social relations. Works like 'Urban Sorcery, Segregation, and Spectacle in Twentieth-Century Rio de Janeiro' and 'Black Magic City' demonstrate her innovative approach to understanding the relationship between occult logics and legal frameworks in Brazilian contexts. Her research on diasporic communities, particularly Chinese and Japanese experiences in Latin America, contributes significantly to transnational and comparative race studies. David B. Larson Fellowship in Health and Spirituality, Kluge Center, United States Library of Congress (2025-2026) National Endowment for the Humanities Fellowship (2025) Social Sciences Research Council, Inaugural Recipient of Interdisciplinary Projects in the Social Sciences (2020-2023) Institute for Ideas and Imagination Fellowship, Columbia University in Paris, France (2021-2022) Provost's Tsunoda Senior Fellowship, Visiting Scholar, Waseda University, Tokyo, Japan (2019) Heyman Center Fellow, The Heyman Center for the Humanities, Columbia University (2017-2018) Fulbright, Lisbon, Portugal (2013-2014) Professor Lee's scholarly activities have been generously supported by major research grants from the National Endowment for the Humanities, the Social Sciences Research Council, and the Library of Congress. Her work demonstrates a commitment to interdisciplinary approaches that bridge humanities and social sciences, particularly through projects examining intersections of race, migration, and cultural production in the Americas. Her publication 'Socially Engaged Oral History Pedagogy Amid the COVID-19 Pandemic' indicates a strong commitment to innovative teaching methods and community-based research. While specific details about current advisees aren't provided, her research program suggests engagement with graduate students interested in diaspora studies, urban history, and literary-cultural analysis. Though not explicitly mentioned in the available information, Professor Lee's research on urban spaces, magic, and law in Brazil likely involves collaborative work with scholars across disciplines. Her participation in centers like the Institute for Ideas and Imagination at Columbia University suggests engagement with interdisciplinary research communities focused on global cultural production and social transformation. Her work contributes to ongoing conversations about how narrative practices shape legal frameworks and social structures in the Americas, particularly regarding racial formation and diasporic experiences.

Tom ConteView profile

Professor

• Computer Architecture
• Compiler Optimization
• Manycore Architectures
• +4 more

Tom Conte is an academic leader with a joint appointment in the School of Electrical & Computer Engineering and School of Computer Science at Georgia Institute of Technology. As the founding director of the Center for Research into Novel Computing Hierarchies (CRNCH), he specializes in computer architecture and compiler optimization. His work focuses on manycore architectures, energy-efficient microprocessor design, and embedded system architectures. Prior to Georgia Tech, he directed the Center for Embedded Systems Research at North Carolina State University. He holds IEEE Fellow status and served as 2015 President of the IEEE Computer Society, co-leading the IEEE Rebooting Computing Initiative since 2011. Dr. Conte earned his bachelor’s degree in Electrical Engineering from the University of Delaware (1986), followed by M.S. and Ph.D. degrees in Electrical Engineering from the University of Illinois at Urbana-Champaign (1988 and 1992). His research has been recognized with prestigious awards including the IEEE Computer Society’s Golden Core Member award and the National Science Foundation’s CAREER Award (1996). His research interests span quantum computing, 3D chip architectures, energy-efficient processing, and post-Moore computing innovations. He has pioneered initiatives like the Superstrider architecture and CREEPY energy-efficient processing frameworks. Recent work includes advancements in quantum programming languages (e.g., Qwerty) and hybrid quantum-classical systems. Awards: IEEE Fellow, Young Alumni Achievement Award, CAREER Award Leadership: IEEE Computer Society President (2015), CRNCH Director Key Projects: Rebooting Computing Initiative, Superstrider Architecture His lab’s contributions include novel compiler optimizations for manycore systems, smart NIC offloading techniques, and thermodynamically inspired computing models. Conte’s work bridges academic research with industry needs through interdisciplinary collaborations and standardization efforts.

Simon ShogryView profile

Associate Professor

• Ancient Philosophy
• Stoicism
• Hellenistic Philosophy
• +3 more

Simon Shogry is an Associate Professor in the Faculty of Philosophy at the University of Oxford and a Tutorial Fellow at Brasenose College. He specializes in Ancient Philosophy, with a focus on Stoicism, Hellenistic ethics, and epistemology. His research explores topics such as Stoic moral psychology, the rationality of perception, and the interplay between logic and ethics in ancient thought. Shogry earned his Ph.D. from Princeton University’s Program in Classical Philosophy and holds a B.A. from Claremont McKenna College. Before joining Oxford, he held postdoctoral and lecturing positions at Princeton University, the University of Pennsylvania, and the College of New Jersey. His research interests include Stoic and Epicurean ethics, ancient logic, and moral psychology. He has published extensively on figures like Seneca, Epictetus, and Chrysippus, with a recent monograph Stoic Eros (CUP, 2024). His work bridges ancient philosophical debates with contemporary epistemology and ethics, emphasizing the relevance of Hellenistic thought to modern discussions. Shogry teaches undergraduate courses on ancient philosophy and ethics at Oxford. His scholarship is accessible via his personal webpage, Google Scholar, and PhilPeople profiles.

Elina RönnbergView profile

Professor

• Discrete Optimisation
• Mathematical Optimisation
• Scheduling
• +18 more

Elina Rönnberg is a Professor and Deputy Head of Department at the Department of Mathematics, Linköping University, where she leads research in discrete optimisation and intelligent decision-making. Her work bridges theoretical method development and real-world applications in sectors such as healthcare, aviation, mining, and transportation. She is actively involved in the Wallenberg AI, Autonomous Systems and Software Program (WASP) and has collaborated with industry leaders like Saab and Scania. Her research focuses on advanced optimisation techniques including Dantzig-Wolfe decomposition, Lagrangian relaxation, column generation, branch-and-price, and logic-based Benders decomposition. She also explores hybrid methods combining mathematical programming with constraint programming and machine learning. Applications span nurse rostering, electric vehicle routing, aircraft arrival scheduling, and underground mine planning. Recent publications highlight a strong trend toward integrating AI and machine learning—particularly graph neural networks—with classical optimisation frameworks to accelerate solution methods. Her work emphasizes practical impact, robustness, and scalability in solving complex scheduling and resource allocation problems. Nurse Rostering with Strategic Planning of Skills for Sick-Leave Robustness (2024) Pricing for the EVRPTW with Piecewise Linear Charging (2024) Speeding Up Logic-Based Benders Decomposition with Graph Neural Networks (2024) Elina supervises several PhD students and has co-supervised doctoral research at international institutions including Makarere University (Uganda) and the University of Exeter (UK). She has contributed to applied projects through student theses in collaboration with Scania and Saab, focusing on electric vehicle routing and search-and-rescue optimisation. She previously served as a Specialist in Optimisation at Saab Aeronautics (2014–2020) and co-founded Schemagi, a scheduling tool aimed at improving quality in healthcare. She teaches courses such as Introduction to Optimization (TAOP07) and Project - Applied Mathematics (TATA62). Her research group, 'Mathematics and algorithms for intelligent decision-making,' operates within the Division of Applied Mathematics (TIMA) at the Department of Mathematics. The team develops decision support tools that enhance efficiency and sustainability in complex systems, particularly under the growing demands of electrification and digitalisation in transport and logistics.

Forest AgostinelliView profile

Assistant Professor

• Deep Learning
• Reinforcement Learning
• Search Algorithms
• +7 more

Forest Agostinelli is an Assistant Professor in the Department of Computer Science and Engineering at the Molinaroli College of Engineering and Computing, University of South Carolina, where he is also affiliated with the AI Institute. His research focuses on designing AI algorithms for pathfinding problems, integrating deep learning, reinforcement learning, heuristic search, and formal logic. He holds a Ph.D. in Computer Science from the University of California, Irvine, an M.S. from the University of Michigan, and a B.S. in Electrical and Computer Engineering from The Ohio State University. Research Overview : Agostinelli’s work emphasizes solving pathfinding problems in domains like robotics, theorem proving, and molecular optimization. His group develops explainable AI methods to enable collaboration between humans and machines. Key projects include DeepCubeA (solving the Rubik’s Cube via deep reinforcement learning) and neural activation function research. Funding & Awards : He has secured grants from NSF, NASA EPSCoR, and South Carolina’s ASPIRE and MADE programs. Notable awards include the NSF Graduate Research Fellowship and the Graduate Education for Minority Students Fellowship. Teaching : He teaches courses in Artificial Intelligence (CSCE 580) and Deep Reinforcement Learning and Search (CSCE 790), mentoring over 15 students at undergraduate and graduate levels. Labs & Collaborations : Active in AI-driven education and interdisciplinary projects, his lab contributes to tools like ALLURE for children’s learning and Bioinformatics platforms like CircadiOmics.