Social Networks:

They also link us, often through important but weak ties, to other humans. Their origin is biological: going back to quorum-sensing, swarming, flocking, social grooming, gossiping, etc. Yet, as we have connected our social networks to traditional human institutions (markets, justice systems, education, etc.) through new technologies, the underlying biology has become obscured, but not dormant.

We will select Use Cases of interest to the audience to design a new environment ab initio.

• Understand the evolving environment (e.g., wealth management and investment banking services)
• Model utilities and risks (robust hypotheses, driving rapid and scalable testing and learning: e.g., A/B testing, boosting and reinforcement learning)
• Interact strategically and rationally (use case development, hypothesis testing and production of new value for clients and the firm within and across divisions)
• Design mechanisms such that all stake-holders interact strategically but in a mutually beneficial manners ("good" Nash equilibria; Shapely values, "Cellularization")

1. [Introduction to Networks] (Biological, Social, Economic and Communication): where we study important binary relationships (e.g., friendship, followership, P2P, B2B, etc.) among entities: such as users, objects, contents. Network effect and Tipping points. Degrees of Separation.
2. [Computer science:] where we study historical developments culminating now in ubiquitous social-technological networks: fundamental concepts such as computational universality, undecidability, exponential growth (e.g., Moore's law). Limits and powers of computer science.
3. [Graph Theory:] where we study evolving Social Networks, using theories of combinatorial, probabilistic and algebraic graph theory.
4. [Graph Laplacians*:] where we study modern algebraic graph theory to compute Social Ranks (e.g., PageRanks).
5. [Game theory:] where we study strategic interactions among informed and uninformed stake-holders (sender-receiver signaling games); Signaling, Costly/Honest Signaling and Deception.
6. [Nash equilibria*:] where we study the solutions to games: such as Stable Separating vs Babbling/Pooling Equilibria.
7. [Mechanism Design:] where we study various ways to guide strategic interactions among stake holders. (Tools: Costly Signaling, Recommender-Verifiers)
8. [Signaling:] where we study how communications and data analysis may be organized, especially, in investment services.
9. [Digital Market Places:] where we study traditional and emerging/disruptive ideas in Market Micro-structure.
10. [Crypto-Coins and Payment Systems:] where we study: Distributed Ledgers, Identity and "Insider Threats" etc.
11. [Trust and Privacy:] where we study how and why information asymmetry among stake-holders is (may need to be) preserved; why certain interactions persist.
12. [Verification*:] where we study how various temporal properties of interactions may be verified (Model Checking, Stress Testing, Causality).
13. [Utility Alignment:] where we study conditions under which strategic alignments may emerge (e.g, via Cellularization).
14. [Consensus and Governance:] where we study how a community may (or fail to) organize themselves to operate profitably (e.g., Arrow's paradox).
15. [Hierarchies and Layers:] where we study how complex communities may (re)organize further at various scales.
16. Case Studies: Personal Data Markets, Crypto-coins, (Bit-coins, Zcash, Ripple, Stellar, Ethereum, solidity, Rholang, DAO (Distributed Companies)), Cyber Security (M-coins), Information Finance Markets (Prospero, Betterment, WealthFront, etc.), Market Microstructure (BURPA), ICO/STO/ITO, GDPR, OpenLaw, OpenZeplin,

Prof. Mishra has industrial experience in Computer and Data Science (aiNexusLab, ATTAP, behold.ai, brainiad, Genesis Media, Pypestream, and Tartan Laboratories), Finance (Instadat, Pattern Recognition Fund and Tudor Investment), Robotics and Bio- and Nanotechnologies (Abraxis, Bioarrays, InSilico, MRTech, OpGen and Seqster). He is the author of a textbook on algorithmic algebra and more than two hundred archived publications. He has advised and mentored more than 35 graduate students and post-docs in the areas of computer science, robotics and control engineering, applied mathematics, finance, biology and medicine.

He holds 21 issued and 23 pending patents in areas ranging over robotics, model checking, intrusion detection, cyber security, emergency response, disaster management, data analysis, biotechnology, nanotechnology, genome mapping and sequencing, mutation calling, cancer biology, fintech, adtech, internet architecture and linguistics.

Prof. Mishra’s pioneering work includes: first application of model checking to hardware verification; first robotics technologies for grasping, reactive grippers and work holding; first single molecule genotype/haplotype mapping technology (Optical Mapping); first analysis of copy number variants with a segmentation algorithm, first whole-genome haplotype assembly technology (SUTTA), first clinical-genomic variant/base calling technology (TotalRecaller), first single molecule single cell nanomapping technology, etc.

Prof. Mishra is currently a professor of computer science and mathematics at NYU’s Courant Institute of Mathematical Sciences, professor of engineering at NYUs Tandon School of engineering, professor of human genetics at MSSM Mt. Sinai School of Medicine, visiting scholar in quantitative biology at CSHL Cold Spring Harbor Laboratory and a professor of cell biology at NYU SoM School of Medicine.

Prof. Mishra has a degree in Science from Utkal University, in Electronics and Communication Engineering from IIT, Kharagpur, and MS and PhD degrees in Computer Science from Carnegie-Mellon University. He is a fellow of IEEE, ACM and AAAS, a fellow of National Academy of Inventors (NAI), a Distinguished Alumnus of IIT (Kharagpur), and a NYSTAR Distinguished Pro- fessor.